# MATS: An Interpretable Trajectory Forecasting Representation for Planning and Control

arXiv: https://arxiv.org/abs/2009.07517 · HTML: https://mlsanity.com/paper/2009.07517/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2020.

## Built on by (most cited first, 30 of 30)

- [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022)
- [THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling](https://mlsanity.com/paper/2110.06607/) (2021)
- [Differentiable Integrated Motion Prediction and Planning with Learnable Cost Function for Autonomous Driving](https://mlsanity.com/paper/2207.10422/) (2022)
- [ScePT: Scene-consistent, Policy-based Trajectory Predictions for Planning](https://mlsanity.com/paper/2206.13387/) (2022)
- [Stepwise Goal-Driven Networks for Trajectory Prediction](https://mlsanity.com/paper/2103.14107/) (2021)
- [DTPP: Differentiable Joint Conditional Prediction and Cost Evaluation for Tree Policy Planning in Autonomous Driving](https://mlsanity.com/paper/2310.05885/) (2023)
- [Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-tuning](https://mlsanity.com/paper/2410.05582/) (2024)
- [DiffStack: A Differentiable and Modular Control Stack for Autonomous Vehicles](https://mlsanity.com/paper/2212.06437/) (2022)
- [Dynamic-Resolution Model Learning for Object Pile Manipulation](https://mlsanity.com/paper/2306.16700/) (2023)
- [Injecting Planning-Awareness into Prediction and Detection Evaluation](https://mlsanity.com/paper/2110.03270/) (2021)
- [Heterogeneous-Agent Trajectory Forecasting Incorporating Class Uncertainty](https://mlsanity.com/paper/2104.12446/) (2021)
- [Scene Informer: Anchor-based Occlusion Inference and Trajectory Prediction in Partially Observable Environments](https://mlsanity.com/paper/2309.13893/) (2023)
- [Interactive Joint Planning for Autonomous Vehicles](https://mlsanity.com/paper/2310.18301/) (2023)
- [RAP: Risk-Aware Prediction for Robust Planning](https://mlsanity.com/paper/2210.01368/) (2022)
- [Rethinking Trajectory Forecasting Evaluation](https://mlsanity.com/paper/2107.10297/) (2021)
- [Task-Relevant Failure Detection for Trajectory Predictors in Autonomous Vehicles](https://mlsanity.com/paper/2207.12380/) (2022)
- [SICNav-Diffusion: Safe and Interactive Crowd Navigation with Diffusion Trajectory Predictions](https://mlsanity.com/paper/2503.08858/) (2025)
- [Learning Interaction-aware Guidance Policies for Motion Planning in Dense Traffic Scenarios](https://mlsanity.com/paper/2107.04538/) (2021)
- [DRL-Based Trajectory Tracking for Motion-Related Modules in Autonomous Driving](https://mlsanity.com/paper/2308.15991/) (2023)
- [Hypergraph-Transformer (HGT) for Interactive Event Prediction in Laparoscopic and Robotic Surgery](https://mlsanity.com/paper/2402.01974/) (2024)
- [Trends in Motion Prediction Toward Deployable and Generalizable Autonomy: A Revisit and Perspectives](https://mlsanity.com/paper/2505.09074/) (2025)
- [LOPR: Latent Occupancy PRediction using Generative Models](https://mlsanity.com/paper/2210.01249/) (2022)
- [CARFF: Conditional Auto-encoded Radiance Field for 3D Scene Forecasting](https://mlsanity.com/paper/2401.18075/) (2024)
- [Prediction Horizon Requirements for Automated Driving: Optimizing Safety, Comfort, and Efficiency](https://mlsanity.com/paper/2402.03893/) (2024)
- [P4P: Conflict-Aware Motion Prediction for Planning in Autonomous Driving](https://mlsanity.com/paper/2211.01634/) (2022)
- [Interpretable Deep Tracking](https://mlsanity.com/paper/2210.01266/) (2022)
- [CLiNet: Joint Detection of Road Network Centerlines in 2D and 3D](https://mlsanity.com/paper/2302.02259/) (2023)
- [Occlusion-Aware 2D and 3D Centerline Detection for Urban Driving via Automatic Label Generation](https://mlsanity.com/paper/2311.02044/) (2023)
- [Scalable Multi-modal Model Predictive Control via Duality-based Interaction Predictions](https://mlsanity.com/paper/2402.01116/) (2024)
- [TRACER: Training-Free Closed-Loop Structured Inference for Traffic Accident Reconstruction](https://mlsanity.com/paper/2606.25002/) (2026)

## Abstract (arXiv)

Reasoning about human motion is a core component of modern human-robot interactive systems. In particular, one of the main uses of behavior prediction in autonomous systems is to inform robot motion planning and control. However, a majority of planning and control algorithms reason about system dynamics rather than the predicted agent tracklets (i.e., ordered sets of waypoints) that are commonly output by trajectory forecasting methods, which can hinder their integration. Towards this end, we propose Mixtures of Affine Time-varying Systems (MATS) as an output representation for trajectory forecasting that is more amenable to downstream planning and control use. Our approach leverages successful ideas from probabilistic trajectory forecasting works to learn dynamical system representations that are well-studied in the planning and control literature. We integrate our predictions with a proposed multimodal planning methodology and demonstrate significant computational efficiency improvements on a large-scale autonomous driving dataset.