Advantage of a GPU powered trajectory planning for...

GTC Munich, 12th October 2017

Dipl.-Ing. Jörg Küfen - Senior Manager Engineer

Marius Stärk, M.Sc. - Development Engineer

GPU Technology Conference 2017

Advantage of a GPU powered trajectory planning for autonomous driving using NVidia DrivePX

Forschungsgesellschaft Kraftfahrwesen mbH Aachen

© fka 2017 · All rights reserved2017/10/12Slide No. 2#150 · 17KJ0067

To Start With …… E/E Systems of Vehicles

Power Systems, Infrastructure

ECU Hardware, Communication

Architecture and Software

System Layers

Depending

Interdisciplinary

Perspective

On the E/E System


IntroductionDisruptive Technologies

Potentials of Disruptive Technologies

Manhatten - 1900 Manhatten - 1913

13 years


IntroductionDisruptive Technologies

Potentials of Disruptive Technologies

Mobile Phone - 2007 Mobile Phone - 2017

less than 10 years


IntroductionAutomated 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



Decomposition of an „Autonomous Driving (AD) System“

Localize ControlPlan

Map

Perceive

Sense

Goal Strategy Tactic Execution

Sense Interpret Plan ActArbitrate



Decomposition of an „Autonomous Driving (AD) System“

S S S S S S

Base Perception Actuator Command

A A A A A A

Reflex A

fast reactions

High-Level

Perception

Knowledge Based

Reasoning(respecting system capabilities)

ArbiterReflex B

reactions

µs

ms

s

min

signals

objects

complex objects

actuations

abstract actions

plans

actions


Trajectory PlannerEssential Element of the Functional Network

Trajectory PlannerDecision Layer Dynamic Controllers

Hierarchy Levels


Trajectory PlannerRequirements

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


Trajectory PlannerCharacterisation

Different planner characteristics

Characteristic Alternatives

Calculation Method Direct / Sampling / Numerical Optimization

Optimization Global Optimum / Local Optimum

Value Range Discrecte / Continuous

State Transition Primitives / Vehicle Model

Degrees of Freedom Spatial / Spatial & Temporal


Trajectory PlannerCharacterisation

fka‘s GPU based Trajectory Planner

Characteristic fka‘s Planner

Calculation Method Numerical Optimization

Optimization Local Optimum

Value Range Continuous

State Transition Vehicle Model

Degrees of Freedom Spatial


Trajectory PlannerCost 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

• Cost factor terms can be integrated or removed easily

• Facilitates model adaption based on driving situation

Intermediate States


Trajectory PlannerFunctional Interface

Input and Output

• Input for the planner consists of

• a reference trajectory

• road data

• obstacles defined as polygons

• Output is a trajectory spline

Road Boundary

Reference Trajectory

Static Obstacles


Trajectory PlannerStructure

Draft Workflow

Sample Track /

Create Reference

Trajectory

Output

TrajectoryVehicle Data

(Position,

Velocity, …)

Evaluate Cost

Function and

Derivatives

Solve NLP

Core Loop

Solution found

or max iterations

reached

Update Vehicle Data

According to Trajectory

Simulation

Send commands

Read odometry

Real Vehicle


Trajectory PlannerStructure

Optimization Potentials

Evaluate Cost

Function and

Derivatives

Solve NLP

Core LoopQuestion 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

• The fka GPU based planner utilizes the massive parallel computing

power of GPUs

• 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


Trajectory PlannerCapabilities 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


Performance AnalysisEvaluation

Planning Performance -



Planning Performance

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

Planning performance depends on several factors:

• Number of intermediate steps

• Scenario and cost function complexity

• Parallel computing capability



The Driver as a Reference

Sense Interpret Plan ActArbitrate

Driver reaction time depends on various factors

• physical and mental condition

• degree of experience in order to characterize

situation

• Traffic situation complexity

Driver: Typically 300-500ms can be assumed

corresponds to 8 to 14m @ 100km/h

Planner: average planning time of 25ms

corresponds to 0,75m @ 100km/h


OutlookFurther 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


Phone

Fax

Email

Internet www.fka.de

fka Forschungsgesellschaft Kraftfahrwesen mbH Aachen

Steinbachstr. 7

52074 Aachen

Germany

Contact

Dipl.-Ing. Jörg Küfen

Senior Manager – Electronics Department

Marius Stärk, M.Sc.

Development Engineer – Electronics Department

+49 241 8861 179

+49 241 8861 110

[email protected]

Engineering for the Future of Automotive

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