BMW at AD meetup Munich19.09.2019

ENABLING DATA DRIVEN DEVELOPMENT OF AUTONOMOUS DRIVING AT BMW

Miha Pelko@mpelko

AGENDA

o Why Autonomous Driving Development (ADD) requires lots of data?

o How can the data platform support ADD?

o Deep dives: Validation of AD software

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AUTONOMOUS DRIVING LEVELS

NO SUPPORT

HANDS ON

ASSISTENCE PARTLY AUTOMATED AUTONOMOUSHIGHLY AUTOMATED FULLY AUTOMATED

Vehicle controls forward and sideward motion

Vehicle controls forward motion

Driver has full controlDriver controls steering and

checks forward motion

Driver checks forward and sideward motion

Driver is ready to take control at any time

Driver only required for certain parts of the track

AUFGABEDES FAHRERS

AUFGABE DES FAHRZEUGS

0 1 2 3 4 5

G11 / G30 iNEXT iNEXT Pilotserie tbd.

HANDS ON HANDS TEMP. OFFEYES TEMP. OFF

HANDS OFFEYES OFF

HANDS OFF MIND OFF

PASSENGER

TRANSITION OF REPONSIBILITYHUMAN MACHINE

TECHNO-LOGICAL‘MOONSHOT’

TECHNO-LOGICALQUANTUM LEAP

Vehicle requests driver to take over control based on

situations

Vehicle does not request driver to take over control

No driver required

*Source: SAE (Society of Automotive Engineers) International Level of Automation

HUMANS DO LEARNING WITH BIG DATA

Page 4Koch et al. 2005, Current Biology 16, 1428–1434

Estimated ~1MB/s of information transfer over optic nerve

Over 18 years awake time the brain would have processed ~0.75 PB of visual information stream.

Full Range Radar.

ADAS* SYSTEM SETUP(* AUTONOMOUS DRIVING ASSISTANCE SYSTEMS)

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.

23 SENSORSBMW SERIES 5

DATA COLLECTION FLEET

SEVERAL TB PER HOUR

DEVELOPMENT AND VALIDATION PROCESS

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Several TB/hUp to 500 PB/a

ML Training / AD development

Test drives

Data Ingest to Data Center

OrganizeStructureEvaluate

Deployment of trained algorithms

Target data sets

Deployment of trained

algorithms

Software in a loop, Simulation

Combinatorial boost of scenarios

Synthetic data

Scenario extraction

Ground Truth creation

Automatic generationReference sensors

Manual labeling

ENABLING DEVELOPERS

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I want to develop a function of driving on a roundabout with pedestrians, …

Search for existing relevant scenarios Run the new algorithms on

the data Order the drive to collect relevant scenarios

Export part of the data for labeling and/or local development Train the ML models

Calculate KPIs for the new versions of algorithms

YIN AND YANG OF BIG DATA ASSISTED AD DEVELOPMENT

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BMW GROUP HIGH PERFORMANCE D3 PLATFORM.

MUNICH AREA

On-Premise Managed Infrastructure

Backbone Network

Containerized Computing PlatformGateway

Zone

BMW Group AD Campus

Self Service container orchestration Expert Service Desk

Ingest

HiL Stations

Function Reprocessing (SIL)Simulation

Model Training

Data PlatformBMW Group AD

Campus

Applications Development

Teams

3rd Party Gateway

Multi-tenancy interfacesBMW Group developer, repositories

Copy Station

GLOBAL DATA LOGISTICS

Remote Locations

DCData preparation

SearchTest space analysis

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VALIDATION OF AD SOFTWARE – OPEN LOOP

• Manual labeling+ Precision - Small scope

• Reference sensors+ Fully independent setup- Limited scope

• Automatic generation (alternative offline algorithms)+ Large scope- Correlation with the sensors

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EXTRACTING THE SCENARIOS FROM THE DATA

Vehicle Speed

Number of pedestrians

Observed Scenario

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EXTENDING THE SCENARIO SPACE WITH SIMULATED DATA

Vehicle Speed

Number of pedestrians

Observed Scenario

Simulated Scenario

EXCITING TIMES AHEAD – THANK YOU FOR YOUR INTEREST.