MFIG-Net: Map-Free Multiagent Trajectory Prediction for Signalized Intersection Scenarios

Signalized intersections are among the most crash-prone elements of urban traffic networks. Their complex layouts and heterogeneous road users pose substantial challenges for accurate trajectory prediction. Although high-definition (HD) maps are widely used to boost prediction performance, their high construction and maintenance costs and limited update frequency hinder practical deployment. Meanwhile, existing map-free models still struggle to reach the accuracy required in such complex settings. To address these issues, we propose MFIG-Net (Map-Free Intersection Graph Network), a map-free, multi-agent trajectory prediction framework tailored to signalized intersections. MFIG-Net comprises three components. First, a Multi-Source Feature Encoder (MSFE) builds per-agent node embeddings by jointly encoding motion histories and static attributes together with traffic-signal phase semantics; the latter are represented using remaining-time sinusoidal positional encodings passed through an MLP to capture phase transitions. Second, the interaction layer combines a Conflict-Aware Graph Attention (CAGA) module—modeling dense spatial interactions via physically grounded, directed edge features and enhanced multi-head attention—with a Mamba-based temporal block that captures long-range dependencies through linear-time selective state-space dynamics. Third, a Laplace Mixture Density Network (MDN) decoder producesKmultimodal future trajectories per agent with per-timestep scale parameters and mode probabilities, yielding accurate and calibrated uncertainty. Experiments on the SinD dataset from two-phase signalized intersections in Tianjin and Xi’an validate the effectiveness of the proposed model. Additional evaluations on the Argoverse dataset further demonstrate its applicability across diverse urban scenarios, where it outperforms most existing map-free approaches.