A Lane-Level Road Marking Map Using a Monocular Camera
- Authors
- Jang, Wonje; Hyun, Junhyuk; An, Jhonghyun; Cho, Minho; Kim, Euntai
- Issue Date
- Jan-2022
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Autonomous driving; lane-level map; road lane; road marking map; symbolic road marking; weighted loss
- Citation
- IEEE-CAA JOURNAL OF AUTOMATICA SINICA, v.9, no.1, pp.187 - 204
- Journal Title
- IEEE-CAA JOURNAL OF AUTOMATICA SINICA
- Volume
- 9
- Number
- 1
- Start Page
- 187
- End Page
- 204
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/84623
- DOI
- 10.1109/JAS.2021.1004293
- ISSN
- 2329-9266
- Abstract
- 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 sensors. This approach, however, is highly expensive and ineffective since a single high-end MMS must visit every place for mapping. In this paper, a lane-level RM mapping system using a monocular camera is developed. The developed system can be considered as an alternative to expensive high-end MMS. The 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. The network is named RMNet. The segmented RMs are then gathered to build a lane-level RM map. Second, the lane-level map is improved through loop-closure detection and graph optimization. To train the RMNet and build a lane-level RM map, a new dataset named SeRM set is developed. The 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 images. Finally, the proposed lane-level map building method is applied to SeRM set and its validity is demonstrated through experimentation.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - ETC > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/84623)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.