Détail du document
Identifiant
oai:arXiv.org:2410.10804
Sujet
Computer Science - Computer Vision... Computer Science - Machine Learnin...Auteur
Qingze Liu Li, Danrui Sohn, Samuel S. Yoon, Sejong Kapadia, Mubbasir Pavlovic, VladimirCatégorie
Computer Science
Année
2024
Date de référencement
12/02/2025
Mots clés
model computer prediction trajectoryRésumé
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.
;Comment: Accepted to be published as inpreceedings of the 2024 International Conference on Pattern Recognition (ICPR)
Qingze,Liu,Li, Danrui,Sohn, Samuel S.,Yoon, Sejong,Kapadia, Mubbasir,Pavlovic, Vladimir, 2024, TrajDiffuse: A Conditional Diffusion Model for Environment-Aware Trajectory Prediction
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