International Management Track Open Innovation in Big-Data ...

Amsterdam Business School

International Management Track

Open Innovation in Big-Data Driven Autonomous Driving

Exploratory Study

Open innovation in a world of intellectual land grabbing

Author: Caper Wauters Supervisors: M. Paukku, 1st Supervisor�UVA

Student number: 11671408 V. Scalera, 2nd Supervisor�UVA

Keywords: Big Data, Autonomous Driving, Collaboration, Open Innovation, Second Enclosure Movement, Privatization, Closed Innovation, Intellectual Land Grabbing. Word count: 18065

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Preface

First of all, I would like to thank Dr. Markus Paukku for his insights and supervision during

this Master thesis. Secondly, many thanks to all interviewees, for their willingness to

participate and for their time. Lastly, in advance I would already like to express my gratitude

to Dr. Vittoria Scalera for being the second reader of my thesis.

State of originality This document is written by Wauters Casper who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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Abstract

The introduction of Big Data and AI in the quest for the first autonomously driving car, has a

disruptive effect on the car industry’s way of innovation. The increased complexity seems to

lead to new types of innovation models for the automotive market.

Will the Automotive industry evolve their innovation process from being a closed

innovator to becoming an open one? Original Equipment Manufacturers (OEM’s) have

historically invested in internal R&D to boost their innovativeness, as well as buying up

companies to add knowledge to the firm. The need of increased innovation (speed) and cross-

industry knowledge for developing autonomous cars (Enkel & Gassmann, 2010), forces the

automotive industry to look outside its own boundaries as a firm and industry, to escape a

cross-industry technological innovation dilemma.

The emergence of open innovation models and collaboration, offer a solution for

importing cross-industry knowledge into the innovation process, closing the knowledge gap

between new high-tech market entrants and the OEM’s. Then again, are these incentives for

open innovation being thwarted by the sensitivity of the knowledge in a high technology driven

market? In a market where data is power, the habit of privatizing collected data can counteract

the open innovation movement. Is this Second Enclosure Movement of “intellectual land

grabbing” overpowering collaborative efforts in the autonomous market space?

This research looks if automotive business models are evolving towards more

intermediate forms of open innovation, with an increased emphasis on collaboration and

information sharing and moving away from the ivory towers, where the entire innovation

process is developed internally.

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Contents

Preface ....................................................................................................................................... I

State of originality .................................................................................................................... I Abstract .................................................................................................................................... II

Contents ................................................................................................................................. III 1 Introduction ...................................................................................................................... 1

1.1 The Automotive Playing Field ............................................................................................ 1 1.2 Autonomous Driving and its Cross-Industry Complexity ............................................... 3 1.3 Research and Contribution ................................................................................................ 4 1.4 Thesis outline ....................................................................................................................... 5

2 Literature review ............................................................................................................. 6 2.1 Key Concepts and Processes in Todays Technological Market ...................................... 6

2.1.1 Innovation shift ................................................................................................................. 6 2.1.2 Big Data and AI Driving Innovation Complexity ............................................................. 8 2.1.3 Shifting innovation models ............................................................................................... 9

2.2 Closed Innovation .............................................................................................................. 10 2.2.1 First enclosure movement ............................................................................................... 10 2.2.2 Second enclosure movement ........................................................................................... 13

2.3 Open Innovation ................................................................................................................ 14 2.3.1 Advantages of Open Innovation ..................................................................................... 16 2.3.2 Challenges of Open Innovation ....................................................................................... 18

3 Theoretical framework and Research Contribution .................................................. 21 3.1 Theoretical Frameworks ................................................................................................... 21 3.2 Research Question and working Propositions ................................................................ 26

3.2.1 Research question ........................................................................................................... 26 4 Research design .............................................................................................................. 30

4.1 Research Structure ............................................................................................................ 30 4.2 Interview Procedures ........................................................................................................ 31

4.2.1 Pilot interview ................................................................................................................. 32 4.2.2 Interview pool ................................................................................................................. 33

4.3 Strengths and limitations of research design .................................................................. 36 5 Data and Analysis and Results ..................................................................................... 38

5.1 Data Analysis ..................................................................................................................... 38 5.2 Results ................................................................................................................................. 41

5.2.1 Working Proposition 1: Innovation Speed ...................................................................... 41 5.2.2 Working Proposition 2: Open Innovation Models .......................................................... 45 5.2.3 Working Proposition 3: Second Enclosure Movement ................................................... 50

5.3 Concluding ......................................................................................................................... 54 6 Conclusions and Discussion .......................................................................................... 58

6.1 Research propositions ....................................................................................................... 58 6.2 Research question .............................................................................................................. 59 6.3 Discussion ........................................................................................................................... 60

6.3.1 Market Performance of Innovation Models .................................................................... 62 6.3.2 Application in Other Markets ......................................................................................... 63

6.4 Limitations and future research ...................................................................................... 65 References ............................................................................................................................... 66

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Appendix A - Case (Tesla vs Mobileye) ................................................................................. i Appendix B – Word Tree for Innovation, Collaboration & Data ...................................... ii

Appendix C – Coding structure .............................................................................................. v Appendix D – Interview Structure ........................................................................................ vi

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1 Introduction

The first big disruption in the transportation of people dates back to 1908, when Ford

introduced an automobile that many middle-class Americans could afford. Back then everyone

still got around on horse and carriage, but the model-T Ford revolutionized the transportation

industry (Wikipedia, 2017). Now autonomous (self-)driving transportation is at the forefront

of the next major progression regarding the automotive industry.

Once again, the automotive industry is an area ripe for disruption. Not only are there

sustainable demand-driven changes happening in the form of electrification of cars, but more

importantly, there is the emergence of autonomous driving. Apple CEO Tim Cook notes this

as a major disruption looming in the automotive industry (Bloomberg, 2017). According to

Cook there are three revolutionary vectors of change happening in more or less the same time-

frame: self-driving vehicles, electric vehicles and digital ride-hailing. Of the three, autonomous

driving seems to be the key driver to the next innovative revolution and everyone is trying to

get in on this technology.

1.1 The Automotive Playing Field

Due to the fast innovation and increasing role of technology and data in the 3 converging

revolutions described by Cook, very different business models can be expected in the future

automotive sector. For the first time in 100 years the traditional automotive industry is being

shaken up, where legacy car manufacturers or Original Equipment Manufacturers1 (OEM’s)

face strong new opponents who are disrupting the entire automotive ecosystem (Bratzel, 2017).

These new high-tech players like Google, Uber, Nvidia and Apple are already starting to

1OEM or Original Equipment Manufacturer is a company that produces parts and equipment in the value chain.

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penetrate the automotive sector, experimenting with new business models in this new data

driven playing field. Car manufacturers like Tesla are trying to internalize the entire value

chain, while other firms are looking at collaborating and are trying to find their niche in this

new space (Marshall, 2018).

A host of automotive brands and other tech heavyweights have been investing heavily

in autonomous R&D2, all searching for an effective business model in which innovation and

data are key elements. Tesla Inc. and Uber have used the three vectors of change to fuel the

development of autonomous driving, it being via electrical vehicles or digital ride-hailing.

Tesla uses its connected network of cars to gain insightful knowledge by collecting customers

driving data, while ride-hailing companies like Uber and newcomer Lyft use their taxi customer

base as a starting point for developing self-driving cars. Within the playing field of the

autonomous automotive industry there are more and more competitors popping up by the day.

Lyft has been on a streak of corporate collaborations, recently pairing up with autonomous

driving tech companies Waymo3 and NuTonomy, to accelerate the deployment of autonomous

vehicles. Furthermore, it acquired significant investment from Jaguar Land Rover in addition

to the initial $500 million General Motors gave in December 2015 (Hawkins, 2017).

The traditional players in the automotive industry have been increasingly interested in

mobility and technology companies, as they seek to insulate themselves from the possible

decline in personal car ownership. By 2025, auto manufacturers are predicting that at least half

of today’s drivers are unlikely to want to own a car (Rawlinson, 2017).

All these partnerships and fierce competition have not been without challenges. A

recent example being that Uber was sued by Waymo for allegedly stealing autonomous driving

secrets (Davies, 2017). Even though the case is still ongoing, this has not been without

2 R&D or Research and Development is a firm’s activity for the first stage of developing innovation. 3 Waymo is the self-driving subsidiary of Google parent company Alphabet.

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casualties, including the head of Uber’s self-driving unit, accused of stealing 14,000 document

from Waymo (Carman, 2017). This case shows the importance of knowledge and data in the

new automotive playing field and although firms are collaborating, the degree of knowledge

sharing can vary greatly.

Figure1-TwitterpostHemalShah-source:TheVerge2017

1.2 Autonomous Driving and its Cross-Industry Complexity

Producing an autonomous car is such a new and complex innovation that to do so, a company

will need a wide range of capabilities. These can be summarised in 3 key aspects; it needs to

be knowledgeable in the manufacturing and distribution of cars (like traditional automakers),

it must be able to process Big Data in order to develop the autonomous Artificial Intelligence

(AI) (software start-ups like Waymo) and finally there has to be knowledge and access to the

customer base. The kind of data Lyft has through its ride-hailing network. As market players

don’t have all the needed resources for building a full-fledged autonomous car, an innovators

dilemma arises with cross-industry knowledge gaps. The biggest challenge being how to bring

high-tech knowledge and car manufacturing knowledge together. Currently there are car

manufacturers that know how to build a car and scale production, but know little about big data

collection, processing and AI training with “deep learning”. While on the other hand, you have

these new High-Tech market entrants that poses a lot of knowledge about data AI and sensors,

but have never built cars or managed a fleet.

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How market players try to bridge their knowledge gaps can differ. Some believe in

the more traditional closed innovation approach, where they build on their own strengths and

internalize the lacking knowledge to their existing innovation by buying tech or collecting

knowledge/data and privatizing this. The latter, closing off knowledge by privatization, is also

known as knowledge enclosure. Most closed innovators take this very protective stance in fear

of knowledge-spillovers to their competitors. At the same time, others see open innovation

models as a way of sharing information and solving knowledge gaps, plus increasing their

innovation abilities and speed. Fusing innovation together unlocking new insights and pushing

the autonomous market as a whole, with less paranoia about knowledge-spillovers.

The strategy for how firms deliver the autonomous driving experience vary widely.

There are firms such as Tesla, trying to offer the full package, from car to AI, for the

autonomous solution. On the other hand, there are modular innovators such as TomTom and

Nvidia, who focus on filling in a piece of the technology stack and by doing so excel in a niche

in the value chain of developing an autonomous car. Decisions in this regard greatly influence

the degree and necessity of cooperation between firms.

1.3 Research and Contribution

Technological developments have completely disrupted the automotive industry. The

increased complexity of developing a car that can drive autonomously has pulled new High-

Tech players into the market. Both the old and new market players are now caught up in the

race to get ahead, testing their (new) innovation models in the process. In this high-tech playing

field, a tension has arisen between the closed and open innovation models, however very little

literature can be found that studies both open and closed innovation and none can be found that

apply the theories on the autonomous car test case. Further literature research will create a

better understanding of the 2 business models and how they interact.

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This results in the following research question:

In the currently emerging autonomous driving industry, which innovation models are

being adopted due to high-tech influences?

By conducting semi-structured interviews with the emerging industry leaders in

autonomous driving, as well as attaining extra insight from experts in the closely related fields

of Big Data, Open innovation and AI, an exploratory study is set up.

1.4 Thesis outline

The emergence of autonomous driving solutions is bounded by the use of Big Data for R&D

of AI. To demystify the inner workings of autonomous driving some core concepts used in the

development of autonomous driving will be clarified. The second chapter will start by

describing Big Data as defined in literature. After boiling down the demarcations of core

concepts like Big Data and AI for autonomous driving to showcase the complexities that drive

the automotive market with new High-Tech inputs, the remaining literature review looks into

the different innovation processes, starting by describing closed innovation and its evolving

second enclosure movement, followed by a deep dive into open innovation. Next in chapter 3,

a theoretical framework is made, where the relevance of this research will be further explained

by formulating propositions about the innovation process of autonomous driving and by

constructing a framework for categorizing a firms’ innovation approach. This will be summed

up and visualized in a conceptual model. The conceptual model will be the starting point that

leads to the research design in chapter 4.

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2 Literature review

In this chapter, key concepts and processes influencing today’s technology and data driven

automotive market are analyzed by means of literature, further clarifying complex topics. By

elaborating on Big Data and the use of AI in autonomous driving, the sectors’ increasing

complexity is sketched, highlighting the need of cross-industry knowledge to build an

autonomous car. The resulting knowledge gaps due to these new complexities fuel the

emergence of both fast paced collaborative innovation and Knowledge (En)closure. After

analyzing the open and closed innovation models through literature an underlying tension

between the two is signaled, making for an interesting business case.

2.1 Key Concepts and Processes in Todays Technological Market

2.1.1 Innovation shift

The dominant Resource-Based View in International Strategy states that a firm should strive

to attain a set of unique core capabilities that can be leveraged to a competitive advantage

(Barney, Wright, & Ketchen, 2001). According to Teece (1998), one of the key capabilities a

firm must poses is a certain degree of innovative capability and the role of technology in

innovation is ever increasing. For the development of autonomous driving, especially the

mining of Big Data is generating new insights and opening up new innovation possibilities by

introducing applications of AI (Chen, Chiang, & Storey, 2012).

The role of technology in innovation is resulting in new approaches to R&D.

Traditionally firms kept their competencies and development internal, often with complete

secrecy to the outside world, so that it’s R&D investments could be converted to competitive

advantages and allow the firm a first mover position into a new market. Even though internal

R&D is still a critical source to the innovation process, more firms are moving away from the

practice of keeping all knowledge within one firm (Sang M. Lee, David L. Olson, & Silvana

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Trimi, 2012). This strategy change is fuelled by the fast pace of technological innovation. The

landscape of innovation is changing at such a high pace, that the rate of development in

innovation is a key factor, possibly overpowering the risk of knowledge-spillovers. It is no

longer a question if, but when, a firm is able to develop innovations for new industries, with a

lot more firms stepping into the technological development field. Look for example at the

growth of firms in Silicon Valley alone (Abraham, 2017).

