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Simulation Based Virtual Testing for Safety of ADAS Algorithms - Case Studies
Technical Paper
2021-01-0114
ISSN: 0148-7191, e-ISSN: 2688-3627
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SAE WCX Digital Summit
Language:
English
Abstract
Automated Driving Systems (ADS) make the driving experience safer, more efficient, and comfortable by performing complex maneuvers, preempting potential risky situations, or taking over the driver’s tasks in critical situations. Innovation acceptance research for Advanced Driver Assistance Systems (ADAS) illustrates the increasing demand for safety and comfort as the two prime movers of the ADAS market. Since ADAS technologies have significant impact on human lives, extensive testing and validation throughout the design process is indispensable. Due to complexity of systems, cost of testing, and safety of test engineers, a significant chunk of ADAS calibration and validation needs to be done virtually. Although simulation-based verification and validation (V&V) is not new, the test descriptions, modeling and simulation framework are not yet well established. Off-the-shelf software tools have different architectures, simulation procedures, data standardizations, and tradeoffs. The underlying question remains- “How to ensure that a simulated test scenario actually tested or validated the ADAS algorithm?” This paper attempts to better understand the basic requirements to successfully test common ADAS algorithms by describing a generalized structure of simulation framework essential to successfully run the safety tests. The study also emphasizes how variability across different simulators may cause discrepancies in the results. The scenarios are built in two different simulators which have unique features, methods and assumptions that must be well-understood for the results to be proven valid. Finally, the essential features of simulators are documented to understand the effect of simulator specific scenario parameters on the success of test simulations via test-of-tests.
Authors
Citation
Singh, H., Midlam-Mohler, S., and Tulpule, P., "Simulation Based Virtual Testing for Safety of ADAS Algorithms - Case Studies," SAE Technical Paper 2021-01-0114, 2021, https://doi.org/10.4271/2021-01-0114.Data Sets - Support Documents
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References
- Thorn , E. , Kimmel , S. , and Chaka , M. A Framework for Automated Driving System Testable Cases and Scenarios Blacksburg National Highway Traffic Safety Administration 2018 https://rosap.ntl.bts.gov/view/dot/38824
- Federal Highway Administration Highway statistics, 2018 Washington, DC US Department of Transportation 2019
- Brenner , W. , and Herrmann , A. An Overview of Technology, Benefits and Impact of Automated and Autonomous Driving on the Automotive Industry Berlin, Heidelberg Digital Marketplaces Unleashed. Springer 10.1007/978-3-662-49275-8_39
- National Highway Traffic Safety Administration https://www.federalregister.gov/doscuments/2019/11/21/-2019-25217/advanced-driver-assistance-systems-draft-research-test-procedures
- Huang , W. , Wang , K. , Ly , Y. , and Zhu , F. Autonomous Vehicles Testing Methods Review 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC) 2016
- Kalra , N. , and Paddock , S.M. Driving to Safety: How Many Miles of Driving Would It Take to Demonstrate Autonomous Vehicle Reliability? Transportation Research Part A: Policy and Practice 94 182 193 2016
- Kalra , N. , and Groves , D.G. RAND Model of Automated Vehicle Safety (MAVS): Model Documentation Santa Monica, CA RAND Corporation 2017 https://www.rand.org/pubs/research_reports/RR1902.html
- Fraade-Blanar , L. , Blumenthal , M.S. , Anderson , J.M. , and Kalra , N. Measuring Automated Vehicle Safety: Forging a Framework Santa Monica, CA RAND Corporation 2018 https://www.rand.org/pubs/research_reports/RR2662.html
- Saigol , D.Z. and Peters , D.A. Verifying Automated Driving Systems in Simulation: Framework and Challenges 25th ITS WorldCongress 2018 http://www.zeynsaigol.com/ITS2018VerifyingADSinSimulation.pdf
- Saigol , D.Z. , Peters , D.A. , Barton , M. , and Taylor , M. Regulating and Accelerating Development of Highly Automated and Autonomous Vehicles Through Simulation and Modelling Milton Keynes, UK Transport Systems Catapult https://s3-eu-west-1.amazonaws.com/media.ts.catapult/wp-content/uploads/2018/03/23113301/00299_AV-Simulation-Testing-Report.pdf
- Winner , H. , Lemmer , K. , Form , T. , and Mazzega , J. PEGASUS—First Steps for the Safe Introduction of Automated Driving Meyer , G. , Beiker , S. Road Vehicle Automation 5. Lecture Notes in Mobility Cham Springer 10.1007/978-3-319-94896-6_1
- Dosovitskiy , A. , Ros , G. , Codevilla , F. , Lopez , A. , and Koltun , V. CARLA: An Open Urban Driving Simulator 2017 https://arxiv.org/abs/1711.03938v1
- TASS A Siemens Business https://tass.plm.automation.siemens.com/prescan
- T. International, SIMCenter PreScan Manual PreScan
- CARLA CARLA Documentation 2020 https://carla.readthedocs.io/en/latest/
- Miao , Q. , Tang , X. , Wang , D. , Tideman , M. , and Li , J. The Application of PRESCAN in the Concept Development of Active Safety System Third International Conference on Digital Manufacturing & Automation Shanghai, China 2012
- Ortega , J. , Lengyel , H. , and Szalay , Z. Overtaking Maneuver Scenario Building for Autonomous Vehicles with PreScan Software Transportation Engineering 2 100029 2020
- Chen , X. , Miao , Y. , Jin , M. , and Zhang , Q. Driving Decision-Making Analysis of Lane-Changing for Autonomous Vehicle Under Complex Urban Environment Proceedings of the 29th Chinese Control and Decision Conference, CCDC, 2017 6878 6883 2017 10.1109/CCDC.2017.7978420
- Pilz , C. , Steinbauer , G. , Schratter , M. , and Watzenig , D. Development of a Scenario Simulation Platform to Support Autonomous Driving Verification 2019 8th IEEE Int. Conf. Connect. Veh. Expo, ICCVE 2019 -Proc. 2019
- Barickman , F.S. NHTSA VRTC HV Forward Collision Avoidance and Mitigation Research Government/Industry Brake Research Presentation 2012
- Albrecht , H. 2017
- Rao , S.J. and Forkenbrock , G.J. 2019
- Stevens , S. Baseline Analysis of Driver Performance for Intersection Crossing and Crash Avoidance Applications 2018
- Mateo , S.S. , Perez-Moreno , E. , Jimenez , F. , Naranjo , J.E. , Flores , C.G.P. , and Teruel , J.A. Study of a Driver Assistance Interface for Merging Situations on Highways Proc. 2018 IEEE Int. Conf. on Vehicular Electronics and Safety 2018 1 5
- CARLA 2020 http://carla.org/2020/06/09/talks_2020/
- CARLA 2020 https://carla.readthedocs.io/en/latest/adv_synchrony_timestep/