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Volume 9 Issue 1
Jan.  2022

IEEE/CAA Journal of Automatica Sinica

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Wonje Jang, Junhyuk Hyun, Jhonghyun An, Minho Cho and Euntai Kim, "A Lane-Level Road Marking Map Using a Monocular Camera," IEEE/CAA J. Autom. Sinica, vol. 9, no. 1, pp. 187-204, Jan. 2022. doi: 10.1109/JAS.2021.1004293
Citation: Wonje Jang, Junhyuk Hyun, Jhonghyun An, Minho Cho and Euntai Kim, "A Lane-Level Road Marking Map Using a Monocular Camera," IEEE/CAA J. Autom. Sinica, vol. 9, no. 1, pp. 187-204, Jan. 2022. doi: 10.1109/JAS.2021.1004293

A Lane-Level Road Marking Map Using a Monocular Camera

doi: 10.1109/JAS.2021.1004293
Funds:  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)
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  • 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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    Highlights

    • A lane-level RM map is built using only a monocular camera and wheel encoder
    • RMNet was developed to train on SeRM dataset for road marking segmentation
    • Class-weighted loss and class-weighted focal loss are proposed to handle class imbalance problem
    • The semantic road mark mapping (SeRM) dataset is developed for effective RM segmentation and mapping

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