高通讯时延下融合车道级地图的车路协同感知定位方法  

Vehicle-Road Cooperative Perception and Localization Method with HighDefinition Map Under High Communication Delay

作　　者：胡钊政[1,2] 胡华桦 孟杰[1,2,3] 陈琪莉 张佳楠 Hu Zhaozheng;Hu Huahua;Meng Jie;Chen Qili;Zhang Jianan(ITS Research Center,Wuhan University of Technology,Wuhan 430063;Chongqing Research Institute of Wuhan University of Technology,Chongqing 401120;Wuhan University of Technology,Hubei Key Laboratory of Transportation Internet of Things,Wuhan 430063)

机构地区：[1]武汉理工大学智能交通系统研究中心,武汉430063 [2]武汉理工大学重庆研究院,重庆401120 [3]武汉理工大学,交通物联网技术湖北省重点实验室,武汉430063

出　　处：《汽车工程》2025年第4期598-613,共16页Automotive Engineering

基　　金：国家重点研发计划项目(2022YFB2502904);湖北省重点研发计划项目(2022BAA082);重庆市科技创新重大研发项目(CSTB2020TIAD-STX0003);武汉市人工智能创新专项(2022010702040064)资助。

摘　　要：在车路协同技术运用于路侧孪生地图动态显示中,由于网联设备之间的通讯存在的时延问题及路侧感知误差的存在,对路侧边缘计算单元融合感知精度会产生严重影响,进而导致孪生地图中车辆显示轨迹出现抖动和延迟的现象。为此,本文提出了一种在高通讯时延下融合车道级地图的车路协同感知定位方法。该方法首先针对车路协同系统中智能网联汽车车端与路侧边缘处理单元之间的通讯时延问题进行分析建模,将延迟模型划分为异构传感器频率同步延迟以及通讯传输延迟,并提出了一种同步优化方法。在同步优化后,提出一种面向群车协同的多维度群车粒子滤波算法,其中粒子的状态量表示群车的状态信息。在所提出的多维度群车粒子滤波算法中首先使用利用路侧部分的观测数据和车道级地图中车道线朝向信息,对粒子的状态进行观测更新。然后利用接收到时延同步后的智能网联汽车的自定位信息和左右车道线横向观测信息与车道级地图中车道线方程对粒子中表示智能网联汽车状态的部分进行观测更新。实验结果显示,在通讯干扰较少的低时延场景中,边缘计算单元的感知定位准确度提升59.4%,在通讯干扰严重的高时延场景中,其准确度提升38.6%。因此所提出高通讯时延下融合车道级地图的车路协同感知定位方法可以有效处理通讯延时问题,并提升边缘计算单元多车感知定位精度进而提升孪生地图动态数据的准确性、稳定性和连续性。In the application of vehicle-road cooperative technology for dynamic display of the roadside twin maps,due to the delay problem of the communication between networked devices and the existence of the roadside perception error,the fusion perception accuracy of the roadside edge computing unit will be seriously affected,which will lead to the jitter and delay of the vehicle display track in the twin map.Hence,in this paper a vehicleroad cooperative sensing and localization method that fuses high definition map under high communication delay is proposed.The method first analyzes and models the communication delay between the vehicle end of the connected vehicle and the roadside edge processing unit in the vehicle-road cooperative system,divides the delay model into sensor synchronization delay and communication transmission delay,and proposes a synchronization optimization method for the delay.After the synchronization optimization,a collaborative multidimensional particle filter algorithm for swarm vehicles is proposed,where the states of the particles represent the pose of different connected vehicles and non-connected vehicles in the swarm vehicles.In the proposed multidimensional particle filter algorithm,the state of the particles is firstly updated using the observation of the state of the particles by utilizing the roadside RSU observation data and the curvature information of the lanes in the high-definition map.Then the self-localization information of the received delayed synchronized smart connected cars combined with the left and right lane line lateral constraint information and the lane line equations of the lanes in the high definition map are used to update the observation of the state portion of the particle that represents the smart connected cars.The experimental results show that the perceptual and localization accuracy of the edge server is improved by 59.4% in the low delay scenario with less communication interference,and its accuracy is improved by 38.6% in the high delay scenario with sever

关 键 词：时延同步 车路协同 车道级地图 粒子滤波 

分 类 号：U495[交通运输工程—交通运输规划与管理]