TrajDiffuse: A Conditional Diffusion Model for Environment-Aware Trajectory Prediction

Accurate prediction of human or vehicle trajectories with good diversity that captures their stochastic nature is an essential task for many applications. However, many trajectory prediction models produce unreasonable trajectory samples that focus on improving diversity or accuracy while neglecting...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2024-10
Main Authors: Qingze, Liu, Li, Danrui, Sohn, Samuel S, Yoon, Sejong, Kapadia, Mubbasir, Pavlovic, Vladimir
Format: Article
Language:English
Subjects:
Accuracy
Collision avoidance
Prediction models
Trajectory planning
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Accurate prediction of human or vehicle trajectories with good diversity that captures their stochastic nature is an essential task for many applications. However, many trajectory prediction models produce unreasonable trajectory samples that focus on improving diversity or accuracy while neglecting other key requirements, such as collision avoidance with the surrounding environment. In this work, we propose TrajDiffuse, a planning-based trajectory prediction method using a novel guided conditional diffusion model. We form the trajectory prediction problem as a denoising impaint task and design a map-based guidance term for the diffusion process. TrajDiffuse is able to generate trajectory predictions that match or exceed the accuracy and diversity of the SOTA, while adhering almost perfectly to environmental constraints. We demonstrate the utility of our model through experiments on the nuScenes and PFSD datasets and provide an extensive benchmark analysis against the SOTA methods.
ISSN:2331-8422