Advances in vision-based lane detection: algorithms, integration, assessment, and perspectives on ACP-based parallel vision

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dc.contributor.authorXing, Yang
dc.contributor.authorLv, Chen
dc.contributor.authorChen, Long
dc.contributor.authorWang, Huaji
dc.contributor.authorWang, Hong
dc.contributor.authorCao, Dongpu
dc.contributor.authorVelenis, Efstathios
dc.contributor.authorWang, Fei-Yue
dc.date.accessioned2019-03-07T16:25:39Z
dc.date.available2019-03-07T16:25:39Z
dc.date.issued2018-05-01
dc.description.abstractLane detection is a fundamental aspect of most current advanced driver assistance systems (ADASs). A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowledge background and the low-cost of camera devices. In this paper, previous vision-based lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods. Next, considering the inevitable limitations that exist in the camera-based lane detection system, the system integration methodologies for constructing more robust detection systems are reviewed and analyzed. The integration methods are further divided into three levels, namely, algorithm, system, and sensor. Algorithm level combines different lane detection algorithms while system level integrates other object detection systems to comprehensively detect lane positions. Sensor level uses multi-modal sensors to build a robust lane recognition system. In view of the complexity of evaluating the detection system, and the lack of common evaluation procedure and uniform metrics in past studies, the existing evaluation methods and metrics are analyzed and classified to propose a better evaluation of the lane detection system. Next, a comparison of representative studies is performed. Finally, a discussion on the limitations of current lane detection systems and the future developing trends toward an Artificial Society, Computational experiment-based parallel lane detection framework is proposed.en_UK
dc.identifier.citationYang Xing, Chen Lv, Long Chen, et al., Advances in vision-based lane detection: algorithms, integration, assessment, and perspectives on ACP-based parallel vision. IEEE/CAA Journal of Automatica Sinica, Volume 5, Issue 3, May 2018 pp. 645-661en_UK
dc.identifier.issn2329-9266
dc.identifier.urihttps:doi.org/10.1109/JAS.2018.7511063
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/13972
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectAdvanced driver assistance systems (ADASs)en_UK
dc.subjectACP theoryen_UK
dc.subjectbenchmarken_UK
dc.subjectlane detectionen_UK
dc.subjectparallel visionen_UK
dc.subjectperformance evaluationen_UK
dc.titleAdvances in vision-based lane detection: algorithms, integration, assessment, and perspectives on ACP-based parallel visionen_UK
dc.typeArticleen_UK

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