Video Based Simulation of Daytime and Nighttime Rain Affecting Driver Visibility

Neale, William; Marr, James C.; Hessel, David; Gillihan, Robert

doi:10.4271/2021-01-0854

Cited by 1 publication

(3 citation statements)

References 11 publications

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“…Tese simulations could be used mainly in the visualisation component of the ADAS/AD functions to display appropriate visual warnings to the driver to assist them in making decisions while driving in rainy conditions. In [75], both day and night conditions are considered, where visibility in rain conditions is simulated using an in-vehicle camera and a simulated environment using Nuke for projection mapping. Furthermore, there are also factors that infuence visibility based on contrast, refections, lighting, colour particles in the air, and rain intensity, such as light (0.0to0.1 in/h), moderate (0.11 − 0.29 in/h), and heavy (0.30 in/h or more), which are taken into account.…”

Section: Discussionmentioning

confidence: 99%

“…In [75], the authors establish a technique to simulate the impact of rain on driver vision by analysing and modelling real video footage taken in dry and rainy settings, during the day and at night. Baseline raw video footage that is not afected by rain is monitored, and using projection mapping and rain modelling, this baseline flm is generated to accurately depict the impact of rain on driver vision.…”

Section: Cnnmentioning

confidence: 99%

“…Custom facility Hardware recommendation for sensor-integrated headlamps [75] Generating video realistic computer-simulated rain and the efect of the rain it has on driver visibility Custom simulation Simulates rain by altering material refexions, refractions, and lighting [7] 1997 Adaptive iterative picture fltering Custom flterer Produces real-time realistic and optical efects [79]…”

Section: Perception and Controlmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Discussionmentioning

confidence: 99%

Section: Cnnmentioning

confidence: 99%