Deep learning-based perception systems for autonomous driving: A comprehensive survey

被引:50
作者
Wen, Li-Hua [1 ]
Jo, Kang-Hyun [1 ]
机构
[1] Univ Ulsan, Grad Sch Elect Engn, Ulsan 44610, South Korea
关键词
Survey; LiDAR; Camera; Deep learning; Road detection; 2-D object detection; 3-D object detection; Traffic sign detection; Traffic light detection; Autonomous driving; VEHICLE DETECTION; POINT CLOUD; 3D; MODEL; SEGMENTATION; RECOGNITION; FUSION;
D O I
10.1016/j.neucom.2021.08.155
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With the rapid development of society and the economy, autonomous driving techniques are widely applied in many areas, such as autonomous vehicles, autonomous drones, and robotics. As a dominating technique, deep learning has become more and more popular for 2-D and 3-D object detection. Numerous deep learning-based methods have been proposed to solve various vision issues. To further help with the development of unmanned systems, this paper presents a comprehensive survey of the recent processes from the past five years for 3-D object detection, road detection, traffic sign detection, and traffic light detection and classification. To summarize and analyze previous works in detail, this paper only focuses on deep learning-based object detection tasks in autonomous driving that take place when the input is a point cloud or image(s). It also presents comparative results for insight comparison and inspiring future researches. (c) 2022 Elsevier B.V. All rights reserved.
引用
收藏
页码:255 / 270
页数:16
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