Abnormal Behavior Detection Based on D–S Evidence Theory for Air–Ground-Integrated Vehicular Networks

The advancement of intelligent connected vehicles and aerial computing has garnered extensive attention from scholars worldwide. In particular, high-altitude platforms (HAPs) and autonomous aerial vehicles (AAVs) have emerged as effective tools to extend vehicular network coverage and enhance real-time monitoring capabilities. This study, grounded in the data from the Yizhuang Intelligent Connected Autonomous Driving Demonstration Zone, introduces an air-ground integrated vehicular network model for abnormal driving behavior detection using D-S evidence theory. The model integrates aerial computing and vehicular networks in a cohesive manner, scrutinizes the data associated with routine driving behaviors, and integrates the outcomes of various analyses through evidence theory at the decision-making level, culminating in the estimation of the probability of abnormal driving behaviors. Simulation experiments results demonstrate that this algorithm not only enhances the precision of abnormal behavior identification to a remarkable 97.1% but also significantly accelerates the detection process and improves the robustness.