DriveR: Towards Generating a Dynamic Road Safety Map with Causal Contexts

Road safety remains a critical global concern, with millions of crashes reported annually. Understanding the safety of individual road junctions is vital, especially in areas prone to road rage and reckless driving. However, current navigation systems lack detailed safety information, increasing risk for drivers and pedestrians. Recognizing this need, this paper introduces \ourmethod{} that automatically annotates the road segments with a driving safety level to aid cautious maneuvering and safe driving practices. By leveraging onboard sensors, \ourmethod{} identifies \textit{causal chains} behind poor driving maneuvers, enabling the modeling of safety levels for various road segments. We perform a thorough evaluation of \ourmethod{} over publicly available and collected datasets from multiple countries and observe $>80\%$ accuracy (in terms of F1-score) in correctly annotating the safety concerns. In addition, a thorough user study indicates the generalizability and usability of the proposed approach for its practical deployment considerations.