Dargahi Nobari, Khazar2025-08-282025-08-282025http://hdl.handle.net/2003/4389910.17877/DE290R-25668The present dissertation introduces a holistic driver model aimed at predicting driver state and reaction during the transition from automated driving to manual driving. Common driver models are typically confined to specific scenarios with limited situational criteria and simplified driver states, often reducing the complexity of the driving situations to a single variable and the human decision-making process to a few measuring factors. These limitations hinder the generalizability of such models to real-world, complex driving environments, where a wide array of variables interact dynamically. Addressing these shortcomings, this research proposes a novel driver model that accounts for a broad spectrum of driver states, integrating emotional and cognitive structures to reflect the nature of real-world driving better. The driver model is grounded in two core theories: the adaptive control of thought-rational cognitive architecture and the theory of constructed emotion. The cognitive architecture provides a robust computational framework for predicting driver performance and reactions. However, its application has been limited to scenarios involving drivers in an emotionally neutral state, overlooking the impact of human affect. On the other hand, the theory of constructed emotion posits that emotions are not fixed responses but are dynamic constructs shaped by the interplay of cognitive, social, and physiological factors. By leveraging these theories, the proposed model offers a more holistic approach, capable of handling a wide range of driver states, considering affect dynamics and emotional variations. The model integrates the driving context with the driver state to predict subsequent states and reactions more accurately, using a generative machine learning algorithm inspired by neuroscientific insights into the human brain. Consequently, the model represents an advancement over the adaptive control of thought-rational cognitive architecture by incorporating the complexity of affective process into its predictive capabilities. While this dissertation focuses on takeover situations, the model’s design is inherently generalizable, allowing for future applications across various driving scenarios. To validate the proposed model, a comprehensive dataset, manD 1.0, is collected through a precisely designed subject study using a driving simulator. This dataset captures a range of driving situations, variations in driver state, and detailed information about vehicle state, all synchronized to provide a rich source of training data. From this dataset, segments involving takeover situations are extracted for model training and assessment. Given that individual differences, such as past experiences and physiological characteristics, can significantly influence driver behavior, the model is individualized for each person by training it on individual-specific data. A comparative analysis with a baseline model demonstrates that incorporating a psychological architecture enhances the accuracy of predicting driver states and reactions, improves the model’s robustness against variability, and increases its generalizability across diverse driving situations. This model holds potential for use in driver assistance systems, particularly in critical situations where predicting driver state and reaction could prevent severe safety consequences.enDriver modelingAutomated drivingTakeover situationsHuman-machine interactionCognitive architectureACT-RConstructed emotion theoryAffective computingDriver state predictionHuman factors in transportation620PhDThesisAutonomes FahrzeugMensch-Maschine-Interaktion