Navigation under uncertainty: Trajectory prediction and occlusion reasoning with switching dynamical systems
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arXiv
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| Autores principales: | , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929541278072832 |
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| author | Wei, Ran Lee, Joseph Wakayama, Shohei Tschantz, Alexander Heins, Conor Buckley, Christopher Carenbauer, John Thiruvengada, Hari Albarracin, Mahault de Prado, Miguel Horling, Petter Winzell, Peter Rajagopal, Renjith |
| author_facet | Wei, Ran Lee, Joseph Wakayama, Shohei Tschantz, Alexander Heins, Conor Buckley, Christopher Carenbauer, John Thiruvengada, Hari Albarracin, Mahault de Prado, Miguel Horling, Petter Winzell, Peter Rajagopal, Renjith |
| contents | Predicting future trajectories of nearby objects, especially under occlusion, is a crucial task in autonomous driving and safe robot navigation. Prior works typically neglect to maintain uncertainty about occluded objects and only predict trajectories of observed objects using high-capacity models such as Transformers trained on large datasets. While these approaches are effective in standard scenarios, they can struggle to generalize to the long-tail, safety-critical scenarios. In this work, we explore a conceptual framework unifying trajectory prediction and occlusion reasoning under the same class of structured probabilistic generative model, namely, switching dynamical systems. We then present some initial experiments illustrating its capabilities using the Waymo open dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_10653 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Navigation under uncertainty: Trajectory prediction and occlusion reasoning with switching dynamical systems Wei, Ran Lee, Joseph Wakayama, Shohei Tschantz, Alexander Heins, Conor Buckley, Christopher Carenbauer, John Thiruvengada, Hari Albarracin, Mahault de Prado, Miguel Horling, Petter Winzell, Peter Rajagopal, Renjith Robotics Machine Learning Predicting future trajectories of nearby objects, especially under occlusion, is a crucial task in autonomous driving and safe robot navigation. Prior works typically neglect to maintain uncertainty about occluded objects and only predict trajectories of observed objects using high-capacity models such as Transformers trained on large datasets. While these approaches are effective in standard scenarios, they can struggle to generalize to the long-tail, safety-critical scenarios. In this work, we explore a conceptual framework unifying trajectory prediction and occlusion reasoning under the same class of structured probabilistic generative model, namely, switching dynamical systems. We then present some initial experiments illustrating its capabilities using the Waymo open dataset. |
| title | Navigation under uncertainty: Trajectory prediction and occlusion reasoning with switching dynamical systems |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2410.10653 |