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
马彬 1周世亚 2姜文龙 3史立峰 4赵宇5
作者信息
- 1. 北京信息科技大学机电学院,北京 100192;新能源汽车北京实验室,北京 100192;北京电动车辆协同创新中心,北京 100192
- 2. 北京信息科技大学机电学院,北京 100192
- 3. 中国人民公安大学交通管理学院,北京 100038
- 4. 中国公路车辆机械有限公司,北京 100010
- 5. 天津中德应用技术大学汽车与轨道交通学院,天津 300350
- 折叠
摘要
切入工况的高精度车速预测是保证自动驾驶切入安全的关键依据.为提高自动驾驶汽车切入工况安全,开展了基于车车耦合风险聚类的切入场景自车速度高精度预测方法的研究.首先,依据实验所得自然驾驶数据进行车辆切入切出片段提取,使用K-means方法依据碰撞风险与加速度关联特征进行聚类分析.其次,基于支持向量机(SVM)模型,对切入切出工况车车交互状态进行在线识别,对切入危险工况进行实时预测.最后,提出基于自回归综合移动平均(ARIMA)模型的改进车速预测方法,结合在线识别结果进行车速在线优化.仿真结果表明,所提出的基于碰撞风险聚类的改进ARIMA车速预测方法对提高切入安全效果明显,较传统的预测方法车辆的碰撞风险降低了 10％-20％.研究结果表明,ARIMA模型的改进车速预测方法对提高自动驾驶车切入安全具有重要的研究意义.
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引用本文复制引用
基金项目
北京市自然科学基金面上项目(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)
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)
天津市科技计划项目(21JCQNJC00810)
出版年
2024
国家科技期刊平台
NSTL
万方数据