DOI: 10.11908/j.issn.0253-374x.21019

作者:

作者单位: 同济大学 道路与交通工程教育部重点实验室，上海 201804


作者简介: 汪 敏（1997—），男，博士生，主要研究方向为智能网联汽车道路测试风险评估、驾驶行为分析等。 E-mail： 18900560959@163.com


通讯作者: 涂辉招（1977—），男，教授，博士生导师，工学博士，主要研究方向为交通风险管理、智能网联汽车与智慧交通、交通行为分析和交通规划等。E-mail： huizhaotu@tongji.edu.cn

中图分类号: U491


基金项目: 国家重点研发计划重点专项（2019YFE0108300）




Prediction of Car-Following Risk Status Based on Car-Following Behavior Spectrum
Author:

Affiliation: Key Laboratory of Road and Traffic Engineering of the Ministry of Education， Tongji University， Shanghai 201804， China


Fund Project:




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摘要:跟驰风险状态反映车辆跟驰行驶过程中的危险程度。为了实时预测跟驰风险状态，基于跟驰行为谱，提取碰撞时间倒数、横向摆动系数、速度不稳定系数3种跟驰风险状态指标，并确定状态划分阈值。采用精确决策树、提升树、线性回归拟合、支持向量机、K-近邻、集成树6种机器学习预测模型进行跟驰风险状态预测，选取准确率、四级风险状态召回率、平均召回率对模型预测效果进行评价，对不同预测时长和不同特征指标序列时长下的预测效果进行对比。基于驾驶模拟器采集的不同道路类型和交通状态组合下6种典型跟驰场景驾驶行为数据进行分析，结果表明，精确决策树预测跟驰风险状态效果最佳；拥挤交通流下支路跟驰的预测效果显著好于其他场景，其他5种场景下的预测效果无显著差异；通过增加特征指标序列时长，可缓解预测效果因预测时长增大而变差的问题。可为车辆主动安全预警与防控提供技术支撑。



Abstract:Car-following risk status reflects the degree of risk situation in the car-following process. For the sake of real-time prediction of car-following risk status （CFRS）， this paper， by extracting three CFRS indicators： the reciprocal of time to collision， the transverse oscillation coefficient， and the velocity instability coefficient， determines the status division thresholds based on car-following behavior spectrum. Six machine learning prediction models： the precise decision tree， the lifting tree， the linear regression fitting， the support the vector machine， the K-nearest neighbor， and the ensemble tree， are used for CFRS prediction. The accuracy rate， the recall rate of four-level risk status， and the average recall rate are selected to evaluate the prediction effects of the model. The prediction effects in different duration of prediction and different duration of feature index sequence are compared. Based on the driving behavior data of six typical car-following scenarios in different road types and traffic state combinations collected by the driving simulator， the analysis indicates that the precise decision tree is the best way to predict CFRS. The prediction effect of car-following on the branch in congested traffic flow is significantly better than that in other scenarios， and there is no significant difference between the other five scenarios. By increasing the duration of the feature index sequence， the problem that the deterioration of the prediction effects due to the increase of the duration of prediction can be alleviated. The research results provide technical support for early warning and prevention of vehicle active safety.





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