文献类型：期刊文献

英文题名：Assessment of Rear-End Collision Risk Based on a Deep Reinforcement Learning Technique: A Break Reaction Assessment Approach

作者：Sheikh, Muhammad Sameer[1];Peng, Yinqiao[1]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Peoples R China

年份：2025

卷号：13

起止页码：20171

外文期刊名：IEEE ACCESS

收录：SCI-EXPANDED(收录号：WOS:001412136300009)、、EI(收录号：20250617822279)、Scopus(收录号：2-s2.0-85216835046)、WOS

基金：This work was supported in part by the Program for Scientific Research Start-Up Funds of Guangdong Ocean University under Grant 060302112202.

语种：英文

外文关键词：Accidents; Hidden Markov models; Vehicles; Safety; Merging; Road safety; Data models; Adaptation models; Analytical models; Mathematical models; Braking reaction time; collision avoidance; rear-end collision; risk assessment; traffic conflict; vehicle safety

外文摘要：Rear-end crashes are a major type of traffic crash that occur more frequently on the road, leading to a large number of injuries and fatalities each year around the world. Examining the overtaking behaviors and predicting the collision risk probability are essential issues for preventing a rear-end collision risk and improving road safety. To this end, we proposed the rear-end collision model to examine the risks associated with the lagging vehicle (LAV) movements. Specifically, this research aims to develop a model for assessing rear-end collision risks by considering different braking reaction times (BRTs) of the LAV. Firstly, we introduce the deep neural network (DNN) to learn the movements of LAV. Then, a collision-free modeling based on the deep reinforcement model (DRM) is proposed to mitigate the collision risks associated with LAV movements to nearby vehicles thus improving traffic safety. Finally, we incorporate the generalized linear model (GLM)-based BR time with driver's driving behavior, aiming to identify the driver's distraction with different vehicle movements. Various performance metrics, such as modified time to collision (MTTC), deceleration rate to avoid collision (DRAC), and post-collision change in velocity (Delta-V), are used to identify rear-end conflicts and to demonstrate the effectiveness of the developed model. The simulation results indicated that the developed model could reduce rear-end collision risks with the leading vehicle (LEV) based on different LAV speeds. Furthermore, the North Carolina (NC) traffic real-time crash data is used to demonstrate the efficacy of the developed model. The results indicated that different traffic conditions, such as driver behaviors, and road and climate conditions influence the severity of rear-end crashes.