DNA for Automated Driving. Jeremy Dahan May 8 th, 2017

Transcription

1 Jeremy Dahan May 8 th, 2017

2 Radar Camera LIDAR Sonar Steering Wheel Sensors Wheel Speeds IMU / Gyro 5 0 Global Position 1999: Mercedes S-Class Distronic 2002: VW Phaeton ACC Moving objects Items to specify Static 2006: obstacles Audi Q7 ACC plus / AEB 2005: Mercedes S-Class Lanes Distronic plus Signs 2010: Audi A8 GPS-guided ACC Trajectory planning & 2013: control Mercedes S-Class Dist.+ / Steering Assistant Fail-safe / -degraded mechanisms 2015: Audi Q7 Traffic Jam Assistant + brakes + sensor fusion + GPS maps + steering + automatic steering Motor Speed Control Regenerative Braking Brake Pressure Steering Angle Control Head Unit Side Tasks Functional safety Maps Vehicle ego motion Items to specify: 24 Redundancies 2

3 Moving objects Steering Wheel Sensors Vehicle ego motion Automated Driving Wheel Speeds Radar Static obstacles Lanes Automated Emergency Brake IMU/ Gyro Camera Signs Automated Valet Parking Global Position LIDAR Sensor Data Fusion Maps Sonar Interactions: n + m + k for n sensors, m functions, k abstraction components 3

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5 Vehicle Abstraction - Sensors Function Specific Views Vehicle Abstraction - Actuators DNA for Automated Driving Software Framework for ADAS and Automated Driving Sensor Data Fusion Situative Behavior Arbitration Motion Management Positioning Situation Analysis Behavior Path Planning Trajectory Control Object Fusion Grid Fusion Road & Lane Fusion Vehicle Database Situation Analysis Situation Analysis Behavior Behavior Path Planning Path Planning Longitudinal Control Lateral Control HMI Management Safety Management 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 5

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 6

7 Map onto concrete ECU architecture HAD EB modules Your modules 3rd-party modules NCAP Ensure differentiation, shorten time to market Upgrade across models / Upgrade over time 7

8 Vehicle Abstraction - Sensors Function Specific Views Vehicle Abstraction - Actuators DNA for Automated Driving Application example: Automated Valet Parking Sensor Data Fusion Positioning Situative Behavior Arbitration Automated Valet Parking Motion Management Trajectory Control Object Fusion Grid Fusion Road & Lane Fusion Vehicle Database Situation Analysis Situation Analysis Behavior Behavior Path Planning Path Planning Longitudinal Control Lateral Control HMI Management Safety Management 8

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10 Software framework in action Positioning Grid Freespace Extraction Highway Driving Short/Mid-term Path Planning 10

11 What about maps? Range of ego sensors are limited Recognition algorithms are limited Not all information needed can be derived from sensor observations 11

12 Sensor-based Learning for Predictive Driving Sensor learning 2 Incremental NDS Compilation 3 Incremental Map Update Service 4 Data aggregation Map matching Sensor fusion Enriched map updates on daily basis Delta maps Collect sensor data Consumer experience 1 5 Secure connection Always up to date and transfer electronic horizon for ADAS functions 12

13 How sensor data is collected and sent Image processing 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 13

14 How sensor data is collected and sent Top View Height Map

15 Example: Zebra crossing recognition on 2.5D maps

16 How to learn from sensor data and enrich maps Data processing 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

17 Example: Zebra crossing in the Cloud

18 Maps boost ADAS and Automated Driving 18

19 DNA for automated driving DNA for Automated Driving and NVIDIA EB robinos EB Assist ADTF EB tresos Rapid prototyping C, C++, Model based Rapid embedding C, C++ Automotive grade software PC Evaluation hardware ECU 19

20 EB robinos implements the open robinos specification provides software modules for prototyping in EB Assist ADTF for rapid embedding on AUTOSAR / DRIVE PX for production on vehicle ECU developed, tested, verified according to functional safety standards Download the open robinos specification Open robinos specifies a reference platform for automated driving up to Level 5 (SAE) architecture interfaces data flow control mechanisms software modules functional safety aspects freely available and licensed as Creative Commons Available for download 20

21 Conclusion The problem Q4/2016 is More not EB difficulty robinos components but complexity. Software frameworks and functional architectures help solve it. We should talk about software, EB robinos is a software framework for automated driving, applicable across car lines not and hardware models architectures. it is DNA for automated driving 21

22 Positioning (robinos, ground truth) Get in touch! Thank you! Automated Valet Parking Electronic Horizon Automated Highway Driving