V2N-Based Comprehensive Safety Framework by Prediction of VRU Movement on Community Roads with Management of Route Branching at Intersections

Traffic accidents involving Vulnerable Road Users (VRUs) frequently occur at unsignalized intersections on Japanese community roads. To prevent such accidents, collision avoidance systems need to predict VRUs' movements throughout the entire road network while explicitly handling uncertainty degraded by sparse observations and frequent route branching at intersections. Based on this motivation, this study proposes a Vehicle-to-Network (V2N)-based comprehensive safety framework for estimation of VRU movement and prediction of future intersection entry for community roads. The framework integrates estimation results provided from Roadside Edges and Vehicle Edges at a Central Server. In addition, road geometry from map information is incorporated as pseudo-observations into the estimation, and multiple route hypotheses are explicitly managed to represent route branching at intersections. For intersection-entry prediction, entry certainty is calculated by integrating a predicted distribution. For evaluation of the proposed framework, we conduct Monte Carlo simulations on simplified grid road networks. The results show that the proposed framework maintains conservative estimation under sparse observations and improves prediction when additional observation information from surrounding vehicles becomes available. Furthermore, a simulation-based case study using an actual community road-network geometry shows the feasibility of the proposed framework for cooperative collision avoidance on actual community roads.