Bayesian networks for modelling the uncertainty associated with drivers’ emotional and mental states

C. Armero, D. Mlynarczyk, G. Calvo, F. Palmi-Perales, V. Gómez-Rubio, A. De la Torre, R. Bayona

A Driver Behavioural Model (DBM) is a framework that integrates data from various sources to study how different factors influence driver actions. DBMs can be used to improve road safety, support driver assistance systems, or design autonomous vehicles. Affective driving components are an important part of DBMs designed to understand and accommodate drivers' emotional and mental states, which can have a relevant impact on their behavior behind the wheel.
We present a Bayesian approach to develop part of an affective module, within a DBM, to model driver mental states, such as mental workload and active fatigue, using Bayesian Networks (BN) to understand their effects on driving performance. BN use a probabilistic and graphical framework to analyse stochastic dependencies between relevant variables, such as physiological indicators and demographic conditions, in order to estimate the probability of a driver being in a specific mental state.

Palabras clave: Bayesian Statistics; Directed Acyclic Graph; Prediction

Métodos Bayesianos
4 de septiembre de 2026  11:10
Aula 20

A. Forte Deltell, J. Martínez-Minaya, J. Piqueras Marqués

B. González Pérez, J. M. Velasco Cabo, D. Parra Rodriguez, O. Garnica, J. I. Hidalgo Pérez, E. Maqueda, M. Botella Serrano

J. M. Gutiérrez Díez

A. Olivares Canal, V. Peña Pizarro, M. Jauch, A. F. Barrientos

Y. Torres Favier, A. Monleon Getino, C. Crespo Palomo