基于贝叶斯网络的车辆换道模型

车辆换道行为是微观交通流中最基本的驾驶行为之一,研究车辆换道行为可以提高车辆换道模型的仿真精度和减少由不合适的车辆换道行为引发的交通事故.当前车辆换道模型大多是基于驾驶员的决策思维方式建立的决策模型,这类模型的缺点是很难捕捉到驾驶员在决策过程中一些潜在决策模式和考虑的影响因素.鉴于此,本文引入了一种典型的人工智能方法——贝叶斯网络,建立了一个全新的车辆换道模型,试图通过机器学习的途径来提高车辆换道模型的精度.采用了分段离散化的方法对数据进行预处理,然后使用处理后的数据对贝叶斯网络的结构和参数进行学习,并分别建立了与两种贝叶斯网络结构相对应的车辆换道模型,最后对建立的模型分别进行验证.模型的验证结果表明,建立的基于贝叶斯网络的车辆换道模型对换道行为的识别率可以达到88%以上.此模型还可进一步应用到驾驶员辅助系统的开发中.

A Lane Change Model Based on Bayesian Networks

Lane change behavior is one of the most foundational driving behaviors in microscopic traffic flow. Researching the lane change behavior contributes to improving the simulation accuracy of lane change models and reducing traffic accidents caused by improper lane change behavior. The current lane change model is the decision model mostly based on the way of driver's thinking. The shortcoming of current models is difficult to catch certain potential decision-making model and influencing factors in the driver's decisionmaking process. In view of this, this paper introduces a typical artificial intelligence method, Bayesian networks, to establish a new lane change model, and tries to improve the accuracy of the lane change model by machine learning. It uses a segmented discrete method to preprocess vehicle trajectory measurement data, and uses the processed data to training and verification this model. The verification results show that, this model's recognition rate to lane change behavior can reach more than 88%. In addition, this model can be further applied to the development of a driver assistance system.