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© fka 2016 · All rights reserved 2016/04/06 Slide No. 1 #150 · 16HD0008.pptx

San Jose, 6th April 2016

Dipl.-Inform. Janek Hudecek, Dipl.-Ing. Christoph Klas

A Universal Trajectory Generator for Automated Vehicles

GPU Technology Conference

Forschungsgesellschaft Kraftfahrwesen mbH Aachen


Motivation and Introduction Information Processing in Automated Vehicles

Automation tasks can typically be divided into

1. Perception: Digitalization of environment – object detection, freespace detection, characterization

2. Interpretation: Gathering of semantic scene understanding

3. Motion Planning: Behavior generation and trajectory planning

4. Actuation: Trajectory tracking and vehicle dynamics controller

1. Perception &

2. Interpretation

3.1 Behavior

Generation

3.2 Trajectory

Planning 4. Actuation Sensing

src: Augsburger Allgemeine


Trajectory Planning via Nonlinear Model-Predictive Control General Idea: NMPC Based Realization

TPS is based on nonlinear model-predictive control

NMPC embeds an optimal control problem (OCP)

A control function is applied to a vehicle model and the resulting trajectory is calculated

Evaluation of state and control constraints of resulting trajectory

Control Function Vehicle Model & Rating Drivable Trajectory

Obstacle

Ego-Vehicle

State Trajectory


Trajectory Planning via Nonlinear Model-Predictive Control Optimal Control Problem

General formulation of OCP:

Follow a given reference, optimize comfort aspects

Stay inside a (dynamic) driving corridor

Respect vehicle dynamics, represented by a

single track model (STM)

State definition depends on STM, usually

Underlying OCP is formulated as NLP:

Rating of resulting trajectory by cost functional

Evaluation of feasibility by constraint definition

Collision awareness

Observe dynamic and kinematic constraints

Respect underlying vehicle model Driving Corridor

Intermediate State

Collision Circles

Elimination of

State-Defects


Trajectory Planning via Nonlinear Model-Predictive Control Implementation Details – Planner Architecture

System is two-part

Numerical Solver:

Fast interior-point algorithm

Gradient based

Restricted to local convergence

Iterative optimization of parameter

candidates

Metric Evaluation:

Reconstruction of control function

based on parameters

Calculation of state transitions

Calculation of , and

1st and 2nd partial derivations of

metrics


Trajectory Planning via Nonlinear Model-Predictive Control Implementation Details – Decomposition Strategy

Implementation Details

Derivations are calculated via

Hyperdual Numbers (AutoDiff)

Partial derivations are

independent of each other

Each derivation correlates to a job

Depending on no. of parameters

~500 – 10.000 jobs

Data decomposition: Jobs are

assigned to blocks

Per job 128 – 512 samples

Task decomposition: Samples are

assigned to threads


Trajectory Planning via Nonlinear Model-Predictive Control Implementation Details – Parallelization Example


Experimental Results Simple Examples – Lane Change and S-Curve


Experimental Results Vehicle Models – Vehicle and Vehicle/Trailer Combination

Vehicle Only Vehicle/Tailer Combination


Experimental Results Real Driving Scenario – Double Curve with Obstacle


Experimental Results Real Driving Scenario – Narrow Passage


Experimental Results Optimizing the Cost Functional

Cost Functional represents several

optimization criteria

Lateral jerk/acceleration/smoothness

Distance to reference curve

Exemplary variation of the parameters

adjusting distance to given reference

Uprating leads to undesired driving

behavior in case of a bad guess

Tuning to subjectively comfortable and

safe behavior is a key factor

Clear distinction of tasks between

behavior decision and trajectory

planner needs to be defined

E.g. behavior layer has to decide when

to execute lane changes


Conclusions:

Presented TPS implements a versatile, adaptive and ultra fast approach

System is currently ported to run on NVIDIA DRIVE PX platform in realtime

First results are very promising in terms of runtime

Next Steps:

Integration of TPS into test vehicle as part of automated driving system

Environment detection e.g. by Nvidia DriveWorks

Closed-loop trajectory tracker

Further research:

Development and integration of human inspired behavior decision system

Subjective decisions respecting traffic rules

Gaining understanding in online-parameterization for subjective evaluation

Integration of overall system into electric vehicle research platform SpeedE

Summary and Outlook

Documents

GPU Technology Conference A Universal Trajectory …on-demand.gputechconf.com/gtc/2016/presentation/s6572-christoph...A Universal Trajectory Generator for Automated Vehicles ... usually