Diffusion models for multi-agent trajectory prediction are limited by iterative denoising, which causes inference latency that hinders their use in time-critical settings like autonomous driving. Fast-sampling variants using DDIM and informed initial noise distributions partially alleviate this issue, but they either fail to achieve true single-step generation or are constrained by the chosen noise distribution. Consistency Models (CMs) offer high-quality one-step generation by mapping noise directly to data, but are difficult to train from scratch . We propose ECTraj, an enhanced CM pipeline with improved training and conditional generation for trajectory prediction. Our framework extends the student-teacher consistency training scheme: the student produces standard outputs, while the teacher explicitly fuses its predictions with parts of the ground truth to give stronger supervision. We also exploit CMs' direct denoising for top-K multi-shot generation during training. Combining conditional generation with this enhanced consistency objective yields faster inference and improved prediction accuracy, establishing competitive new benchmarks on the large-scale Argoverse 2 dataset.
@article{arxiv.2605.08572,
title = {Enhancing Consistency Models for Multi-Agent Trajectory Prediction},
author = {Alen Mrdovic and Qingze and Liu and Danrui Li and Mathew Schwartz and Kaidong Hu and Sejong Yoon and Mubbasir Kapadia and Vladimir Pavlovic},
journal= {arXiv preprint arXiv:2605.08572},
year = {2026}
}