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

TL;DR

This paper introduces a Bayesian network-based method to model drivers' mental states like fatigue and mental load, aiming to enhance understanding of driver behavior for traffic safety and autonomous vehicles.

Contribution

It presents a novel application of Bayesian networks to model the affective states of drivers based on physiological and demographic data.

Findings

Bayesian networks effectively model dependencies between driver states and variables.

The approach improves understanding of how mental states influence driving performance.

Potential applications include traffic safety and autonomous vehicle systems.

Abstract

This paper focuses on the affective component of a driver behavioural model (DBM). This component specifically models some drivers' mental states such as mental load and active fatigue, which may affect driving performance. We have used Bayesian networks (BNs) to explore the dependencies between various relevant random variables and assess the probability that a driver is 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.