Zhang, Y;
Lu, Z;
Zhang, X;
Xue, JH;
Liao, Q;
(2021)
Deep Learning in Lane Marking Detection: A Survey.
IEEE Transactions on Intelligent Transportation Systems
10.1109/TITS.2021.3070111.
(In press).
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Abstract
Lane marking detection is a fundamental but crucial step in intelligent driving systems. It can not only provide relevant road condition information to prevent lane departure but also assist vehicle positioning and forehead car detection. However, lane marking detection faces many challenges, including extreme lighting, missing lane markings, and obstacle obstructions. Recently, deep learning-based algorithms draw much attention in intelligent driving society because of their excellent performance. In this paper, we review deep learning methods for lane marking detection, focusing on their network structures and optimization objectives, the two key determinants of their success. Besides, we summarize existing lane-related datasets, evaluation criteria, and common data processing techniques. We also compare the detection performance and running time of various methods, and conclude with some current challenges and future trends for deep learning-based lane marking detection algorithm.
| Type: | Article |
|---|---|
| Title: | Deep Learning in Lane Marking Detection: A Survey |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TITS.2021.3070111 |
| Publisher version: | https://doi.org/10.1109/TITS.2021.3070111 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | Lane marking detection, Traffic dataset, Deep network, Objective function, Evaluation metric |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10125670 |
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