Probabilistic Prediction of Collisions between Cyclists and Vehicles Based on Uncertainty of Cyclists’ Movements

碰撞 撞车 概率逻辑 毒物控制 避碰 蒙特卡罗方法 计算机科学 模拟 工程类 统计 数学 人工智能 计算机安全 医学 环境卫生 程序设计语言
作者
Di Pan,Yong Han,Q.Q. Jin,Jin Kan,Hongwu Huang,Koji Mizuno,Robert R. Thomson
出处
期刊:Transportation Research Record [SAGE Publishing]
卷期号:: 036119812211212-036119812211212
标识
DOI:10.1177/03611981221121270
摘要

The uncertainty of cyclists’ movements has a significant impact on predicting the risk of collisions between cyclists and vehicles. The purpose of this study was to provide a method for assessing collision risk using probability, taking into account the uncertainty of cyclists’ movements. A cyclist model was first developed using a first-order Markov model. Then, based on Monte Carlo sampling, the distribution characteristics of the minimum distance and the time-to-collision (TTC) between the vehicle and the cyclist were extracted. By fitting these features, the probability density functions of the collision distance and TTC were estimated to derive the collision probabilities. The effectiveness of the collision probability prediction model was benchmarked against a deterministic crash risk prediction model (autonomous emergency braking [AEB] system) applied to three real-world cases previously reconstructed in an in-depth crash database. The results show that the collision probability prediction model can effectively predict the risk of collisions between cyclists and vehicles with better accuracy than AEB systems using a fixed trigger threshold. This study is a valuable reference for the development of advanced vehicle collision avoidance systems to protect cyclists and other vulnerable road users.
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