@inproceedings{bc8e421e411a492aab527071e5312688,
title = "Optimal method of extreme scenarios for intelligent driving vehicle testing based on time window",
abstract = "With the advancement and reform of the automotive industry, intelligent driving vehicles have gradually penetrated the automobile market and entered a phase of peak development. Concurrently, the significant progress has been made in testing technology for intelligent driving vehicles. However, designing extreme testing scenarios remains a crucial and challenging problem in the field of vehicle testing. Therefore, this paper proposes an optimal method based on time window to create extreme scenarios for intelligent driving vehicle testing. By analyzing vehicle movement trajectories and incorporating the information such as size and volume of vehicles, more rational and realistic test scenarios are optimized. And simulation results demonstrate that the proposed method is reliable and effective.",
keywords = "Time window, extreme scenarios, intelligent driving vehicle testing",
author = "Ge Qu and Juan Shi and Zhiqiang Zhang and Kuiyuan Guo",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; 7th International Conference on Traffic Engineering and Transportation System, ICTETS 2023 ; Conference date: 22-09-2023 Through 24-09-2023",
year = "2024",
doi = "10.1117/12.3015756",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Ghanizadeh, \{Ali Reza\} and Hongfei Jia",
booktitle = "Seventh International Conference on Traffic Engineering and Transportation System, ICTETS 2023",
address = "United States",
}