A Multi-Party Conversation-Based Effective Robotic Navigation System for Futuristic Vehicle

Abstract

In response to the growing need for advanced in-car navigation systems that prioritize user experience and aim to reduce driver cognitive workload, this study addresses the research question of how to enhance the interaction between drivers and navigation systems. The focus is on minimizing distraction while providing personalized and geographically relevant information. The research introduces an innovative in-car robotic navigation system comprising three subsystem models: geofencing,
personalization, and conversation. The dynamic geofencing model acquires geographic details related to the user's current location and provides information about required destinations. The personalization model tailors suggestions based on user preferences, while the conversation model, employing two virtual robots, fosters interactive multiparty conversations aligned with the driver's interests. The study's scope is specifically confined to interactive conversations centered on nearby restaurants and the driver's dietary preferences. Evaluation of the system indicates a notable prevalence of neutral expressions among
participants during interaction, suggesting that the implemented system successfully mitigates cognitive workload. Participants in the experiments express higher usability and interactivity levels, as evidenced by feedback collected at the study's conclusion, affirming the system's effectiveness in enhancing the user experience while maintaining a driver-friendly environment.

Keywords: Human-Robot Interaction, Multiparty Conversation, In-Car Navigation