# Navigation under uncertainty: Trajectory prediction and occlusion reasoning with switching dynamical systems

arXiv: https://arxiv.org/abs/2410.10653 · HTML: https://mlsanity.com/paper/2410.10653/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 28 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Builds on (most cited first, 18 of 18)

- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Object-Centric Learning with Slot Attention](https://mlsanity.com/paper/2006.15055/) (2020)
- [Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset](https://mlsanity.com/paper/2104.10133/) (2021)
- [MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction](https://mlsanity.com/paper/1910.05449/) (2019)
- [Learning Lane Graph Representations for Motion Forecasting](https://mlsanity.com/paper/2007.13732/) (2020)
- [Wayformer: Motion Forecasting via Simple & Efficient Attention Networks](https://mlsanity.com/paper/2207.05844/) (2022)
- [MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction](https://mlsanity.com/paper/2111.14973/) (2021)
- [PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings](https://mlsanity.com/paper/1905.01296/) (2019)
- [Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research](https://mlsanity.com/paper/2310.08710/) (2023)
- [MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Querying](https://mlsanity.com/paper/2306.17770/) (2023)
- [Evaluating and Calibrating Uncertainty Prediction in Regression Tasks](https://mlsanity.com/paper/1905.11659/) (2019)
- [Recurrent switching linear dynamical systems](https://mlsanity.com/paper/1610.08466/) (2016)
- [Scene Informer: Anchor-based Occlusion Inference and Trajectory Prediction in Partially Observable Environments](https://mlsanity.com/paper/2309.13893/) (2023)
- [Graph Switching Dynamical Systems](https://mlsanity.com/paper/2306.00370/) (2023)
- [Stay on Track: A Frenet Wrapper to Overcome Off-road Trajectories in Vehicle Motion Prediction](https://mlsanity.com/paper/2306.00605/) (2023)
- [Resolving uncertainty on the fly: Modeling adaptive driving behavior as active inference](https://mlsanity.com/paper/2311.06417/) (2023)
- [Probabilistic Semantic Data Association for Collaborative Human-Robot Sensing](https://mlsanity.com/paper/2110.09621/) (2021)
- [Gradient-free variational learning with conditional mixture networks](https://mlsanity.com/paper/2408.16429/) (2024)

## Built on by (most cited first, 4 of 4)

- [The Recurrent Sticky Hierarchical Dirichlet Process Hidden Markov Model](https://mlsanity.com/paper/2411.04278/) (2024)
- [Bayesian Inference in Recurrent Explicit Duration Switching Linear Dynamical Systems](https://mlsanity.com/paper/2411.04280/) (2024)
- [PhysVarMix: Physics-Informed Variational Mixture Model for Multi-Modal Trajectory Prediction](https://mlsanity.com/paper/2507.19701/) (2025)
- [Tracking the Unseen: An Occlusion-Robust Framework for Target Tracking Under Full and Long-Term Occlusion](https://mlsanity.com/paper/2609.17427/) (2026)

## Abstract (arXiv)

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.