Figure3-PatentRegistrationsSiliconValley//Datasource:U.SPatentandTrademarkOffice

As seen in figure 3 Computers, Data Processing & Information Storage, all directly related to

Big Data, are some of the newest but by far the fastest growing fields in Silicon Valley. These

sectors facilitate new types of innovation due to data mining and machine learning, further

speeding-up the rate of innovation change.

The new driving technological forces of Big Data and AI through machine learning,

not only change the way innovation can be applied but also cause higher levels of complexity

for the development of an autonomous car, generating a cross-industry knowledge gap between

the High-Tech sector and car manufacturers. The complexities will be further explored by

describing how the key concepts of Big Data and AI apply to autonomous driving.

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2.1.2 Big Data and AI Driving Innovation Complexity

First of all, let’s take a closer look at this fuzzy term ‘Big Data’. It is a trending topic and nearly

impossible to avoid in any new business opportunity. There seem to be many different

definitions for Big Data and not without reason. Business is shaping up to be more and more

data-driven and it is understandable that along the way the terms’ definition has been refined.

Gartner’s 3V’s model is one of the first to define Big Data some 15 years ago. The definition

endures until today, in which Big Data is characterized according to its Volume, Variety and

Velocity (De Mauro et al., 2015). Big Data is large in volume, has a wide variety of data and a

high speed of data streaming in. Beside the 3V’s, the definition of Big Data has been expanded,

to include the need for a specific technology to capture and create value out of that data (De

Mauro et al., 2015).

This is a good starting point from where to further specify what Big Data is and how

its application can deliver solutions in the autonomous driving sector. Big Data functions as

the key resource for developing machine/deep learning, which is crucial input for the

autonomous vehicles Artificial Intelligence (AI). Applying algorithms to the Big Data,

identifying patterns in the data and upon these insights predicting new patterns in upcoming

data, creates an algorithmic system that learns by itself without further human interference

(Hall et al., 2016). The availability of Big Data on human drivers’ behaviour is the most

essential resource for the development of the car’s Artificial Intelligence (AI) and will

determine the rate of innovation (Nothdurft et al., 2011; Thorpe, et al., 1991). Both studies

from Nothdurft (2011) and Thorpe (1991) indicate that the biggest challenge in autonomous

driving is the split decision making and anti-collision reaction of the AI by visual input. In

order to create a car with autonomous driving decisions, specific Big Data is needed for the

AIs ‘deep learning’, which comprises of discovering patterns so the cars’ AI can successfully

and safely process the real-time visual and other input metrics of the car, letting it make the

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right decisions in its surroundings. The safety and success of an autonomous vehicle depends

on what Big Data can be attained and what deep learning insights can be added to the

algorithms of the AI in support of its decision making.

Here a crucial cross-industry knowledge gap arises. New High-Tech market entrants

like Nvidea, Waymo, etc. are more knowledgeable about developing AI and processing big

amounts of data, but lack the data mining capabilities due to a lack of fleet and inability to

build cars as a whole. For legacy car manufacturers or even ride-hailing services it’s the other

way around.

Thus, good user-data sets are crucial for the self-learning AI algorithms used in

autonomous driving. It is impossible to pre-program all scenarios a car will encounter when

driving on the road, so the AI must learn by repetition and replication (just like a human).

Luckily, once it has seen a scenario it will never forget the lesson learned and make the same

mistake twice, something that can’t be said for all people driving around.

2.1.3 Shifting innovation models

The previous paragraphs have described how in today’s technological marketplace, a paradigm

change is happening in innovation due to complex cross-industry knowledge gaps. From

literature two important innovation movements can be discerned: open versus closed

innovation. On the one hand, there are industry leaders who resort to a resource based strategy

for the accumulation of valuable technology assets and take an aggressive stance in protecting

their intellectual property. On the other hand, successful high-tech global players are seen

disconnecting the value chain to work together with part specific industry experts (Teece &

Pisano, 1994), opening the doorway to open innovation models.

Within high technological markets like autonomous driving there are different forces

pushing and pulling towards open and closed innovation models, creating a tension and

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uncertainty of best practices within the automotive industry. Freeman & Engel (2007) argue

that innovation requires two important underlying conditions. Firstly, the accessibility and

mobility of resources and secondly, incentives must be aligned so that the collaborating parties

(within a company or between companies) complement each other where needed. Current

autonomous driving collaborations poses a lot of information asymmetry due to specialization

and diversifications of collaborating partners. Within this organizational chaos the distribution

of knowledge seems to be the main source of the problem in the form of knowledge-spillovers.

The exploration of firms into new markets, coupled with the liberalization is stripping firm-

level competitive advantages back to its fundamental core of innovation and making intangible

know-how (or intellectual property, IP) the leading asset for competitive advantage (D. J.

Teece, 1998). Pulling back open innovation initiatives towards closed internal innovation with

higher IP protection.

To properly outline the market activities in terms of this innovation tension in the autonomous

driving industry, a deep dive into the concepts of closed innovation and open innovation will

follow.

2.2 Closed Innovation

2.2.1 First enclosure movement

Building on the tension between closed and open innovation models in the autonomous driving

industry, are the phenomenon called the first and second enclosure movements that add

contradictory logic. Closed-off innovation derives from a thought of protecting intellectual

property (IP) by privatising it and enclosing it from the public. This concept is called the first

enclosure movement and it is a principle that dates back to the very beginning of economics

(Boyle, 2003). All the way back to the simple economics of herders moving around their stock.

In the beginning, herder’s sheep wandered around on all grasslands, without restrictions (the

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commons). The first steps towards enclosure where taken by people when they started to fence

of pieces of these common grasslands for themselves. There was a positive side to this process,

because unlike with commonly owned land, people were willing to invest in their own piece

of land. This could be through fertilisation, irrigation, etc., increasing the production and

efficiency of that piece of land. A fenced of area motivated people to invest, because there was

a clear reward in the form of increased production from the specified piece of land. According

to Boyle (2003), this first move of privatization made a strong case, the enclosure of property

of the commons was a good thing and privatization leads to specialization and increased

efficiency. To safeguard this movement, a development of property rights was needed to fuel

progress and keep incentives for investment. Over the centuries people have applied this

concept to almost all physical and even non-physical elements of life. Thus, privatization of

intellectual property (IP) was born.

Safeguarding property rights for intellectual property is obviously not as simple as

building a fence around it. The rewards of investment and creation of IP can be easily lost. In

order to maintain the market incentives for innovation, protective steps must be taken for the

creator/innovator. When unable to exclude others from copying their innovation, an innovator

cannot get returns for their innovation investment. Thus, a limited monopoly must be created

in order to reward the innovator and pay back his investment. This is called the intellectual

property right. For intellectual information with low barriers and easy reproduction capabilities

IP rights play a big role.

First Enclosure Privatization counteracting Open Innovation

Thoughts derived from this first enclosure movement still has lingering influences on the

development of open innovation. The first enclosure movement started as a positive movement,

creating economic growth. The closed innovation and privatization leads to optimization and

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efficiency of the privatized product and created trust for future investment. Especially in more

software based platforms, privatization is the dominating strategy with a winner-take-all

ideology, argued via logics of network externalities, natural monopolies and first-mover

advantages (Eisenmann et al., 2010; Parker & Van Alstyne, 2005). The transition to more open,

knowledge sharing models is still very limited and the progression of open innovation is yet

unclear. Ferraro and O’mahony (2004) discovered that even today, seemingly open innovation

projects still create (contradictory) organizational boundaries to safeguard the income from

these projects, in some way still looking how to fence off certain pieces of the innovation

process. From his research the question arises, do collaborative projects have a chance of

remaining open at all, or do they funnel and narrow over time, becoming increasingly closed

to a select few and eventually privatizing? While producing knowledge goods like software, a

company can ensure security and stability of the knowledge flows by determining what to open

and what to close off. But doing so handicaps the possibility for fully open innovation.

Further research indicates collective vs individual tensions arising in growing

collaborative (open) networks (Wareham et al., 2014). As the ecosystem of a collaborative

network expands in scale and scope, a contradictory tension is triggered in the form of a tragedy

of commons4. The growth and sheer size of the network can create contradictory logic and have

adverse effect on the behaviour of the ecosystems actors. Actors may decide to choose for their

own wellbeing above the overall health of the ecosystem by say privatization, creating actual

tragedy of commons. . As such effects multiply, the ecosystem risks becoming increasingly

unsustainable and the same network that has enabled its growth facilitate its demise (Wareham

et al., 2014). In other words, when actors of a collaborative innovation project start privatizing

4 Tradgedy of Commons: An economic theory within a sharing ecosystem, where individual users acting according to their own self-interest behave contrary to the common good of all users and by these collective actions harming the ecosystem.

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new IP fuelled by the open collaboration, tragedy of commons may apply, for example when

privatizing image processing algorithms built on collected data from the collaboration. These

actions of self-interest risk triggering a closed attitude and counteract the possibility to openly

share information in the collaboration.

These tragedies of commons due to enclosure are becoming increasingly visible in

software/Big Data based solutions. Where firms go a step further by rushing to enclose ever

larger stretches of potential information.

2.2.2 Second enclosure movement

In the software development sector, there is talk of the second enclosure movement (Boyle,

2003) and seeing as autonomous driving, for a large part, consists of software development and

Big Data utilization, this is also applicable to this emerging industry. The second enclosure

movement surpasses protective privatization and overreaches towards the likes of “intellectual

land grabbing”! In a world where information/data is power, the sole purpose of this enclosure

movement is fencing off future data sets and using potential open/common information. Firms

are trying to utilize collaborative/open information, plus fence off the first adopters data in the

autonomous market, by privatizing their products based on these information sources.

It can be argued that privatizing firms have a right to protect their collected data because they

created the circumstances to harvest it and even though it is normal from a closed off innovators

standpoint to protect IP regarding complex algorithms and computations, justification for the

raw user data is less obvious. Firms even try to fence off future raw driver’s data without

actually fully using the data right now. More focused on mining data and privatizing it then

actual development for the future. Pulling knowledge and learning out of the (future) shared

information pool. This second movement endangers the sustainability of open innovation

projects as market incentives are destroyed and tragedy of commons apply.

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In the new autonomous driving industry space, there is a risk that leading (high-tech) firms

will try to internalize as much future information as possible by locking-in customers to their

products and fencing off their user data. By doing so these firms could harvest potential future

common knowledge or “intellectual land grabbing”, creating an advantage for the private firm

itself but slowing down or even blocking some of the general generation of autonomous

knowledge. As Boyle (2003) refers to this intellectual land grabbing like the environment, it is

an information pool of commons that is yet to be discovered. “Like the environment, the public

domain must be invented before it can be saved”. The question remains if this second

movement of privatizing future data will potentially kill off all open innovation efforts in the

autonomous driving scene.

2.3 Open Innovation

Open innovation is a powerful framework consisting of the employment, capture and

generation of intellectual property (IP) at firm level (West & Gallagher, 2006). The model

stresses the importance of using a broad range of knowledge sources for a firms’ innovation

process, stretching from rivals, academics to even cross-industry firms. In the last decade, the

concept of open innovation has been extensively researched and still it ranks high on the agenda

for the management in technology and innovation. More research is demanded to gain an

understanding of this emerging innovation management paradigm (Boscherini, Cavaliere,

Chiaroni, Chiesa, & Frattini, 2009).

The open innovation paradigm is often contrasted to the traditional closed “proprietary”

model by which internal R&D activities lead to innovative products (Chandle et al., 2009).

Within closed innovation firms, IP that could not be commercialized can be licensed to others

or “sits on the shelf” until internal development can put it to other use, reducing risk of

‘knowledge-spillovers’ to other firms (Chesbrough & Rosenbloom, 2002; Smith & Alexander,

1999). According to Chesbrough however, this traditional, closed innovation model is eroding.

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The emergence of a more open model is presenting itself, where companies recognize not all

innovation can come from inside the organization and the speed of innovation is increased by

means of collaboration. First signs of an innovation transition from closed to open innovation

models are emerging by a hybrid option, where even between competitors there is cooperation

for certain parts of the innovation process also known as coopetition. By cooperating in parts

of the value chain, certain innovation hurdles can be overcome to grow the market as a whole

and still compete in other area’s of the value chain (Bengtsson & Kock, 1999).

The concept of open innovation is primarily applicable to the high-tech sector

(Chesbrough & Crowther, 2006), with the automotive industry as a good example. According

to a study by lli, Albers and Miller (2010), the open innovation movement was already present

in the traditional legacy automotive industry. Table 1 shows the degree to which this was the

case and outlines the two opposing innovation ideas. The table shows that open innovation

already showed promise, but the tendency towards closed innovation prevailed. Nonetheless,

the shift towards more open models appears at the horizon, with a more knowledge/technology

intensive automotive market developing.

Table1:ContrastingPrinciplesofClosedandOpenInnovation(lli,AlbersandMiller2010)

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In the knowledge/technology driven autonomous driving industry, the shift from closed to

more open innovation models seems to be happening right now. External sources of knowledge

play a bigger role, complicating the evolution of early stage technology projects, involving

technological and market uncertainties (Chesbrough, 2004). In these circumstances firm’s need

to rethink their business models to better facilitate collaborative/open innovation, they are

required to change the way they manage innovation. A firm needs to “play poker” in risk taking

as well as play “chess” for strategic risk reduction. Strategic reconfiguration is needed to

determine what knowledge to share and what to close off.

2.3.1 Advantages of Open Innovation

In todays increasingly high-tech environments, firms are required to reach outside the firms’

boundaries in order to access technological knowledge and enhance their innovation

performance. Furthermore, it is often argued that a company actually starts to boost its’

innovation speed the moment information is openly shared. Cohen and Levinthal (1989)

suggested a combination of internal and external R&D and in doing so serving a dual purpose.

On the one hand this will boost internal development by complementing it with input of

external knowledge and also this allows a firm to evaluate/observe innovation outside its own

boundaries. In this context, firms with higher investment in their own (internal) R&D benefit

more from external R&D and spillovers in future partnerships.

