基于时空图模型的车-路-图协同定位方法

doi:10.13229/j.cnki.jdxbgxb.20221519

摘要/Abstract

摘要：

将车路协同系统中车辆的位置估计问题转化为时空图模型构建与优化问题，提出一种时空图优化协同定位（STGO-CL）方法。其中，感知区域中不同时刻的车辆位置构成图模型中的节点；车端与路端通过融合高精地图计算出来的车辆绝对位置与相对位置构成图模型的边，并加入时延补偿约束。在求解过程中采用Levenberg-Marquardt （LM）法求解目标函数实现对感知区域中的车辆位置最优状态估计，实现车-路-图协同定位。利用CARLA建立直道和弯道仿真实验场景以验证算法，结果表明：时空图优化协同定位方法平均定位误差为0.29 m，定位性能较GPS或路侧单元（Road side unit， RSU）单独定位分别提高了97.1%和55.4%，较不融合高精地图的时空图优化协同定位方法提高了42.0%。在时延补偿上，可将200 ms时延下的定位性能提高67.0%。本文利用时空图模型实现车-路-图协同定位可有效提升车路协同系统的环境感知性能。

关键词: 交通运输工程, 协同定位, 时空图模型, 智能网联汽车, 车路协同

Abstract:

The problem of vehicle location estimation in the vehicle-infrastructure cooperative systems was formulated into the construction and optimization of a spatial-temporal graph model， and a spatial-temporal graph optimization cooperative localization（STGO-CL） method was proposed. In the graph model， the location of the vehicle at different times in the perception area constituted the nodes， and the absolute and relative location of the vehicle calculated by the vehicle end and the road end fused with high-precision（HD） map constituted the edges. And time delay compensation constraints are added. In the solution process， the LM method was used to solve the objective function to realize the optimal state estimation of the vehicle location in the perception area and realize the vehicle-infrastructure-map cooperative localization. Used CARLA to establish straight and curve simulation experimental scenes to verify the proposed method. The experimental results demonstrate that the average localization error of the proposed method is 0.29 m. The localization performance is improved by 97.1% and 55.4% respectively compared with GPS or RSU localization alone. Compared with the proposed method without HD map， the proposed method is improved by 42.0%. In terms of time delay compensation， the localization performance under 200 ms time delay can be improved by 67.0%. The use of spatial-temporal graph model to realize the vehicle-infrastructure-map cooperative localization can effectively improve the environmental perception performance of the vehicle-infrastructure cooperative systems.

Key words: traffic and transportation engineering, cooperative localization, spatial-temporal graph model, intelligent connected car, vehicle-infrastructure cooperative systems

中图分类号:

U495

胡钊政,孙勋培,张佳楠,黄戈,柳雨婷. 基于时空图模型的车-路-图协同定位方法[J]. 吉林大学学报(工学版), 2024, 54(5): 1246-1257.

Zhao-zheng HU,Xun-pei SUN,Jia-nan ZHANG,Ge HUANG,Yu-ting LIU. Vehicle-infrastructure-map cooperative localization method based on spatial-temporal graph model[J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(5): 1246-1257.

图/表 23

图1

图2

图3

图4

图5

图6

图7

图8

图9

图10

图11

表1

直道场景协同定位提升性能"

定位方式	RMSE/m	$λ$ /%
仅GPS	10.13	97.1
仅RSU	0.65	55.4
STGO-CL（No HD Map）	0.50	42.0
STGO-CL	0.29	—

表1

表2

直道场景时延补偿提升性能"

性能	时延/ms
性能	50	100	150	200
原始RMSE/m	0.65	1.21	1.67	2.21
补偿后RMSE/m	0.37	0.48	0.60	0.73
$λ$ /%	43.1	60.3	64.1	67.0

表2

图12

图13

图14

图15

图16

图17

表3

弯道场景协同定位提升性能"

定位方式	RMSE/m	$λ$ /%
仅GPS	10.02	96.7
仅RSU	0.64	48.4
STGO-CL（No HD Map）	0.54	38.9
STGO-CL	0.33	—

表3

表4

弯道场景时延补偿提升性能"

性能	时延/ms
性能	50	100	150	200
原始RMSE/m	0.68	1.25	1.73	2.36
补偿后RMSE/m	0.41	0.53	0.67	0.79
$λ$ /%	39.7	57.6	61.3	66.5

表4

图18

图19

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