Navigation under uncertainty: Trajectory prediction and occlusion reasoning with switching dynamical systems
- URL: http://arxiv.org/abs/2410.10653v1
- Date: Mon, 14 Oct 2024 16:03:41 GMT
- Title: Navigation under uncertainty: Trajectory prediction and occlusion reasoning with switching dynamical systems
- Authors: Ran Wei, Joseph Lee, Shohei Wakayama, Alexander Tschantz, Conor Heins, Christopher Buckley, John Carenbauer, Hari Thiruvengada, Mahault Albarracin, Miguel de Prado, Petter Horling, Peter Winzell, Renjith Rajagopal,
- Abstract summary: We propose a conceptual framework unifying trajectory prediction and occlusion reasoning under the same class of structured probabilistic generative model.
We then present some initial experiments illustrating its capabilities using the open dataset.
- Score: 36.18758962312406
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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