When using internal and external innovation, firms must be aware that this is not a one-

way street. Once available, firms often assume that the sources of external innovations will

continue to flow, but what happens when everyone in the partnership tries to be a ‘free rider’

by only absorbing external innovations? Firms start to show first signs of the Second Enclosure

Movement. This behaviour is primarily linked to fear of knowledge-spillovers to a direct

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competitor. Even though knowledge-spillovers can be problematic, studies have shown that it

is still economically rational to keep sharing information and that open innovation generally

pays off (Sofka An Grimpe 2010). Firms in the same industry complement each other in

creating the market but compete in dividing it (Nalebuff et al., 1996). So, if a firm stands to

benefit from an innovation that increases the market size, it will accept spillovers if the return

from its share of market growth is attractive enough. Moreover, firms need to and seem to be

willing to contribute back to the existing projects, to assure that the external innovations

continue to meet their perspective needs, to maintain absorptive capacity, and to avoid

discouraging current and future innovators for collaboration (West & Gallagher, 2006). A study

by Henkel (2006) shows that the development of the Linux operating system is a good example

of a successful open innovation platform. Firms utilizing Linux, contributed a lot of

development back to the public embedded code, despite it not being directly in their best

interest but in the best interest of the Personal Computer Marketplace. Similar for the

autonomous driving industry, developing the market first by means of open innovation benefits

all market players. If big steps towards autonomous driving can be made due to sharing

information and the knowledge gaps can be filled, they will accept the risk of knowledge-

spillovers.

Besides the boost it gives to a firms internal innovation and general market growth,

another advantage of committing to open innovation is that it makes firms a more attractive

partner with resource connections and expertise (Powell et al., 2005). The open innovation

investment acts as a “ticket of admission” to the open innovation game. Only partners with

considerable expertise, reputation, plus willingness to play together are invited. Resulting

collaborative networks create more innovation by benefiting from technological discussion and

knowledge exchange, which provides new opportunities and future business diversity (Lee et

al., 2010).

18

What makes open innovation as a business strategy attractive, is the possibility of

exploiting imported knowledge and building on idle intellectual capital, enabling even large

corporations to become more entrepreneurial and increasing the speed of innovation (Dodgson

et al., 2006). Open innovation models provide considerable efficiency gains, as it supports

accumulation of knowledge (David, 2003) and reduces duplication efforts in the innovation

process. In the case of autonomous driving the open innovation models overcomes industry

knowledge gaps by sharing information between high-tech entrants and car manufacturers. Car

manufacturers can share their user data, while the high-tech players can feedback the processed

user data for say better imaging process algorithms.

2.3.2 Challenges of Open Innovation

Although there are a lot of potential gains when utilizing the open innovation model, it also

bares considerable challenges. The costs of managing/coordinating the network of added

external expertise are uncertain. Particularly as the number of interdependencies increases with

more sophisticated and often competing demands on multiple relationships. Who bears the

costs and which partner reaps the benefits from an open model still remain somewhat unclear.

Open innovation is a powerful approach for generating extra innovation capabilities, but also

can create intellectual Property (IP) issues at firm level. Thus, another source of uncertainty is

concerning how to manage and protect a firm’s intellectual property (IP). Some firms address

this issue by revealing selectively. For example, in programming language, this would

encompass revealing most of the main source code without disclosing crucial add-ons on the

main framework.

Beside the practical issues such as cost and methods for IP protection, the main

challenge for most firms is that they are stuck in a proprietary attitude and their unfamiliarity

with openness. Openness is new territory for most firms, that need to rethink their business

19

models in order to innovate. Based on innovation research by West and Gallagher (2006), 3

fundamental adaptations can be identified for applying the open innovation concept to a firm:

exploit internal innovation, incorporate external innovation into the internal development and

ensure information exchange both ways of the partnership. The latter creates a tension in most

sharing models. Companies instinctively only want to receive the necessary information

without disclosing much of its own knowledge (fear of knowledge-spillovers). This train of

thought must first be broken before the actual full potential/benefits of collaborative/open

innovation can be realized. Hughes and Wareham (2010) conducted a study focusing on

building open innovation capabilities via external information sharing, creating uncertain

knowledge boundaries in the innovation network. They discuss the importance a firm’s

absorptive capacity5 has in relation to the shared knowledge. Not only how to import gained

knowledge into the existing firms capabilities, but also the growth of absorptive capacity for

the entire innovation network. The conclusion of their research was that firms must adjust to

bi-directional knowledge sharing to some degree in order to embrace and utilize the open

innovation model and enjoy the benefits of experimental learning and sharing of best practices.

Exactly how much outbound knowledge must be shared is very industry and firm depended.

In the case of autonomous driving the question remains if legacy car manufacturers can

switch their traditional proprietary mind toward open innovation without fear for knowledge-

spillovers of IP, focussing more on the joined gains and increased innovation speed due to open

innovation. Of course, there are hybrid transition methods by partial cooperation in certain

parts of autonomous development, while competing in the rest (Khanna et al., 1998), but

5 Absorptive Capacity: a firm's ability to recognize the value of new information, assimilate it, and apply it.

20

working so closely with your direct competitors only intensifies fear of knowledge-spillovers,

potentially feeding the fear and limiting open innovation in other areas.

Of all challenges, how to distribute firm knowledge seems to be the biggest hurdle to overcome.

The protection of IP keeps many firms at bay from open innovation.

21

3 Theoretical framework and Research Contribution

Available research and literature on the application of open innovation models to the highly

complex, technological and data driven autonomous driving industry is very limited, if not

completely unavailable. Research is limited to the traditional automotive industry (Chiaroni,

Chiesa and Frattini, 2012) and more general research on open R&D and open innovation

((Enkel & Gassmann, 2010; Chesbrough & Crowther, 2006; D. J. Teece, 1986). Based on these

sources and news articles the situation can be sketched of a fast changing industry, with large

new players and varying business approaches. The technology push has created new innovation

pressure, shifting automotive innovation structures to new forms and it is still unclear which

business approaches will prevail. This makes the automotive industry an interesting case study

for research of innovation models in modern technological markets, by combining the available

generic literature and interviews of important market players. This chapter describes what

contribution this research makes to a better understanding of the industries innovation

processes. To start with a theoretical framework is constructed, which is used as an important

tool for this research. Following this, the research question and several working propositions

are presented.

3.1 Theoretical Frameworks

The concept of open innovation shows promising application within the automotive innovation

shift, but can happen in many different forms and to varying degrees. Based on the concepts of

closed and open innovation, a framework has been constructed to identify the type of

innovation model each interviewed firm utilizes. By looking further into innovation literature

and searching for existing categorizing frameworks, a base innovation framework was found.

An adaptation has been made of the existing framework by introducing an extra hybrid option

of open innovation, relating to more of a coopetition type of collaboration. Collaborating

22

openly in certain areas of autonomous development while competing in the rest with a more

protective closed innovation model. In the following paragraphs the framework is presented.

Research from Haeussler (2006) indicates, the type of competitive relationship with

your partners, as well as the knowledge sources and access channels, determines the level of

knowledge control between collaborating partners. Furthermore, when the level of external

knowledge inflow is considerable, firms also regulate the outflowing knowledge more loosely.

If a firm acquires external knowledge from competitors by entering joint projects, more actions

are taken to control outgoing knowledge. Hence, these firms try to establish rock solid fences

around them. Contrarily, in an environment with high potential spillovers both ways, firms

control the knowledge flows from a “take and give” angle instead of a “knowledge catcher”

approach. Concluding, the extent of a firms’ collaborative innovation depends on the

management of the inflowing and outflowing knowledge. Based on similar theory, Ellen,

Gassmann and Chesbrough (2009) approach open innovation by way of knowledge flows. By

categorizing the directions of information flow, they determine 3 broad archetypes of “open

innovation”: inbound, outbound and coupled (bi-directional knowledge sharing), as illustrated

in Figure 2.

23

Figure2:InnovationModelsbywayofKnowledgeFlows(Ellen,GassmannandChesbrough(Enkeletal.,2009)

Although the concept of knowledge flow identification for innovation model determination is

a clear method, for this case study of open innovation in the autonomous driving scene, there

is need of a concreter framework to be able to link firms’ structures to their innovation process.

West and Gallagher (2006) made a framework that describes knowledge flow structures

related to the level of openness and collaboration. For the purposes of this report, the three

innovation structures proposed by them have been expanded to include a fourth. Making an

adaptation to the initial knowledge framework set-up by West and Gallagher (2006), results in

the next 4 R&D knowledge sharing structures to identify the firms’ level of openness, see

Figure 3. The additional structure is outlined in blue.

Scanning for new

Techonolgies & Capabilities

DevelopmentPilots &Prototypes

R&D Configuration R&D Development

Internal R&D

Technolgy Push

External R&D(Cross industry

Technology)

InboundProcess

CoupledProcess

OutboundProcess

24

Figure3:knowledgeflowsforincreasinglyopeninnovationmodels.Anadjustedframework,initiallybyWestandGallagher(2006)

At the top, the closed innovation model refers to the traditional internalization of innovation.

By developing R&D within the company without external innovation sources. Secondly, is the

modular innovation framework. This hybrid model was added because it was the missing link

that demonstrates the first step for a firms’ adoption towards open innovation. It’s a hybrid

between closed internal innovation and open development within the innovation partnership or

network. By adding the hybrid form based of the coopetition concept, the transitional phase is

also covered. A firm can cooperate for a module of development to overcome knowledge gaps

but still compete in the other modules of the developing value chain (Bengtsson & Kock, 1999).

The firm can yield returns by combining internal and external technologies to offer a product

otherwise not available. Not all member of the partnership share full information. Still showing

signs of primarily external knowledge input (inbound knowledge) and trying to “freeride” on

other partners. A lot of the R&D is divided into modules or silos, developing their R&D in

parts, being a cog in the entire system. Information and knowledge sharing is kept to a

minimum and only the absolutely necessary is shared, in order to protect their IP as much as

FirmR&D

ClosedProprietaryInnovation

HybridModular

Silo

PooledInnovation

SpinoutInnovation

FirmR&D

Module 1

Firm

Firm

FirmR&D

R&D

Module 1 Module 2

FirmR&D

Module 2

FirmR&D

Module 3

Public Community

Leve

l of o

penn

ess

25

possible. The range of openness can vary within this model from an absolute minimum to extra

innovation sharing for increased innovation speed and insights. Thirdly, pooled innovation

shares a lot more information. Knowledge is shared between partners of the innovation

network. Here joined “pilot” projects of innovation form a central joint project open to all

partners. Generated feedback, information and knowledge is freely shared within the

boundaries of the partnership, to further optimize each partners’ contributing module. The

speed of innovation is greatly increase compared to the more conservative hybrid silo

framework. Lastly, spinout innovation projects are the true forms of open innovation. Where

the innovation process and progress is actively shared to the public. By dumping the R&D into

the public domain, it enables the community to freely access and track the growth of the R&D.

This knowledge framework is the closest thing to actual fully open innovation. The Mozilla

web browser is a good example of a public/community grown project in responds to the lacking

capabilities of Internet Explorer.

Figure4:Degree&TypeofCollaboration(basedonWestandGallagher(2006))

Closed Innovation

Closed Innovation

Modular(Silo)

Innovation

Competition

Cen

tral

R&

DD

ecen

tral

R&

D

Collaboration

Pooled Innovation

Spinout Innovation

External Orientation

Collaborative Orientation

26

Figure 4 plots the 4 innovation models by competitiveness and centralization of R&D, quickly

giving a clear overview. The primary factor for choosing the openness of the partnership is

based on knowledge-spillovers. Depending the risk and potential of spillovers for IP, firms

choose their collaborative orientation and external orientation.

3.2 Research Question and working Propositions

Using the adapted innovation framework in Figure 3, an initial investigation can be conducted

on how innovation is developing in the autonomous driving industry. By matching industry

innovation processes to the constructed framework, development trends can be identified. Is

there a transition from closed to open innovation models in the autonomous driving industry

due to cross-industry knowledge gaps? Or is the risk of knowledge-spillovers to big, so firms

show signs of enclosure movement and are counteracting possible open innovation? Once the

dust settles which innovation model will prevail? As a result, the following research question

is formulated to oversee the new research angle for the test case of autonomous driving.

3.2.1 Research question



To operationalize the research question, a conceptual model is built as a guideline for setting

up the exploratory study, see Figure 5: Conceptual model research.

27

Figure5:Conceptualmodelresearch

On the basis of this conceptual model, working proposition are made to help support the

research question and facilitate direction during interviews, making sure to collect the correct

feedback from the correspondents. The working propositions will work together with the

innovation framework to discover firms innovation models and indicate how these innovation

methods perform and interact in the market.

Working Proposition 1

Collaboration and sharing of information increases your partnerships’ innovation speed.

Openness and sharing of information between collaborating parties increases a partnership’s

innovation speed as it supports accumulation of knowledge and reduces duplication efforts in

the innovation process. But does this also apply to the autonomous market where the risk of

Firm Innovation Process

Open innovation?

Closed Innovation?

level of knowledge

openess

level of knowledge

openess

Comparering to Innovation Framework

Innovation Performance

interviewedfirm

matched innovation

process

28

knowledge-spillovers can be grave and we have already seen partnerships end in lawsuits over

IP.

Based on quotes from the interviewed firms, their stance on the use of openness in

innovation for facilitating increased innovation speed can be observed. By identifying if

collaborative openness increases the innovation speed for the firm and if the rate of innovation

has become prioritized above the risk of knowledge-spillovers.


Open innovation models prevail in the big-data driven autonomous driving industry.

Within the autonomous driving space there are different modes of openness towards sharing

information about innovation development. This proposition says that the general consensus is

that more open/collaborative business models generate better innovation result and perform

better then parties that try to internalize all development.

With the innovation framework, the level of openness of each interviewed firm can be

identified. Which type of innovation models is encountered the most, plus how do they perform

in the autonomous driving market. Based on a recent report by Navigant Consulting (2018) a

current standing of the autonomous development of firms is made and this can be compared to

their innovation models used. Within the interviewed market players are there more open or

closed innovators and who of them is taking the lead in autonomous development?

