Scalable Intention Sharing for ETSI VAMs

2026-06-29

AI Summary

This work addresses the scalability limitations of existing ETSI-compliant vehicle intent sharing methods in dense V2X environments, where reliance on trajectory vector transmission leads to rapidly increasing communication and computational overhead as the number of neighbors and prediction horizon grow. To overcome this, the authors propose encoding intent using uncertainty ellipses instead of conventional trajectory vectors, integrating geometric compression with an extended Kalman filter to generate short-term predictions. This approach maintains a constant message size while reducing computational complexity by an order of magnitude. Experimental evaluation using real-world cyclist trajectories demonstrates that the method significantly enhances system scalability under ETSI compliance, enabling reliable, low-overhead multi-second intent sharing.

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.

Problem

• scalability
• intention sharing
• V2X
• ETSI VAMs
• maneuver coordination

Innovation

• intention sharing
• uncertainty ellipses
• ETSI VAM
• scalable V2X communication
• maneuver prediction