Advances in Vision-Based Lane Detection: Algorithms, Integration, Assessment, and Perspectives on ACP-Based Parallel Vision
Lane 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...
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| Vydáno v: | IEEE/CAA journal of automatica sinica Ročník 5; číslo 3; s. 645 - 661 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Chinese Association of Automation (CAA)
01.05.2018
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| ISSN: | 2329-9266, 2329-9274 |
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| Abstract | Lane 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. |
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| AbstractList | Lane 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. |
| Author | Velenis, Efstathios Cao, Dongpu Wang, Fei-Yue Wang, Hong Chen, Long Xing, Yang Wang, Huaji Lv, Chen |
| Author_xml | – sequence: 1 givenname: Yang surname: Xing fullname: Xing, Yang organization: Advanced Vehicle Engineering Centre, Cranfield University, and also with Vehicle Intelligence Pioneers Ltd, China – sequence: 2 givenname: Chen surname: Lv fullname: Lv, Chen email: c.lyu@cranfield.ac.uk organization: Advanced Vehicle Engineering Centre, Cranfield University, UK – sequence: 3 givenname: Long surname: Chen fullname: Chen, Long email: chenl46@mail.sysu.edu.cn organization: School of Data and Computer Science, Sun Yat-Sen University, China – sequence: 4 givenname: Huaji surname: Wang fullname: Wang, Huaji email: huaji.wang@cranfield.ac.uk organization: Advanced Vehicle Engineering Centre, Cranfield University, UK – sequence: 5 givenname: Hong surname: Wang fullname: Wang, Hong email: hong.wang@uwaterloo.ca organization: Mechanical and Mechatronics Engineering, University of Waterloo, Canada – sequence: 6 givenname: Dongpu surname: Cao fullname: Cao, Dongpu email: dongpu.cao@uwaterloo.ca organization: Mechanical and Mechatronics Engineering, University of Waterloo, Canada – sequence: 7 givenname: Efstathios surname: Velenis fullname: Velenis, Efstathios email: e.velenis@cranfield.ac.uk organization: Advanced Vehicle Engineering Centre, Cranfield University, UK – sequence: 8 givenname: Fei-Yue surname: Wang fullname: Wang, Fei-Yue email: feiyue@ieee.org organization: State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, China |
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| Snippet | Lane detection is a fundamental aspect of most current advanced driver assistance systems ( ADASs ). A large number of existing results focus on... |
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| SubjectTerms | ACP theory Advanced driver assistance systems (ADASs) benchmark Detection algorithms Feature extraction Image color analysis Image edge detection lane detection parallel vision performance evaluation Roads Robustness Transforms |
| Title | Advances in Vision-Based Lane Detection: Algorithms, Integration, Assessment, and Perspectives on ACP-Based Parallel Vision |
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