Spicetech GmbH i. G.Schloßstraße 59 C70176 Stuttgart

Confidential

Testing the Unexpected:NewFrontiersinAutomotiveDevelopment

Stuttgart

04.12.2017

3Confidential

User

Engine&Transmission

Assistance

SafetyStandardsand laws onCO2 /NOX /Noise

2015

Efforts indevelopmentand validation

4Confidential

Viewpoint ofSupercomputing

Thermodynamics(CFD)

Crash(FEM) Fluiddynamics (CFD)

2015

5Confidential

User

Engine&Transmission

Assistance

SafetyStandardsand laws onCO2 /NOX /Noise

2025

Efforts indevelopmentand validation

6Confidential

Viewpoint ofSupercomputing

User

Engine&Transmission

Assistance

SafetyStandardsand laws onCO2 /NOX /Noise

2025

7Confidential

Assistance(ADAS)

Challenge:algorithm-baseddecision making

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image source:www.onlinedatahub.com

8Confidential

Testing the Unexpected:Model-based system validation

9Confidential

Cyber Physical Systems

DistributedSysteme

Coupled Systems

LifeCycleManagement

Autonomic Computing

IoT

Cybernetics

Self-organizing Systems

Requirements Management

Cognitive SystemsEngineering

SystemIntegrationSystemVerification

Interfaces

Stakeholdermanagement

FMIFMU

Collaborativedevelopment

Requirements Workflow Modellierung/Simulation

MBSEModel-Based SystemsEngineering

Model-based development‘til 2015

10Confidential

MBSEModel-Based SystemsEngineering

Cyber Physical Systems

DistributedSysteme

Coupled Systems

LifeCycleManagement

Autonomic Computing

IoT

Cybernetics

Self-organizing Systems

Requirements Management

Cognitive SystemsEngineering

SystemIntegrationSystemVerification

Interfaces

Stakeholdermanagement

FMIFMU

Collaborativedevelopment

DataRequirements Workflow Model/Simulation

BigData

Test-DataManagement

CustomerData

Self-Learning

Artificial Intelligence

Model-based development‘til 2015

11Confidential

DevelopmentofAdvanced DriverAssistentSystems

Known basic virtualtest scenarios

Unspecified virtual testscenarios

Roadtests

Thedevelopment process

12Confidential

sensormodel HPC machine-

learningscenariorendering

Fortissimo2Experiment908MassivelyParallelVirtualTestingofSafety-RelevantDrivingSystems

ISV&Domainexpert: SpicetechGmbHHPCexpert: XLABd.o.o.HPCcentre: Höchstleistungsrechenzentrum StuttgartEnd-user: Automotivesuppliers/Carmanufacturers

representedbyValeo andPorsche

Our approach

13Confidential

Challenge1:Scenariomodeling

Classification of Object A

Classification of Object B

Density of fog (VarB)

Intensity of ambient light(VarA)

Colorof pavement (VarG)

Texture of pavement (VarF)

Colorof lane marks (VarE)

Colorof object B(VarD)

Speedof object B(VarC)

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14Confidential

Challenge2:Multi-dimensionality

• strictly n-dimensionaldata model

• pre- and postprocessingseparated to condense results

• systematic quantification ofvariational space

• MPI-parallelscenario distribution

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15Confidential

Challenge3:Machine Learning

• validation basis:broad grid inn-dimensionalspace

• fastexplorer:based onlocalsupport vector machines

• systematic enhancement ofvalidation:test priorization byuncertainty /cost function

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16Confidential

Fortissimo2Experiment908MassivelyParallelVirtualTestingofSafety-RelevantDrivingSystems

ISV&Domainexpert: SpicetechGmbHHPCexpert: XLABd.o.o.HPCcentre: Höchstleistungsrechenzentrum StuttgartEnd-user: Automotivesuppliers/Carmanufacturers

representedbyValeo andPorsche

Keybenefits

• Servicegives fastand reliable feedback for functional development and maturity

of systems

• Iterativeimprovements of systems leads to multipleusage of service

• Servicewillsignificantly reduce onroad testing costs and time