4.7 Article

A Lane-Level Road Marking Map Using a Monocular Camera

Journal

IEEE-CAA JOURNAL OF AUTOMATICA SINICA
Volume 9, Issue 1, Pages 187-204

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JAS.2021.1004293

Keywords

Autonomous driving; lane-level map; road lane; road marking map; symbolic road marking; weighted loss

Funding

  1. Industry Core Technology Development Project - Ministry of Trade, industry & Energy (MOTIE, Republic of Korea) [20005062]

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This paper proposes a method for lane-level road marking mapping using a monocular camera and develops the SeRM dataset for training and validation. The method utilizes a deep learning network for road marking segmentation and improves the lane-level map through loop-closure detection and graph optimization.
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.

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