ECTraj: Enhanced Consistency Training for Multi-Agent Trajectory Prediction

Diffusion models for multi-agent trajectory prediction arelimited by iterative denoising, which causes inference latency that hinderstheir use in time-critical settings like autonomous driving. Fast-samplingvariants using DDIM and informed initial noise distribution partiallyalleviate this issue, but they either fail to achieve true single-step gen-eration or are constrained by the chosen noise distribution. ConsistencyModels (CMs) offer high-quality one-step generation by mapping noisedirectly to data, but are difficult to train from scratch. We propose EC-Traj , an enhanced CM pipeline with improved training and conditionalgeneration for trajectory prediction. Our framework extends the student-teacher consistency training scheme: the student produces standard out-puts, while the teacher explicitly fuses its predictions with parts of theground truth to give stronger supervision. We also exploit CMs’ directdenoising for top-K multi-shot generation during training. Combiningconditional generation with this enhanced consistency objective yieldsfaster inference and improved prediction accuracy, establishing compet-itive new benchmarks on the large-scale Argoverse 2 dataset.3