Data-driven artificial intelligence applications for tyre-road-noise prediction and road condition monitoring: A review and future directions

Noise is an important environmental issue that affects quality of life and health, especially in urban areas. With the widespread adoption of electric vehicles, engine noise inside the car has decreased significantly, making tyreroad noise the main noise source, which also accounts for a large proportion of traffic noise. The powerful tool that is artificial intelligence (AI) has emerged in recent years for noise management and monitoring. AI-based systems can classify noise sources, create noise maps and develop control strategies. As a result, some studies
have focused on improving road, vehicle mechanics, and tyre textures and improving the sound quality of tyreroad noise. However, research specifically on tyre-road noise prediction is quite limited. Studies in the literature have generally focused on predicting road damage, surface quality and weather conditions, with less emphasis on tyre-road noise prediction. Many of these studies estimate tyre-road noise by modeling. However, it is not possible for modeling to capture real environment data. Therefore, more data-based studies on tyre-road noise
optimization, monitoring and prediction are needed in this area. ... mehrThis paper focuses on data-based studies and is a discussion of techniques such as data acquisition, feature extraction and selection, and artificial intelligence
algorithms that have been or could be used in this area. Data-driven artificial intelligence methods, such as deep learning, are highlighted for their significant potential in tyre-road noise monitoring and prediction. As a result, future research is expected to focus more on deep learning applications, opening new perspectives for further development in this field..