# Active inference as a unified model of collision avoidance behavior in human drivers

arXiv: https://arxiv.org/abs/2506.02215 · HTML: https://mlsanity.com/paper/2506.02215/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2025.

## Built on by (most cited first, 3 of 3)

- [Resolving space-sharing conflicts in road user interactions through uncertainty reduction: An active inference-based computational model](https://mlsanity.com/paper/2604.19838/) (2026)
- [General-purpose LLMs as Models of Human Driver Behavior: The Case of Simplified Merging](https://mlsanity.com/paper/2604.09609/) (2026)
- [Emotion in an active inference model of human driving](https://mlsanity.com/paper/2608.07480/) (2026)

## Abstract (arXiv)

Collision avoidance -- involving a rapid threat detection and quick execution of the appropriate evasive maneuver -- is a critical aspect of driving. However, existing models of human collision avoidance behavior are fragmented, focusing on specific scenarios or only describing certain aspects of the avoidance behavior, such as response times. This paper addresses these gaps by proposing a novel computational cognitive model of human collision avoidance behavior based on active inference. Active inference provides a unified approach to modeling human behavior: the minimization of free energy. Building on prior active inference work, our model incorporates established cognitive mechanisms such as evidence accumulation to simulate human responses in two distinct collision avoidance scenarios: front-to-rear lead vehicle braking and lateral incursion by an oncoming vehicle. We demonstrate that our model explains a wide range of previous empirical findings on human collision avoidance behavior. Specifically, the model closely reproduces both aggregate results from meta-analyses previously reported in the literature and detailed, scenario-specific effects observed in a recent driving simulator study, including response timing, maneuver selection, and execution. Our results highlight the potential of active inference as a unified framework for understanding and modeling human behavior in complex real-life driving tasks.