# Resolving uncertainty on the fly: Modeling adaptive driving behavior as active inference

arXiv: https://arxiv.org/abs/2311.06417 · HTML: https://mlsanity.com/paper/2311.06417/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 82 references that are in the map.
- First posted on arXiv 10 Nov 2023.

## Builds on (most cited first, 15 of 15)

- [MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction](https://mlsanity.com/paper/1910.05449/) (2019)
- [TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors](https://mlsanity.com/paper/2101.06557/) (2021)
- [Deep Variational Reinforcement Learning for POMDPs](https://mlsanity.com/paper/1806.02426/) (2018)
- [Active inference on discrete state-spaces: a synthesis](https://mlsanity.com/paper/2001.07203/) (2020)
- [Deep active inference agents using Monte-Carlo methods](https://mlsanity.com/paper/2006.04176/) (2020)
- [Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation](https://mlsanity.com/paper/2205.03195/) (2022)
- [Active Inference in Robotics and Artificial Agents: Survey and Challenges](https://mlsanity.com/paper/2112.01871/) (2021)
- [Scaling active inference](https://mlsanity.com/paper/1911.10601/) (2019)
- [The Free Energy Principle for Perception and Action: A Deep Learning Perspective](https://mlsanity.com/paper/2207.06415/) (2022)
- [Designing Ecosystems of Intelligence from First Principles](https://mlsanity.com/paper/2212.01354/) (2022)
- [Action-Based Representation Learning for Autonomous Driving](https://mlsanity.com/paper/2008.09417/) (2020)
- [Designing explainable artificial intelligence with active inference: A framework for transparent introspection and decision-making](https://mlsanity.com/paper/2306.04025/) (2023)
- [Microscopic Calibration and Validation of Car-Following Models -- A Systematic Approach](https://mlsanity.com/paper/1403.4990/) (2014)
- [Measuring Surprise in the Wild](https://mlsanity.com/paper/2305.07733/) (2023)
- [Learning An Active Inference Model of Driver Perception and Control: Application to Vehicle Car-Following](https://mlsanity.com/paper/2303.15201/) (2023)

## Built on by (most cited first, 6 of 6)

- [Value of Information and Reward Specification in Active Inference and POMDPs](https://mlsanity.com/paper/2408.06542/) (2024)
- [Navigation under uncertainty: Trajectory prediction and occlusion reasoning with switching dynamical systems](https://mlsanity.com/paper/2410.10653/) (2024)
- [Active Inference and Human--Computer Interaction](https://mlsanity.com/paper/2412.14741/) (2024)
- [Resolving space-sharing conflicts in road user interactions through uncertainty reduction: An active inference-based computational model](https://mlsanity.com/paper/2604.19838/) (2026)
- [A Hardware-oriented Approach for Efficient Bayesian Inference Computation and Deployment](https://mlsanity.com/paper/2607.17855/) (2026)
- [Emotion in an active inference model of human driving](https://mlsanity.com/paper/2608.07480/) (2026)

## Abstract (arXiv)

Understanding adaptive human driving behavior, in particular how drivers manage uncertainty, is of key importance for developing simulated human driver models that can be used in the evaluation and development of autonomous vehicles. However, existing traffic psychology models of adaptive driving behavior either lack computational rigor or only address specific scenarios and/or behavioral phenomena. While models developed in the fields of machine learning and robotics can effectively learn adaptive driving behavior from data, due to their black box nature, they offer little or no explanation of the mechanisms underlying the adaptive behavior. Thus, a generalizable, interpretable, computational model of adaptive human driving behavior is still lacking. This paper proposes such a model based on active inference, a behavioral modeling framework originating in computational neuroscience. The model offers a principled solution to how humans trade progress against caution through policy selection based on the single mandate to minimize expected free energy. This casts goal-seeking and information-seeking (uncertainty-resolving) behavior under a single objective function, allowing the model to seamlessly resolve uncertainty as a means to obtain its goals. We apply the model in two apparently disparate driving scenarios that require managing uncertainty, (1) driving past an occluding object and (2) visual time sharing between driving and a secondary task, and show how human-like adaptive driving behavior emerges from the single principle of expected free energy minimization.