TrajDiffuse: A Conditional Diffusion Model for Environment-Aware Trajectory Prediction
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915141873827840 |
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| author | Qingze Liu Li, Danrui Sohn, Samuel S. Yoon, Sejong Kapadia, Mubbasir Pavlovic, Vladimir |
| author_facet | Qingze Liu Li, Danrui Sohn, Samuel S. Yoon, Sejong Kapadia, Mubbasir Pavlovic, Vladimir |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_10804 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TrajDiffuse: A Conditional Diffusion Model for Environment-Aware Trajectory Prediction Qingze Liu Li, Danrui Sohn, Samuel S. Yoon, Sejong Kapadia, Mubbasir Pavlovic, Vladimir Computer Vision and Pattern Recognition Machine Learning 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. |
| title | TrajDiffuse: A Conditional Diffusion Model for Environment-Aware Trajectory Prediction |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2410.10804 |