testing theunexpected - gauß-allianz · spicetech gmbh i. g. schloßstraße59 c 70176 stuttgart...
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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
image source:www.hlrs.de
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