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

2025-02-05

AI Summary

This study addresses the need to better understand driver behavior in dynamic driving environments by modeling the influence of psychological states—specifically mental workload and active fatigue—on driving performance. It innovatively introduces a Bayesian network framework for the first time to model drivers’ affective states, integrating physiological signals and demographic variables into an affective module capable of probabilistic inference over latent psychological conditions. This approach enables quantitative assessment of mental workload and fatigue, thereby enhancing the explanatory power of driver behavior models. The resulting framework not only advances theoretical understanding of driver state dynamics but also provides a robust foundation for real-time safety interventions in intelligent transportation systems and autonomous vehicles.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• affective module
• driver behavioural model
• mental states
• mental load
• active fatigue

Innovation

• Bayesian networks
• driver behavioural model
• affective computing
• mental state estimation
• probabilistic reasoning