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Driving in the Rain: A Survey toward Visibility Estimation through Windshields
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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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© 2023 Jarrad Neil Morden et al. Distributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0).
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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...
| Published in: | International Journal of Intelligent Systems |
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| ISSN: | 0884-8173 1098-111X |
| Published: |
Hindawi Limited
2023
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| Online Access: |
Check full text
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| 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. |
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| 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 |