A Dynamic Bayesian Network Model for Real-Time Risk Propagation of Secondary Rear-End Collision Accident Using Driving Risk Field

Abstract

In order to take more active measures to prevent and control secondary accidents, it is necessary to describe risk propagation process after the accident. To this end, this paper deeply analyzed the risk propagation mechanism between vehicles and proposed a novel secondary rear-end collision accident risk propagation model, which could real-time evaluate vehicle rear-end collision risk. The research scene of the paper is a single-lane road rear-end collision scene, so a driving risk field model suitable for this scene is first established. Based on this, the vehicle operation interaction risk field force is calculated. Then, the risk field force is converted into risk probability through the hyperbolic tangent function, and the vehicle operation interaction risk model is obtained. In addition, a risk propagation framework based on dynamic Bayesian network is constructed to describe the propagation process of rear-end collision risk from the accident vehicle to following vehicles. Finally, according to the probabilistic reasoning process of the framework, combined with the accident vehicle risk, a secondary rear-end collision accident risk propagation model is established. Simulation experiments show that the paper model can describe the evolution trend of rear-end risk and the risk assessment results are more accurate. And after the accident, the rear-end collision risk propagation speed will increase with the increase of traffic flow, and the number of secondary rear-end vehicles will also increase with the increase of traffic speed. These conclusions are of great significance in formulating vehicle anti-collision strategies and deploying risk management and control facilities.