  Precise and prompt identification of road surface conditions enables vehicles
to adjust their actions, like changing speed or using specific traction control
techniques, to lower the chance of accidents and potential danger to drivers
and pedestrians. However, most of the existing methods for detecting road
surfaces solely rely on visual data, which may be insufficient in certain
situations, such as when the roads are covered by debris, in low light
conditions, or in the presence of fog. Therefore, we introduce a multimodal
approach for the automated detection of road surface conditions by integrating
audio and images. The robustness of the proposed method is tested on a diverse
dataset collected under various environmental conditions and road surface
types. Through extensive evaluation, we demonstrate the effectiveness and
reliability of our multimodal approach in accurately identifying road surface
conditions in real-time scenarios. Our findings highlight the potential of
integrating auditory and visual cues for enhancing road safety and minimizing
accident risks
