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

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

doi:10.1155/2023/9939174

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

Jarrad Neil Morden

, Fabio Caraffini

, Ioannis Kypraios

, Ali H. Al-Bayatti

, Richard Smith

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

Swansea University Author: Fabio Caraffini

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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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first_indexed	2023-09-04T11:00:44Z
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spelling	v2 64331 2023-09-02 Driving in the Rain: A Survey toward Visibility Estimation through Windshields d0b8d4e63d512d4d67a02a23dd20dfdb 0000-0001-9199-7368 Fabio Caraffini Fabio Caraffini true false 2023-09-02 SCS 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. Journal Article International Journal of Intelligent Systems 2023 1 26 Hindawi Limited 0884-8173 1098-111X Driving, driver visibility, harsh weather, visibility estimation, ADAS/AD functions 31 8 2023 2023-08-31 10.1155/2023/9939174 http://dx.doi.org/10.1155/2023/9939174 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University 2023-10-10T15:09:11.2062968 2023-09-02T21:31:52.5258113 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Jarrad Neil Morden 0000-0001-5679-9059 1 Fabio Caraffini 0000-0001-9199-7368 2 Ioannis Kypraios 0000-0002-7649-302x 3 Ali H. Al-Bayatti 0000-0002-8062-1258 4 Richard Smith 5 64331__28520__ead02112975c443aba3048512f05d2ae.pdf 64331.VOR.pdf 2023-09-13T09:34:15.5889380 Output 559180 application/pdf Version of Record true © 2023 Jarrad Neil Morden et al. Distributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0). true eng https://creativecommons.org/licenses/by/4.0/
title	Driving in the Rain: A Survey toward Visibility Estimation through Windshields
spellingShingle	Driving in the Rain: A Survey toward Visibility Estimation through Windshields Fabio Caraffini
title_short	Driving in the Rain: A Survey toward Visibility Estimation through Windshields
title_full	Driving in the Rain: A Survey toward Visibility Estimation through Windshields
title_fullStr	Driving in the Rain: A Survey toward Visibility Estimation through Windshields
title_full_unstemmed	Driving in the Rain: A Survey toward Visibility Estimation through Windshields
title_sort	Driving in the Rain: A Survey toward Visibility Estimation through Windshields
author_id_str_mv	d0b8d4e63d512d4d67a02a23dd20dfdb
author_id_fullname_str_mv	d0b8d4e63d512d4d67a02a23dd20dfdb_***_Fabio Caraffini
author	Fabio Caraffini
author2	Jarrad Neil Morden Fabio Caraffini Ioannis Kypraios Ali H. Al-Bayatti Richard Smith
format	Journal article
container_title	International Journal of Intelligent Systems
container_volume	2023
container_start_page	1
publishDate	2023
institution	Swansea University
issn	0884-8173 1098-111X
doi_str_mv	10.1155/2023/9939174
publisher	Hindawi Limited
college_str	Faculty of Science and Engineering
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department_str	School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url	http://dx.doi.org/10.1155/2023/9939174
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description	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.
published_date	2023-08-31T15:09:13Z
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Driving in the Rain: A Survey toward Visibility Estimation through Windshields

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