重庆理工大学学报2024，Vol.38Issue(1)：67-76,10.DOI:10.3969/j.issn.1674-8425(z).2024.01.008
切入场景下基于碰撞风险聚类的改进车速预测方法
The modified velocity prediction strategy based on the collision risk clustering in cut-in scenarios
摘要
Abstract
High-precision vehicle speed prediction in cut-in scenarios is the key to ensuring the safety of autonomous driving cut-ins.To improve the safety of autonomous driving vehicles in cut-in scenarios,this paper studies the high-precision prediction method of ego-vehicle speed in cut-in scenarios based on vehicle-vehicle coupling risk clustering.First,the vehicle cut-in and cut-out segments are extracted based on the natural driving data obtained from the experiments,and the clustering analysis is performed based on the collision risks and acceleration correlation features using the K-means method.Second,based on the support vector machine(SVM)model,the online classification of vehicle-vehicle interaction state of cut-in and cut-out conditions is performed,and the real-time prediction of dangerous cut-in conditions is made.Finally,an improved vehicle speed prediction method based on ARIMA model(Autoregressive Integrated Moving Averaged Model)is proposed,optimizing real-time vehicle speed with online recognition results.Simulation results show the improved ARIMA vehicle speed prediction based on collision risk clustering significantly improves cut-in safety,cutting the vehicle collision risks by 10％～20％when compared to the traditional prediction methods.Our research may provide some references for improving the cut-in safety of autonomous driving vehicles.关键词
车速预测/碰撞风险/K-means聚类/支持向量机/ARIMA模型Key words
vehicle speed prediction/collision risk/K-means clustering/support vector machine/ARIMA model分类
交通工程引用本文复制引用
马彬,周世亚,姜文龙,史立峰,赵宇..切入场景下基于碰撞风险聚类的改进车速预测方法[J].重庆理工大学学报,2024,38(1):67-76,10.基金项目
北京市自然科学基金面上项目(3212005) （3212005）
公共安全行为科学与工程2022年公安学一流学科培优行动科技创新项目(2022KXGCKJ06) （2022KXGCKJ06）
运输车辆运行安全技术交通运输行业重点实验室对外开放研究课题(KFKT2022-06) （KFKT2022-06）
天津市科技计划项目(21JCQNJC00810) （21JCQNJC00810）
