Reverse deduction of vehicle group situation based on dynamic Bayesian network

• Main Authors: Xiaoyuan Wang, Jianqiang Wang, Yaqi Liu, Zhenxue Liu, Jingheng Wang
• Format: Article
• Language: English
• Published: SAGE Publishing 2018-03-01
• Series: Advances in Mechanical Engineering
• Online Access: https://doi.org/10.1177/1687814017747708

Vehicle group is the basic unit of microscopic traffic flow, and also a concept that often involved in the research of active vehicle security. It is of great significance to identify vehicle group situation accurately for the research of traffic flow theory and the intelligent vehicle driving system. Three-lane condition was taken as an example and the privacy protection of driver (only the data of travel time were used) was a premise in this article. Poisson’s distribution was used to identify vehicle group situation which was constituted by target vehicle and its neighboring vehicles when the target vehicle arrived at the end of study area. And the dynamic Bayesian network was used to build the reverse deduction model of vehicle group situation. The model was verified through actual and virtual driving experiments. Verification results showed that the model established in this article was reasonable and feasible.