Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (5): 1246-1257.doi: 10.13229/j.cnki.jdxbgxb.20221519

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Vehicle-infrastructure-map cooperative localization method based on spatial-temporal graph model

Zhao-zheng HU1,2(),Xun-pei SUN1,Jia-nan ZHANG1,Ge HUANG1,Yu-ting LIU1   

  1. 1.Intelligent Transportation Systems Research Center，Wuhan University of Technology，Wuhan 430063，China
    2.Chongqing Research Institute，Wuhan University of Technology，Chongqing 401120，China
  • Received:2022-11-28 Online:2024-05-01 Published:2024-06-11

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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

CLC Number: 

  • U495

Fig.1

Flow chart of the proposed algorithm"

Fig.2

Spatial-temporal graph model for VICS"

Fig.3

Distance observation between vehicles"

Fig.4

Lateral distance observation on vehicle from HD map"

Fig.5

Time delay compensation constraints"

Fig.6

Vehicle sensor settings"

Fig.7

Sensor data in simulation scene"

Fig.8

Straight scene based on HighD dataset"

Fig.9

Trajectory in straight scene"

Fig.10

RMSE in straight scene"

Fig.11

CDF with localization error in straight scene"

Table 1

Improve performance of CL in straight scene"

定位方式RMSE/mλ/%
仅GPS10.1397.1
仅RSU0.6555.4
STGO-CL（No HD Map）0.5042.0
STGO-CL0.29—

Table 2

Improve performance of time delay compensation in straight scene"

性能时延/ms
50100150200
原始RMSE/m0.651.211.672.21
补偿后RMSE/m0.370.480.600.73
λ/%43.160.364.167.0

Fig.12

Effect of the number of connected vehicles on RMSE in straight scene"

Fig.13

Effect of permeability on RMSE in straight scene"

Fig.14

Curve scene"

Fig.15

Trajectory in curve scene"

Fig.16

RMSE in curve scene"

Fig.17

CDF with localization error in curve scene"

Table 3

Improve performance of CL in curve scene"

定位方式RMSE/mλ/%
仅GPS10.0296.7
仅RSU0.6448.4
STGO-CL（No HD Map）0.5438.9
STGO-CL0.33—

Table 4

Improve performance of time delay compensation in curve scene"

性能时延/ms
50100150200
原始RMSE/m0.681.251.732.36
补偿后RMSE/m0.410.530.670.79
λ/%39.757.661.366.5

Fig.18

Effect of the number of connected vehicles on RMSE in curve scene"

Fig.19

Effect of permeability on RMSE in curve scene"

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