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Safety Validation and Edge Case Testing for Autonomous Vehicles

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How the heavy tail ceiling is a problem, and different ways to augment sensor inputs with edge cases to improve autonomous vehicle/self-driving car robustness.
Presentation at China Autonomous Driving Testing Technology Innovation Conference, June 2018

Published in: Automotive
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Safety Validation and Edge Case Testing for Autonomous Vehicles

  1. 1. AutonomousVehicles: SafetyValidationand EdgeCaseTesting Shanghai,June2018 ©2018EdgeCaseResearchLLC Prof.PhilipKoopman
  2. 2. 2©2018EdgeCaseResearchLLC Limitationstotesting&simulation WhyEdgeCasesmatter TheHeavyTailCeiling Testing&trainingonEdgeCases Overview https://goo.gl/J3SSyu
  3. 3. 3©2018EdgeCaseResearchLLC Goodforidentifying“easy”cases  Expensiveandpotentiallydangerous  Shouldconcentrateondatacollection,notdebugging DefaultAVValidation:PublicRoadTesting http://bit.ly/2toadfa http://bit.ly/2lsj6Qu
  4. 4. 4©2018EdgeCaseResearchLLC Safer,butexpensive  Notscalable  Onlyteststhingsyouhavethoughtof! ClosedCourseTesting Volvo/ MotorTrend
  5. 5. 5©2018EdgeCaseResearchLLC Highlyscalable;lessexpensive  Realismvarieswidely(nicelookingisnotnecessarilyrealistic)  Onlyteststhingsyouhavethoughtof! Simulation http://bit.ly/2K5pQCN Udacity http://bit.ly/2toFdeT Apollo
  6. 6. 6©2018EdgeCaseResearchLLC SomeEdgeCasesare extreme,weird,unusual  Unusualroadobstacles  Extremeweather  Strangebehaviors ManyEdgeCasesurprises  Youwon’tseetheseintesting  Thisisthestuffyoudidn’tthinkof! WhatAboutEdgeCases? https://www.clarifai.com/demo http://bit.ly/2In4rzj
  7. 7. 7©2018EdgeCaseResearchLLC Unusualroadobstacles&obstacles Extremeweather Strangebehaviors JustAFewEdgeCases http://bit.ly/2top1KD http://bit.ly/2tvCCPK https://ind.pn/2ltnAWW https://dailym.ai/2K7kNS8 https://en.wikipedia.org/wiki/Magic_Roundabout_(Swindon)
  8. 8. 8©2018EdgeCaseResearchLLC Wherewillyoubeafter1Billionmilesofvalidationtesting? Assume1Millionmilesbetweenunsafe“surprises”  Example#1: 100“surprises”@100Mmiles/surprise –Allsurprisesseenabout10timesduringtesting –Withluck,allbugsarefixed  Example#2: 100,000“surprises”@100Bmiles/surprise –Only1%ofsurprisesseenduring1Bmiletesting –Bugfixesgivenorealimprovement(1.01Mmiles/surprise) WhyEdgeCasesMatter https://goo.gl/3dzguf
  9. 9. 9©2018EdgeCaseResearchLLC TheRealWorld:HeavyTailDistribution CommonThings SeenInTesting EdgeCases NotSeenInTesting (HeavyTailDistribution)
  10. 10. 10©2018EdgeCaseResearchLLC TheHeavyTailTestingCeiling
  11. 11. 11©2018EdgeCaseResearchLLC Falsepositiveonlanemarking Falsenegativerealbicyclist Falsenegativewhen infrontofdarkvehicle Falsenegativewhen personnexttolightpole LessonsLearnedWithEdgeCaseTesting Perceptionfailuresareoftencontext-dependent  Falsepositivesandfalsenegativesarebothaproblem
  12. 12. 12©2018EdgeCaseResearchLLC EdgeCaseTesting:SceneAlteration Removeormodifyobjects  Removeinformationtotestbrittleness  Removeobjects: –Checksforhiddenlearnedcorrelations –Usefulfordataaugmentation Vehicleremovedfromscene
  13. 13. 13©2018EdgeCaseResearchLLC EdgeCaseTesting:DataAugmentation Addobjectstoscenes  Objectscantrigger context-dependentfaults  Slightlymutatedobjects checkforbrittleness Multi-sensorcoordination  Vision  Lidar  Radar Recordedscene Darkvehicleaddedtoscene Synthesizedscene
  14. 14. 14©2018EdgeCaseResearchLLC EdgeCaseTesting:DataDegradation https://goo.gl/5sKnZV QuocNet: Car Nota Car Magnified Difference Bus Nota Bus Magnified Difference AlexNet: Szegedy,Christian,etal."Intriguingpropertiesofneuralnetworks."arXivpreprintarXiv:1312.6199(2013).
  15. 15. 15©2018EdgeCaseResearchLLC  SensordatacorruptionexperimentsatNREC MLIsBrittleToEnvironmentChanges SyntheticEnvironment RobustnessTestingSyntheticEquipmentFaults Gaussianblur ExploringtheresponseofaDNNtoenvironmental perturbationsfrom“RobustnessTestingfor PerceptionSystems,”RIOTProject,NREC, DIST-A.
  16. 16. 16©2018EdgeCaseResearchLLC EdgeCaseTesting:DataFuzzing Addsmallamountsofsensornoise Costvs.speedtradeoff  Useapproximatemodelsfortraining  Userealisticmodelsforvalidation Pedestrian Missed: Gaussian Noise+ BlackCar Pedestrian Missed: Gaussian Blur
  17. 17. 17©2018EdgeCaseResearchLLC Builda“zoo”ofsurprises  Lookforsurprises  Movethemtoaffectvehicle Inject“unrealistic”faults tostresssystem  Robotssusceptibletoexceptionaldata –Floatingpoint:NaN,Infinity,etc. –Integer:0x8000000,nullpointers,… –Sensordata:malformedarrays,… AdditionalEdgeCaseApproaches Cowsfallingoutofatruck https://youtu.be/uy5OEoyEiAI
  18. 18. 18©2018EdgeCaseResearchLLC Ascalablewaytotest&trainonEdgeCases TheHologramTool Yourfleetand yourdatalake Hologram clustertests yourCNN Hologram clustertrains yourCNN YourCNN becomes morerobust
  19. 19. 19©2018EdgeCaseResearchLLC ThankYou! http://bit.ly/2MTbT8F(signmodified) Mars

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