Enhancing Driver Safety in Challenging Weather Conditions for Efficient Vision Driver Assistance Systems
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Abstract
Traffic accidents pose a heightened level of severity under various conditions that result in reduced visibility, such as wet days, dark nights, gloomy and/or rainy nights, foggy days, and similar circumstances. Contemporary vision-based driver assistance systems are specifically engineered to operate optimally in favorable weather conditions. Classification is a systematic approach utilized to ascertain the optical attributes of vision enhancement algorithms, with the aim of enhancing their efficiency. In order to enhance the performance of machine vision systems under adverse weather conditions, this study introduces a multi-class weather classification approach that relies on a combination of several weather features and supervised learning techniques. Initially, the extraction of underlying visual characteristics is performed on multitraffic scene photos. Subsequently, these characteristics are represented as an eight-dimensional feature matrix. Furthermore, a total of five supervised learning methods are employed in order to train the classifiers. The findings of the investigation indicate that the extracted features possess the capability to effectively express the semantics of the image. Moreover, the classifiers demonstrate a notable level of accuracy in recognizing the image, as well as the ability to adapt to varying conditions. The strategy described in this study serves as a foundation for improving the identification of vehicles in front during nighttime light variations, as well as extending the driver's visual range in foggy weather conditions.
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