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

TL;DR

This paper introduces a unified computational model based on active inference to simulate and explain human collision avoidance behaviors across different scenarios, aligning well with empirical data.

Contribution

The paper presents a novel active inference-based model that unifies various aspects of human collision avoidance behavior, addressing gaps in existing fragmented models.

Findings

Model reproduces empirical response times and maneuver choices.

Aligns with meta-analyses and simulator data.

Demonstrates active inference as a comprehensive framework.

Abstract

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.