RideGuide: multimodal conversational tour guide for passenger engagement and spatial learning in robotaxis

Robotaxis are gradually taking the place of traditional taxis, removing the human driver as a source of local insights and conversation, fostering passive travel and potentially hindering passengers' spatial learning of their surroundings. This paper introduces RideGuide, a customizable multimodal conversational tour guide system for autonomous vehicles that integrates voice, touch, and vision capabilities to provide context-aware and engaging interactions. In an exploratory lab study (n = 12), participants customized RideGuide 's language, voice, and chatbot personality before experiencing a pre-recorded robotaxi ride (t = 10 min, 270 $_{}^{\circ }$ car back-seat video). Results indicate a positive hedonic user experience (UEQ-S, 1.10), moderate chatbot usability (CUQ, 67.6%), and low workload (NASA-TLX, 32.4%). On average, participants recalled 3.3 landmarks by name, suggesting a potential in supporting spatial learning. Participants reported a greater willingness to converse with RideGuide compared to human drivers and expressed openness to data sharing for personalization. The results show expectations for future robotaxi interfaces, including real-time contextual information, vehicle explainability, and adaptable conversation styles. The findings inform design recommendations for developing engaging, human-centered multimodal interfaces in autonomous mobility contexts.