Enhancing Consistency Models for Multi-Agent Trajectory Prediction

• URL: http://arxiv.org/abs/2605.08572v1
• Date: Sat, 09 May 2026 00:23:08 GMT
• Title: Enhancing Consistency Models for Multi-Agent Trajectory Prediction
• Abstract summary: ECTraj is an enhanced CM pipeline with improved training and conditional generation for trajectory prediction.<n>We exploit CMs' direct denoising for top-K multi-shot generation during training.
• Score: 9.313920952984104
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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