Advantage of a GPU powered trajectory planning for...
Transcript of 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
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IntroductionDisruptive Technologies
Potentials of Disruptive Technologies
Manhatten - 1900 Manhatten - 1913
13 years
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IntroductionDisruptive Technologies
Potentials of Disruptive Technologies
Mobile Phone - 2007 Mobile Phone - 2017
less than 10 years
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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
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IntroductionAutomated Driving (AD) Systems
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IntroductionAutomated Driving (AD) Systems
Decomposition of an „Autonomous Driving (AD) System“
Localize ControlPlan
Map
Perceive
Sense
Goal Strategy Tactic Execution
Sense Interpret Plan ActArbitrate
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IntroductionAutomated Driving (AD) Systems
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
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Trajectory PlannerEssential Element of the Functional Network
Trajectory PlannerDecision Layer Dynamic Controllers
Hierarchy Levels
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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
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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
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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
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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
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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
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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
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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
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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
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Performance AnalysisEvaluation
Planning Performance -
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Performance AnalysisEvaluation
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
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Performance AnalysisEvaluation
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
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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
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Phone
Fax
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
Engineering for the Future of Automotive