DNA for Automated Driving - GTC On Demand · 2017. 5. 19. · DNA for Automated Driving Conclusion Software frameworks and functional architectures help solve it. We should talk about

DNA for Automated Driving

Jeremy DahanMay 8th, 2017

© Elektrobit (EB) 2017 | Confidential


2

Radar

Camera

LIDAR

Sonar

SteeringWheel Sensors

WheelSpeeds

IMU /Gyro

GlobalPosition

Maps

Motor SpeedControl

Brake Pressure

SteeringAngle Control

RegenerativeBraking

Static obstacles

Lanes

Moving objects

Vehicleego motion

Signs

Trajectoryplanning &

control

Fail-safe /-degraded

mechanisms

Head Unit

Side Tasks

Functionalsafety

Redundancies

Items to specify: 24

0

5

10

15

20

25

30

2002 2004 2007 2010 2013 2015

Items to specify

1999: Mercedes S-ClassDistronic

2002: VW PhaetonACC

2005: Mercedes S-ClassDistronic plus

2010: Audi A8GPS-guided ACC

2006: Audi Q7ACC plus / AEB

2013: Mercedes S-ClassDist.+ / Steering Assistant

2015: Audi Q7Traffic Jam Assistant

+ brakes + sensor fusion + GPS maps + steering + automatic steering


Sensor Data Fusion


3

Radar

Camera

LIDAR

Sonar

SteeringWheel Sensors

WheelSpeeds

IMU/Gyro

GlobalPosition

Maps

Static obstacles

Lanes

Moving objects

Vehicleego motion

Signs

Interactions: 𝒏 +𝒎+ 𝐤for 𝒏 sensors, 𝒎 functions, 𝒌 abstraction components

Automated Driving

Automated Emergency Brake

Automated Valet Parking



4



Software Framework for ADAS and Automated Driving

5

Interfaces for• Interoceptive sensors – wheel ticks,

steering angle, accelerometers / gyros• „Smart“ environment sensors – point

clouds, object lists• ADASISv2/3 for map, SENSORIS for cloud

Integrated safety concept• System health monitoring and diagnosis• Safe-state triggering• Options for redundant environment

model and functions (e.g. minimal risk

Interfaces for • Kinematic vehicle components• Instrument cluster• Infotainment display

www.open-robinos.com

Trajectory Control

Longitudinal Control

Lateral Control

Positioning

Object Fusion

Grid Fusion

Behavior

Road & Lane Fusion

Vehicle DatabaseFu

nct

ion

Sp

eci

fic

Vie

ws

Ve

hic

le A

bst

ract

ion

-Se

nso

rs

Ve

hic

le A

bst

ract

ion

-A

ctu

ato

rsMotionManagement

Safety Management

Behavior

Behavior

Situative Behavior ArbitrationSensorData Fusion

HMI Management

SituationAnalysis

SituationAnalysis

SituationAnalysis

PathPlanning

PathPlanning

PathPlanning

http://www.open-robinos.com/

© Elektrobit (EB) 2017 | Confidential 6

Every software component has• scalable, documented and standardized interfaces to other components• exchangeable interfaces to the base system / OS• a pre-industrialized algorithm core




7

Map onto concrete ECU architecture

Upgrade across models / Upgrade over time

NCAP

HAD

Ensure differentiation, shorten time to market

EB modules 3rd-party modulesYour modules



8

Trajectory Control

Longitudinal Control

Lateral Control

Positioning

Object Fusion

Grid Fusion

Road & Lane Fusion

Vehicle Database

Fun

ctio

n S

pe

cifi

c V

iew

s

Ve

hic

le A

bst

ract

ion

-Se

nso

rs

Ve

hic

le A

bst

ract

ion

-A

ctu

ato

rsMotionManagement

Safety Management

Behavior

Behavior

Situative Behavior ArbitrationSensorData Fusion

HMI Management

SituationAnalysis

SituationAnalysis

PathPlanning

PathPlanning

Automated ValetParking

Application example: Automated Valet Parking



9


Short/Mid-term Path Planning

Grid Freespace Extraction

Highway Driving

Positioning

Software framework in action


10



Range of ego sensors are limited

•

•

Recognition algorithms are limited

Not all information needed can be derived from sensor observations

•

•

•

•

11

What about maps?



