Multi-factor coupled friction coefficient map spatiotemporal modeling and driving risk fusion for rainy roads

The reduction in tire-road friction coefficient (TRFC) during rainy weather is a major cause of traffic accidents. TRFC values result from the spatiotemporal coupling of road, vehicle, and environmental factors, yet existing estimation methods struggle to fully integrate these factors, hindering the accuracy of TRFC-related driving risk assessments. To address this, a framework for spatiotemporal coupling of these factors at the lane level to generate friction coefficient maps is proposed and two fusion methods for multi-driving risks are established for rainy roads. A tire-fluid-road simulation model is developed to output a TRFC dataset for training a surrogate prediction model. Lanes are divided into grids to align TRFC-related factors, which are then input into the surrogate model to estimate TRFC and create friction coefficient maps. Three TRFC-related driving risks (hydroplaning, rear-end collisions, and sideslip) are analyzed and normalized to construct multi-risk maps, evaluated using max-risk and weighted-sum fusion strategies. The proposed method was validated using rainy-day car-following trajectory data on an urban expressway in Shanghai. Results show that heavy rainfall and high speeds reduce TRFC levels and increase its variability between wheel and non-wheel paths, augmenting the hydroplaning and sideslip risks, while reduced TRFC increases safe following distance and rear-end collision risk as vehicle convergence. The risk fusion results evidence that max-risk fusion excels in scenarios with a dominant risk, while weighted-sum fusion suits scenarios with multiple high-risk types. This study offers a lane-level driving risk assessment for rainy roads, providing insights for developing safety measures in wet road conditions.