ENABLING DATA DRIVEN DEVELOPMENT FOR …...MACHINE LEARNING MACHINE LEARNING [ML] enables the...

ENABLING DATA DRIVEN DEVELOPMENT FOR AUTONOMOUS DRIVING.

BORIS SCHAUERTE | BMW GROUP2. DECEMBER 2019

LESS TRAFFIC

LESS ACCIDENTS

LESS EMISSIONS

MORE TIME

HIGHER FLEXIBILITY

MORE COMFORT

HIGHER SAFETY

NEW MOBILITY CONCEPTSVEHICLES BEING PART OF OUR LIVING SPACE

PROMISES OF AUTONOMOUS DRIVING TO CUSTOMERS AND SOCIETY.

2. December 2019 | Boris Schauerte

LESS TRAFFIC

LESS ACCIDENTS

LESS EMISSIONS

MORE TIME

HIGHER FLEXIBILITY

MORE COMFORT

HIGHER SAFETY




CHALLENGE 1: COMPLEX FUNCTION DEVELOPMENT

LESS TRAFFIC

LESS ACCIDENTS

LESS EMISSIONS

MORE TIME

HIGHER FLEXIBILITY

MORE COMFORT

HIGHER SAFETY




CHALLENGE 1: COMPLEX FUNCTION DEVELOPMENTCHALLENGE 2: PROVE SAFETY

ARTIFICIAL INTELLIGENCE VALUE LOOP.

AUTONOMOUS DRIVING.

TRAINING

DATA COLLECTION

DATA

ECO SYSTEM

ARTIFICIAL

INTELLIGENCE

ARTIFICIAL INTELLIGENCE VALUE LOOP.AUTONOMOUS DRIVING.



AUTONOMOUS DRIVING.

ON-BOARD OFF-BOARD

PROCESSING

TRAININGDEPLOYMENT

DATA COLLECTION

CONNECTED

ENVIRONMENT

DATA

ECO SYSTEM

ARTIFICIAL

INTELLIGENCE

ARTIFICIAL INTELLIGENCE VALUE LOOP.AUTONOMOUS DRIVING.


SEVERAL TB PER HOUR PER CAR

TARGETED DATA COLLECTION

FREQUENTLY UPDATED SW & HW

DEDICATED DATA COLLECTION AND TEST FLEET.


CAN COVER MILLIONS OF MILES

FULL RANGE RADAR.

NIGHT VISION.SIDE VIEW CAMERA.

SIDE RANGERADAR

SURROUND VIEW CAMERA.

ULTRA-SONIC.

STEREO FRONT CAMERA.

REAR VIEW CAMERA.

SIDE RANGE RADAR.

ULTRA-SONIC.

STEERING AND LANE CONTROL ASSISTANT INCL. LANE CHANGE ASSISTANT.

SURROUND VIEW.

ACTIVE CRUISE CONTROL.

SPEED LIMIT ASSIST.

EMERGENCY STEERING ASSIST.

WRONG WAY ASSIST.

CROSSROAD ASSIST.

CUSTOMER FLEET DATA COLLECTION.


SEVERAL MB PER HOUR

TARGETED DATA COLLECTION

STABLE SW & HW

CAN COLLECT BILLIONS OF MILES


AUTONOMOUS DRIVING.DATA LANDSCAPE.


COLLECT STORE

ACCESSPROCESS

Data Quality

Data Retention

Indexing

Data transfer and IngestFleet control

Data recorder

Campaign mgmt.

Synthethic Data

Reprocessing

Deployment

KPIs

Function dev.

Analytics (e.g, test space coverage)(Auto-)labeling

Data(set) export

Versioning

Privacy (e.g., GDPR) Distributed Infrastructure (due to

to technical or legal constraints)

SecurityVarying sources (e.g., sensor

setups, versions, …)

Data Trustworthiness

Scalability and Orchestration

BUILDING A AUTONOMOUS DRIVING DATA FACTORY.

AUTOMATION & ARCHITECTURE:Automated processes are necessary to handle the volume.

INTRODUCTION & DATA SOURCES: Data-driven development needs large amounts of data.


Development Fleet

Customer Fleet

Data Collection & Preparation

Raw Data

Ground truth

generationData clearing & post

processingLabeling & Indexing

Classify sequences

Data quality

assurance,

Data access

API

DATA FACTORY.


