RoadSafeAI: Predicting Crash Risk from Road Map Images

Urban mobility is undergoing a profound transformation driven by multiple forces, including sustainability, autonomous vehicles, and safety. In this study, we focus on road safety, which remains a global concern with 1.19 million road traffic deaths reported annually by the World Health Organization. Among the key factors contributing to crash risk, infrastructure design has been recognized as one of the most actionable. This paper proposes an AI-based tool that provides automatic urban risk assessment through topological imagery, even in the absence of detailed crash records in a specific area of interest. We develop a regression model based on a modified ResNet-18 architecture, trained to predict a risk index from map-based images of urban areas and real-world telematics data, specifically harsh braking events. The model is trained on a dataset of over 80,000 harsh events collected from approximately 10,000 vehicles in the city of Milan during 2024.The results show that the model can effectively infer infrastructure-related road risk from topological inputs only, providing a lightweight and data-efficient prior for intelligent vehicles, and supporting enhanced global path planning in an autonomous driving context, even in scenarios with limited or no incident data.