A Lane-Level Road Marking Map Using a Monocular Camera

作     者：Wonje Jang Junhyuk Hyun Jhonghyun An Minho Cho Euntai Kim Wonje Jang;Junhyuk Hyun;Jhonghyun An;Minho Cho;Euntai Kim

作者机构：School of Electrical and Electronic EngineeringYonsei UniversitySeoul 120-749Korea 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报（英文版）)

年 卷 期：2022年第9卷第1期

页      面：187-204页

核心收录：

学科分类：0710[理学-生物学] 08[工学] 082304[工学-载运工具运用工程] 080203[工学-机械设计及理论] 0804[工学-仪器科学与技术] 080204[工学-车辆工程] 0802[工学-机械工程] 0823[工学-交通运输工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported by the Industry Core Technology Development Project,20005062 Development of Artificial Intelligence Robot Autonomous Navigation Technology for Agile Movement in Crowded Space,funded by the Ministry of Trade,industry&Energy(MOTIE,Republic of Korea) 

主　　题：Autonomous driving lane-level map road lane road marking map symbolic road marking weighted loss 

摘      要：The essential requirement for precise localization of a self-driving car is a lane-level map which includes road markings(RMs).Obviously,we can build the lane-level map by running a mobile mapping system(MMS)which is equipped with a high-end 3D LiDAR and a number of high-cost *** approach,however,is highly expensive and ineffective since a single high-end MMS must visit every place for *** this paper,a lane-level RM mapping system using a monocular camera is *** developed system can be considered as an alternative to expensive high-end *** developed RM map includes the information of road lanes(RLs)and symbolic road markings(SRMs).First,to build a lane-level RM map,the RMs are segmented at pixel level through the deep learning *** network is named *** segmented RMs are then gathered to build a lane-level RM ***,the lane-level map is improved through loop-closure detection and graph *** train the RMNet and build a lane-level RM map,a new dataset named SeRM set is *** set is a large dataset for lane-level RM mapping and it includes a total of 25157 pixel-wise annotated images and 21000 position labeled ***,the proposed lane-level map building method is applied to SeRM set and its validity is demonstrated through experimentation.