# GRIT: Fast, Interpretable, and Verifiable Goal Recognition with Learned Decision Trees for Autonomous Driving

arXiv: https://arxiv.org/abs/2103.06113 · HTML: https://mlsanity.com/paper/2103.06113/

## Key facts

- Cited by 19 papers in the map (data through 13 Oct 2026).
- First posted on arXiv 10 Mar 2021.

## Built on by (most cited first, 19 of 19)

- [Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions](https://mlsanity.com/paper/2112.11561/) (2021)
- [TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction](https://mlsanity.com/paper/2303.04116/) (2023)
- [Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review](https://mlsanity.com/paper/2402.10086/) (2024)
- [Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2408.09675/) (2024)
- [Behavioral Intention Prediction in Driving Scenes: A Survey](https://mlsanity.com/paper/2211.00385/) (2022)
- [Explaining Autonomous Driving Actions with Visual Question Answering](https://mlsanity.com/paper/2307.10408/) (2023)
- [Causal Explanations for Sequential Decision-Making in Multi-Agent Systems](https://mlsanity.com/paper/2302.10809/) (2023)
- [Verifiable Goal Recognition for Autonomous Driving with Occlusions](https://mlsanity.com/paper/2206.14163/) (2022)
- [Disentangled Neural Relational Inference for Interpretable Motion Prediction](https://mlsanity.com/paper/2401.03599/) (2024)
- [Advancing Explainable Autonomous Vehicle Systems: A Comprehensive Review and Research Roadmap](https://mlsanity.com/paper/2404.00019/) (2024)
- [DiPA: Probabilistic Multi-Modal Interactive Prediction for Autonomous Driving](https://mlsanity.com/paper/2210.06106/) (2022)
- [Deep Reinforcement Learning for Multi-Agent Interaction](https://mlsanity.com/paper/2208.01769/) (2022)
- [Explainable Goal Recognition: A Framework Based on Weight of Evidence](https://mlsanity.com/paper/2303.05622/) (2023)
- [Interpretable Goal-Based model for Vehicle Trajectory Prediction in Interactive Scenarios](https://mlsanity.com/paper/2308.04312/) (2023)
- [DIDER: Discovering Interpretable Dynamically Evolving Relations](https://mlsanity.com/paper/2208.10592/) (2022)
- [Data-Driven Goal Recognition in Transhumeral Prostheses Using Process Mining Techniques](https://mlsanity.com/paper/2309.08106/) (2023)
- [Interpretable Long Term Waypoint-Based Trajectory Prediction Model](https://mlsanity.com/paper/2312.06219/) (2023)
- [An End-to-End Decision-Aware Multi-Scale Attention-Based Model for Explainable Autonomous Driving](https://mlsanity.com/paper/2605.00291/) (2026)
- [Preference-Conditioned Multi-Objective Reinforcement Learning for Tactical Decision Making in Heavy-Duty Vehicles](https://mlsanity.com/paper/2601.18783/) (2026)

## Abstract (arXiv)

It is important for autonomous vehicles to have the ability to infer the goals of other vehicles (goal recognition), in order to safely interact with other vehicles and predict their future trajectories. This is a difficult problem, especially in urban environments with interactions between many vehicles. Goal recognition methods must be fast to run in real time and make accurate inferences. As autonomous driving is safety-critical, it is important to have methods which are human interpretable and for which safety can be formally verified. Existing goal recognition methods for autonomous vehicles fail to satisfy all four objectives of being fast, accurate, interpretable and verifiable. We propose Goal Recognition with Interpretable Trees (GRIT), a goal recognition system which achieves these objectives. GRIT makes use of decision trees trained on vehicle trajectory data. We evaluate GRIT on two datasets, showing that GRIT achieved fast inference speed and comparable accuracy to two deep learning baselines, a planning-based goal recognition method, and an ablation of GRIT. We show that the learned trees are human interpretable and demonstrate how properties of GRIT can be formally verified using a satisfiability modulo theories (SMT) solver.