利用行车记录仪视频提取路面车道线

doi:10.13474/j.cnki.11-2246.2024.1201

摘要/Abstract

摘要： 路面车道线是高精地图的关键组成部分,搭载在网约车上的海量行车记录仪视频是对道路信息的实时观测,是一种较为经济的车道线数据提取的重要数据源。本文基于海量的滴滴网约车行车记录仪视频,探讨了基于LaneNet深度网络模型的路面车道线数据提取方法的可行性。该方法首先利用LaneNet网络模型对每帧视频图像进行语义分割,进而通过预测透视变换矩阵,实现对车道线像素点位置的拟合提取,最后采用模拟数据和复杂场景下的滴滴行车记录仪数据进行试验结果评价。试验结果表明,本文采用模型在车载视频图像中具有较好的车道线提取性能。

关键词: 车载视频, 高精地图, 车道线, 语义分割, 道路提取

Abstract: Road lane lines are a key component of high-precision maps. The massive driving recorder videos mounted on online ride-hailing vehicles are real-time observations of road information and are an important data source for more economical lane line data extraction. Based on the massive Didi ride-hailing driving record videos, this paper explores the feasibility of the road lane line data extraction method based on the LaneNet deep network model. This method first uses the LaneNet network model to perform semantic segmentation on each frame of the video image, and then predicts The perspective transformation matrix realizes the fitting and extraction of the lane line pixel position. In the experimental analysis, simulation data and Didi driving recorder data in complex scenes were used to evaluate the experimental results. The experimental results show that the model used in this article has better lane line extraction performance in vehicle video images.

Key words: vehicle-mounted video, high-precision map, lane lines, semantic segmentation, road extraction

中图分类号:

P237

黄金彩, 李诗逸, 石岩. 利用行车记录仪视频提取路面车道线[J]. 测绘通报, 2024, 0(12): 1-5.

HUANG Jincai, LI Shiyi, SHI Yan. Extracting road lane lines from driving recorder video[J]. Bulletin of Surveying and Mapping, 2024, 0(12): 1-5.

参考文献

[1] 王嫣然,陈清亮,吴俊君.面向复杂环境的图像语义分割方法综述[J].计算机科学,2019,46(9):36-46.
[2] 吴伟,刘洋,刘威,等.自动驾驶环境下交叉口车辆路径规划与最优控制模型[J].自动化学报,2020,46(9):1971-1985.
[3] 章军辉,陈大鹏,李庆.自动驾驶技术研究现状及发展趋势[J].科学技术与工程,2020,20(9):3394-3403.
[4] YIN Jiateng,TANG Tao,YANG Lixing,et al.Research and development of automatic train operation for railway transportation systems:a survey[J].Transportation Research Part C:Emerging Technologies,2017,85:548-572.
[5] WEI Wenting,YANG Ruying,GU Huaxi,et al.Multi-objective optimization for resource allocation in vehicular cloud computing networks[J].IEEE Transactions on Intelligent Transportation Systems,2022,23(12):25536-25545.
[6] MCCALL J C,TRIVEDI M M.Video-based lane estimation and tracking for driver assistance:survey,system,and evaluation[J].IEEE Transactions on Intelligent Transportation Systems,2006,7(1):20-37.
[7] BAR HILLEL A,LERNER R,LEVI D,et al.Recent progress in road and lane detection:a survey[J].Machine Vision and Applications,2014,25(3):727-745.
[8] YENIKAYA S,YENIKAYA G,DVVEN E.Keeping the vehicle on the road[J].ACM Computing Surveys,2013,46(1):1-43.
[9] ZHU Hao,YUEN K V,MIHAYLOVA L,et al.Overview of environment perception for intelligent vehicles[J].IEEE Transactions on Intelligent Transportation Systems,2017,18(10):2584-2601.
[10] XING Yang,LV Chen,CHEN Long,et al.Advances in vision-based lane detection:algorithms,integration,assessment,and perspectives on ACP-based parallel vision[J].IEEE/CAA Journal of Automatica Sinica,2018,5(3):645-661.
[11] NAROTE S P,BHUJBAL P N,NAROTE A S,et al.A review of recent advances in lane detection and departure warning system[J].Pattern Recognition,2018,73:216-234.
[12] 苏欣欣.基于Deeplabv3+算法的交通场景语义分割模型研究及应用[D].西安:长安大学,2022.
[13] 彭程里.基于编解码网络的图像语义分割方法及其应用研究[D].武汉:武汉大学,2021.
[14] ZOU Qin,SUN Qin,CHEN Long,et al.A comparative analysis of LiDAR SLAM-based indoor navigation for autonomous vehicles[J].IEEE Transactions on Intelligent Transportation Systems,2022,23(7):6907-6921.
[15] WU Jianqing,XU Hao,ZHAO Junxuan.Automatic lane identification using the roadside LiDAR sensors[J].IEEE Intelligent Transportation Systems Magazine,2020,12(1):25-34.
[16] PAN Xingang,SHI Jianping,LUO Ping,et al.Spatial as deep:spatial CNN for traffic scene understanding[C]//Proceedings of 2017 AAAI Conference on Artificial Intelligence.[S.l.]:IEEE,2017.
[17] NEVEN D,DE BRABANDERE B,GEORGOULIS S,et al.Towards end-to-end lane detection:an instance segmentation approach[C]//Proceedings of 2018 IEEE Intelligent Vehicles Symposium.Changshu:IEEE,2018:286-291.
[18] YOO S,LEE H S,MYEONG H,et al.End-to-end lane marker detection via row-wise classification[C]//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops.Seattle:IEEE,2020:4335-4343.
[19] QIN Zequn,WANG Huanyu,LI Xi.Ultra fast structure-aware deep lane detection[M]//Lecture Notes in Computer Science.Cham:Springer International Publishing,2020:276-291.
[20] LI Xiang,LI Jun,HU Xiaolin,et al.Line-CNN:end-to-end traffic line detection with line proposal unit[J].IEEE Transactions on Intelligent Transportation Systems,2020,21(1):248-258.
[21] TABELINI L,BERRIEL R,PAIXÃO T M,et al.Keep your eyes on the lane:real-time attention-guided lane detection[C]//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Nashville:IEEE,2021:294-302.