research-article

CSG: Critical Scenario Generation from Real Traffic Accidents

Authors:

Wu XiangbinAuthors Info & Claims

2020 IEEE Intelligent Vehicles Symposium (IV)

Pages 1330 - 1336

https://doi.org/10.1109/IV47402.2020.9304609

Published: 19 October 2020 Publication History

Abstract

Autonomous driving (AD) is getting closer to our life, but the severe traffic accidents of autonomous vehicle (AV) happened in the past several years warn us that the safety of AVs is still a big challenge for the AD industry. Before volume production, the automotive industry and regulators must ensure the AV can deal with dangerous scenarios. Although road test is the most common method to test the performance and safety of an AV, it has some manifest disadvantages, e.g., highly risky and unrepeatable, low efficiency and lack of useful critical scenarios. Critical-scenario-based simulation can effectively address these problems and become an important complement to road test. In this paper, we present a novel approach to extract critical scenarios from real traffic accident videos and re-generate them in a simulator. We also introduce our integrated toolkit for scenario extraction and scenario test. With the toolkit, we can build a critical scenario library quickly and use it as a benchmark for AV safety assessment, among other purposes. On top of this, we further introduce our safety assessment criteria and scoring method.

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Cited By

Song QAnderberg ROlsson HRuneson PBaresi LMa XPasquale L(2024)Generating Executable Test Scenarios from Autonomous Vehicle Disengagements using Natural Language ProcessingProceedings of the 19th International Symposium on Software Engineering for Adaptive and Self-Managing Systems10.1145/3643915.3644098(98-104)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643915.3644098
Song QEngström ERuneson P(2024)Industry Practices for Challenging Autonomous Driving Systems with Critical ScenariosACM Transactions on Software Engineering and Methodology10.1145/364033433:4(1-35)Online publication date: 11-Jan-2024
https://dl.acm.org/doi/10.1145/3640334
Tang SZhang ZZhang YZhou JGuo YLiu SGuo SLi YMa LXue YLiu Y(2023)A Survey on Automated Driving System Testing: Landscapes and TrendsACM Transactions on Software Engineering and Methodology10.1145/357964232:5(1-62)Online publication date: 24-Jul-2023
https://dl.acm.org/doi/10.1145/3579642
Show More Cited By

Index Terms

CSG: Critical Scenario Generation from Real Traffic Accidents
1. Computing methodologies
  1. Computer graphics
  2. Modeling and simulation
2. Information systems
  1. Information systems applications

Index terms have been assigned to the content through auto-classification.

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cover image Guide Proceedings

2020 IEEE Intelligent Vehicles Symposium (IV)

Oct 2020

1558 pages

Copyright © 2020.

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IEEE Press

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Published: 19 October 2020

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Cited By

Song QAnderberg ROlsson HRuneson PBaresi LMa XPasquale L(2024)Generating Executable Test Scenarios from Autonomous Vehicle Disengagements using Natural Language ProcessingProceedings of the 19th International Symposium on Software Engineering for Adaptive and Self-Managing Systems10.1145/3643915.3644098(98-104)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643915.3644098
Song QEngström ERuneson P(2024)Industry Practices for Challenging Autonomous Driving Systems with Critical ScenariosACM Transactions on Software Engineering and Methodology10.1145/364033433:4(1-35)Online publication date: 11-Jan-2024
https://dl.acm.org/doi/10.1145/3640334
Tang SZhang ZZhang YZhou JGuo YLiu SGuo SLi YMa LXue YLiu Y(2023)A Survey on Automated Driving System Testing: Landscapes and TrendsACM Transactions on Software Engineering and Methodology10.1145/357964232:5(1-62)Online publication date: 24-Jul-2023
https://dl.acm.org/doi/10.1145/3579642
Wang SSheng ZXu JChen TZhu JZhang SYao YMa X(2022)ADEPT: A Testing Platform for Simulated Autonomous DrivingProceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering10.1145/3551349.3559528(1-4)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3551349.3559528
Kolb NJordan CHuber FPretschner A(2022)Automatic Evaluation of Automatically Derived Semantic Scenario Instance Descriptions2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)10.1109/ITSC55140.2022.9922013(1565-1571)Online publication date: 8-Oct-2022
https://dl.acm.org/doi/10.1109/ITSC55140.2022.9922013

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