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DNA for Automated Driving Jeremy Dahan May 8 th , 2017

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  • DNA for Automated Driving

    Jeremy DahanMay 8th, 2017

  • © Elektrobit (EB) 2017 | Confidential

    DNA for Automated Driving

    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

  • © Elektrobit (EB) 2017 | Confidential

    Sensor Data Fusion

    DNA for Automated Driving

    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

  • © Elektrobit (EB) 2017 | Confidential

    DNA for Automated Driving

    4

  • © Elektrobit (EB) 2017 | Confidential

    DNA for Automated Driving

    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

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    ctu

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    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

    DNA for Automated Driving

  • © Elektrobit (EB) 2017 | Confidential

    DNA for Automated Driving

    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

  • © Elektrobit (EB) 2017 | Confidential

    DNA for Automated Driving

    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

  • © Elektrobit (EB) 2017 | Confidential 9

    DNA for Automated Driving

    9

  • © Elektrobit (EB) 2017 | Confidential

    Short/Mid-term Path Planning

    Grid Freespace Extraction

    Highway Driving

    Positioning

    Software framework in action

    DNA for Automated Driving

    10

  • © Elektrobit (EB) 2017 | Confidential

    DNA for Automated Driving

    Range of ego sensors are limited

    Recognition algorithms are limited

    Not all information needed can be derived from sensor observations

    11

    What about maps?

  • © Elektrobit (EB) 2017 | Confidential

    DNA for Automated Driving

    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

  • © Elektrobit (EB) 2017 | Confidential 13

    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

    DNA for Automated Driving

    2 3 4

    51

  • © Elektrobit (EB) 2017 | Confidential

    How sensor data is collected and sent

    DNA for Automated Driving

    Top View

    14

    Height Map

    2 3 4

    51

  • © Elektrobit (EB) 2017 | Confidential

    Example: Zebra crossing recognition on 2.5D maps

    15

    DNA for Automated Driving

    2 3 4

    51

  • © Elektrobit (EB) 2017 | Confidential

    DNA for Automated Driving

    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

  • © Elektrobit (EB) 2017 | Confidential

    Example: Zebra crossing in the Cloud

    17

    DNA for Automated Driving

    2 3 4

    51

  • © Elektrobit (EB) 2017 | Confidential 18

    Maps boost ADAS and Automated Driving

    DNA for Automated Driving

  • © Elektrobit (EB) 2017 | Confidential

    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

  • © Elektrobit (EB) 2017 | Confidential

    DNA for Automated Driving

    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]

  • © Elektrobit (EB) 2017 | Confidential

    DNA for Automated Driving

    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]