Development Fleet

Customer Fleet


Raw Data

Ground truth



Classify sequences

Data quality

assurance,

Data access

API

DATA FACTORY.


Post-

Pro

cessed

Data

Evaluation FrameworkEvaluation of safety,

performance, comfort, test

coverage etc.

Development Fleet

Customer Fleet


Raw Data

Ground truth



Classify sequences

Data quality

assurance,

Data access

API

DATA FACTORY.


Post-

Pro

cessed

Data



coverage etc.

Simulation

Bus Simulation

Traffic Models

Sensor Models

Vehicle Model

Environment Simulation

Development Fleet

Customer Fleet


Raw Data

Ground truth



Classify sequences

Data quality

assurance,

Data access

API

DATA FACTORY.


Post-

Pro

cessed

Data



coverage etc.

Simulation

Bus Simulation

Traffic Models

Sensor Models

Vehicle Model


Scenario

Descriptions

Development Fleet

Customer Fleet


Raw Data

Ground truth



Classify sequences

Data quality

assurance,

Data access

API

DATA FACTORY.


Simulation

Bus Simulation

Traffic Models

Sensor Models

Vehicle Model


Data, Simulation & Test Management



coverage etc.

Traffic to DescriptionDerive (simulatable) scenario

from post-processed data

Scenario

Descriptions

Post-

Pro

cessed

Data


AUTONOMOUS DRIVING.EXTENDING THE SCENARIO SPACE WITH SIMULATED DATA.


Vehicle

Speed

Number of

pedestriansObserved

Scenario


AUTONOMOUS DRIVING.EXTENDING THE SCENARIO SPACE WITH SIMULATED DATA.


Vehicle

Speed

Number of

pedestriansObserved

ScenarioSimulated

Scenario

Seite 18

DATA VARIATION. C S

AP



Scenario EvaluationEvaluation Framework

Evaluation of safety,


coverage etc.

Scenario Extraction

Traffice to DescriptionDerive (simulatable) scenario


Development Fleet

Customer Fleet


Raw Data

Ground truth



Classify sequences

Data quality

assurance,

Data access

API

Scenario Variation

Auto. Scenario

Variation

DATA FACTORY.


Simulation

Bus Simulation

Traffic Models

Sensor Models

Vehicle Model

Environment SimulationP

ost-

Pro

cessed

Data

Scenario

Descriptions





coverage etc.

Scenario Extraction



Development Fleet

Customer Fleet


Raw Data

Ground truth



Classify sequences

Data quality

assurance,

Data access

API

Scenario Variation

Auto. Scenario

Variation

DATA FACTORY.


Simulation

Bus Simulation

Traffic Models

Sensor Models

Vehicle Model


ost-

Pro

cessed

Data

Scenario

Descriptions

Test Space

ExplorationGenerate necessary

parameter variations

Test Space AnalyticsDerive model and statistics

for scenarios

Test Space CoverageStatistical coverage of the

test space





coverage etc.

Scenario Extraction



Development Fleet

Customer Fleet


Raw Data

Ground truth



Classify sequences

Data quality

assurance,

Data access

API

Scenario Variation

Auto. Scenario

Variation

DATA FACTORY.


Simulation

Bus Simulation

Traffic Models

Sensor Models

Vehicle Model


Scenario

Descriptions

Test Space




for scenarios


test space

Reprocessing Platform

Post-

Pro

cessed

Data





coverage etc.

Scenario Extraction



Development Fleet

Customer Fleet


Raw Data

Ground truth



Classify sequences

Data quality

assurance,

Data access

API

Scenario Variation

Auto. Scenario

Variation

DATA FACTORY.


Simulation

Bus Simulation

Traffic Models

Sensor Models

Vehicle Model


Scenario

Descriptions

Test Space




for scenarios


test space

Version

management

Testcase

s

Scenario

Evaluatio

n results


Post-

Pro

cessed

Data





coverage etc.

Scenario Extraction



Development Fleet

Customer Fleet


Raw Data

Ground truth



Classify sequences

Data quality

assurance,

Data access

API

Scenario Variation

Auto. Scenario

Variation

DATA FACTORY.


Simulation

Bus Simulation

Traffic Models

Sensor Models

Vehicle Model


Scenario

Descriptions

Test Space




for scenarios


test space

Version

management

Testcase

s

Scenario

Evaluatio

n results


Post-

Pro

cessed

Data

Data

retention

Data

retention





coverage etc.

