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Related Experiment Video

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Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior

Published on: June 9, 2020

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

Julian F Schumann¹, Johan Engström², Leif Johnson³

¹Department of Cognitive Robotics, Delft University of Technology, Delft, Netherlands.

Nature Communications

|June 10, 2026

Summary

This study introduces a computational cognitive model for collision avoidance behavior in driving, using active inference to unify fragmented existing models. The model accurately simulates human responses across various scenarios, improving our understanding of driving safety.

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Published on: December 18, 2020

Area of Science:

Cognitive Science
Computational Neuroscience
Human Factors Engineering

Background:

Existing models of human collision avoidance behavior are fragmented, focusing on specific scenarios or aspects like response times.
A unified computational cognitive model is needed to comprehensively understand driving behavior during critical events.

Purpose of the Study:

To propose and validate a computational cognitive model of human collision avoidance behavior based on active inference.
To address gaps in existing models by providing a unified framework for diverse collision scenarios.

Main Methods:

Developed a computational cognitive model using active inference and evidence accumulation.
Simulated human responses in three distinct collision avoidance scenarios: lead vehicle braking, lateral incursion, and intersection failure to yield.
Validated the model against aggregate meta-analysis results and detailed driving simulator study data.

Main Results:

The active inference model successfully explains a wide range of empirical findings in human collision avoidance.
The model closely reproduces aggregate results from literature meta-analyses.
The model accurately predicts scenario-specific effects, including response timing, maneuver selection, and execution, from driving simulator studies.

Conclusions:

Active inference offers a generalizable framework for understanding and modeling human behavior in complex driving tasks.
The proposed model provides a unified approach to collision avoidance, integrating cognitive mechanisms.
This research advances the understanding of human factors in driving safety and evasive maneuvers.