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Reverse deduction of vehicle group situation based on dynamic Bayesian network:

机译:基于动态贝叶斯网络的车辆群状况逆推:

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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.
机译:车辆组是微观交通流的基本单位,也是主动车辆安全研究中经常涉及的一个概念。准确识别车辆群状况对交通流理论和智能车辆驾驶系统的研究具有重要意义。以三车道条件为例,以驾驶员的隐私保护(仅使用行驶时间的数据)为前提。泊松分布用于识别目标车辆到达研究区域末端时由目标车辆及其附近车辆构成的车辆组情况。并利用动态贝叶斯网络建立了车辆状况逆推演模型。通过实际和虚拟驾驶实验验证了该模型。验证结果表明,本文建立的模型是合理可行的。

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