# Simultaneous Policy Learning and Latent State Inference for Imitating Driver Behavior

arXiv: https://arxiv.org/abs/1704.05566 · HTML: https://mlsanity.com/paper/1704.05566/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2017.

## Built on by (most cited first, 16 of 16)

- [Explainability of deep vision-based autonomous driving systems: Review and challenges](https://mlsanity.com/paper/2101.05307/) (2021)
- [Modeling Human Driving Behavior through Generative Adversarial Imitation Learning](https://mlsanity.com/paper/2006.06412/) (2020)
- [Learning Multimodal Rewards from Rankings](https://mlsanity.com/paper/2109.12750/) (2021)
- [STEER: Flexible Robotic Manipulation via Dense Language Grounding](https://mlsanity.com/paper/2411.03409/) (2024)
- [Towards Socially Responsive Autonomous Vehicles: A Reinforcement Learning Framework with Driving Priors and Coordination Awareness](https://mlsanity.com/paper/2309.09726/) (2023)
- [Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation](https://mlsanity.com/paper/2311.16091/) (2023)
- [Learning to Navigate Intersections with Unsupervised Driver Trait Inference](https://mlsanity.com/paper/2109.06783/) (2021)
- [Contrastively Learning Visual Attention as Affordance Cues from Demonstrations for Robotic Grasping](https://mlsanity.com/paper/2104.00878/) (2021)
- [Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving](https://mlsanity.com/paper/2403.12176/) (2024)
- [PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory Systems](https://mlsanity.com/paper/2308.00864/) (2023)
- [Learning Temporal Strategic Relationships using Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1805.04969/) (2018)
- [Generalization Guarantees for Imitation Learning](https://mlsanity.com/paper/2008.01913/) (2020)
- [Learning Interpretable Policies in Hindsight-Observable POMDPs through Partially Supervised Reinforcement Learning](https://mlsanity.com/paper/2402.09290/) (2024)
- [Cooperative Advisory Residual Policies for Congestion Mitigation](https://mlsanity.com/paper/2407.00553/) (2024)
- [Learning Latent Traits for Simulated Cooperative Driving Tasks](https://mlsanity.com/paper/2207.09619/) (2022)
- [Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models](https://mlsanity.com/paper/2607.06328/) (2026)

## Abstract (arXiv)

In this work, we propose a method for learning driver models that account for variables that cannot be observed directly. When trained on a synthetic dataset, our models are able to learn encodings for vehicle trajectories that distinguish between four distinct classes of driver behavior. Such encodings are learned without any knowledge of the number of driver classes or any objective that directly requires the models to learn encodings for each class. We show that driving policies trained with knowledge of latent variables are more effective than baseline methods at imitating the driver behavior that they are trained to replicate. Furthermore, we demonstrate that the actions chosen by our policy are heavily influenced by the latent variable settings that are provided to them.