Furthermore, interviews can indicate if a firm switched to a different innovation modes and for

what reason.

29


The second enclosure movement is a real threat to open innovation.

Although the Second enclosure movement refers to the privatization of existing open data sets,

this concept is still valid in the autonomous driving industry. In this case, the risk lies mainly

in the information pool of commons that is yet to be discovered. “Like the environment, the

public domain must be invented before it can be saved” (Boyle 2003). The premier bottleneck

for autonomous driving is training the AI and for that great amounts of Big Data are needed.

Closed innovators are actually performing “intellectual land grabbing” by committing

customers to their product and with this lock-in effect removing potential future data sets out

of the commons information pool. How much impact this movement will have compared to

the rate of development of open innovators is to be seen.

Will the market become separated by companies chasing the silver bullet and achieving to

be the ‘Apple standard’ in the market. Offering the entire product or will there be more of a

modular separation where niches of the value chain get filled with a more Android like

technology platforms and different companies running their cars on the platforms?

Based on the interviews, are firm moving away from collaboration efforts and

refocussing on the internal closed-off innovation, like for example in the past Tesla and

Mobileye split up after safety issues, see Appendix A. This was a while ago, but are there

similar examples where firms have knowledge-spillover issues and reframe form openly

sharing development? Furthermore, do the interviews show clues of firms actively focused on

pure driver data collection with intent of shielding this off from the possible future openly

accessible data pools, inhibiting open autonomous development in the process?

30

4 Research design

The level of analysis in this research is at firm-level. Big Data, Machine Learning and the

Artificial Intelligence (AI) needed for an autonomous (self-driving) car is a crossover field that

is newly developing. With the many cutting-edge initiatives, there is not enough viable data

available for a quantitative research approach. Therefore, an exploratory qualitative approach

has been used by interviewing the industry leaders and experts. Whenever possible, the

interviews have been conducted in English for easier codification.

4.1 Research Structure

The development of the autonomous car industry was chosen as a specific case study for this

research, so that the practical implications of disruptive technology and Big Data on

collaborative knowledge structures can be clearly determined. This research makes an analysis

of each firms’ different approach to autonomous innovation, within the specified developing

industry, making it an embedded single case study (Yin, 2009). Both Yin (2009) and Zucker

(2009) argue that in order to capitalize on the benefits of such a case design, there must be

replication logic in either a literal or theoretical form (Zucker, 2009). In this case, a theoretical

comparison applies. Theoretical replication logic looks at similar or contrasting approaches for

the same innovation problems and the motivation behind these decisions.

31

The interviews conducted for this research are semi-structured,

so there is a starting point in questions from where the direction

of the conversation can be determined. The initial structure of the

questions forms the basis of a comparison between interviews

and enable linkage between interview insights. According to

Saunders (2011) inductive methodology like an interview, see

Figure 8, is a good approach to explore new theory/strategy and

closely examine processes. To this end, the interviews were

matched with the different innovation frameworks. By

conducting the interviews, initial market developments for

autonomous innovation can be drafted. During the interviews,

patterns of innovation are observed and matched with the

adjusted working propositions to eventually determine theoretical understanding of the markets

innovation.

4.2 Interview Procedures

The interviews are semi-structured, forming the basis of the research instrument and allowing

for the exploration of deeper motivations of interviewees, leading to higher quality answers

and thereby achieving greater insights, as proposed by Horton et al., (2004). The interview

structure, containing its questions, can be found in Appendix D. Autonomous driving is a

reasonably unknown field and developing at a rapid pace. For this reason, (semi-)open

questioning was deemed necessary. The interviews started from a semi-structured question

followed by an open discussion on the topic. The structure provides a memory que for the

interviewer and helps guide the conversation with follow-up questions if the discussion strays

Figure6:Induction=Bottom-upapproach

32

off topic. By recording the interviews, the interviewer could focus his full attention on the

conversation. These recordings were later transcribed, to enable comparison between

interviews through data analysis, a method prescribed by Pope et al., (2000).

Interviews were in English or alternatively in Dutch, depending on the interviewees

preference and ability to communicate in English (judgement made by the interviewer.

Whenever possible, interviews were conducted face-to-face to better understand the

interviewees meaning by also reading their body language during the interview. Firms Amber

Mobility and TomTom were interviewed in person. Due to interviewees time constraints or

their location abroad, other interviews were conducted over Skype or Google Hangouts. The

interviews varied in length, the shortest being 20 minutes with TomTom and up to 45 minutes

with Amber Mobility. A second interview was conducted with Koen Lekkerkerker (RCS),

because he indicated to have some new insights. To smoothen the conversation a switch was

made from English in the first interview to Dutch in the second.

4.2.1 Pilot interview

To start with, a pilot interview was conducted at a small autonomous driving start-up from the

TU-Eindhoven called Amber Mobility. Doing so enabled the interviewer to optimize the

research design and practice his interview techniques. Making sure the structured questions

point the conversations into the right direction and enabling the interviewer to cope with the

flexibility and deepening on subjects from semi-structured follow-up questions and

conversation (Kvale, 1996). The interviews were all recorded and transcribed, plus notes were

taken during the interview itself. Probing during the conversation and recapping with a

reflection or example helps structure the answers and minimised the risk of going off topic, as

proposed by Leech (2002).

33

4.2.2 Interview pool

Multiple interviews were conducted from which data was collected and analysed. Most

interviewees where approached though direct emailing or through the author’s own network.

The interviewees are listed in table 1 below. Table 2: List of interviewees, their organizations and functions

Name Organization Function Frank Poort

Amber Mobility CTO (autonomous/electric ride-hailing service)

Vincent Laurense Stanford University PhD Researcher Autonomous Vehicle Control

Vincent Demunynck

TomTom Senior Product Marketing

Ryan Samaan

Waymo (Alphabet/Google) Mapping Operations

Carlo van de Weijer

TU-Eindhoven & ex-TomTom Reseach Track Automotive

Koen Lekkerkerker Robot Care Systems & TU-Delft

Lead Engineer of WEpods self-driving shuttles

Tom Westendorp NVIDIA Sr. Business Development Manager Autonomous Driving

Koen Lekkerkerker (2) follow-up

Robot Care Systems & TU-Delft

Lead Engineer of WEpods self-driving shuttles

Wiebe Janssen Ex-Telsa / Lightyear Powertrain mechanical engineer at Lightyear / Ex-Tesla engineer model 3

Of the 9-interviewed market players, not all were free to openly share their thoughts and

information. Especially from car manufacturers, there was a lot of hesitance to participate and

restrictions during the interviews. An exception was Amber Mobility, who utilize more of an

open, start-up culture with less fear of disclosing sensitive information. On the other hand, an

example of a very closed attitude is that of a contact at Tesla (Tom van Rijndorp) who is

responsible for AI development in Tesla’s autonomous program. He agreed to do an interview,

but after checking with Tesla headquarters he was not allowed to continue. After further

pursuit, it was possible to talk to an ex-Tesla employee Wiebe Janssen who used to be in charge

of development of the model 3 Tesla car. However, he was very restricted in what he was able

to share. With Uber there were similar circumstances, after speaking to 4 people at the firm

34

who were willing to participate, the Europe regional director closed down the conversation.

Even though initial contact with leading car manufacturers in the autonomous driving

industry was not successful, other big players were contacted and interviewed. The interview

pool can be categorized into 3 groups: Research Institutes / High-Tech firms / Car

manufacturers or ride-hailing services.

The universities of Stanford (Vincent Laurense) and TU-Eindhoven (Carlo van de Weijer) were

approached in the category of Research Institutes.

- Carlo van de Weijer is in charge of the research track Automotive at the TU-Eindhoven

and supports Amber Mobility plus other autonomous initiative. He is regarded as one

of the leading experts in autonomous development in the Netherlands.

- Vincent Laurense does his PhD research in the field of Autonomous Vehicle Control.

The lab where he does all of his testing is called the Center for Automotive Research

at Stanford; CARS is the acronym, which unites many car manufacturers (OEMs), but

also suppliers, car insurance companies, tire manufacturers to share ideas on mobility

and solve automotive problems. Vincent is directly involved in a project where it works

closely and openly together with Audi and Volkswagen.

Although getting through to car manufacturers actively participating in the development of

autonomous driving was a big challenge, two parties were interviewed.

- First off, Amber Mobility shared their insights and innovation development for the

future. At this time, Amber Mobility is a ride-hailing service with electrical cars that

have semi-autonomous features. In the near future, they plan to no longer build on their

BMW I3 test cars, but want to build their own car from the ground up, called the Amber

One. Their motivation for building their own car is to better integrate their own

35

autonomous bridging systems, as conventional cars do not really enable fully

autonomous support.

- Second, Koen Lekkerkerker of Robotic Care Systems (RCS) involved in autonomous

shuttle bus WePod was interviewed. RCS is the leading firm in the collaboration for

building the WePod shuttle bus. Initially, the collaborations started-off with many

participants, but after problems with closed innovators the collaboration slimmed down

to only actively open collaborators, like for example Nvidia.

- And Thirdly Wiebe Janssen an ex-Tesla employee was interviewed, but sadly the

interview was not fruitful without any new information disclosure or insights. The aim

of the interview was to circumvent the Tesla HQ shutdown of my interview with Tom

van Rijndorp at Tesla, by interviewing an ex-Tesla employee and gain some insights of

how they innovate, the company culture and his personal experiences. But during the

interview he was reluctant to disclose even the smallest detail with regard to Tesla

because of a signed NDA. This gives a good indication of how closed-off Tesla

innovates and instils this into its (ex-)employees.

In the category of High-Tech firms entering the autonomous driving industry, Waymo, Nvidia

and TomTom were contacted.

- The content driven mapping company TomTom was interviewed about how they plan

to position themselves in the developing autonomous market and what degree of open

innovation is possible for a company so heavily dependent on data collection.

- Nvidia is the leading computer chip manufacturer for autonomous driving systems.

Beside their hardware, they offer an open software development platform for

autonomous driving. Nearly all autonomous vehicle developers use Nvidia hardware

and software, even fully closed companies like Tesla have switched to integrating their

36

system. As the computing power of the Nvidia chip and software is 10x bigger than

anything else on the market right now.

- Lastly, Ryan Samaan in charge of mapping at Waymo was interviewed. Formerly

Waymo was known as Googles self-driving car project that started in 2009. Now

Waymo is the subsidiary of Alphabet focussing on autonomous car development.

Waymo stated “we have no plans to become a vehicle manufacturer or supplier to the

auto industry. Instead, Waymo intends to partner with other companies to provide

vehicle platforms while retaining control of the automated driving stack and providing

mobility services to consumers”. In 2017, Waymo announced a partnership with Intel

to develop autonomous driving technology together and develop better processing.

In conclusion, a good spread of market players was interviewed. Giving a good representation

of different innovators in the autonomous driving industry. Talking to car manufacturers, High-

Tech entrants and highly collaborating Research Institutes. The fact that research institutes like

Stanford also were able to give big insights into their collaborations with big car manufacturers

like Audi and Volkswagen compensate partially for the failed interview attempts with Telsa

and Uber. Combining these insights with the car manufacturers from Amber Mobility and

WePod covers the car manufacturing side of the market to some degree. Enabling an

assessment of the field of how they innovate and how they perform in the race for technological

development.

4.3 Strengths and limitations of research design

Autonomous driving is a reasonably unknown field and a small market, making for small

sample sizes, further supporting a qualitative approach but making the rigour of the study

strongly depended on how close to the actual developing source the interviewee is. This is a

primary market research where not all leading experts were willing to cooperate or could not

37

be reached. Yet looking at the array of interviewees and their place in the autonomous driving

space, shows important players with leading innovations structures that seem to overlap and

show market conform developing practices of how to solve the autonomous driving problem.

For a more thorough assessment of the field, more developers must be interviewed.

There is still room for better representation of big car manufacturer, but for an initial insight

into market innovation the interview pool suffices.

38

5 Data and Analysis and Results

This chapter analyses and presents the results from the conducted interviews. Based on the

literature derived working propositions stated in chapter 3, the interviews will be structured by

codification. By coding the key inter-related themes, market behavior towards the propositions

is interpreted and explains opposing views towards open innovation models. Further learning

effects from market performance can be added to the innovation stance of each market player

and under what circumstances.

First, the discovered inter-related themes and enclosing codes are identified and

structured, by executing a word query analysis.

Second, the propositions are analyzed in comparison to the interviewed firms and their

stance to the matter. This is done by presenting the support or resistance of the proposition in

a table combined with factual quotes.

Third, a cross-table overview summarizes the important findings per interviewee with

regard to the propositions to create a clear overview.

5.1 Data Analysis The initial step of the data analysis is coding the imported transcriptions. Coding qualitative

data consists of summarizing parts of the interview in words or short phrases, assigning

attributes to the text (Saldaña, 2015). By doing so, comparisons can be made between

interviews by looking at overlapping codes and pattern recognition. To process the transcribed

interviews the coding is analyzed, recoded, annotated and eventually the basis is created for a

summarization of the interview, as can be seen in Figure 7.

39

Figure7:QualitativeanalysisusingNvivo

Nvivo 11 is the software package used to analyse the interviews in a thematic way

(Braun & Clarke, 2006). It provides different types of analysis options. First a word cloud is

generated, see Figure 8, displaying an overview of the most frequently used words during

interviews. These create the first indicators for important theme’s and topics.

