A Lane-Level Road Marking Map Using a Monocular Camera

Wonje Jang, Junhyuk Hyun, Jhonghyun An, Minho Cho, Euntai Kim

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)187-204
Number of pages18
JournalIEEE/CAA Journal of Automatica Sinica
Issue number1
Publication statusPublished - 2022 Jan 1

Bibliographical note

Publisher Copyright:
© 2014 Chinese Association of Automation.

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Information Systems
  • Artificial Intelligence


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