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Vulnerable objects detection for autonomous driving: A review
Affiliation:1. Key Laboratory of Conveyance and Equipment Ministry of Education, East China Jiaotong University, Nanchang, 330013, China;2. Shenzhen Key Laboratory of Smart Sensing and Intelligent Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China;3. School of Automotive Studies, Tongji University, Shanghai, 201804, China;4. Shanghai Tongyu Automotive Technology Co., LTD., Shanghai, 201806, China;1. Electrical and Computer Engineering Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA;2. Electrical and Computer Engineering Department, Duke University, Durham, NC 27708, USA;3. Institute of Applied Physical Sciences and Engineering, Duke Kunshan University, Kunshan, Jiangsu 215316, China;4. Boeing Research and Technology, Huntsville, AL 35758, USA
Abstract:Object detection performed by Autonomous Vehicles (AV)s is a crucial operation that comes ahead of various autonomous driving tasks, such as object tracking, trajectories estimation, and collision avoidance. Dynamic road elements (pedestrians, cyclists, vehicles) impose a greater challenge due to their continuously changing location and behaviour. This paper presents a comprehensive review of the state-of-the-art object detection technologies focusing on both the sensory systems and algorithms used. It begins with a brief introduction on the autonomous driving operations and challenges. Then, different sensory systems employed on existing AVs are elaborated while illustrating their advantages, limitations and applications. Also, sensory systems employed by different research are reviewed. Moreover, due to the significant role Deep Neural Networks (DNN)s are playing in object detection tasks, different DNN-based networks are also highlighted. Afterwards, previous research on dynamic objects detection performed by AVs are reviewed in tabular forms. Finally, a conclusion summarizes the outcomes of the review and suggests future work towards the development of vehicles with higher automation levels.
Keywords:Autonomous driving  Objects detection  Sensor fusion  Deep learning
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