Road Environment Perception for Safe and Comfortable Driving

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Road Environment Perception for Safe and Comfortable Driving

Guo, Sicen / Jiang, Yu / Li, Jiahang / Zhou, Dacheng / Su, Shuai / Bocus, Mohammud Junaid / Zhu, Xingyi / Chen, Qijun / Fan, Rui

Abstract

With the ongoing evolution of autonomous driving technology, road environment perception systems have become a significant focus of research. However, there is currently a paucity of comprehensive survey articles that provide a systematic overview of state-of-the-art (SoTA) computer vision techniques and vibration methods for road defect detection, particularly with regards to deep learning methods. This chapter aims to fill this gap by describing the sensing technologies for vision-based and vibration-based road environment data acquisition, summarizing several public datasets for pothole and crack detection, and providing a comprehensive review of SoTA road defect detection algorithms. Additionally, this chapter also discusses the core competencies of autonomous vehicle software systems, such as planning and control. Finally, we offer a glimpse into the future of autonomous driving systems, envisioning the integration of both vision and motion sensors. We believe that this survey will act as a helpful guide for advancing road defect detection technology, providing strategic advice and practical guidance to those involved in developing such systems.

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Document information

Title :

Road Environment Perception for Safe and Comfortable Driving

Additional title:

Advs Comp. Vision, Pattern Recognition

Contributors:

Fan, Rui (editor) / Guo, Sicen (editor) / Bocus, Mohammud Junaid (editor) / Guo, Sicen (author) / Jiang, Yu (author) / Li, Jiahang (author) / Zhou, Dacheng (author) / Su, Shuai (author) / Bocus, Mohammud Junaid (author) / Zhu, Xingyi (author)

Published in:

Autonomous Driving Perception ; Chapter : 11 ; 357-387

Advances in Computer Vision and Pattern Recognition

Publication date :

2023-10-07

Size :

31 pages

ISBN :

978-981-99-4287-9 , 978-981-99-4286-2

ISSN :

2191-6594 , 2191-6586

DOI :