GroupModes: Learning to Couple Goals and Interactions for Human Trajectory Prediction

Accurate human trajectory prediction is essential for autonomous driving, mobile robotics, and crowd monitoring. Yet, existing models can achieve low individual trajectory errors without necessarily generating coherent group-level futures that account for interactions among agents. To address this, we propose GroupModes, a trajectory prediction architecture that conditions agent-specific goals and evolving social interactions on a shared group-level mode. Position-aware encoding captures observed motion and spatial context. A structured mode bank generates observation-conditioned modes, augmented with bounded stochastic residuals to represent within-mode variation. Each mode specifies individual goals and modulates time-varying interaction logits through a low-rank parameterization. A goal-conditioned synchronous decoder jointly generates future trajectories for the target and its neighboring agents. Experimental results demonstrate that GroupModes achieves competitive prediction accuracy and that aligning goal and interaction realization under a shared mode improves target and joint forecasting. Further analyses indicate that the learned group modes organize individual trajectory candidates into coordinated joint hypotheses, providing benefits beyond individual candidate coverage. Our code is available at https://anonymous.4open.science/r/groupmodes-review-0F6B.