基于联合概率数据融合的多目标车辆安全跟随  

Safety-enhanced multi-vehicle tracking based on joint probability data association

作　　者：章军辉 郭晓满 王静贤 付宗杰 陈大鹏 ZHANG Jun-hui;GUO Xiao-man;WANG Jing-xian;FU Zong-jie;CHEN Da-peng(School of Electrical and Automatic Engineering,Changshu Institute of Technology,Suzhou 215500,China;Wuxi Internet of Things Innovation Center Co.Ltd,Wuxi 214029,China;Kunshan Department,Jiangsu Internet of Things Innovation Center,Wuxi 215347,China)

机构地区：[1]常熟理工学院电气与自动化工程学院,江苏苏州215500 [2]无锡物联网创新中心有限公司,江苏无锡214029 [3]江苏省物联网创新中心昆山分中心,江苏苏州215347

出　　处：《浙江大学学报（工学版）》2023年第11期2170-2178,共9页Journal of Zhejiang University：Engineering Science

基　　金：江苏省博士后科研资助计划(2020Z411).

摘　　要：为了实现密集杂波环境下多目标车辆安全跟随,提出多源传感器数据融合的多目标车辆跟踪算法与纵向避撞预警策略.针对多源传感器观测序列因采样周期、采样起始时刻、通信时延差异等引起的时间异步,以及空间上存在不同维度、不同坐标系的问题,给出时间配准与空间融合的软同步方法.采用基于改进的联合概率数据关联(JPDA)的单一传感器多目标状态估计算法对目标轨迹进行滤波估计,能够在保证有效关联的同时,在一定程度上降低计算复杂度.基于多源传感器联合概率数据融合(MSJPDA)序贯滤波算法对目标的运动状态进行序贯更新,将最后一级的输出作为融合中心的最终状态估计,再根据威胁估计模型对追尾危险的发展态势进行评估与分级.实车试验与仿真结果验证了该算法的可行性与有效性.A novel multi-sensor data fusion based multi-vehicle tracking algorithm and a longitudinal collision warning strategy were proposed,in order to achieve the safety-enhanced multi-vehicle tracking in dense clutter environments.The time registration and spatial fusion approaches were introduced,with respect to the problems of time asynchronous sampling sequences of multi-source sensors caused by nonidentity of sampling periods,sampling start-up time and inherent communication time delay,as well as different coordinate systems and different space dimensions.The single sensor multi-object state estimation based on the improved joint probability data association(JPDA)was utilized to estimate the multi-object trajectory,which not only ensured the effective association,but also could reduce the computational complexity to a certain extent.Furthermore,the multi-sensor joint probability data association(MSJPDA)sequential filtering algorithm was employed to update the motion state of targets serially,and the output of the last sensor was utilized as the state estimation of the fusion center,by which the situation of longitudinal collision could be well-estimated.Results of comparative experiment demonstrated the feasibility and effectiveness of the tracking algorithm proposed.

关 键 词：智能车辆 多源数据融合 多车辆跟踪 威胁估计 联合概率数据关联 

分 类 号：U461.91[机械工程—车辆工程]