Vulnerable objects detection for autonomous driving: A review

• Published in: Integration (Amsterdam) Vol. 78; pp. 36 - 48
• Main Authors: Khatab, Esraa, Onsy, Ahmed, Varley, Martin, Abouelfarag, Ahmed
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.05.2021; Elsevier BV
• Subjects: Algorithms; Artificial neural networks; Autonomous driving; Autonomous vehicles; Collision avoidance; Collision avoidance systems; Collision dynamics; Deep learning; Neural networks; Object recognition; Objects detection; Pedestrians; Sensor fusion; Sensors; State-of-the-art reviews; Studies; Trajectory analysis; Vehicles; Objects detection; Deep learning; Sensor fusion; Autonomous driving
• ISSN: 0167-9260; 1872-7522
• DOI: 10.1016/j.vlsi.2021.01.002

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.