Title
Measuring Driver’s Visibility in Rainy Conditions for Semi-Autonomous Vehicle Take-over
PhD Project abstract
The demand for advanced driver-assistance systems (ADAS), which help with monitoring, warning, braking, and steering, is rising, ADAS is predicted to rise over the next decade, fuelled mostly by regulatory and consumer interest in safety applications that protect drivers and prevent accidents. When it rains heavily, human vision is impaired, making the appropriate operation of driver assistance systems even more important for safe driving. One of the most pressing concerns to be addressed in this area is the development of autonomous vehicles and driver aid technologies to assist drivers in these situations. On-board cameras give critical visual cues and information to drivers to ensure safe driving. The data may be used to identify pavement markings, road signs, and potential dangers, such as barriers. These camera systems perform admirably in more normal weather situations, particularly in bright sunlight. However, system performance should not decrease in severe weather, and the camera system should continue to offer useful information in these situations, particularly given the increased burden of the driver.
Rain on a windshield has several detrimental effects for video-based car applications, including pedestrian recognition and lane detection. As a result, it is critical to understand the intensity of rain on a windshield when operating a driving safety assistance system or an autonomous driving car. Regardless of the background, a person can clearly detect rain on a windshield. However, deciphering the condition of the raindrop from a picture shot via a windshield is challenging since a raindrop mix with the texture of the backdrop; consequently, it is critical to identify the rain. Additionally, rain on a windshield blur since a camera's emphasis is often on a background. This complicates rain detection.