Abstract
More than 70% of vehicle to pedestrian accident occurred in night time. Improvement of visibility in the night time is one of the most important tasks due to the frequency of fatal accidents. The adaptive lighting system helps to improve the headlamp illumination by means of continuous adaptation off the headlamps according to the current driving situation and current environment. Through use of camera-based driver assistance systems versatile possibilities have evolved to support the driver. Likewise, the human optical perception, the ability to see is constricted by visibility conditions. Unfortunately in the real world there are plenty of situations and influences which can degrade camera sight, such as adverse weather conditions, lens occlusions through ice or dirt, blurring effects through water or translucent occlusions, cracks in the lens or windshield.
The weather or the road geometry cause a reduced illumination range or glare which is critical for driver safety. But in the presence of rain and snow, headlights can paradoxically reduce visibility due to light reflected off of precipitation back towards the driver. Precipitation also scatters light across a wide range of angles that disrupts the vision of drivers in oncoming vehicles. An optimal headlamp system should be able to adapt on a multitude of parameters from the ego vehicle and the environment to ensure a sufficient illumination of the street without glaring other road users. A perfect road illumination without providing glare for other traffic participants and giving the maximum additional information about the environment to the driver is still a hard task for these systems.
A new approach based on mono camera for rain detection system is introduced and experimental results are discussed.
KEYWORDS Rain detection, Weather recognition, Self-diagnostics, Lighting system, Intelligent camera