Using V2X-Information for Trajectory Prediction at Urban Intersections

Crossing an urban intersection is one of the major challenges in automated/autonomous driving. This is due to a manifold of possible interactions with other traffic participants. In this paper, we propose a trajectory prediction service based on historical V2X-information gathered from Cooperative Awareness Messages (CAMs)/Basic Safety Messages (BSMs). The service allows connected vehicles to more easily navigate the intersection by identifying possibly critical encounters, especially with traffic participants which are not covered by the vehicle’s sensors. In comparison with approaches relying on video or other sensor data sources, this has the advantage that Road-Side Units (RSUs), which are used for Vehicle-to-Everything (V2X) communication, are more and more available at public intersections, e.g., due to equipment rollouts all over Europe in projects like C-Roads. The prediction service introduced in this paper is a first step in ongoing research and will act as a baseline for f urther projects, where additional sensors and also more involved prediction algorithms, e.g., based on neural networks, will be considered.