V2N-Based Comprehensive Safety Framework by Prediction of VRU Movement on Community Roads with Management of Route Branching at Intersections
Kota Watanabe, Takuma Ito
TL;DR
This paper introduces a new safety framework using vehicle-to-network technology to predict vulnerable road user movements and improve safety at community road intersections.
Contribution
The novel contribution is a V2N-based safety framework that integrates sparse observations and manages route branching at intersections for VRU movement prediction.
Findings
The framework maintains conservative estimation under sparse observations.
Prediction accuracy improves with additional observation data from surrounding vehicles.
Simulation results confirm feasibility for cooperative collision avoidance on real community roads.
Abstract
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.…
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Taxonomy
TopicsTraffic control and management · Traffic and Road Safety · Autonomous Vehicle Technology and Safety