吉林大学学报(工学版) ›› 2025, Vol. 55 ›› Issue (9): 2935-2945.doi: 10.13229/j.cnki.jdxbgxb.20240526

• 交通运输工程·土木工程 • 上一篇    

基于交通环境信息的虚拟车道线拟合方法

朱冰1(),孟鹏翔1,刘斌2,韩嘉懿1(),赵健1,陈志成1,宋东鉴1,陶晓文1   

  1. 1.吉林大学 汽车底盘集成与仿生全国重点实验室，长春 130022
    2.中国第一汽车集团有限公司，长春 130013
  • 收稿日期:2024-05-15 出版日期:2025-09-01 发布日期:2025-11-14
  • 通讯作者: 韩嘉懿 E-mail:zhubing@jlu.edu.cn;jiayi_han@jlu.edu.cn
  • 作者简介:朱冰（1982-），男，教授，博士.研究方向：汽车智能化技术.E-mail：zhubing@jlu.edu.cn
  • 基金资助:
    国家自然科学基金青年基金项目(52302494);吉林省自然科学基金面上项目(20240101116JC);中国博士后科学基金面上项目(2023M741339)

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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摘要：

针对车辆行驶道路中部分路段因磨损、冰雪覆盖等引发的车道线缺失问题，提出了一种基于交通环境信息的虚拟车道线拟合方法。首先，基于highD自然驾驶数据集建立了交通车辆在车道保持状态下的车道线横向偏移概率模型，以确定虚拟车道线横向位置；其次，基于双向长短期记忆网络建立了交通车辆意图识别和轨迹预测模型，以实现虚拟车道线产生条件判别和形状生成；最后，根据上述信息，将满足拟合条件的交通车车道线横向偏移统计学特征转化为主车坐标系下的虚拟车道线风险场，以实现虚拟车道线拟合。仿真和实车数据验证结果表明，该方法能够很好地生成安全、有效的虚拟车道线，与车道线真值误差较小，可为主车提供参考行驶空间，确保行车安全。

关键词: 车辆工程, 虚拟车道线, 交通环境信息, 车道线横向偏移特性, 风险场

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

中图分类号: 

  • U463.6

图1

研究场景"

图2

本文方法整体框架"

表1

样本量统计"

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

图3

左右侧相对车道线横向偏移概率密度分布"

表2

分布参数"

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

图4

Bi-LSTM神经网络结构图"

图5

风险场示意图"

图6

仿真试验"

图7

实车试验平台架构"

图8

试验场景"

图9

实车数据验证结果"

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