Figure8:WordCloud

Import Code Query & Visualise

AnnotateSummarize

cardata cars

company

system

autonomous

amber

buy

companies

innovationdevelopment

driving

model

newtechnology

time

together

work

bmw

case

level

moving

better

build

collaboration

drive

software

vehicle

business

future

lidar

mobility

oem

partnership

problem

visionadvantage

algorithm

feeding

integrate

knowmain

making

maps

mercedes

part

parts

platform

sensors

smart

speeds

tier

years

algorithms

approach

buying

closed

competitive

control

details

driver

edge

effort

faster

instead

knowledge

law

learning

leave

legal

made

next

oems

pilot

possibility

private

sensor

sharing

subscription

systems

account

advanced

amsterdam

another

40

As can be seen, key words like Innovation, Collaboration and Data have a high frequency of

reoccurrence across the interviews. This collection of words forms the first indicators for

potential coding theme’s. Additional to the Word Cloud some text searches have been

performed with regards to innovation, collaboration and partnership (See Appendix B). Giving

a broad overview about the context where these topics are used. The relevance of these key

words compared to the working propositions determine the resulting theme’s: Collaboration,

Innovation and Data, due to their influence on the working propositions. During codification,

more codes are added which can be placed under these global themes and further clarify

interview stances towards the proportions. Figure 9 showcases the coding spread under each

chosen theme, also indicating the levels in coding structure.

Figure9:Thematiccodingspread

Next up, with a better understanding of the important themes, the actual coding itself was

executed on the transcriptions. Starting with a base coding structure under each theme. During

processing of the transcribed interviews, relevant sentences where matched with the coding

structure, plus new insights during processing generated extra codes that where added during

codification, restructuring the code hierarchy after each interview. After all interviews were

Big

Data

Collaboration

Innovation

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coded, the resulting final coding structure was established and can be found in Appendix C.

Codification plays a big part in the quality of the research, as research excellence rest in a large

part on the excellence of the coding (Strauss, 1987).

5.2 Results For the results of the interviews the following steps were undertaken. For each proposition,

relevant results are presented in a table. Each table consist of 3 elements: Firm, if the person in

question is in favour of the proposition or not and in the third column additional supporting

interview quotes are illustrated. An interviewees stance on the propositions is expressed in

“Pro” (in favour), “Con” (not in favour), “Unclear” (no relevant answer was given). Based on

the performed coding, specific sections of the interviews were pinpointed in search of

answering the working propositions. The context of referring codes to the propositions were

analysed for positive or negative connotations within the sentences itself and its surrounding.

5.2.1 Working Proposition 1: Innovation Speed Collaboration and sharing of information increases your partnerships’ innovation speed.

The proposition states that collaborating during the innovation process and sharing information

for the development of autonomous cars, enables the collaborators to innovate at a higher

speed. By analysing the interviews for relevant answers the proposition can be tested. In order

to filter the interviews, the codes “innovation speed” and sub-nodes were analysed for concrete

quotes with regards to increased innovation speed. Table 3 shows the results.

Table3:QuotesWorkingProposition1

Company Stance Proposition

Supporting statements

Amber Mobility

Pro -“At the moment everybody's using for example Nvidia graphics processors. They have the means to improve that and in a much faster rate than Tesla is, because they're never going to be as big as what everyone else is using.”

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- “If all the OEM’s come to the same point and all the companies that generate that data are feeding that back into the map data, feeding it back into the map maker, the Lidar data you sent back to the Lidar supplier and so everybody feeds back the data to the manufacturer, that can improve it which also leads to improved performance at the side of the one that's feeding the data back. So, you have a business model and you have a vicious circle where it only becomes more positive.” - So actually, opening up everything, speeds up the process of innovation instead of closed private innovation? Frank: “Yea” -“It's impossible to do something very quickly and in a flexible way (for a big company), so collaboration there is key.”

Stanford University

Pro -“if you are directly involved in research you can see more than the results of a published paper. You can see the history of how you come to a conclusion and which is the best framework you can develop for.”

TomTom Con (onesided- inbound)

-“And yet it (closed off innovation) could probably slow down innovation, than if we would all openly work together. But I think everyone is just trying to protect their own asses.” -“Right, open innovation by everyone collaborating and being friends would be better. But this is a really competitive market and I think everyone sees a lot of value to be captured, that they want to capture themselves.”

Waymo (Alphabet)

Pro (recognition)

-“We are using a little bit of the AI stuff of Alphabet …There are some tools that I use directly that are training the AI just while I've been there. When I started up to now, we've started mapping certain road features in last year and a half. So it's accelerating really quickly and it's really interesting to see how easily the software engineers are having an easy time of it. It seems like it's been very easy to port things over from labelling images of selecting road signs, to being able to label road features on the mapping end.” -“We wanted to use Waze as kind of like a crowdsourcing for scouting missions around the city that we're mapping. … It would have saved a lot of headaches.”

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TU-Eindhoven & ex-TomTom

Pro I think that autonomous driving will arrive very soon. Mainly in the form of comfort actions, like taking over in traffic and for long halls on the high-way.”

Robot Care Systems, TU-Delft & Wepod

Pro -“Lyft often is compared to being the Android of autonomous taxis. They appear to have entered a wide range of collaborations with an open attitude and this seems to pay off. They are starting to overtake Uber as a ride-haling service.” - “With some people there was a very open collaboration. Usually that that works best.” - “Actually, you see that if you manage to do these collaborations, you can indeed form a serious challenge to competitors in the markets that are much bigger and have much more financing.” …. “Technology wise, the differences aren't that big like the one with the 10x bigger budgets aren’t 10x times better and I think that's compensated by the parties with lower financing such as ours, due to good collaborations.”

NVIDIA Pro -“We find it very important to also work together with start-ups. There are many small companies that produce good software.” -“By offering our Nvidia development platform to start-ups, they also have access to the different developed tools added to the platform by others.”…“These kind of tools (for example the recording tool) help starting companies to link their cameras to the platform and enable them to run their network and judge the potential of their added value proposition.”

All interviewees but one (TomTom) gave a positive confirmation in their belief of increased

innovation speed due to collaboration. Although TomTom does recognize that working

together and sharing information has a high chance of increased innovation, it looks at the

emerging market space from a very protective point of view with high risk of knowledge -

spillovers. TomTom tries to find its FMA6 by being the first to offer a real HD maps7 option

for future autonomous cars. It believes the market consists of such a competitive nature due to

6 FMA: First Mover Advantage. 7 HD maps: Precise roadmaps up to 10cm for accurately locating autonomous cars.

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it being technology driven and thinks all other players share their protective vision, disabling

fruitful collaboration.

And yet within this market space there are all sorts of development collaborations

gaining traction with positive outcomes. Nivdia’s research and development platform is not

intended to earn money, but to help as many market players as possible. They believe in

growing the market first, being highly involved and supporting where able. Of course, Nvidia’s

platform is not just charity. From a strategic standpoint, Nvidia doesn’t need to develop much

software itself, but enables others to do it for them and with the spider web of collaborations it

runs with their platform, they keep a pulse on the market and stay ahead of the curb.

Amber Mobility acknowledged that because everyone is using the Nvidia GPU’s and

shares a lot of its user data back to Nvidia, it enables Nvidia to improve their GPU’s and

supporting software at a much faster rate than their competitors. The sheer amount of data

feedback they receive cannot be match by any other competitor in the processing segment.

Other market players like RCS clearly state its willingness to share their generated data sets

with the public, because they believe in acceleration of innovation through sharing and

collaboration. Helping others innovate, boosts all research and others research is for a large

share what provided RCS with new insights and capabilities. Giving to the community will be

rewarded with new development insights and growth as an entire industry.

Conclusion

There seems to be recognition of increased innovation speed due to collaboration in general,

but for the application in the emerging autonomous driving industry the consensus is split due

to some innovators who think the potential for value capture in a data driven market with the

risk of knowledge-spillovers outweigh the benefits and speed of collaboration.

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5.2.2 Working Proposition 2: Open Innovation Models Open innovation models prevail in the big-data driven autonomous driving industry.

This working proposition refers to the seemingly growing use of open innovation models for

developing autonomous cars. Are the firms’ innovation with openness and shared information

exchange out growing more closed innovation models? Is the gap between late market entry

open innovation initiatives and closed market leaders closing or are they even overtaking

conventional models? For the text analysis, the main focus is directed on the codes: “Open

Innovation”, “Modular”, “Pooled”, “Spinout”, “Bi-Directional Collaboration”. Following

result can be found in Table 4.




Amber Mobility

Pro -“That’s the deal that we trying to make with Here and TomTom, is to give our data back and they can turn it into Maps and then give us the maps back.” (bi-directional information sharing) -“We're working with TNO which is working with NXP but also BMW / Volkswagen and all the major OEM’s are selling their software too. Which then gives them data back and so they have multiple means of improving that. They have multiple means of getting data, something that would have taken us a lot of effort or money to you to get that point.” - “Nvidia is very good in doing the graphics processing platforms, but they also make algorithms for vision. So, you have different parties all around that are very good at the very specific things they do and what the big OEM’s do is, also what we're doing, put everything together and then mainly now looking at all the gaps in that system.” (modular innovation)

Stanford University

Pro -“We have the Center for Automotive Research at Stanford; CARS that's the acronym, which unites many OEM’s, but also suppliers, car insurance companies, tire manufacturers to have share ideas on mobility.” -“You would think that the OEM’s would not be willing to take those risks I guess, or to give away their rights to see things first hand. That's not even true. They're surprisingly open to work with this open model. So not too nervous about protecting everything.”

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TomTom Con -“Maybe 6 or 7 years ago, car manufacturers were thinking: 'we

built the cars so we will build self-driving cars'. And then there's a whole range of other companies, you mentioned Tesla, that are car manufacturers or a technology company that happens to make cars. And I think it's true that these OEMs, they saw this, they recognize the threat and said; 'hold on, if we don't really work with these technology companies, we're going to miss the train. So, I think that kind of shift happened a few years ago and that the OEMs really have to work, or even acquire these technology start-ups because there are more and more moving towards a mobility company, or technology company.” -“there are many companies that have shown interest in our HD map. I think we've provided more than 50 companies with a sample, actively working with a few OEM’s, some disclosed like Volvo for example or some are not disclosed. And also, with technology companies like say Nvidia, we also have collaboration in specific areas.” - “There are some collaborations, if zooming in to the Bosch for example that we announced a few months ago, we create some content in our HD-maps which works really well with their radars. So, what you need is a two-way conversation, saying 'What do you need, what do we need', to build that. But that really goes per project or on projects with a specific focus, then yes I guess we would open up but I think in general TomTom has a somewhat closed approach to innovation. Saying, our content on a case by case basis we would open up and disclose information.”

Waymo (Alphabet)

Con -“The Waze thing is my prime example for that as far as collaboration with others. Even though that's internal. This would solve so many of our issues. One of our biggest issues on mapping is world changes related to construction. We create the map and it's the state of the world at a certain point in time and when it changes the car gets confused in those areas. When the world changes we have to close the map. It cuts off routes, increases commute time, it's a problem that we're fighting right now on mapping. With the people using Waze as our scouting vehicles, we'll be able to know immediately when to close the map and begin data collection in those areas immediately.” “But it isn't that simple to get that data specifically. But for example, we wanted to use Waze as kind of like a crowdsourcing for scouting missions around the city that we're mapping. For one reason or another, Waze is cut-off from Waymo.”

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-“In the company itself, there is there's a lot of knowledge sharing on a need to know basis, if that makes sense.”…“A lot of the parts of the brain aren't touching they're not communicating.”


Pro -“Tesla got into problems by trying to do everything on its own (internal innovation). But after the failed collaboration with Mobileye, they were forced to switch and collaborate with Nivdia. Internal development didn’t go as smoothly as they hoped. The Tesla car right now can do less autonomously then 2 years back.”


Pro -“For a company of our scale we're simply very small on limited budgets and limited amount of men power we can spend on such a project. We are very much dependent on setting up good collaborations.” - “With some people there was a very open collaboration. Usually that that works best.” -“Well in general I think if there's things that we can open up, that is helpful for others to actually help them innovate, we pretty open to open sourcing it.” -“We actually open sourced the annotation tool for example, which we use to build our data sets. that tool is available to the broader public, everyone can use it. Because these are the things that if people actually like it they can start contributing to it as well and you both benefit.” (spinout innovation) -“You actually see the Android vs IOS concept in platforms development from a Baidu an with a certain degree also Nvidia and NXP but the latter with vendor lock-in motives, offering an open source platform for autonomous driving.”

NVIDIA Pro -“We can try and compete with them or we can work together with them and support them with our chips that they cannot produce, with which they add their speciality to our hardware to present a total finished product.” -“At the end of the day you don’t earn much money with the development platform and it’s there to help who we can.” -“With a few OEM’s we have far reaching collaborations, where the innovation models can differ, where we fully co-develop instead of just enabling access to our development platform for use.”

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According to TomTom, collaboration is needed due to the nature of the automotive market that

is changing from a standard car industry towards more of a technology driven market. Although

TomTom does collaborate introducing its HD-maps to market players, it doesn’t disclose much

information, but expects it users to unidirectionally share their data with them to optimize their

product. Although TomTom identifies the need for a modular approach to the autonomous

driving problem, they still approach the innovation process from a closed perspective, and state

they are a content driven firm.

In the collaborations TomTom is very careful in what information they disclose for risk

of knowledge-spillovers. TomTom is even working together with mapping competitors like

Here for German car manufacturers, thus the risk in these collaborations is very high for

knowledge-spillovers. TomTom underlines how competitive the market is right now in their

view. In this market, you can be complementary in certain fields and competitors on other,

further complicating the innovation development and risk of knowledge-spillovers.

Ryan in charge of mapping for Waymo, perceives his company as innovating via a

closed model. The rate of change in technology and data collection is at such high pace, that

there is no time for meeting with oversight and discussing bridging problems with other parties.

All activities are focused on direct problem solving and keeping up with the rest of the market.

Waymo is part of the Alphabet umbrella and poses possibilities for collaboration between the

firms, yet very little openness is observed, even within Alphabet. Some basic AI algorithms

are used for mapping but merely output itself and running it without real opened back-end

coding for personal tweaking. Even between sister companies, knowledge-spillovers are

perceived as a risk. Further user data from sister companies like Waze would have great

potential for data collection and increased innovation speed for mapping, but are not utilized.

A very segregated closed-off innovation process is used.