12

Sensor-based Learning for Predictive Driving

• Secure connection

and transfer

Collect sensor data

1

• Data aggregation• Map matching• Sensor fusion

Sensorlearning

2

• Enriched map

updates on

daily basis

IncrementalNDS Compilation

3

• Delta maps

Incremental MapUpdate Service

4

Consumerexperience

• Always up to date

electronic horizon for ADAS functions

5

2 3 4

51


How sensor data is collected and sent

• Objects form camera recognition modules

• Create 2.5D maps of the road

• Algorithms for feature extraction:

image processing

machine learning

• Objects that can be extracted:

lane markings

lane Geometry

road boundaries

lane arrows

traffic signs

Image processing


2 3 4

51


How sensor data is collected and sent


Top View

14

Height Map

2 3 4

51


Example: Zebra crossing recognition on 2.5D maps

15


2 3 4

51



16

How to learn from sensor data and enrich maps

• Data cleanup

Incoming data is validated regarding location and timestamp

Wrong movement profiles (e.g. ferries or trains) are discarded

• Data aggregation

Collected data is aggregated into clusters to ensure reliability

Clusters are matched on a recent map

User data is anonymized to ensure privacy

Data processing

2 3 4

51


Example: Zebra crossing in the Cloud

17


2 3 4

51


Maps boost ADAS and Automated Driving



DNA for Automated Driving and NVIDIA

19

EB robinos

EB Assist ADTF EB tresos

DNA for automated driving

Rapid prototyping

C, C++, Model based

Rapid embedding

C, C++

Automotive gradesoftware

ECUEvaluation hardware

PC



Open robinos

• specifies a reference platform forautomated driving up to Level 5 (SAE)• architecture• interfaces• data flow• control mechanisms• software modules• functional safety aspects

• freely available and licensed asCreative Commons

• Available for download

EB robinos

• implements the open robinosspecification

• provides software modules• for prototyping

in EB Assist ADTF• for rapid embedding

on AUTOSAR / DRIVE PX• for production

on vehicle ECU

• developed, tested, verifiedaccording to functional safetystandards

www.try-eb-robinos.com

20

Download the open robinos specification

www.open-robinos.com

http://www.try-eb-robinos.com/mailto:[email protected]



ConclusionSoftware frameworks and functional

architectures help solve it.

We should talk about software, not hardware architectures.

Q4/2016 – More EB robinos componentsThe problem is not difficulty but complexity.

EB robinos is a software framework for automated driving, applicable across car lines and models – it is DNA for automated driving

21

Get in touch! Thank you!

www.eb-robinos.com

www.try-eb-robinos.com

[email protected]

Positioning (robinos, ground truth)

Automated Valet Parking

Automated Highway Driving

Electronic Horizon
https://www.facebook.com/EBAutomotiveSoftwarehttps://www.facebook.com/EBAutomotiveSoftwarehttps://twitter.com/EB_automotivehttps://twitter.com/EB_automotivehttps://www.youtube.com/user/EBAutomotiveSoftwarehttps://www.youtube.com/user/EBAutomotiveSoftwarehttps://www.linkedin.com/company/elektrobit-eb-automotivehttps://www.linkedin.com/company/elektrobit-eb-automotivehttp://www.eb-robinos.com/http://www.try-eb-robinos.com/mailto:[email protected]

DNA for Automated Driving - GTC On Demand · 2017. 5. 19. · DNA for Automated Driving Conclusion Software frameworks and functional architectures help solve it. We should talk about

Documents