Scalable Intention Sharing for ETSI VAMs
2026-06-29 • Networking and Internet Architecture
Networking and Internet Architecture
AI summaryⓘ
The authors looked at how vehicles and cyclists can share their intended moves in very busy traffic using short-term predictions. They compared different ways to send this information and found that using 'uncertainty ellipses' is much faster for computers and keeps message sizes small. They used a method called the Extended Kalman Filter to predict short moves and tested it with real cyclist data. Their approach worked well for predicting what will happen next while being practical for crowded traffic communication.
V2X communicationmaneuver coordinationExtended Kalman Filteruncertainty ellipsestrajectory predictionETSI standardsGNSS trajectoriesintention sharingcomputational complexity
Authors
Felipe E. Valle, Oscar Amador, Johan Thunberg, Elena Haller, Alexey Vinel
Abstract
Efficient maneuver coordination in dense V2X environments requires accurate short-term prediction while maintaining low communication and computational overhead. Current European Telecommunications Standards Institute (ETSI)-compliant approaches rely on intention detection and trajectory vector transmission, which scale poorly with neighborhood size and prediction horizon. This paper revisits maneuver coordination from an intention sharing perspective and investigates geometric encodings that enable scalable communication. First, we analyze three ETSI-compliant encodings, trajectory vectors, N-polygons, and uncertainty ellipses, through complexity analysis and simulation-based CPU measurements. Results show that uncertainty ellipses reduce computational complexity by an order of magnitude compared with trajectory vectors while maintaining a constant message size. Building on this, an Extended Kalman Filter is used to generate short-horizon predictions, which are encoded as uncertainty ellipses to represent the intended maneuver. The prediction pipeline is evaluated using real-world GNSS trajectories collected from cyclist maneuvers on a controlled test track, demonstrating that the approach achieves reliable multisecond prediction horizons while maintaining scalability for dense V2X environments.