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Generating Critical Test Scenarios for Autonomous Driving Systems via Influential Behavior Patterns

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Dan YeAuthors Info & Claims

ASE '22: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering

Article No.: 46, Pages 1 - 12

https://doi.org/10.1145/3551349.3560430

Published: 05 January 2023 Publication History

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Abstract

Autonomous Driving Systems (ADSs) are safety-critical, and must be fully tested before being deployed on real-world roads. To comprehensively evaluate the performance of ADSs, it is essential to generate various safety-critical scenarios. Most of existing studies assess ADSs either by searching high-dimensional input space, or using simple and pre-defined test scenarios, which are not efficient or not adequate. To better test ADSs, this paper proposes to automatically generate safety-critical test scenarios for ADSs by influential behavior patterns, which are mined from real traffic trajectories. Based on influential behavior patterns, a novel scenario generation technique, CRISCO, is presented to generate safety-critical scenarios for ADSs testing. CRISCO assigns participants to perform influential behaviors to challenge the ADS. It generates different test scenarios by solving trajectory constraints, and improves the challenge of those non-critical scenarios by adding participants’ behavior from influential behavior patterns incrementally. We demonstrate CRISCO on an industrial-grade ADS platform, Baidu Apollo. The experiment results show that our approach can effectively and efficiently generate critical scenarios to crash ADS, and it exposes 13 distinct types of safety violations in 12 hours. It also outperforms two state-of-art ADS testing techniques by exposing more 5 distinct types of safety violations on the same roads.

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Cited By

Meiser MZinnikus I(2024)A Survey on the Use of Synthetic Data for Enhancing Key Aspects of Trustworthy AI in the Energy Domain: Challenges and OpportunitiesEnergies10.3390/en1709199217:9(1992)Online publication date: 23-Apr-2024
https://doi.org/10.3390/en17091992
Guo AZhou YTian HFang CSun YSun WGao XLuu ALiu YChen ZFilkov VRay BZhou M(2024)SoVAR: Build Generalizable Scenarios from Accident Reports for Autonomous Driving TestingProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695037(268-280)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695037
Chen YHuai YLi SHong CGarcia J(2024)Misconfiguration Software Testing for Failure Emergence in Autonomous Driving SystemsProceedings of the ACM on Software Engineering10.1145/36607921:FSE(1913-1936)Online publication date: 12-Jul-2024
https://doi.org/10.1145/3660792
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Index Terms

Generating Critical Test Scenarios for Autonomous Driving Systems via Influential Behavior Patterns
1. Networks
  1. Network properties
    1. Network reliability
2. Security and privacy
  1. Software and application security
    1. Software security engineering

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ASE '22: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering

October 2022

2006 pages

ISBN:9781450394758

DOI:10.1145/3551349

Copyright © 2022 ACM.

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Published: 05 January 2023

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National Key R&D Program of China
National Natural Science Foundation of China

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ASE '22

ASE '22: 37th IEEE/ACM International Conference on Automated Software Engineering

October 10 - 14, 2022

MI, Rochester, USA

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Overall Acceptance Rate 82 of 337 submissions, 24%

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Cited By

Meiser MZinnikus I(2024)A Survey on the Use of Synthetic Data for Enhancing Key Aspects of Trustworthy AI in the Energy Domain: Challenges and OpportunitiesEnergies10.3390/en1709199217:9(1992)Online publication date: 23-Apr-2024
https://doi.org/10.3390/en17091992
Guo AZhou YTian HFang CSun YSun WGao XLuu ALiu YChen ZFilkov VRay BZhou M(2024)SoVAR: Build Generalizable Scenarios from Accident Reports for Autonomous Driving TestingProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695037(268-280)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695037
Chen YHuai YLi SHong CGarcia J(2024)Misconfiguration Software Testing for Failure Emergence in Autonomous Driving SystemsProceedings of the ACM on Software Engineering10.1145/36607921:FSE(1913-1936)Online publication date: 12-Jul-2024
https://doi.org/10.1145/3660792
Gambi AMathews SSteininger BPoienko MBobek DChristakis MPradel M(2024)The Flexcrash Platform for Testing Autonomous Vehicles in Mixed-Traffic ScenariosProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3685299(1811-1815)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3650212.3685299
Yu XLiu LHu XKeung JXia XLo DChristakis MPradel M(2024)Practitioners’ Expectations on Automated Test GenerationProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3680386(1618-1630)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3650212.3680386
Lu YTian YBi YChen BPeng XChristakis MPradel M(2024)DiaVio: LLM-Empowered Diagnosis of Safety Violations in ADS Simulation TestingProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3652135(376-388)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3650212.3652135
Neelofar NAleti A(2024)Identifying and Explaining Safety-critical Scenarios for Autonomous Vehicles via Key FeaturesACM Transactions on Software Engineering and Methodology10.1145/364033533:4(1-32)Online publication date: 11-Jan-2024
https://dl.acm.org/doi/10.1145/3640335
Hou ZWang SLiu HYang YZhang Y(2024)Twin Scenarios Establishment for Autonomous Vehicle Digital Twin Empowered SOTIF AssessmentIEEE Transactions on Intelligent Vehicles10.1109/TIV.2023.33043539:1(1965-1976)Online publication date: Jan-2024
https://doi.org/10.1109/TIV.2023.3304353
Zhang STak T(2023)Risk analysis of autonomous vehicle test scenarios using a novel analytic hierarchy process methodIET Intelligent Transport Systems10.1049/itr2.1246618:5(794-807)Online publication date: 5-Dec-2023
https://doi.org/10.1049/itr2.12466

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