IPLG: Intent-Propagated Lane Graph for Multimodal Trajectory Prediction at Urban Intersections

Vehicle trajectory prediction at urban intersections is challenging due to strong maneuver ambiguity, dense agent interactions, and complex lane branching structures. Existing methods frequently suffer from mode collapse or fail to provide sufficient coverage across all competing exit branches, limiting their reliability in safety-critical scenarios. This paper proposes IPLG, an Intent-Propagated Lane Graph framework for multimodal vehicle trajectory prediction at UAV-observed urban intersections. The proposed framework addresses two core challenges: the semantic ambiguity of lane nodes at divergence points under competing agent intentions, and the tendency of standard decoders to generate trajectories that deviate from valid lane regions. To this end, IPLG introduces a closed-loop interaction module that propagates agent intent cues into the lane graph and along its topology, a topology-aware diversified goal selection strategy that suppresses goal clustering across competing exit branches, and a residual decoder that enforces local lane consistency at each prediction step. Experiments on the XJROAD dataset show that IPLG achieves consistent performance across multiple evaluation metrics, with noticeable improvements in Miss Rate and Off-road Rate. Additional validation on the public nuPlan dataset demonstrates that the proposed model can generate plausible trajectories across different intersection geometries, further supporting its generalizability across diverse road structures and driving scenarios.