Hitch-hikers guide to AI for Connected and Autonomous Vehicles

Artificial Intelligence for

Connected and Autonomous Vehicles

The hitch-hikers guide to

12/04/2017 © Astius Technology 2017 1


This talk was given to an audience of enthusiasts at

the ‘Self-driving and Autonomous Vehicles’ meetup

group.

The event was held at Barclays Eagle Labs at Brighton,

on April 12th 2017

Bill Harpley MSc

• 30+ years in technology sector

• Founder of Astius Technology

• Organiser of Brighton IoT meetup group (700+ members)

• Initiator of Brighton node of the global Things Network

• Organiser of the Self-driving Cars & Autonomous Vehicles meetup group

https://uk.linkedin.com/in/billharpley

[email protected]

www.astius.co.uk


https://www.meetup.com/Internet-of-Things-Brighton

https://www.thethingsnetwork.org/

https://www.meetup.com/Self-Driving-and-Autonomous-Vehicles/




mailto:[email protected]

http://www.astius.co.uk/

• A brief history of Artificial Intelligence

• The difference between

– Artificial Intelligence (AI)

– Machine Learning (ML)

– Deep Learning (DL)

• Vehicles are getting smarter

• Key industry players and partners

• The future of motive AI


A brief history of Artificial Intelligence (AI)


Key milestones since 1950


1950 • Alan Turing published

the Turing Test • Isaac Asimov published

the Three Laws of Robotics

1956 Term artificial intelligence created by John McCarthy

1951 First game-playing AI programs written at Manchester University

1958 LISP programming language invented

1959 MIT AI Lab founded

1961 First industrial robot deployed on an assembly line

1972 PROLOG programming language invented

1969 SRI developed Shakey the Robot

1979 Stanford AI Lab built the Stanford Cart

1986 Team at Munich University build first robot cars

Mid-1980s Neural networks become widely used

1995 CMU Navlab Vehicle drives from Pittsburgh to San Diego

2009 Google builds self-driving car

2004 DARPA Grand Challenge

How AI has contributed to the development of Autonomous Vehicles

Evolution of AI technologies


Source: Nvidia

GROWING DEMAND

FOR COMPUTATIONAL

RESOURCES

AI applications used within CAV context


Expert Systems

Natural Language Processing

Language Translation

Object Recognition

Facial / Gesture

Recognition

Voice Recognition

Diagnostics Character /

Symbol Recognition

Robotic control

Flavours of Artificial Intelligence (AI)



Artificial Intelligence

•“Weak AI” – programs developed to perform specific tasks

•Used when the problem domain can be modelled as a clear set of rules

•Example: expert systems

Machine Learning

• Deploys algorithms which can learn to make predictions or decisions, based on input from a set of training data

•“neural networks” are commonly used for this task

•Used in situations in which it is hard to programme a clear set of rules

•Examples: Computer Vision, Speech Recognition, Vehicle Routing

Deep Learning

•You can think of this as an advanced form of Machine Learning

•Data is consumed at the input and processed through a series of stages

•Each processing layer adds an additional layer of meaning to the result

•Example: vehicle onboard computer can be trained to interpret camera images using a 360° field of view

GROWING DEMAND FOR

COMPUTATIONAL RESOURCES

Overview of principal AI technologies

Acceleration of AI learning


GPUs (Graphics Processing Unit)

FPGAs (Field Programmable Gate Array)

NVIDIA Drive PX2 board (2 x GPUs)

• A GPU consists of hundreds of

processing cores.

• Facilitates massively parallel

processing of a data set.

Spartan-6 FPGA evaluation kit

• The programme logic is defined by the

customer after the board has been

manufactured.

• Facilitates optimisation for a specific task.

GPUs and FPGAs compared


GPU

• Provides “software acceleration”

• Cheaper than FPGA boards

• Less energy efficient than FPGAs

FPGA

• Provides “hardware acceleration”

• More expensive than GPU boards

• More energy efficient than GPUs

These are the basic factors which differentiate GPU and FPGA technology

Why vehicles are getting smarter


SAE –J3016 classification standard


Timeline for deployment


Each level of autonomy requires increased “intelligence” and computational power

Advanced Driver Assistance System (ADAS)

28/03/2017 Connected Cars & Autonomous Vehicles 16

What is ADAS?

ADAS can be thought of as

a set of technologies

which provide improved

safety to the driver and

other road users:

• LIDAR • RADAR • Embedded Vision • GPS and Digital Mapping

Examples of ADAS applications

Cruise control

Assisted parking

Collision avoidance

Vehicle navigation

Assisted lane changing

Example: ADAS vision system


Example: Nissan Leaf


Nissan Leaf cars avoid collisions by utilising 12 cameras, five radars and four lasers to enable them to identify surrounding objects and calculate their position on the road to within a few centimetres.

Players and partners in automotive AI


Nvidia Xavier chip


Photo (left) shows the result of

collaboration between Nvidia and

Bosch to produce an AI automotive

computer based in the Xavier chip.

Key partnerships


BMW + Intel + Mobileye

Audi + Qualcomm

Audi + Nvidia

BMW + IBM Watson

Just a few of the key partnerships which have emerged between automakers and hardware / software vendors. Notice how automakers are hedging their bets!

Example: Audi


Artificial Intelligence (AI) is already used in automotive applications such as Smart Parking, Driver alertness monitoring, Automated lane changing.

Audi and Nvidia have announced partnership to bring AI-controlled car to market by 2020 (SAE Level 4)

Audi virtual cockpit Audi AI test vehicle

Other auto-makers such as BMW, Volvo, Toyota and Mercedes are exploring use of AI / ML in connected cars

These types of AI applications employ neural networks. These need to be trained using large data sets, hence a role for Cloud Computing and Big Data.

What the future holds for automotive AI


The car of the future


https://www.toyota.com/concept-i/ http://www.discover-sedric.com/en/

All major motor manufacturers are developing futuristic ‘concept cars’.

Here are two examples from Toyota and Volkswagen.

https://www.toyota.com/concept-i/



http://www.discover-sedric.com/en/



The taxi of the future


There seems to be universal consensus that driverless taxis are the way forward.

In fact, they are already being trialled in major cities around the world.

http://nutonomy.com/

nuTonomy ( Singapore ) Uber (Pittsburgh, USA)

http://uber.com

http://nutonomy.com/

https://newsroom.uber.com/pittsburgh-self-driving-uber/

The transport revolution


AI motive technology is now being deployed to build the next generation of personal

transportation and commercial logistics

Driverless buses

Driverless trucks Rolls Royce autonomous ship Commuter drones

Hyperloop

Summary of key points


1. Research into AI goes back more than half a century

2. Flavours are “Weak” AI, Machine Learning and Deep Learning

3. AI is fundamental to progress towards Autonomous vehicles

4. GPUs and FPGAs are used to implement “intelligent” functions

5. Many key partnerships have emerged between different players

6. Motive AI technology now used in other areas of transport

Advanced System Technologies in Urban Spaces™


Hitch-hikers guide to AI for Connected and Autonomous Vehicles

Automotive

Transcript of Hitch-hikers guide to AI for Connected and Autonomous Vehicles