期刊
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷 63, 期 2, 页码 540-555出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2013.2281199
关键词
Autonomous vehicles; drivable-region detection; lane detection; light detection and ranging (LIDAR); multilevel feature fusion; vision
资金
- Fundamental Research Funds for the Central Universities
- National Natural Science Foundation of China [91120002, 41001306]
- Shenzhen Dedicated Funding of Strategic Emerging Industry Development Program [JCYJ20121019111128765]
- German Academic Exchange Service under Grant PPP VR China [54368573]
- Sino-German Center for Research Promotion [GZ 692]
Autonomous vehicle navigation is challenging since various types of road scenarios in real urban environments have to be considered, particularly when only perception sensors are used, without position information. This paper presents a novel real-time optimal-drivable-region and lane detection system for autonomous driving based on the fusion of light detection and ranging (LIDAR) and vision data. Our system uses a multisensory scheme to cover the most drivable areas in front of a vehicle. We propose a feature-level fusion method for the LIDAR and vision data and an optimal selection strategy for detecting the best drivable region. Then, a conditional lane detection algorithm is selectively executed depending on the automatic classification of the optimal drivable region. Our system successfully handles both structured and unstructured roads. The results of several experiments are provided to demonstrate the reliability, effectiveness, and robustness of the system.
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