@inproceedings{9b13bda838fe4c968aa5e1577048d70e,
title = "Collision Risk Quantification Method for Trajectories with Uncertainty",
abstract = "As the number and variety of traffic tools continue to increase, avoiding collisions is becoming increasingly challenging. To proactively prevent collisions, it{\textquoteright}s essential to quantify the associated risk. While the Monte Carlo method offers superior accuracy in this regard, its reliance on extensive sampling renders it time-consuming and challenging to implement. This paper introduces a quantification method for collision risk that incorporates a safety distance and calculates collision probabilities for trajectories with uncertainty. Simulation results show that the proposed method performs well in terms of both accuracy and efficiency compared to the MC method and can be applied to large-scale trajectory data.",
keywords = "Collision avoidance, Collision risk quantification, Trajectory safety",
author = "Zhanwei Hu and Fengzhe Zhang and Jinyong Chen and Rui Zhou",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; International Conference on Guidance, Navigation and Control, ICGNC 2024 ; Conference date: 09-08-2024 Through 11-08-2024",
year = "2025",
doi = "10.1007/978-981-96-2216-0\_21",
language = "英语",
isbn = "9789819622153",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "217--228",
editor = "Liang Yan and Haibin Duan and Yimin Deng",
booktitle = "Advances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control Volume 5",
address = "德国",
}