A spatiotemporal learning approach to safety-oriented individualized driving risk assessment in a vehicle-to-everything (V2X) environment

Abstract

Advances in real-time basic safety message (BSM) data from sensor-equipped vehicles have created new opportunities for driving risk assessments. This paper presents a machine learning approach using BSM data to provide fine-grained risk assessments, focusing on safety-critical events (SCEs) related to driving profiles, vehicle states, and road conditions. This approach formulates a bi-level risk indicator: one level measures the observable frequency of SCEs, while the other estimates their likelihood. The coarse level calculates risk scores by classifying driving profiles as normal or risky based on SCE frequency. The fine level refines these scores by comparing normal and risky profiles using key features from a feature learning model. This combined system accounts for recent driving behaviours and road/weather conditions within a vehicle-to-everything (V2X) environment, addressing high data dimensionality and imbalance. A comprehensive case study using 1 year of data from pilot V2X infrastructure in Tampa, Florida, demonstrates the efficacy of this approach, showing practical applications of the SCE-based risk indicator and combinatorial feature learning while also highlighting the real-world utility of the assessment method in providing a detailed and actionable view of driving risk based on V2X information.