D. Mlynarczyk, G. Calvo, F. Palmí Perales, C. Armero, V. Gómez-Rubio, Ú. Martínez-Iranzo, A. de la Torre-García
A Bayesian Network (BN) is a probabilistic model that uses conditional probability distributions to describe how different variables influence one another. It enables the estimation of probabilities for unknown outcomes based on known evidence, while accounting for uncertainty and interactions within the network. This study applies a BN approach to explore the affective component of a Driver Behavioural Model (DBM), aiming to understand and predict how drivers' mental states, such as mental load and active fatigue, impact driving performance. The resulting BN integrates various data, including demographic and physiological factors like heart rate variability and respiration rate, to model complex dependencies and estimate the likelihood of a driver being in a specific mental state. This research has the potential to contribute to intelligent transportation systems by predicting mental states that might impair driving and, consequently, enhancing road safety.
Palabras clave: Directed acyclic graph, Driver Behavioural Model, Mental states
Programado
Jóvenes investigadores en Estadística Bayesiana
10 de junio de 2025 19:00
Sala de prensa (MR 13)