# Learning to Navigate Intersections with Unsupervised Driver Trait Inference

arXiv: https://arxiv.org/abs/2109.06783 · HTML: https://mlsanity.com/paper/2109.06783/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 39 references that are in the map.
- First posted on arXiv 14 Sep 2021.

## Builds on (most cited first, 19 of 19)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Generating Sentences from a Continuous Space](https://mlsanity.com/paper/1511.06349/) (2015)
- [The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems](https://mlsanity.com/paper/1810.05642/) (2018)
- [INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps](https://mlsanity.com/paper/1910.03088/) (2019)
- [Social NCE: Contrastive Learning of Socially-aware Motion Representations](https://mlsanity.com/paper/2012.11717/) (2020)
- [Learning to Transduce with Unbounded Memory](https://mlsanity.com/paper/1506.02516/) (2015)
- [Multimodal Probabilistic Model-Based Planning for Human-Robot Interaction](https://mlsanity.com/paper/1710.09483/) (2017)
- [Multimodal Deep Generative Models for Trajectory Prediction: A Conditional Variational Autoencoder Approach](https://mlsanity.com/paper/2008.03880/) (2020)
- [Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement Learning](https://mlsanity.com/paper/2011.04820/) (2020)
- [Reinforcement Learning for Autonomous Driving with Latent State Inference and Spatial-Temporal Relationships](https://mlsanity.com/paper/2011.04251/) (2020)
- [Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning](https://mlsanity.com/paper/1705.01196/) (2017)
- [Towards Full Automated Drive in Urban Environments: A Demonstration in GoMentum Station, California](https://mlsanity.com/paper/1705.01187/) (2017)
- [Simultaneous Policy Learning and Latent State Inference for Imitating Driver Behavior](https://mlsanity.com/paper/1704.05566/) (2017)
- [The Value of Inferring the Internal State of Traffic Participants for Autonomous Freeway Driving](https://mlsanity.com/paper/1702.00858/) (2017)
- [Belief State Planning for Autonomously Navigating Urban Intersections](https://mlsanity.com/paper/1704.04322/) (2017)
- [Autoencoder Regularized Network For Driving Style Representation Learning](https://mlsanity.com/paper/1701.01272/) (2017)
- [A Taxonomy and Review of Algorithms for Modeling and Predicting Human Driver Behavior](https://mlsanity.com/paper/2006.08832/) (2020)

## Built on by (most cited first, 8 of 8)

- [Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation](https://mlsanity.com/paper/2311.16091/) (2023)
- [HEIGHT: Heterogeneous Interaction Graph Transformer for Robot Navigation in Crowded and Constrained Environments](https://mlsanity.com/paper/2411.12150/) (2024)
- [Robust Driving Policy Learning with Guided Meta Reinforcement Learning](https://mlsanity.com/paper/2307.10160/) (2023)
- [PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory Systems](https://mlsanity.com/paper/2308.00864/) (2023)
- [Cooperative Advisory Residual Policies for Congestion Mitigation](https://mlsanity.com/paper/2407.00553/) (2024)
- [An Expert Ensemble for Detecting Anomalous Scenes, Interactions, and Behaviors in Autonomous Driving](https://mlsanity.com/paper/2502.16389/) (2025)
- [Casper: Inferring Diverse Intents for Assistive Teleoperation with Vision Language Models](https://mlsanity.com/paper/2506.14727/) (2025)
- [Goal-conditioned Hierarchical Reinforcement Learning for Sample-efficient and Safe Autonomous Driving at Intersections](https://mlsanity.com/paper/2506.16336/) (2025)

## Abstract (arXiv)

Navigation through uncontrolled intersections is one of the key challenges for autonomous vehicles. Identifying the subtle differences in hidden traits of other drivers can bring significant benefits when navigating in such environments. We propose an unsupervised method for inferring driver traits such as driving styles from observed vehicle trajectories. We use a variational autoencoder with recurrent neural networks to learn a latent representation of traits without any ground truth trait labels. Then, we use this trait representation to learn a policy for an autonomous vehicle to navigate through a T-intersection with deep reinforcement learning. Our pipeline enables the autonomous vehicle to adjust its actions when dealing with drivers of different traits to ensure safety and efficiency. Our method demonstrates promising performance and outperforms state-of-the-art baselines in the T-intersection scenario.