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In sharp contrast with these 2 parties the rest of the interviewees firmly believe in the

application of semi- to fully open innovation models. Stanford collaborates openly with a wide

array of firms, all cooperatively searching for solutions in autonomous driving. The joint

innovation shows signs of the pooled innovation model. The publication of the research further

indicates open spinout models. “The goal of the project is to show that autonomous vehicle

control can be as good as the best race car drivers”, if all cars drive with the skill of a race car

drivers’ precision, the safety in traffic will go up! Stanford has community driven motives to

showcase the capabilities of autonomous transportation and showcasing the strengths of the

technology. Growing the interest for the market in an open way for the whole world to see.

The Success of Nvidia’s collaborative modular innovation model becomes apparent as

it is the market leader in hardware chips and supporting software for image processing in the

autonomous car. It has successfully found its niche and build from there. There is some

carryover from years of experience in GPU production from the gaming industry, but compared

to Intel it is 2 to 5 years ahead, thanks to all the collaborative input into the Nvidia platform by

the entire market. Nearly all autonomous driving initiatives are using Nvidia processors, even

Tesla, due to Nvidia being so far ahead of the competition, thanks to its open platform.

This success of their innovation model has not gone unnoticed as others enter the

market with similar idea’s. Baidu’s fully open sourced development platform for autonomous

driving just emerged and NXP soon followed.

Conclusion

Overall the market seems to be shifting towards more and more open innovation models. From

more conservative hybrid modular models like TomTom and Waymo to pooled and spinout

models like Stanford and in some degree also Nvidia. Most market players confirm the need

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for a modular solution in developing the autonomous car, as fully internal innovation can’t

handle the autonomous complexities.

5.2.3 Working Proposition 3: Second Enclosure Movement The second enclosure movement is a real threat to open innovation.

The privatization of user data in closed innovation models with the sole intent to generate

inbound information flows without reciprocation, destroy open collaboration incentives. For

interview analysis the codes: “Innovation - closed”, “Collaboration – Unidirectional”,

“Knowledge Spillovers”, “Information Asymmetry” and “privatization” are utilized. The result

can be found in Table 5.




Amber Mobility

Con -”Tesla has a very smart model. They put all the sensors in the car and they have people driving around and you have a machine learning algorithm that learns from everybody's driving, from which they generate the pathfinding algorithm and the sensor vision themselves. This is a very smart thing to do because they can gather a lot of data this way.” --“Tesla’s really trying to do everything. They built their own motors, they build their own batteries. Trying to do everything themselves because they do not trust anyone. In my eyes, I think they are very Paranoid.” - “But everyone wants a competitive edge and if you integrate in a partnership, where also all your other competitors are allowed. it's not a competitive edge, you become better from it, but all the rest of it does too and that's why I guess they keep buying other companies to also take the possibility out that other people do the same thing.”

Stanford University

Con -“There are so many aspects to solve in the self-driving car problem. I don't see anyone that's going to take the big prize and solve everything.”

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-“I'm very fortunate to be in an academic environment where it is all about being open. That being said I do see that people do believe in that model (Closed privatizing innovation). In the past, we have been working with contracts for manufacturers directly. But we are moving to a model where we get sponsors for the lab and there might not be as a strong tie to that manufacturer, but we will work on this together and share all the data. So, it's more like a gift rather than a one to one project that we do to get where others might not benefit as much. And people seem surprisingly open to that model. You would think that the OEM’s would not be willing to take those risks I guess, or to give away their rights to see things first hand. That's not even true. They're surprisingly open to work with this open model. So not too nervous about protecting everything.”

TomTom Pro - “The HD map is built with our own mobile mapping vans, so that is our material, our equipment, only used and administered by us. There's only 50 or 100 of these vans driving around the world mapping basically the road for us. Everything the HD-maps does today is built on our own sources, but in the future I think we understand that once these HD-maps get deployed they need to stay up to date and that's something you cannot do or is not sustainable to do just by yourself. So, we would ask also customers to feedback what their sensors observe and then from that basically we would kind of cherry pick the data that we see is missing when we need as additional open source.” - “I don't think we would move to an open platform I think the technology is too sensitive to do that. Maybe other domains besides the mapping pillar in autonomous driving could do that but for ourselves we'd like to collaborate with a lot of partners but I don't think we would throw open our data because that's what we are, a content driven company. So, we need to preserve that.”

Waymo (Alphabet)

Pro - “We send cars out to collect data and we build maps off of the data they collect (privatization).” - “It is collecting public data and privatizing it, but people opt into it whenever they decide, or whenever they will decide to have a Waymo come pick them up. In the same way that we're giving up our data to Lyft an Uber when they come and get us or even when we use it we're giving up our data. I mean that's going to be the future. Our world is how people privatize public data… I think that Uber and tons of other companies see it as a public transport service

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and that's how you get people to opt into your privatization of their data. Just make it cheap, make it possible and useful.” -“At Waymo specifically there's no collaboration with any other any other company at all.” (Closed Innovation model)


unclear -


Con -“The automotive industry is from itself a very conservative and slow adapting (closed off) market.” - “As long as you make good agreements on the exact rights of IP, so that you cannot borrow someone else's contribution for a different project without their approval. That kind of agreement. But some are more protective and actually throughout the project that actually caused us to put an end to part of the collaboration that we started off with.” - “But if you look more at, for example collision avoidance algorithms or path planning algorithms and you develop something in-house. That would really give you strategic advantage over competitors, those things I doubt we will open up for the broader public.”

NVIDIA Con -“If you look at processing power we have a major head start. We are ahead about 2 year compared to the rest and with some OEM’s we have far-reaching collaborations, surpassing standard models of innovation, where we develop for and with them and not only enable our platform for them.” -“Tesla that in principle wants to do everything on its own with regard to building autonomous cars, but it is hard to do everything yourself. Even other suppliers like Mobileye or even Baidu need help.”

Stanford hasn’t encountered any potential risks of privatization limiting their open pooled and

spinout innovation. They work closely together with big OEM’s for testing and pooling

resources together in solving the autonomous driving problem, but at the end of the day still

reports all result to the public. As Vincent at Stanford notes, there are advantages at being

involved with the innovation process itself and not only waiting for development result. A lot

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extra can be learned by the development path towards a result. So, there is still incentive to

participate with open innovation initiatives. As a result, many OEM’s still participate in the

open innovation model and are not to bothered with knowledge-spillovers.

Amber Mobility and RCS both think the protective attitude from parties like Tesla,

Waymo and even TomTom are paranoid. They don’t see a single market player finding the so-

called “silver bullet” and be able to offer a stand-alone autonomous car developed in-house.

RCS actually broke off ties with certain partners that showed to much of a protective attitude

during their innovation process of a self-driving shuttle called WEpod. The one-sided

information flows in collaboration killed off the partnership. Not the project as a whole but the

consortium of market player in the end only consisted of active sharing partners.

Nvidia is blowing the competition (Intel) out of the water thanks to all its

collaborations. The sheer amount of data collected from collaborating users to improve the

platform is immense.

The automotive industry is traditionally a very conservative and slowly adapting

market, from which many OEM’s originally started off with a closed off innovation model,

trying to find its competitive edge in the emerging autonomous market. TomTom notes that

depending which stage (pillar) of autonomous driving a firm is developing, it can differ how

protective you as a firm need to be. Certain modules of autonomous innovation are highly

content driven like mapping, while other might be more hardware driven with less risk of

knowledge-spillovers, e.g. Nvidia GPU’s. In the end TomTom wants to privatize as much user

feedback as possible into its maps and sell this content to the market.

Waymo agreed that many of the open mapping data like Open Street Map turned out to

be insufficiently accurate. Forcing them to build their own maps. Waymo personally

acknowledged it is collecting public data and privatizing it. But that is the world today. In their

vision, the world consists of people privatizing public data in one way or another. Customers

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will decide to opt in and give their user data for enabling the service that is delivered. Waymo

looks at the autonomous driving problem from a public transport perspective. Just make it

cheap, make it possible and make it useful.

Conclusion

The bottom line is that not one player seems to be able to solve the autonomous driving problem

on his own. Even a Tesla is now using Nvidia processing chips and software imaging platform,

after a failed in-house attempt. The old-fashioned attitude of trying to privatize all new progress

seems to fail and the need for collaboration overpowers all closed initiatives. But within the

different innovation models more software driven innovation does seem susceptible for

privatization and thwart a possible collaboration. In the field of mapping for example there is

a lot of separated closed innovation happening with parties like Waymo, TomTom and Here.

5.3 Concluding Within the interviews, all aspects of the autonomous car industry where covered, High-Tech

firms, research institutions and car manufacturers or OEM’s. By which showcasing different

views towards the developing innovation roadmap of autonomous cars. Via the data of the

interview cases a cross-case analysis is generated from the codification and innovation

framework (see Table 6). By matching a firms’ innovation codes to the constructed

innovation framework from the theoretical framework, an identification of the autonomous

driving innovation process appears.

Table 6: cross-case analysis

Organization (Type)

Type of Innovation

Elements of Innovation Model Increased Collaborative Innovation Speed

Amber (OEM)

-Open Modular

-Information exchange partners e.g. Nvidia -Sharing use of partner product

Yes

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-Each building its niche capability on top of shared information -Bridging technology into 1 system

Stanford University (Research Institute)

Open Spinout/Pooled

-(Publishing) Open Sharing Research result -Co-development + Collaborative sharing with OEM partners

Yes

TomTom (High-Tech)

-Closed Modular Unidirectional

-Accessibility API to HD maps -Feedback map users to improve own maps -Modular approach by offering maps to collaborations.

no

Waymo/Alphabet (High-Tech)

Closed (Semi-Open) Modular

-Initially trying to offer total vertically integrated autonomous solution. -Forced into collaboration by market competition. -Partnering up with Intel(Mobileye) + ride haling services like Lyft. -Data from sister company Waze would contribute significantly for quicker mapping. Yet no access.

no

TU-Eindhoven (Research Institute)

Spinout -support autonomous initiatives like Amber Mobility with new research insights from the TU-Eindhoven

yes

Robot Care Systems & TU-Delft (EOM)

-Open Modular + (Partial Spinout)

-Consortium of developing partners for Self-Driving Shuttle. -Bridging software components of other partners require information sharing. -Small firm with limited man power and budget requires collaboration and shared innovation. -through collaborative open modular innovation able to compete with much bigger initiatives. -Sharing generated data sets (deep learning) -Open source annotation tool -Contribute back to public available information and tools. -Share piece of code to a German University

yes

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NVIDIA (High-Tech)

-Open Modular + Pooled

-Engage in a lot of collaboration with start-ups in the hope for future platform growth and supporting good software initiatives with growth potential. -Nvidia Drive development platform for R&D and prototyping+ support from their processors. -Spinoff and pooled initiatives like when connecting to the Nvidia development platform you can use all tools available on it.

- free network training tools - free camera recording tools - availability to the neural

network of Nvidia (black box, no direct algorithm coding)

yes

Based on the previous table an overview is created for showcasing which innovation models

are predominantly present in the autonomous driving industry. The cross-table registers a rise

of more open innovation models, starting from pooled and spinout models from research

institutes like Stanford and RCS. Stanford does pooled research with big OEM’s for

autonomous driving but then follows the spinout innovation model for its research results by

publishing them for everyone to do with and continue as they like. On the more commercial

business side this initial participation with more open innovation models has translated to new

business models with a more open character. Nvidia for example is really embracing its hybrid

modular open innovation model, where it tries to share as much as possible with as many

interested parties possible. Feeding of the joint effort for innovation. The amount of data

collection due to collaboration seems to really generate a big competitive edge in the market.

And at the very end of the spectrum there are now even full open source autonomous platform

initiative like Baidu. With that forging strong alliances and partnerships with big player.

On the opposing side, the initially closed innovators like Waymo and TomTom have

shifted their fully closed innovation model towards partial collaboration with other parties, but

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remaining very closed in knowledge transfer and internal innovation. Waymo for example

partnered up with Intel to co-develop particular processing software and uses Lyft’s ride-

hailing service to generate user-data for both AI training and mapping. All the while still trying

to create one directional inbound information flows on top of their internal innovation.

TomTom itself displays a bit of a hybrid in terms of openness as it tries to commercialize its

mapping with restricted access, while it still askes for user feedback. By doing so actually

privatizing collected data and potential future open data.

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6 Conclusions and Discussion

This final chapter answers the working propositions and research question and further

describes its findings and implications on the basis of a discussion.

6.1 Research propositions

First 3 working propositions were put forward in search of answering the research question.

The working propositions were analyzed through a set of semi-structured interviews with

market players and industry expert in the field of autonomous driving.

• WP1: Collaboration and sharing of information increases your partnerships’

innovation speed.

Based on the interviews this proposition is correct. All interviewed parties acknowledged that

sharing information and collaborating with other partners increases their speed of innovation.

Only TomTom showed concern of how a technology driven market like autonomous cars, has

high risk of knowledge-spillovers and that a content driven firm like TomTom still choses to

restrict much information sharing, especially from their side. TomTom does see room for

increased innovation speed due to collaboration outside the mapping pillar of autonomous

development.

• WP2: Open innovation models prevail in the big-data driven autonomous driving

industry.

This proposition is also correct. There are a large number of open innovation initiatives in the

autonomous car industry. Ranging from partially open information sharing like Nvidia and

Amber Mobility till full-fledged open source platforms like Baidu. Five of the seven

interviewed firms share a lot of their information with an open innovation model, and even the

closed innovators like Waymo and TomTom switched innovation models towards hybrid

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coopetition, where it still can remain a closed innovator, but collaborates in certain areas where

it misses expertise.

• WP3: The second enclosure movement is a real threat to open innovation. This proposition is true till a certain degree, when looking at the present business approaches

of Waymo and TomTom. Both public user data and data collected internally is being privatized

for mapping and AI training purposes by interviewees TomTom and Waymo. As Waymo puts

it, “our world is about how people privatize public data.” Waymo indicated that much of the

publicly available mapping data turned out to be insufficiently accurate, forcing them to collect

their own data and build their own maps. However, neither companies were willing to

contribute their own data to the public domain, openly justifying “intellectual land grabbing”.

6.2 Research question

Based on the findings the research question can now be answered.



