Administrator by China Associction for Science and Technology
Sponsored by China Society of Automotive Engineers
Published by AUTO FAN Magazine Co. Ltd.

Automotive Engineering ›› 2024, Vol. 46 ›› Issue (11): 1962-1972.doi: 10.19562/j.chinasae.qcgc.2024.11.003

Previous Articles     Next Articles

Interactive Scenarios Strategy Modeling and Simulation for Automated Driving Testing

Jian Sun,He Zhang,Xiaocong Zhao(),Yiru Liu,Ye Tian   

  1. Tongji University,The Key Laboratory of Road and Traffic Engineering of the Ministry of Education,Shanghai 201804
  • Received:2024-03-30 Revised:2024-06-12 Online:2024-11-25 Published:2024-11-22
  • Contact: Xiaocong Zhao E-mail:zhaoxc@tongji.edu.cn

Abstract:

The interaction ability between Highly Automated Vehicles (HAV) with human-driven vehicles is critical to the operational safety and efficiency of hybrid traffic in future. In order to test the interactivity of HAV, the background vehicle in the testing scenario needs to have naturalistic interaction characteristics and reflect the heterogeneous interaction strategy of human drivers. Based on the game theory, the Game-theoretical Strategic Interaction Model (GSIM) is developed in this paper. In the individual utility function, the interactive social characterization parameters with distinguishable values are introduced to directionally regulate the interaction strategy of the background vehicle. The test results of unprotected left-turning scenarios at intersections show that GSIM preserves the interpretability of natural driving stepwise planning and mutual interactions to ensure simulation accuracy of interactive behaviors. GSIM is also able to effectively reflect the interactive strategy of human driving in high-risk scenarios, helping to provide challenging and valuable testing scenarios. Compared to traditional Intelligent Driver Models, GSIM improves average simulation accuracy by 42.8% in unprotected left turn scenarios and serious conflicts recurrence rate by 25.8%.

Key words: highly automated vehicles, scenario testing, interaction strategy, game theory, driving behavior