Browsing by Author "Chen, Long"

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    Advances in vision-based lane detection: algorithms, integration, assessment, and perspectives on ACP-based parallel vision
    (IEEE, 2018-05-01) Xing, Yang; Lv, Chen; Chen, Long; Wang, Huaji; Wang, Hong; Cao, Dongpu; Velenis, Efstathios; Wang, Fei-Yue
    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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    Milestones in autonomous driving and intelligent vehicles - Part 1: control, computing system design, communication, HD map, testing, and human behaviors
    (IEEE, 2023-05-29) Chen, Long; Li, Yuchen; Huang, Chao; Xing, Yang; Tian, Daxin; Li, Li; Hu, Zhongxu; Teng, Siyu; Lv, Chen; Wang, Jinjun; Cao, Dongpu; Zheng, Nanning; Wang, Fei-Yue
    Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits. Although a number of surveys have reviewed research achievements in this field, they are still limited in specific tasks and lack systematic summaries and research directions in the future. Our work is divided into three independent articles and the first part is a survey of surveys (SoS) for total technologies of AD and IVs that involves the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions. This is the second part (Part 1 for this technical survey) to review the development of control, computing system design, communication, high-definition map (HD map), testing, and human behaviors in IVs. In addition, the third part (Part 2 for this technical survey) is to review the perception and planning sections. The objective of this article is to involve all the sections of AD, summarize the latest technical milestones, and guide abecedarians to quickly understand the development of AD and IVs. Combining the SoS and Part 2, we anticipate that this work will bring novel and diverse insights to researchers and abecedarians, and serve as a bridge between past and future.
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    Milestones in autonomous driving and intelligent vehicles: survey of surveys
    (IEEE, 2022-11-24) Chen, Long; Li, Yuchen; Huang, Chao; Li, Bai; Xing, Yang; Tian, Daxin; Li, Li; Hu, Zhongxu; Na, Xiaoxiang; Li, Zixuan; Teng, Siyu; Lv, Chen; Wang, Jinjun; Cao, Dongpu; Zheng, Nanning; Wang, Fei-Yue
    Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits. Although a number of surveys have reviewed research achievements in this field, they are still limited in specific tasks, lack of systematic summary and research directions in the future. Here we propose a Survey of Surveys (SoS) for total technologies of AD and IVs that reviews the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions. To our knowledge, this article is the first SoS with milestones in AD and IVs, which constitutes our complete research work together with two other technical surveys. We anticipate that this article will bring novel and diverse insights to researchers and abecedarians, and serve as a bridge between past and future.