Bayesian inference of visibility in fog and smoke artificial conditions from 3D-LiDAR point clouds

Montalban, Karl; Atchuthan, Dinesh; Reymann, Christophe; Ravet, Alexandre; Dupouy, Paul-Édouard; Rivière, Nicolas; Lacroix, Simon

doi:10.1016/j.jqsrt.2023.108666

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Cited by 2 publications

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Accessible Framework for Automation in Precision Agriculture Leveraging Boston Dynamics' SPOT

Boyle,

Cui,

Dougherty

et al. 2023

2023 IEEE MIT Undergraduate Research Technology Conference (URTC)

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Accessible Framework for Automation in Precision Agriculture Leveraging Boston Dynamics' SPOT

Boyle,

Cui,

Dougherty

et al. 2023

2023 IEEE MIT Undergraduate Research Technology Conference (URTC)

View full text Add to dashboard Cite

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

Morden,

Caraffini,

Kypraios

et al. 2023

International Journal of Intelligent Systems

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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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Bayesian inference of visibility in fog and smoke artificial conditions from 3D-LiDAR point clouds

Cited by 2 publications

References 28 publications

Accessible Framework for Automation in Precision Agriculture Leveraging Boston Dynamics' SPOT

Accessible Framework for Automation in Precision Agriculture Leveraging Boston Dynamics' SPOT

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

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