# GCBAM-UNet: Sun Glare Segmentation Using Convolutional Block Attention Module
## 1 Introduction  
Clear vision is crucial for safe driving on any road. Indeed, the more effectively drivers can observe their surroundings, the more likely they will make secure and timely decisions when facing road hazards. Any obstacle to a driver's sight can weaken their driving capabilities and diminish road safety. Many studies have shown that reduced visibility significantly impacts traffic flow and heightens the risk of vehicle collisions [1]. Even though roads are typically engineered to satisfy minimum sight distance standards, driver visibility can still be hindered by environmental factors such as inadequate nighttime lighting and unfavorable weather conditions like rain, snow, and fog [2, 3]. These factors negatively impact driver visual perception by limiting visibility and viewing distances, creating a significant threat to their safety. While many studies have evaluated the dangers of driving in adverse weather conditions, few have investigated the effect of sun glare on road safety [4, 5]. Sun glare is also a significant cause of vision obstruction and warrants further investigation. It can be a significant hazard while driving, leading to traffic accidents. As the sun sits low on the horizon, typically during sunrise and sunset, its light can shine directly into drivers' eyes, reducing visibility and making it difficult to see the road, other vehicles, pedestrians, and traffic signals. Sun glare poses significant challenges not only for human drivers but also for Advanced Driver Assistance Systems (ADAS). Such systems rely on sensors, cameras, and other technologies to function effectively, and sunlight can impair their performance. For instance, sun glare can obscure the view of cameras, making it difficult for systems to detect and recognize objects, road markings, and traffic signals. Intense sunlight can saturate camera sensors, leading to a loss of image details. Also, glare can create artifacts or reflections that may be misinterpreted by the ADAS system as obstacles or ignored as harmless reflections. Addressing the problem of sun glare in ADAS involves several approaches and strategies that leverage technology, design, algorithms, and the integration of various sensors and systems. In the literature, there exist various approaches to handle the problem of sun glare that can be divided into hardware, image processing, and deep learning approaches. Hardware-based approaches can use different cameras and sensors such as High Dynamic Range (HDR) cameras [6], which can capture a wide range of light intensities, making them more effective in handling bright light and glare, or infrared sensors that are less affected by visible light and can provide clear images even in conditions of strong sunlight. Also, polarized filters can be added to camera lenses to reduce glare and improve image quality [7]. Other approaches combine data from various sensors (e.g., cameras, radar, LiDAR) to help compensate for the shortcomings of individual sensors [8]. For example, radar and LiDAR are less affected by glare and can provide reliable data when camera performance is compromised. Overall, these approaches can improve visual clarity and contrast and reduce glare translating to better reaction times and improved safety on the road. However, the cost of ADAS hardware can be very expensive based on factors like sensor quality, system integration, and the specific ADAS features offered. Moreover, the image processing-based approaches employ algorithms and methods that can detect and mitigate the effects of glare, enhancing the quality of images captured by cameras [9]. These methods adjust the contrast of images to redistribute the intensity levels of an image to improve contrast, which can help reduce the effect of glare on the overall image quality. Also, image processing algorithms can identify regions in an image that are significantly brighter due to glare. These spots can then be targeted for specific processing.