Scenario Extraction



Development Fleet

Customer Fleet


Raw Data

Ground truth



Classify sequences

Data quality

assurance,

Data access

API

Scenario Variation

Auto. Scenario

Variation

DATA FACTORY.


Simulation

Bus Simulation

Traffic Models

Sensor Models

Vehicle Model


Scenario

Descriptions

Test Space




for scenarios


test space

Version

management

Testcase

s

Scenario

Evaluatio

n results


Post-

Pro

cessed

Data

Embedded test

platforms

Test

automation

Data

retention

Data

retention





coverage etc.

Scenario Extraction



Development Fleet

Customer Fleet


Raw Data

Ground truth



Classify sequences

Data quality

assurance,

Data access

API

Scenario Variation

Auto. Scenario

Variation

DATA FACTORY.


Simulation

Bus Simulation

Traffic Models

Sensor Models

Vehicle Model


Scenario

Descriptions

Test Space




for scenarios


test space

Version

management

Testcase

s

Scenario

Evaluatio

n results


Embedded test

platforms

Machine Learning Platform

New Software

Increment

Post-

Pro

cessed

Data

Test

automation

Data

retention

Data

retention





coverage etc.

Scenario Extraction




for scenarios

Embedded test

platforms

Orchestration of

processesSecurity ConceptData Center Integration

Platform

Access Management & IP Protection (e.g.

Partners)


Machine Learning Platform

Development Fleet

Customer Fleet


Raw Data

Ground truth



Classify sequences

Data quality

assurance,

Data access

API

Scenario Variation

Auto. Scenario

Variation

Test Space




test space

DATA FACTORY.


New Software

Increment

Simulation

Bus Simulation

Traffic Models

Sensor Models

Vehicle Model


ost-

Pro

cessed

Data

Scenario

Descriptions

Version

management

Testcase

s

Scenario

Evaluatio

n results

Data

retention

Data

retention

THANK YOU FOR YOUR [email protected]



TEST LANDSCAPE.

INFORMATION TECHNOLOGY [IT]

WHAT IS ARTIFICIAL INTELLIGENCE?THE AUTOMATION OF INTELLIGENT BEHAVIOR.

ARTIFICIAL INTELLIGENCE

Knowledge

representation

Perception and

cognition

Planning and

reasoning…

ARTIFICIAL INTELLIGENCE [AI] is a subdivision

of computer science. AI describes a set of algorithmic

methodologies to solve complex optimization problems.

Automates and supports decisions.

MACHINE LEARNING

Random forest Neural networks

Support vector

machine…

MACHINE LEARNING [ML] enables the recognition of

patterns and correlations in data automatically as apposed to

applying static rules and definitions. Allows the identification

of formerly hidden semantics.

DEEP LEARNING

DEEP LEARNING [DL] describes a class of

optimization methods loosely inspired by the

structure of biological nervous systems.

DL is able to produce results comparable to

and

in some cases superior to human experts,

especially in the field of image recognition

and natural language processing.

Deep

neural nets

Artificial

neural nets

…

19801950 2010EXPERT SYSTEMS SELF-

LEARNING SYSTEMS

rule based data driven



INFORMATION TECHNOLOGY [IT]


ARTIFICIAL INTELLIGENCE

ARTIFICIAL INTELLIGENCE [AI] is a subdivision

of computer science. AI describes a set of algorithmic

methodologies to solve complex optimization problems.

Automates and supports decisions.

MACHINE LEARNING

MACHINE LEARNING [ML] enables the recognition of

patterns and correlations in data automatically as apposed to

applying static rules and definitions. Allows the identification

of formerly hidden semantics.

DEEP LEARNING

DEEP LEARNING [DL] describes a class of

optimization methods loosely inspired by the

structure of biological nervous systems.

DL is able to produce results comparable to

and

in some cases superior to human experts,

especially in the field of image recognition

and natural language processing.

ImageNet ’15

14m pics & 20k

cats

MS COCO ’14

2.5m inst & 91

cats

…

19801950 2010EXPERT SYSTEMS SELF-

LEARNING SYSTEMS

rule based data driven

WHAT MADE THESE BREAKTHROUGHS POSSIBLE?ONE FACTOR, THE EMERGENCE OF ACCESS TO HIGH-QUALITY DATA.

MNIST ’98

70k pics & 10 cats

PASCAL VOC ’05

20k pics & 20 cats

ImageNet ’09

3.2m pics & >5k

cats


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