Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (3): 663-673.doi: 10.13229/j.cnki.jdxbgxb20220895

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Enhanced localization system based on camera and lane markings

Ke HE(),Hai-tao DING,Nan XU,Kong-hui GUO   

  1. College of Automotive Engineering，Jilin University，Changchun 130022，China
  • Received:2022-07-13 Online:2023-03-01 Published:2023-03-29

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Abstract:

In the traditional technology of lateral localization using lane markings identified by cameras， incorrect matching of lanes or their left and right boundary points causes large localization errors under driving conditions such as lane changing. A multi-indicator weighted evaluation map matching algorithm combined with lane change recognition method was proposed. Further， a lane left and right boundary point determination method was designed， and a camera-based bilateral boundary line lateral localization method was proposed based on accurate matching to lanes and their boundary points， which can improve the lateral localization accuracy relative to lanes， and then fuse the camera with GPS， IMU， wheel odometer， and lightweight lane level map to form a complete localization system. The experimental results show that the accuracy and stability are significantly improved compared with the traditional fusion localization method using cameras and lane markings. The localization system designed in this paper provides a low-cost and high-accuracy solution for autonomous driving localization.

Key words: vehicle engineering, camera, lane marking, sensor fusion, localization, map matching

CLC Number: 

  • U469.79

Fig.1

Diagram of lane boundary points and boundary lines"

Fig.2

Framework of the localization system"

Fig.3

Two-wheel odometer model"

Fig.4

Simulation experiment： trajectory and change of C0 of left and right lane markings when vehicle changes lanes left first and then right"

Fig.5

Determination algorithm about left and right boundary points on the lane"

Fig.6

Schematic diagram of camera-based lateral localization of bilateral boundaries"

Fig.7

Schematic diagram of localization using left and right boundary lines"

Fig.8

Camera-based lateral localization effect of bilateral boundaries"

Fig.9

Driving scene where four roads intersect at an intersection"

Table 1

Parameter settings in the simulation"

参数误差均值误差标准差
x坐标/m01.5
y坐标/m01.5
航向角/rad00.1
横摆角速度/（rad·s-1）00.01
车速/（m·s-1）00.3

Fig.10

Double lane change simulation scenario"

Fig.11

Complete route trajectory"

Fig.12

Comparison of experimental trajectories of the double lane change"

Fig.13

Comparison of lateral errors of driving on the road"

Fig.14

Comparison between heading errors of algorithms"

Table 2

Comparison of localization results"

方法指标均值标准差
GPS+IMU+轮式里程计侧向误差/m0.330.62
整体误差/m0.580.82
航向角误差/rad0.0030.048
文献［16］算法侧向误差/m0.300.66
整体误差/m0.570.88
航向角误差/rad0.0070.126
本文算法侧向误差/m0.030.18
整体误差/m0.340.67
航向角误差/rad0.0030.048

Fig.15

Map matching effect in double lane change"

Table 3

Comparison of map matching results"

方法总体匹配正确率/%
GPS点对点匹配89.8
文献［16］使用算法93.6
本文算法97.8

Fig.16

Constructed driving scenario and selected test route （with colors highlighting the roads to be traveled）"

Fig.17

Test results on the whole route"

Table 4

Comparison of localization results"

算法指标均值标准差
文献［16］定位误差/m2.303.03
航向角误差/rad0.0170.462
本文定位误差/m0.771.69
航向角误差/rad0.0090.176
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