The research has shown that within the interview pool, a transition towards more open

innovation is happening. The increased complexity in the autonomous driving market due to a

higher technological demand for development has created a market that is dependent on cross-

industry knowledge. As also concluded in literature, the cross-industry knowledge gaps are an

important driving force for open innovation, because to bridge these gaps collaboration and

information sharing is needed. No single innovator with a fully closed-off innovation solution

can keep pace with collaborating innovators.

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The wide array of technical competencies needed to develop an autonomous car is

pulling apart the value chain and making a modular innovation solution a new dominant market

strategy, further fueling the need for collaboration. For example, Nvidia’s specialization in the

technology stack of AI and its processing chips, while another party Lyft is trying to be market

dominant in ride-haling solution and data-collection for autonomous driving. Interestingly

these companies are openly sharing as much information as possible and havening as many

collaborations with different parties as possible. In a modular market, open innovation is a

strategy used with which firms try to integrate their technology in many different innovation

processes in the hope to become the universal standard e.g. Nvidia’s semi-open autonomous

development platform.

The conventional closed-off automotive industry is shifting towards more open

innovation models by the day. Many hybrid forms of open-innovation are being adopted but a

general trend can be seen, with a clear difference between the traditional and the new players

in the automotive industry. Traditional closed OEM’s are collaborating in order to close the

high-tech knowledge gap. These traditionally closed-innovators are transitioning to hybrid

modular coopetition, a module of development to overcome knowledge gaps but still compete

in the other modules of the developing value chain. This hybrid form allows them to maintain

some control over their IP and reduce knowledge-spillovers. On the other hand, high-tech

entrants are going beyond the hybrid model toward completely open innovation. They are

collaborating more openly with companies who own a fleet to generate as much user data as

possible, seeing as they have little experience with the process of building actual cars and no

car fleet to test pilot their technology on.

6.3 Discussion

Within the interview pool, a clear transition towards open innovation models is seen. However,

the question remains if this transition will be extrapolated to the whole autonomous car market,

61

or even all high-tech markets. A long history of successful closed innovation models, due to

the ‘first enclosure movement’, is still effecting firms’ decisions and for several traditional

firms the tendency towards closed innovation prevails. It is clear that knowledge and the

protection of IP plays a vital role in the application of open innovation. It forms the crux that

determines a firms’ willingness to open their innovation processes or keep it closed.

The key lies in completely different circumstances of today’s markets. The innovation

speed plays an increasingly important role in such a data driven technological market space. It

no longer is a question if, but when a firm and its collaborators are able to produce a fully

autonomous car. The fact that the interviewed companies recognize the importance of

collaboration for innovation speed (see working proposition 1) is one of the most important

factors pushing them towards open innovation models. Wanting to be the developers of the

first autonomous car, openly collaborating firms are mainly interested in innovation speed and

that reduces the importance and fear of knowledge-spillovers. In an industry that is innovation

in such a high pace the importance of innovation speed trump the risks of knowledge spillovers.

It is worth taking some risk with then missing the boat. As a result of this changed mind-set,

open innovation models generate a greater appeal and the autonomous driving industry will

continue to shift towards more open innovation models.

A good illustration are the movements of Tesla, where they try to commit the Tesla car

buyers to their product and create a lock-in effect, pulling these first adopters out of the

potential innovation data pool of self-driving cars. Tesla is clearly afraid of losing control of

its IP and trying to close-off and privatize the data and following development. In the

meantime, open innovation initiatives like Nvidia’s development platform for autonomous AI,

are already overtaking most vertically integrated solutions such as Tesla, due to the increased

freedom in development and increased innovation speed due to information sharing. Open

innovation trumps the enclosure movement due to innovation speed.

62

6.3.1 Market Performance of Innovation Models

A report by Navigant Consulting (2018) further supports the finding of this exploratory

research. Navigant made a market performance assessment of car manufacturers in the field of

autonomous driving in 2017 and 2018, see Figure 10.

2017 2018

Figure10:Performanceautonomousdrivingmarket2017and2018(NavigantConsulting,2018)

When looking at which firms perform well and what innovation model they use, firms with

open innovation models start to become market dominant and confirming the suspicions of

open innovation effectivity. When looking at the level of openness in Figure 11 and comparing

how some of these firm perform, a clear pattern can be identified. Tesla with its closed

innovation went from a contender in 2017 to a challenger in 2018, falling behind to the high

pace of innovation development in the market. While firms like Waymo who started to open

up innovation via coopetition, rise from contender to market leader. Also, Volkswagen was

able to become a market leader partially due to its open collaboration with the Stanford CARS

lab. Similar effect can be found for Baidu that just entered the autonomous market in 2014 and

was still small in 2017. With its fully open source platform pulled in a lot of collaborations and

63

moved to a contender in 2018 (Statt, 2017). Thanks to Baidu’s open source platform and its

collaboration it is one of the fastest growing entities right now.

Figure11:Leveloffirmopenness

All these market trends can be linked to the way these firms innovate and showcase the

importance of innovation speed. Innovators more focused on internal innovation and

knowledge-spillovers miss the boat and fall behind in a market space with such high innovation

pace.

6.3.2 Application in Other Markets

Enkel and Gassmann (2010) state that the more an industry’s idiosyncrasies correspond to

development trends like (1) globalization, (2) technology intensity, (3) technology fusion, (4)

new business models and (5) knowledge leveraging, the more appropriate the Open Innovation

model seems to be, see Figure 12.

Level of Innovation Openness

Fully Closed Fully Open

64

Figure 12:Development trends in the automotive industry (Ili et al., 2010)

Even though these 5 elements create a clear habitat for open innovation models to thrive, this

research has shown that the need for collaboration can differ between the different parts of the

autonomous technology stack.

In the last decennia, the globalization has pulled apart much of the traditional value

chains, creating more specialization and collaboration with niche partners. An IPhone for

example is not fully produced in America, but many of the component are made by Chinese

producers or even competitors like Samsung. Following the trend of global markets, innovation

also has globalized. Especially when looking in high-technological markets. Technological

innovation is developing at much higher speeds due to this globalized collaboration. It can be

argued that all technological innovation markets are subjected to an increased importance of

innovation speed, in order to keep up with fast innovating competitors. Potentially this can pull

many technological innovators out of their closed innovation and open the door for more open

innovation.

When for example looking at the Mobile Market, how much technological difference

is there between a flag ship Samsung phone or Iphone (None). Even newcomers like OnePlus

produce on par mobile phones in a matter of a year. It seems everything related to high-

65

technological development knows such high innovation space, that all focus must be to

develop your product as quickly as possible or risk falling behind the fast-moving market. It

is better to develop quickly in a collaboration then slow on your own. The trend of transition

towards open innovation models can be expected to nestle itself in all high-technological

markets as a result.

6.4 Limitations and future research

This research is an exploratory assessment of the autonomous driving field and how its

innovation models are being influenced by high-tech. Even though many types of market

players were interviewed, to extrapolate these results to the entire market would be precipitous

as the market remains somewhat closed, as some of the big OEM’s were not open for an

interview. And yet, market research by Navigant Consulting (2018) further supports the

findings, building rigor of the sample size of this interview pool. Making it more believable

that this is an actual representation of how the market is shifting towards more open innovation.

To further support the initial findings of an innovation transition happening in the

automotive market, a broader follow-up research is necessary. A larger number of market

players can be interviewed, perhaps including the presently closed-off car manufacturers who

by that time may have joined the transition to becoming more open. A future research will not

only allow for more interviews, it will also be able to incorporate a clearer market trend as the

market will have continued to develop in a certain direction.

66

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Appendix A - Case (Tesla vs Mobileye)

Tesla’s autopilot system is designed in-house but relies on a fusion of externally-developed

component technologies. The current autopilot can handle a limited amount of situations

(without the driver’s intervention) based on a variety of on-board sensors and equipment

including GPS, radar, cameras and real-time connectivity to Tesla servers. Mobileye was one

of the suppliers to Tesla’s semi-autonomous driving kit by delivering chips and software for

the image analysis, which help the car steer and stay in lane (Berlowitz&VonAhn,2016). How

and under which conditions to apply this technology in the car is disputed between the 2

partners. The Israeli founded Mobileye, a heavy hitter in the fast-moving race for developing

sensors systems for self-driving technology, acknowledged certain limitations to the provided

technology in terms of its capabilities for processing visual situations. Tesla blew this caution

into the wind and was determined to push for an actual semi-driving pilot, giving an other

interpretation to the information shared by Mobileye. Resulting in an accusation from

Mobileye towards Tesla for releasing the update prematurely knowing in certain braking

scenario’s the autopilot would not properly respond and have to leaning on other sensors to

cope with this. After the first deadly Tesla car crash these opposing views created a surprising

amount of daylight between the 2 normally tight partners. Resulting in Tesla terminating its

collaboration with Mobileye (Hull, 2016). Also Chris Lattner, Tesla’s head AI and autopilot

software, leaves after coming over 6 months earlier from Apple. Showing in-housing autopilot

technology to be a challenge and again forcing Tesla to new collaborations and expertise.

ii

Appendix B – Word Tree for Innovation, Collaboration & Data

innovation

Yeah . that's I guess holdingYeah . that's I guess holding

would be more fruitful forwould be more fruitful for

TU - delft our companies , SpringTU - delft our companies , Spring

thethe

They try to increaseThey try to increase

the larger OEM’s allthe larger OEM’s all

see a slowdown ofsee a slowdown of

screen . so it's notscreen . so it's not

coverage right now andcoverage right now and

speed . It's picking up inspeed . It's picking up in

so you're closely related towardsso you're closely related towards

privateprivateinnovation instead of closedinnovation instead of closed

actually more feeding towardsactually more feeding towards

open innovation , pushing for fasteropen innovation , pushing for faster

openopen

utilize open data andutilize open data and

try to develop withtry to develop with

thatthat

where you seewhere you see

to the theoryto the theory

in other industriesin other industries

ofof

the whole ideathe whole idea

the whole conceptthe whole concept

sortsortsense they'resense they're

see anysee any

moremoreand with someand with some

and collaboration towardsand collaboration towards

in that sense semi -in that sense semi -

And instead of actuallyAnd instead of actually

a society perspective , righta society perspective , right

ofof

the there's a lotthe there's a lot

speeds up the processspeeds up the process

speedspeedyou see youryou see your

and increases theand increases the

last couple years can’t dolast couple years can’t do

it could probably slow downit could probably slow down

have been a lot quickerhave been a lot quicker

also more knowledge and morealso more knowledge and more

a somewhat closed approach toa somewhat closed approach to

which is mostly on thewhich is mostly on the

was done in - house . Nowwas done in - house . Now

to speed up . Are theyto speed up . Are they

that comes from the bigthat comes from the big

speedspeed

It's picking up inIt's picking up in

because of all thebecause of all the

and try to catchand try to catch

sort of gets killed offsort of gets killed off

projects of Tomtom and autonomousprojects of Tomtom and autonomous

processprocessitself because like fiveitself because like five

especially regarding mapping andespecially regarding mapping and

or open sourcing software sometimesor open sourcing software sometimes

of the roadmap for Tomof the roadmap for Tom

isisshared because that isshared because that is

actually speeding up theactually speeding up the

instead of closed private innovation ?instead of closed private innovation ?

ininthe future in thisthe future in this

and more information sharing .and more information sharing .

growing a lot with sharinggrowing a lot with sharing

builds on each other andbuilds on each other and

back and the model ofback and the model of

andand

then fence off oncethen fence off once

killing off the wholekilling off the whole

because of this youbecause of this you

aeveryone collaborating and being friendsaeveryone collaborating and being friends

? Frank? FrankYea Casper : Tesla wasYea Casper : Tesla was

I Think they haveI Think they have

..

Well in your sense you'reWell in your sense you're

Saying our content on aSaying our content on a

Is that the same sortIs that the same sort

But it's going there IBut it's going there I

And I was kind ofAnd I was kind of

,,

than if we would allthan if we would all

so the thing they doso the thing they do

pushing for faster innovation , it'spushing for faster innovation , it's

it's actually more feeding towardsit's actually more feeding towards

iii

collaboration

you manage to do theseyou manage to do these

tried to do it withtried to do it with

there's nothere's nodifferent teams , butdifferent teams , but

At Waymo specificallyAt Waymo specifically

there was a very openthere was a very open

thethe

withinwithinpartners actually growingpartners actually growing

guess you don'tguess you don't

of Partners . So weof Partners . So we

flexible way yeah , soflexible way yeah , so

end to part ofend to part of

content slipping away duringcontent slipping away during

Casper : Okay so likeCasper : Okay so like

Tesla due to sharing andTesla due to sharing and

shifting a bit more towardsshifting a bit more towards

say that there are somesay that there are some

say Invidia , we also havesay Invidia , we also have

requires quite a lot ofrequires quite a lot of

only read about the Intelonly read about the Intel

manufactured by us . There's obviouslymanufactured by us . There's obviously

is there between the Wepodis there between the Wepod

inin

model of doing everythingmodel of doing everything

die veel samenwerking doendie veel samenwerking doen

development , that we're doingdevelopment , that we're doing

having to do that . Thathaving to do that . That

for that as far asfor that as far as

doing a lot of partnerships ,doing a lot of partnerships ,

do that but looking atdo that but looking at

Casper : So within the corporate /Casper : So within the corporate /

cars or whatever and formingcars or whatever and forming

at this moment with itsat this moment with its

academische wereld juist heel veelacademische wereld juist heel veel

you don't see any riskyou don't see any risk

wouldwouldmake it like , we'remake it like , we're

be more fruitful forbe more fruitful for

withwith

the parts manufacturers , butthe parts manufacturers , but

others . Even though that'sothers . Even though that's

other partiesother partiesso areso are

. What is. What is

Intel etc . It wasIntel etc . It was

any other any otherany other any other

all the other companiesall the other companies

a lot of partnersa lot of partners

which share data and buildingwhich share data and building

was that Intel is goingwas that Intel is going

verwachten Ik sprak ook iemandverwachten Ik sprak ook iemand

towards more open innovation ? Frank :towards more open innovation ? Frank :

there is key I guessthere is key I guess

that we started off with .that we started off with .

started with TU - delft ourstarted with TU - delft our

sort ofEnded up with onlysort ofEnded up with only

outside of manufacture , which isoutside of manufacture , which is

in specific areas . Casper : Yeah .in specific areas . Casper : Yeah .

how much information is shared ?how much information is shared ?

en wat meer informatie uitwisselenen wat meer informatie uitwisselen

because you have got everythingbecause you have got everything

as well you can indeedas well you can indeed

andandwith some more openwith some more open

in that sense semi -in that sense semi -

? Koen? KoenWell it's actually veryWell it's actually very

For a company ofFor a company of

. Usually that that works best .. Usually that that works best .

