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

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.