摘要
高精度车辆轨迹数据对于实现智慧交通具有重要意义,然而现有的车辆轨迹感知技术受到采集范围的限制,难以获得全时段全区域的车辆轨迹数据,无法满足实际应用中对车辆轨迹跟踪精度以及实时性的要求。考虑到跨雷达场景下的车辆轨迹数据特征,提出一种基于雷达数据的跨设备车辆轨迹跟踪方法。首先,根据数据特点应用置信区间下限筛选轨迹数据,并通过卡尔曼滤波对车辆轨迹的位置和速度分别进行平滑和降噪处理。其次,将雷达探测区域的车辆轨迹时间戳、位置坐标、行驶速度、行驶方向以及车道编号作为模型输入,非重叠区域的位置信息作为输出,构建了基于长短期记忆(long short term memory,LSTM)的非重叠区域轨迹预测模型。然后,提出基于轨迹条件约束和搜索区域限制的快速动态时间规整算法(dynamic time warping,DTW)用以计算轨迹相似度,从而实现跨雷达设备车辆轨迹跟踪。最后,以高速公路上跨雷达检测的重叠场景和非重叠场景数据为例进行验证,实验结果表明,重叠场景下车辆轨迹跟踪准确度达到92.2%,非重叠区域车辆轨迹匹配正确率达到90.3%。
High-precision vehicle trajectory data is crucial for the realization of intelligent transportation systems.However,existing vehicle trajectory sensing technologies are limited by the range of data collection,making it challenging to obtain full-period and full-area vehicle trajectory data,which cannot meet the demands for trajectory tracking accuracy and real-time performance in practical applications.Considering the characteristics of vehicle trajectory data across radar scenarios,a cross-device vehicle trajectory tracking method was proposed based on radar data.Firstly,trajectory data was filtered based on the lower bound of the confidence interval,and the position and velocity of vehicle trajectories were smoothed and denoised using Kalman filtering.Next,the trajectory timestamp,position coordinates,speed,direction,and lane number from the radar detection area were used as model inputs,while the position information of non-overlapping areas was used as output to construct a non-overlapping area trajectory prediction model based on long short-term memory(LSTM).Subsequently,a fast dynamic time warping(DTW)algorithm based on trajectory condition constraints and search area limitations was proposed to compute trajectory similarity,enabling cross-radar device vehicle trajectory tracking.Finally,verification was conducted using overlapping and non-overlapping scenario data from radar detection on highways.Experimental results show that the trajectory tracking accuracy in overlapping scenarios reaches 92.2%,and the trajectory matching accuracy in non-overlapping areas reaches 90.3%.
作者
尤鑫
薛金银
张北海
高宇航
田向丽
赵建东
YOU Xin;XUE Jin-yin;ZHANG Bei-hai;GAO Yu-hang;TIAN Xiang-li;ZHAO Jian-dong(Beijing Sutong Technology Co.,Ltd.,Beijing 100161,China;School of Traffic and Transportation,Beijing Jiaotong University,Beijing 100044,China)
出处
《科学技术与工程》
北大核心
2025年第17期7373-7379,共7页
Science Technology and Engineering
基金
国家重点研发计划(2022ZD0115605)。
