# The Value of Inferring the Internal State of Traffic Participants for Autonomous Freeway Driving

arXiv: https://arxiv.org/abs/1702.00858 · HTML: https://mlsanity.com/paper/1702.00858/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2017.

## Built on by (most cited first, 15 of 15)

- [Partially Observable Markov Decision Processes in Robotics: A Survey](https://mlsanity.com/paper/2209.10342/) (2022)
- [Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions](https://mlsanity.com/paper/2112.11561/) (2021)
- [Active Uncertainty Reduction for Safe and Efficient Interaction Planning: A Shielding-Aware Dual Control Approach](https://mlsanity.com/paper/2302.00171/) (2023)
- [Learning to Navigate Intersections with Unsupervised Driver Trait Inference](https://mlsanity.com/paper/2109.06783/) (2021)
- [Compositional Learning-based Planning for Vision POMDPs](https://mlsanity.com/paper/2112.09456/) (2021)
- [A Hybrid Rule-Based and Data-Driven Approach to Driver Modeling through Particle Filtering](https://mlsanity.com/paper/2108.12820/) (2021)
- [Intention-Aware Navigation in Crowds with Extended-Space POMDP Planning](https://mlsanity.com/paper/2206.10028/) (2022)
- [Dynamically Switching Human Prediction Models for Efficient Planning](https://mlsanity.com/paper/2103.07815/) (2021)
- [A Bayesian Approach to Identifying Representational Errors](https://mlsanity.com/paper/2103.15171/) (2021)
- [Efficient Sampling in POMDPs with Lipschitz Bandits for Motion Planning in Continuous Spaces](https://mlsanity.com/paper/2106.04206/) (2021)
- [Filter-Aware Model-Predictive Control](https://mlsanity.com/paper/2304.10246/) (2023)
- [Rao-Blackwellized POMDP Planning](https://mlsanity.com/paper/2409.16392/) (2024)
- [Discrete Contrastive Learning for Diffusion Policies in Autonomous Driving](https://mlsanity.com/paper/2503.05229/) (2025)
- [TreeIRL: Safe Urban Driving with Tree Search and Inverse Reinforcement Learning](https://mlsanity.com/paper/2509.13579/) (2025)
- [Scalable Rao-Blackwellized Online Planning for High-Dimensional POMDPs](https://mlsanity.com/paper/2609.01351/) (2026)

## Abstract (arXiv)

Safe interaction with human drivers is one of the primary challenges for autonomous vehicles. In order to plan driving maneuvers effectively, the vehicle's control system must infer and predict how humans will behave based on their latent internal state (e.g., intentions and aggressiveness). This research uses a simple model for human behavior with unknown parameters that make up the internal states of the traffic participants and presents a method for quantifying the value of estimating these states and planning with their uncertainty explicitly modeled. An upper performance bound is established by an omniscient Monte Carlo Tree Search (MCTS) planner that has perfect knowledge of the internal states. A baseline lower bound is established by planning with MCTS assuming that all drivers have the same internal state. MCTS variants are then used to solve a partially observable Markov decision process (POMDP) that models the internal state uncertainty to determine whether inferring the internal state offers an advantage over the baseline. Applying this method to a freeway lane changing scenario reveals that there is a significant performance gap between the upper bound and baseline. POMDP planning techniques come close to closing this gap, especially when important hidden model parameters are correlated with measurable parameters.