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Title: Real-time robust multi-lane detection and tracking in challenging urban scenarios
Authors: Zhou, Hui
Zhang, Handuo
Hasith, Karunasekera
Wang, Han
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: 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
Project: MRP1A
Abstract: 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.
ISBN: 9781728100654
DOI: 10.1109/ICARM.2019.8834317
Rights: © 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:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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