Training and Assessing Hazard Perception in High-Risk Occupations: Toward an AI-Driven Adaptive and Immersive Simulation

Abstract

Work-related accidents in high-risk occupations remain a recurring and costly challenge. Emergency vehicle driving (EVD) is ranked among the most hazardous occupations, with a large proportion of accidents attributed to poor risk perception and inadequate situation awareness (SA). The capacity to identify hazards is a multidimensional cognitive process that may benefit from training and cannot be assumed to develop through experience alone. Yet most occupational safety programs focus on technical skills. The objective of this research is to present the conceptual framework behind a simulation-based cognitive training approach and to discuss the ongoing development and validation of a proof-of-concept: an AI-powered adaptive simulation for hazard perception (HP) training in which eye-tracking is the enabling technology for real-time performance assessment and feedback. The conceptual framework is grounded in the NSEEV model (Noticing, Salience, Effort, Expectancy, Value) and the concept of SA. In the case of EVD, the platform presents drivers with short video clips recorded from the perspective of an emergency vehicle, with hazardous events time-stamped by experienced field instructors. Eye-tracking provides an objective measure of attentional allocation and can reveal which hazards drivers fail to detect and whether specific scan patterns are associated with anticipated hazard detection. These oculometric and behavioural data feed a Bayesian knowledge-tracing algorithm that adjusts training content. Preliminary results from early versions of the platform provide support for the feasibility of the approach and for its potential as a low-cost, portable complement to high-fidelity simulation training.

Keywords: Hazard Perception, Situation Awareness, Emergency Vehicle Driving, Eye-tracking, Adaptive Training, Simulation

DOI: 10.54941/ahfe1007921