Adaptive Human-Machine Interaction for Autonomous Passenger Vehicles: Towards Personalization and Enhanced User Experience

Autonomous passenger vehicles represent a transformative step towards intelligent mobility solutions. This study focuses on developing adaptive human-machine interaction (HMI) systems for these vehicles, aiming to address user needs through personalization, contextual modeling, and intelligent interaction mechanisms. The proposed system integrates user modeling, context-awareness, and adaptive user interface techniques to create a seamless and humanized experience. Key features include voice- and touch-based interaction with an articulated touch screen, real-time context adaptation, and compliance with data protection regulations. The research highlights the importance of user-centric design in ensuring comfort, usability, and engagement while leveraging well-established technologies like collaborative filtering, deep transfer learning, and pervasive computing. Based on the theoretical model developed, it is anticipated that the proposed solution will enhance user satisfaction and operational efficiency. This work contributes to the broader field of autonomous robotics by exploring innovative approaches to interaction design for intelligent systems in smart cities.