Inferring Hidden Attentional States in Driving: A Bayesian Approach to Modeling Distraction and Secondary Task Engagement.

Abstract

ObjectiveTo develop and validate a computational framework that infers individualized attention strategies and latent distraction states to support personalized modeling of multitasking behavior and intervention.BackgroundDriver distraction from in-vehicle systems is a growing safety concern. However, the level of distraction is often latent and varies significantly across individuals. Existing models typically overlook these differences, limiting their effective use for personalized interventions.MethodWe introduce a Partially Observable Semi-Markov Decision Process (POSMDP) to model hidden attentional dynamics and attention allocation decisions. Using behavioral data, including glance behavior, velocity, and pupillometry, from a high-fidelity driving simulator with 18 participants, we estimate personalized reward functions that reflect each driver's subjective valuation of secondary task utility versus safety cost.ResultsThe method accurately infers distraction states and recovers participant-specific utility weights governing the trade-off between secondary task benefit and driving cost. Compared to a well-established 2-s glance rule, it improves detection of distraction events and reveals individual variability in attention strategies. Some drivers exhibit highly conservative profiles, while others assign greater value to secondary tasks, even under high distraction. Counterfactual simulations show how perceived task importance could modulate visual attention behavior across individuals.ConclusionOur POSMDP-based framework provides an interpretable and individualized account of driver attention allocation, capturing both latent states and behavioral variability.ApplicationThis model enables the development of personalized, risk-sensitive driver assistance systems that adapt to individual attention strategies, enhancing road safety through context-aware, graded interventions.