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<p>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 (<i>n</i> = 12), participants customized RideGuide ’s language, voice, and chatbot personality before experiencing a pre-recorded robotaxi ride (t = 10 min, 270<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(^\circ \)</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mo>∘</mo> </mmultiscripts> </math></EquationSource> </InlineEquation> 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月3日

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  • Eve Schade

摘要

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.