TY - GEN
T1 - Safety-Violation Scenarios Search for ADS via Multi-Objective Genetic Algorithm
AU - Zong, Haoxin
AU - Hou, Zhonglin
AU - Liu, Hong
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Simulation testing of Autonomous Driving Systems (ADS) has become prominent, but prevalent techniques often miss bugs due to insufficient cross-layer integration and employ simplistic participant models, leading to a mismatch with real-world complexities and flawed ADS evaluation. The paper presents a Multi-Objective Genetic Algorithm (MOGA)-based method for creating scenarios that lead to safety violations by the ego vehicle. It considers complex conditions with various actor types and behaviors, using Behavior Trees for actor trajectories. The method includes a multi-dimensional metric to optimize MOGA for critical scenarios and reduce irrelevant ones, considering the ego vehicle's performance and the environment. Tests show the method produced 1, 5 5 2 scenarios in 24 hours, identifying 53 that led to 1 0 different safety violations, proving the effectiveness of behavior tree actors in increasing complexity. It also outperforms traditional methods in identifying a wider range of violations faster and triples the occurrence of critical safety scenarios.
AB - Simulation testing of Autonomous Driving Systems (ADS) has become prominent, but prevalent techniques often miss bugs due to insufficient cross-layer integration and employ simplistic participant models, leading to a mismatch with real-world complexities and flawed ADS evaluation. The paper presents a Multi-Objective Genetic Algorithm (MOGA)-based method for creating scenarios that lead to safety violations by the ego vehicle. It considers complex conditions with various actor types and behaviors, using Behavior Trees for actor trajectories. The method includes a multi-dimensional metric to optimize MOGA for critical scenarios and reduce irrelevant ones, considering the ego vehicle's performance and the environment. Tests show the method produced 1, 5 5 2 scenarios in 24 hours, identifying 53 that led to 1 0 different safety violations, proving the effectiveness of behavior tree actors in increasing complexity. It also outperforms traditional methods in identifying a wider range of violations faster and triples the occurrence of critical safety scenarios.
KW - Autonomous Driving System
KW - Evolutionary Strategy
KW - Multi-Objective Genetic Algorithm
KW - Test Scenario Generation
UR - https://www.scopus.com/pages/publications/105002236201
U2 - 10.1109/SWC62898.2024.00301
DO - 10.1109/SWC62898.2024.00301
M3 - 会议稿件
AN - SCOPUS:105002236201
T3 - Proceedings - 2024 IEEE Smart World Congress, SWC 2024 - 2024 IEEE Ubiquitous Intelligence and Computing, Autonomous and Trusted Computing, Digital Twin, Metaverse, Privacy Computing and Data Security, Scalable Computing and Communications
SP - 1961
EP - 1966
BT - Proceedings - 2024 IEEE Smart World Congress, SWC 2024 - 2024 IEEE Ubiquitous Intelligence and Computing, Autonomous and Trusted Computing, Digital Twin, Metaverse, Privacy Computing and Data Security, Scalable Computing and Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE Smart World Congress, SWC 2024
Y2 - 2 December 2024 through 7 December 2024
ER -