Knowledge-Informed Multi-Agent Trajectory Prediction at Signalized Intersections for Infrastructure-to-Everything

Multi-agent trajectory prediction at signalized intersections is pivotal for the safety of autonomous driving and the efficiency of intelligent transportation systems. However, conventional vehicle-centric approaches are limited by restricted perception ranges and occlusion. Vehicle-to-Everything (V2X) cooperation is widely regarded as an effective approach to alleviating these limitations. Furthermore, existing cooperative systems often suffer from complex coupling and selection bias, hindering universal and real-time service. To address these challenges, this paper introduces a novel Infrastructure-to-Everything (I2X) collaborative prediction scheme. This scheme decouples infrastructure capabilities from vehicle requests by independently forecasting and broadcasting trajectories for all detected vehicles. Building on this scheme, we propose I2XTraj, a dedicated infrastructure-based model that leverages three core mechanisms. First, a continuous signal-informed mechanism to adaptively encode real-time traffic light information. Second, a maneuver strategy awareness mechanism that integrates intersection geometric constraints to estimate maneuver distributions. Third, a spatial-temporal-mode attention network to refine multi-agent interactions. Extensive evaluations on two real-world datasets, V2X-Seq and SinD, demonstrate the superiority of our approach. In both single-infrastructure and collaborative scenarios, I2XTraj outperforms state-of-the-art methods by over 30% and 15%, respectively, confirming its strong generalizability and robustness in complex intersection environments.