Bayesian network approach to building an affective module for a driver behavioural model

2025/02/05 by Dorota Młynarczyk, Gabriel F. Calvo, Młynarczyk, Dorota +9
Engineering · Psychology · #Applications (stat.AP) #Autonomous Vehicle Technology and Safety #Color perception and design #FOS: Computer and information sciences #Human-Automation Interaction and Safety

paper · pdf · doi:10.48550/arxiv.2502.03254

openalex publication_date 2025/02/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper focuses on the affective component of a Driver Behavioural Model (DBM), specifically modelling some driver's mental states, such as mental load and active fatigue, which may affect driving performance. We used Bayesian networks (BNs) to explore the dependencies between various relevant variables and estimate the probability that a driver was in a particular mental state based on their physiological and demographic conditions. Through this approach, our goal is to improve our understanding of driver behaviour in dynamic environments, with potential applications in traffic safety and autonomous vehicle technologies.

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