Citation

Ghasemieh A, Kashef R. Transp. Eng. (Amsterdam) 2022; 8: e100115.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.treng.2022.100115

PMID

unavailable

Abstract

Detection of the surrounding objects of a vehicle is the most crucial step in autonomous driving. Failure to identify those objects correctly in a timely manner can cause irreparable damage, impacting our safety and society. Several studies have been introduced to identify these objects in the two-dimensional (2D) and three-dimensional (3D) vector space. The 2D object detection method has achieved remarkable success; however, in the last few years, detecting objects in 3D have received more remarkable adoption. 3D object recognition has several advantages over 2D detection methods, as more accurate information about the environment is obtained for better detection. For example, the depth of the images is not considered in the 2D detection, which reduces the detection accuracy. Despite considerable efforts in 3D object detection, it has not yet reached the stage of maturity. Therefore, in this paper, we aim at providing a comprehensive overview of the state-of-the-art 3D object detection methods, with a focus on 1) identifying advantages and limitations, 2) revelling a novel categorization of the literature, 3) outlying the various training procedures, 4) highlighting the research gap in the existing methods and 5) building a road map for future directions.

Language: en

Keywords

3D object detection; Autonomous vehicles; LiDAR; Point cloud; Sensors; Stereo images