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Driving in the Rain: A Survey toward Visibility Estimation through Windshields

Jarrad Neil Morden Orcid Logo, Fabio Caraffini Orcid Logo, Ioannis Kypraios Orcid Logo, Ali H. Al-Bayatti Orcid Logo, Richard Smith

International Journal of Intelligent Systems, Volume: 2023, Pages: 1 - 26

Swansea University Author: Fabio Caraffini Orcid Logo

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DOI (Published version): 10.1155/2023/9939174

Abstract

Rain can significantly impair the driver’s sight and affect his performance when driving in wet conditions. Evaluation of driver visibility in harsh weather, such as rain, has garnered considerable research since the advent of autonomous vehicles and the emergence of intelligent transportation syste...

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Published in: International Journal of Intelligent Systems
ISSN: 0884-8173 1098-111X
Published: Hindawi Limited 2023
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa64331
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Abstract: Rain can significantly impair the driver’s sight and affect his performance when driving in wet conditions. Evaluation of driver visibility in harsh weather, such as rain, has garnered considerable research since the advent of autonomous vehicles and the emergence of intelligent transportation systems. In recent years, advances in computer vision and machine learning led to a significant number of new approaches to address this challenge. However, the literature is fragmented and should be reorganised and analysed to progress in this field. There is still no comprehensive survey article that summarises driver visibility methodologies, including classic and recent data-driven/model-driven approaches on the windshield in rainy conditions, and compares their generalisation performance fairly. Most ADAS and AD systems are based on object detection. Thus, rain visibility plays a key role in the efficiency of ADAS/AD functions used in semi- or fully autonomous driving. This study fills this gap by reviewing current state-of-the-art solutions in rain visibility estimation used to reconstruct the driver’s view for object detection-based autonomous driving. These solutions are classified as rain visibility estimation systems that work on (1) the perception components of the ADAS/AD function, (2) the control and other hardware components of the ADAS/AD function, and (3) the visualisation and other software components of the ADAS/AD function. Limitations and unsolved challenges are also highlighted for further research.
Keywords: Driving, driver visibility, harsh weather, visibility estimation, ADAS/AD functions
College: Faculty of Science and Engineering
Funders: Swansea University
Start Page: 1
End Page: 26