Navigation under uncertainty: Trajectory prediction and occlusion reasoning with switching dynamical systems
This addresses safety-critical scenarios in autonomous driving, but it appears incremental as it builds on existing probabilistic models.
The paper tackles the problem of predicting future trajectories of nearby objects under occlusion in autonomous driving by proposing a unified framework using switching dynamical systems, with initial experiments on the Waymo dataset.
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.