Real-time robust multi-lane detection and tracking in challenging urban scenarios
In this paper, we consider multi-lane detection in challenging urban scenarios such as emerging, ending, spitting and merging of lane markings, heavily curved lanes, zig-zag lanes, on/off ramp and disturbance of other road writings. We present a fast robust multi-lane detection and tracking framewor...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/141801 |
| _version_ | 1824453834819239936 |
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| author | Zhou, Hui Zhang, Handuo Hasith, Karunasekera Wang, Han |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Zhou, Hui Zhang, Handuo Hasith, Karunasekera Wang, Han |
| author_sort | Zhou, Hui |
| collection | NTU |
| description | In this paper, we consider multi-lane detection in challenging urban scenarios such as emerging, ending, spitting and merging of lane markings, heavily curved lanes, zig-zag lanes, on/off ramp and disturbance of other road writings. We present a fast robust multi-lane detection and tracking framework to address these challenges. In this method, lane feature elements are first extracted and then grouped into clusters, and clusters are associated through energy minimization. Probabilistic decision making is adopted to track individual lane considering lane cluster measurements and prior lane state. A multi-lane tracking strategy is also presented to manage lane tracks from their appearance to disappearance, which can reduce false detection and improve robustness of the algorithm. Real driving data are used to verify the effectiveness of our algorithm in all mentioned challenging scenarios. |
| first_indexed | 2025-02-19T03:12:43Z |
| format | Conference Paper |
| id | ntu-10356/141801 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:12:43Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1418012020-06-11T00:53:41Z Real-time robust multi-lane detection and tracking in challenging urban scenarios Zhou, Hui Zhang, Handuo Hasith, Karunasekera Wang, Han School of Electrical and Electronic Engineering 2019 IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM) Engineering::Electrical and electronic engineering Lane Detection Computer Vision In this paper, we consider multi-lane detection in challenging urban scenarios such as emerging, ending, spitting and merging of lane markings, heavily curved lanes, zig-zag lanes, on/off ramp and disturbance of other road writings. We present a fast robust multi-lane detection and tracking framework to address these challenges. In this method, lane feature elements are first extracted and then grouped into clusters, and clusters are associated through energy minimization. Probabilistic decision making is adopted to track individual lane considering lane cluster measurements and prior lane state. A multi-lane tracking strategy is also presented to manage lane tracks from their appearance to disappearance, which can reduce false detection and improve robustness of the algorithm. Real driving data are used to verify the effectiveness of our algorithm in all mentioned challenging scenarios. NRF (Natl Research Foundation, S’pore) Accepted version 2020-06-11T00:50:23Z 2020-06-11T00:50:23Z 2019 Conference Paper Zhou, H., Zhang, H., Hasith, K., & Wang, H. (2019). Real-time robust multi-lane detection and tracking in challenging urban scenarios. Proceedings of 2019 IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM), 936-941. doi:10.1109/ICARM.2019.8834317 9781728100654 https://hdl.handle.net/10356/141801 10.1109/ICARM.2019.8834317 2-s2.0-85073265769 936 941 en MRP1A © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/ICARM.2019.8834317. application/pdf |
| spellingShingle | Engineering::Electrical and electronic engineering Lane Detection Computer Vision Zhou, Hui Zhang, Handuo Hasith, Karunasekera Wang, Han Real-time robust multi-lane detection and tracking in challenging urban scenarios |
| title | Real-time robust multi-lane detection and tracking in challenging urban scenarios |
| title_full | Real-time robust multi-lane detection and tracking in challenging urban scenarios |
| title_fullStr | Real-time robust multi-lane detection and tracking in challenging urban scenarios |
| title_full_unstemmed | Real-time robust multi-lane detection and tracking in challenging urban scenarios |
| title_short | Real-time robust multi-lane detection and tracking in challenging urban scenarios |
| title_sort | real time robust multi lane detection and tracking in challenging urban scenarios |
| topic | Engineering::Electrical and electronic engineering Lane Detection Computer Vision |
| url | https://hdl.handle.net/10356/141801 |
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