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

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