CS Events

Qualifying Exam Enhancing Consistency Models for Multi-Agent Trajectory Prediction
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Speaker:

Bio

Location : CBIM 22

Committee: 

Professor Vladimir Pavlovic

Professor Chengzhi Mao

Professor Hao Wang

Professor Minesh Patel

Event Type: Qualifying Exam

Abstract: Diffusion models have achieved impressive results in many prediction tasks. However, their performance is limited by iterative denoising, which leads to inference that is prohibitively slow in time-critical settings, such as autonomous driving. Many alternatives which alleviate this issue have been developed. Among these alternatives, consistency models (CMs) stand out for their capability to generate high-quality samples in a single step. Despite this, CMs are often difficult to train from scratch. We propose ECTraj, an enhanced CM-based trajectory prediction pipeline with an improved training objective. In particular, ECTraj extends the standard student-teacher consistency training scheme: the student produces standard outputs (future trajectories), while the teacher explicitly fuses its predictions with parts of the ground truth future trajectories to achieve stronger supervision. Additionally, we exploit the direct denoising capability of CMs for top-K multi-shot generation during training. Combining conditional generation with this enhanced consistency objective results in faster inference and improved prediction accuracy on the large-scale Argoverse 2 dataset.

Organization: 

Contact  Professor Vladimir Pavlovic

Zoom Link: https://us06web.zoom.us/j/6501959343?pwd=alBIZEtRaU00d1JqVjY2azRvZlNzZz09