Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (9): 2935-2945.doi: 10.13229/j.cnki.jdxbgxb.20240526

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Virtual lane lines fitting method based on traffic environment information

Bing ZHU1(),Peng-xiang MENG1,Bin LIU2,Jia-yi HAN1(),Jian ZHAO1,Zhi-cheng CHEN1,Dong-jian SONG1,Xiao-wen TAO1   

  1. 1.National Key Laboratory of Automotive Chassis Integration and Bionics，Jilin University，Changchun 130022，China
    2.China FAW Group Co. ，Ltd. ，Changchun 130013，China
  • Received:2024-05-15 Online:2025-09-01 Published:2025-11-14
  • Contact: Jia-yi HAN E-mail:zhubing@jlu.edu.cn;jiayi_han@jlu.edu.cn

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Abstract:

A method for virtual lane lines fitting based on traffic environment information was proposed to address the problem of lane lines missing caused by wear， ice and snow coverage， etc. on certain road sections during vehicle driving. Firstly， a probability model for the lateral offset of lane lines under lane-keeping conditions of traffic vehicles was established based on the highD naturalistic driving dataset to determine the lateral position of virtual lane lines. Then， a traffic vehicle intention recognition and trajectory prediction model based on bi-directional long short-term memory networks was established to realize the condition judgment and shape generation of virtual lane lines generation. Finally， based on the information of the above， the statistical characteristics of lateral lane deviation of traffic vehicles that meet the fitting conditions were transformed into the risk field of virtual lane lines in the main vehicle coordinate system to realize the virtual lane lines fitting. Simulation and real vehicle data validation results show that this method can generate safe and effective virtual lane lines with a small error compared to the true value of lane lines， providing a reference driving space for the main vehicle and ensuring driving safety.

Key words: vehicle engineering, virtual lane lines, traffic environment information, lateral offset characteristics of lane lines, risk field

CLC Number: 

  • U463.6

Fig.1

Research scenario"

Fig.2

Overall framework of proposed method"

Table 1

Sample size statistics"

车宽/m左侧交通车辆样本量右侧交通车辆样本量
<1.8881 213406 644
1.8~1.92 739 963884 933
1.9~2.03 219 259773 928
2.0~2.11 777 115384 604
2.1~2.2551 579205 403
2.2~2.3203 123144 733
>2.3138 4155 728 230

Fig.3

Probability density distribution of lateral deviation of left and right relative lane lines"

Table 2

Distribution parameters"

车宽/m左侧（韦布尔分布）右侧（正态分布）
均值/m方差/m2均值/m方差/m2
<1.81.300 20.153 71.308 50.381 0
1.8~1.91.201 50.151 61.225 90.382 6
1.9~2.01.101 50.151 51.188 80.382 7
2.0~2.11.014 20.160 61.143 40.374 1
2.1~2.20.930 90.153 11.096 80.369 1
2.2~2.30.879 40.169 11.081 80.378 2
>2.30.795 70.208 80.955 00.334 2

Fig.4

Bi-LSTM neural network structure diagram"

Fig.5

Risk field diagram"

Fig.6

Simulation experiment"

Fig.7

Real vehicle testing platform architecture"

Fig.8

Experimental scenario"

Fig.9

Validation results of real vehicle data"

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