Toward Adaptive and User-Centered Intelligent Vehicles: AI Models with Granular Classifications for Risk Detection, Cognitive Workload, and User Preferences

Abstract

As artificial intelligence (AI) increasingly integrates into our transportation systems, intelligent vehicles have emerged as research topics. Many advancements aim to enhance both the safety and comfort of drivers and the reliability of intelligent vehicles. The main focus of my research is addressing and responding to the varying states and needs of drivers, which is essential for improving driver-vehicle interactions through user-centered design. To contribute to this evolving field, this thesis explores the use of physiological signals and eye-tracking data to decode user states, perceptions, and intentions. While existing studies mostly rely on binary classification models, these approaches are limited in capturing the full spectrum of user states and needs. Addressing this gap, my research focuses on developing AI-driven models with more granular classifications for cognitive workload, risk severity levels, and user preferences for self-driving behaviours. This thesis is structured into three core domains: collision risk detection, cognitive workload estimation, and perception of user preferences for self-driving behaviours. By integrating AI techniques with multi-modal physiological data, my studies develop ML (Machine Learning) models for the domains introduced above and achieve high performance of the ML models. Feature analytical techniques are employed to enhance model interpretability for a better understanding of features and to improve the model performance. These findings pave the way for a new paradigm of intelligent vehicles that are not only more adaptive but also more aligned with user needs and preferences. This research lays the groundwork for the future development of user-centered intelligent companion systems in vehicles, where adaptive, perceptive, and interactive vehicles can better meet the complex demands of their users.