, it looks at for example ,, it looks at for example ,

iv

data

zit dat met jullie quazit dat met jullie qua

ze gewoon heel veel driverze gewoon heel veel driver

you're generating a lot moreyou're generating a lot more

you have two very largeyou have two very large

with extra data like localisationwith extra data like localisation

Which then gives them themWhich then gives them them

We actually started off usingWe actually started off using

using ? what kind of mappingusing ? what kind of mapping

us velocity , accelerations position , angularus velocity , accelerations position , angular

to use like google mapsto use like google maps

to get their data , criticalto get their data , critical

to collectto collectwould be ablewould be able

send cars outsend cars out

totoquestion might be referringquestion might be referring

public data for matchingpublic data for matching

They are talking about annotatedThey are talking about annotated

theirtheir

many OEMs are usingmany OEMs are using

it and they'll seeit and they'll see

into your privatization ofinto your privatization of

getgetother cars toother cars to

of systema willof systema will

the map maker , the LiDarthe map maker , the LiDar

thethe

you want to viewyou want to view

you have to provideyou have to provide

together and share alltogether and share all

time right ?: Yes yes .time right ?: Yes yes .

the one that's feedingthe one that's feeding

so everybody feeds backso everybody feeds back

sets where you getsets where you get

kind of cherry pickkind of cherry pick

inherently like they owninherently like they own

information ? Vincent : I meaninformation ? Vincent : I mean

everything . It's opening upeverything . It's opening up

build maps off ofbuild maps off of

that the richness of suchthat the richness of such

that back into the mapthat back into the map

thatthat

the companies that generatethe companies that generate

information feel ownership overinformation feel ownership over

getgetthat simple tothat simple to

globally and weglobally and we

communities . Everybody can takecommunities . Everybody can take

car once you've gotcar once you've got

able to crowdsource allable to crowdsource all

talking about the realtime traffictalking about the realtime traffic

sure . And there you needsure . And there you need

still having problems collecting enoughstill having problems collecting enough

Sometimes it comes with extraSometimes it comes with extra

sharingsharing

When it comes toWhen it comes to

they are pushing towardsthey are pushing towards

One of them wasOne of them was

sets , so it's mostly Imagesets , so it's mostly Image

service based on real timeservice based on real time

sensorsensorthis , so you havethis , so you have

differences between how thedifferences between how the

sensitivesensitive

to happen and soto happen and so

overs because of thisovers because of this

learning AI , is thatlearning AI , is that

say okay I give mysay okay I give my

same stuff with the samesame stuff with the same

Ryan : Yes , collecting data , compilingRyan : Yes , collecting data , compiling

right ? so you have someright ? so you have some

rawrawthen to get thisthen to get this

different levels of dat ,different levels of dat ,

question , we don't share anyquestion , we don't share any

publicpublic

stuff you're also usingstuff you're also using

related stuff we userelated stuff we use

openopen

you also useyou also use

they use reallythey use really

really using anreally using an

is how people privatiseis how people privatise

generalized , that it's alsogeneralized , that it's also

collectingcollectingthat ? So actuallythat ? So actually

is . It isis . It is

anyanyyou would loadyou would load

data and isdata and is

a petty there's Noa petty there's No

a little bit ofa little bit of

People assume that we basePeople assume that we base

ownown

youryourguys mostly generateguys mostly generate

course you're buildingcourse you're building

we're moving increasingly towardswe're moving increasingly towards

But it is ourBut it is our

ourour

we're giving upwe're giving upthatthat

itit

we would throw openwe would throw open

we use to buildwe use to build

TomTom , is to giveTomTom , is to give

openopen

somesomesaying There issaying There is

and Tesla usingand Tesla using

right now and privatizingright now and privatizing

off by companies usingoff by companies using

are trying to utilizeare trying to utilize

a lot ofa lot of

utilizingutilizing

useuse

there'sthere's

of course there are bigof course there are big

ofof

you've collected eight yearsyou've collected eight years

which makes the collectingwhich makes the collecting

what kindwhat kind

public datapublic data

Okey , andOkey , and

just wonderingjust wondering

Casper : SoCasper : So

of difference between sensitivityof difference between sensitivity

need exactly the typeneed exactly the type

is scaling in typesis scaling in types

interested in what degreesinterested in what degrees

data is the sourcesdata is the sources

access to that sortaccess to that sort

a lota lot

is thereis there

can gathercan gather

and collectingand collecting

localisation data or maybe laserlocalisation data or maybe laser

lists . Casper : With all thislists . Casper : With all this

like a section there onlike a section there on

is our own data . It'sis our own data . It's

in to giving up yourin to giving up your

I can look at oldI can look at old

hurdle at this point , andhurdle at this point , and

HD map , which is justHD map , which is just

have multiple means of gettinghave multiple means of getting

havehaveSo of course weSo of course we

one time user . Theyone time user . They

generatinggeneratingthe broader public . Butthe broader public . But

own system of carown system of car

generatedgenerated

work with ? Casper : Withwork with ? Casper : With

public data old privatelypublic data old privately

Is that also ownIs that also own

focus on vehicle dynamics , modelling ,focus on vehicle dynamics , modelling ,

examples are more about recordedexamples are more about recorded

every aspect of the sensorsevery aspect of the sensors

do you see the userdo you see the user

controllers . So , you might getcontrollers . So , you might get

computations kunnen maken met huncomputations kunnen maken met hun

collectingcollecting

think Waymos model ofthink Waymos model of

open data ?. Ryan : Yes ,open data ?. Ryan : Yes ,

a little bit , buta little bit , but

close the map and beginclose the map and begin

cameracamera

the car based onthe car based on

So you pump inSo you pump in

nu vrij weinig metnu vrij weinig met

data to augment thisdata to augment this

about machine learning fromabout machine learning from

But they are also sendingBut they are also sending

Baidu , Microsoft en alle groteBaidu , Microsoft en alle grote

and there's also some privateand there's also some private

And then GPS and carAnd then GPS and car

and forming collaboration which shareand forming collaboration which share

and building on each other'sand building on each other's

also sending data back ( closedalso sending data back ( closed

youyou

used if you wouldused if you would

use foruse forthe samethe same

reading outreading out

sent back to thesent back to the

give some back atgive some back at

can go to mydrive.tomtom.com .can go to mydrive.tomtom.com .

with the car . Not thatwith the car . Not that

what kind of data iswhat kind of data is

we need and we needwe need and we need

used I'm really interested inused I'm really interested in

uitwisseling Jullie wisselen alles heeluitwisseling Jullie wisselen alles heel

towards the traffic information servicetowards the traffic information service

toto

Waymo by using theirWaymo by using their

the communities . Everybody canthe communities . Everybody can

make the community safer .make the community safer .

Lyft an Uber whenLyft an Uber when

augment this camera data .augment this camera data .

they collect . And it's verythey collect . And it's very

thethemanufacturer of that canmanufacturer of that can

car are using atcar are using at

that's being usedthat's being usedby rideby ride

as wellas well

thatthat

you're collectingyou're collecting? Ryan : We? Ryan : We

, which makes, which makes

you might use inyou might use in

would serve as awould serve as a

wewe

work with iswork with is

useuse. Casper : Has. Casper : Has

, we localize, we localize

see is missingsee is missing

they're giving Tesla . Butthey're giving Tesla . But

theytheyuse for thisuse for this

actually doing theactually doing the

the car collects fromthe car collects from

than say a Tesla that'sthan say a Tesla that's

specifically But for example wespecifically But for example we

setssets

where you get thewhere you get the

There's some differences betweenThere's some differences between

That's on the deepThat's on the deep

that tool is availablethat tool is available

so it's mostly Imageso it's mostly Image

out there for howout there for how

for for example , that'sfor for example , that's

but then we runbut then we run

set of pictures of childrenset of pictures of children

probably easier at the sameprobably easier at the same

privacy and cyber security stuffprivacy and cyber security stuff

oror

you also use openyou also use open

they're still really openthey're still really open

stuff you're building betweenstuff you're building between

maybe laser data tomaybe laser data to

is that public accessibleis that public accessible

once the car drives theonce the car drives the

onon

what Google Maps iswhat Google Maps is

people who have enoughpeople who have enough

mapping available yet thatmapping available yet that

old privately generated data thatold privately generated data that

of the cameras and theof the cameras and the

nog Koen : Het zit allemaalnog Koen : Het zit allemaal

Might be a bit moreMight be a bit more

maps But seeing as it'smaps But seeing as it's

like localisation data or maybelike localisation data or maybe

kon leegtrekken de hele tijd .kon leegtrekken de hele tijd .

isis

thetheway it willway it will

sources of datasources of data

that ? Koen : They arethat ? Koen : They are

power . Do you seepower . Do you see

build up so actuallybuild up so actually

into something that an autonomousinto something that an autonomous

inin

which Bounding boxes arewhich Bounding boxes are

the sense so itthe sense so it

its raw sense , intoits raw sense , into

I guess . Do you useI guess . Do you use

fromfrom

the car telling usthe car telling us

people and from thepeople and from the

or will it beor will it be

Open Street Map ThatOpen Street Map That

Lyft but present orLyft but present or

GPS traces that screensGPS traces that screens

databases or whatever becausedatabases or whatever because

a one time user .a one time user .

forforthatthat

kind of stuff .kind of stuff .

. We don't have. We don't have

matching to data thatmatching to data that

feeding it back into thefeeding it back into the

do they use , do theydo they use , do they

collectioncollectionin those areas immediately .in those areas immediately .

and stuff , how doand stuff , how do

centers in de wereld , gebruikencenters in de wereld , gebruiken

being generated afterwards is goingbeing generated afterwards is going

because that's what we are ,because that's what we are ,

backback

to the system soto the system so

So you have aSo you have a

from our customers . Sofrom our customers . So

closed data ). That's theclosed data ). That's the

andandthey can turnthey can turn

so they haveso they have

available and people are tryingavailable and people are trying

atat

the same time right ?the same time right ?

all timesall timeswe wouldn'twe wouldn't

. Now it's. Now it's

as much as a Teslaas much as a Tesla

areareyou guys using ? whatyou guys using ? what

feeding that back intofeeding that back into

andand

you get out youryou get out your

we build maps offwe build maps off

privatizing itprivatizing itright orright or

. But people. But people

privacy , my professor wasprivacy , my professor was

open innovation and thenopen innovation and then

knowledge and algorithms andknowledge and algorithms and

is any public datais any public data

I'm sure they willI'm sure they will

for example do simulationsfor example do simulations

building on each other'sbuilding on each other's

actually doing everything themselves .actually doing everything themselves .

analysis and stuff like that .analysis and stuff like that .

after an accident , or likeafter an accident , or like

?.?.Vincent So if you wantVincent So if you want

Ryan Yes , collecting data , compilingRyan Yes , collecting data , compiling

..

you think this is goingyou think this is going

With an ownership model forWith an ownership model for

Waymos business model is , theyWaymos business model is , they

Then once it materializes inThen once it materializes in

SoSo

your question might beyour question might be

you pump in camerayou pump in camera

it's more like ait's more like a

Ryan It's surprising right . YouRyan It's surprising right . You

People assume that we basePeople assume that we base

Just make it cheap , makeJust make it cheap , make

It's data towards the trafficIt's data towards the traffic

Is that also own generatedIs that also own generated

Instead of sharing it , they'reInstead of sharing it , they're

En een paar jaar geledenEn een paar jaar geleden

Depending which segment of autonomousDepending which segment of autonomous

CasperCasperSo it's pretty limited ,So it's pretty limited ,

Of course you're buildingOf course you're building

ButButthey are also sendingthey are also sending

it's it's not givingit's it's not giving

And I mean that's goingAnd I mean that's going

,,

would you open it upwould you open it up

the AI training and thethe AI training and the

something that would have takensomething that would have taken

rather than what's happened internallyrather than what's happened internally

critical data from a onecritical data from a one

creating the map itself andcreating the map itself and

compiling data , creating the mapcompiling data , creating the map

bump it into my controllerbump it into my controller

). That's the product that Here). That's the product that Here

v

Appendix C – Coding structure

Coding nodes References

vi

Appendix D – Interview Structure

Semi-structure interview questions General

- Can you tell me something about yourself and about what you do for the company? - How does your company see/define innovation? o How about open innovation? - What kind of strategies do you have in place for open innovation?

Big Data usage

• Is your company already utilising big amounts of data?

• Is any of this open/public data?

• Are there different levels in sensitivity of data? Like difference

between actual driving data and building algorithms?

Collaboration?

• For the development of self-driving cars there is normally need

for 3 big capabilities: Expertise in sensor technology, data

collection (like actual driving cars collecting data) and

development of autonomous AI by machine learning.

o Are you collaborating with other parties to fill in the

gaps?

• See any risks of knowledge-spillovers or knowledge races

between these partners?

Innovation process:

• Strategic roadmap in development?

• Your vision how Autonomous driving will develop and how you

want to strategically position yourself?

• Do you see a first mover advantage and how this will maybe set

the standard? Or more coexistence along each other so for

example VHS vs DVD or more of a mobile platform like

Android and IOS coexisting?

• Is your company changing its innovation model? And if so what

market influences are forcing the change?

• (Does your company plan to develop by open innovation?)

o Setting a side certain parts of coding and withholding

other crucial ones, so feel any social responsibility?

vii

Propositions

WP1 Do you see collaborative open innovation as a tool to increase the speed of development in autonomous driving? WP2 Do you see a place for open-innovation in Autonomous driving? WP3 Do you see enclosing by intellectual property as a risk for killing off open innovation? Actually, pulling knowledge out of the open data pool and privatising it.

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