Advantage of a GPU powered trajectory planning for autonomous driving using NVidia DrivePX. GPU Technology Conference 2017

Transcription

1 Advantage of a GPU powered trajectory planning for autonomous driving using NVidia DrivePX GPU Technology Conference 2017 GTC Munich, 12 th October 2017 Dipl.-Ing. Jörg Küfen - Senior Manager Engineer Marius Stärk, M.Sc. - Development Engineer Forschungsgesellschaft Kraftfahrwesen mbh Aachen

2 To Start With E/E Systems of Vehicles System Layers Architecture and Software ECU Hardware, Communication Depending Interdisciplinary Perspective On the E/E System Power Systems, Infrastructure Slide No. 2

3 Introduction Disruptive Technologies Potentials of Disruptive Technologies Manhatten Manhatten years Slide No. 3

4 Introduction Disruptive Technologies Potentials of Disruptive Technologies Mobile Phone Mobile Phone less than 10 years Slide No. 4

5 Introduction Automated Driving (AD) Systems Impact on the Future of Mobility 1 Zero Emission Optimize traffic and traffic flow Reduce of fuel consumption and CO2 emission 2 Demographic Change Support unconfident drivers Guarantee mobility for elderly people 3 Vision Zero Avoidance of human driving errors 4 Increase traffic density Optimize traffic and traffic flow Convenient, time efficient 5 Economy Attractive products, by technology leadership Time efficient and comfortable mobility redefine tomorrows mobility Slide No. 5

6 Introduction Automated Driving (AD) Systems Slide No. 6

7 Introduction Automated Driving (AD) Systems Decomposition of an Autonomous Driving (AD) System Goal Strategy Tactic Execution Map Sense Localize Plan Control Perceive Sense Interpret Plan Arbitrate Act Slide No. 7

8 Introduction Automated Driving (AD) Systems Decomposition of an Autonomous Driving (AD) System min complex objects plans Knowledge Based Reasoning (respecting system capabilities) s objects abstract actions High-Level Perception Reflex B reactions Arbiter ms actions Base Perception Reflex A fast reactions Actuator Command µs signals S S S S S S A A A A A A actuations Slide No. 8

9 Trajectory Planner Essential Element of the Functional Network Hierarchy Levels Decision Layer Trajectory Planner Dynamic Controllers Slide No. 9

10 Trajectory Planner Requirements From Acceptance and Technology 1 Trajectories need to feel human to the passengers 2 Trajectory calculation must be stable 3 Trajectory calculation must always provide a safe result 4 Trajectory calculation must be fast Slide No. 10

11 Trajectory Planner Characterisation Different planner characteristics Characteristic Calculation Method Optimization Value Range State Transition Degrees of Freedom Alternatives Direct / Sampling / Numerical Optimization Global Optimum / Local Optimum Discrecte / Continuous Primitives / Vehicle Model Spatial / Spatial & Temporal Slide No. 11

12 Trajectory Planner Characterisation fka s GPU based Trajectory Planner Characteristic Calculation Method Optimization Value Range State Transition Degrees of Freedom fka s Planner Numerical Optimization Local Optimum Continuous Vehicle Model Spatial Slide No. 12

13 Trajectory Planner Cost Function Relevant Aspects for Planning Trajectory planner uses a discrete set of intermediate steps to generate a solution Intermediate state cost depends on factors like Distance to borders Reference trajectory distance Reference trajectory relative orientation and more The overall cost of a trajectory is defined as the sum of all costs of each intermediate states Intermediate States Cost factor terms can be integrated or removed easily Facilitates model adaption based on driving situation Slide No. 13

14 Trajectory Planner Functional Interface Input and Output Input for the planner consists of a reference trajectory road data obstacles defined as polygons Road Boundary Output is a trajectory spline Static Obstacles Reference Trajectory Slide No. 14

15 Trajectory Planner Structure Draft Workflow Core Loop Sample Track / Create Reference Trajectory Evaluate Cost Function and Derivatives Vehicle Data (Position, Velocity, ) Solve NLP Solution found or max iterations reached Output Trajectory Simulation Update Vehicle Data According to Trajectory Real Vehicle Send commands Read odometry Slide No. 15

16 Trajectory Planner Structure Optimization Potentials Question from a point of System Architecture: where are optimization potentials, which can be addressed by new technologies? Cost function solved by NLP solver NLP solver requires 1st and 2nd partial derivatives of cost function Core Loop Evaluate Cost Function and Derivatives The fka GPU based planner utilizes the massive parallel computing power of GPUs Solve NLP GPU is used for function evaluation and derivative calculation Derivatives are calculated implicitly using automatic differentiation (hyperdual numbers) Afterwards the NLP solver operates upon the calculated cost function Slide No. 16

17 Trajectory Planner Capabilities of the Planner fka's Planner Capabilities Capabilites of the Trajectory Planner Realtime usabilty Constraints and conditions Customizable dynamic models Prediction horizon adaptabe Robust solutions not given any prior assumptions Static obstacles Slide No. 17

18 Performance Analysis Evaluation Planning Performance - Slide No. 18

19 Performance Analysis Evaluation Planning Performance Planning performance depends on several factors: Number of intermediate steps Scenario and cost function complexity Parallel computing capability Platform CPU-Only x86 Drive PX 2 dgpu Drive PX 2 igpu Runtime (approx.) 200ms 21ms 35ms Speed-Up (vs CPU) 1x 9.52x 5.71x Slide No. 19

20 Performance Analysis Evaluation The Driver as a Reference Sense Interpret Plan Arbitrate Act Driver reaction time depends on various factors physical and mental condition degree of experience in order to characterize situation Traffic situation complexity Driver: Typically ms can be assumed corresponds to 8 to 100km/h Planner: average planning time of 25ms corresponds to 100km/h Slide No. 20

21 Outlook Further Roadmap Planner and GPU as Component for AD Next steps on fka s GPU based planner Extend functionality and further enhance computation efficiency Natively integrate aspect of situation dependent, adaptive granularity selection and adaptive selection of cost function influencing factors Perform functional safety analysis Integrate complete planner into driving environment Slide No. 21

22 Contact Dipl.-Ing. Jörg Küfen Senior Manager Electronics Department Marius Stärk, M.Sc. Development Engineer Electronics Department fka Forschungsgesellschaft Kraftfahrwesen mbh Aachen Steinbachstr Aachen Germany Phone Fax Internet kuefen@fka.de Slide No. 22

23 Engineering for the Future of Automotive