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Building multimodal dialogue understanding capabilities situated in thein-cabin context is crucial to enhance passenger comfort in autonomous vehicle(AV) interaction systems. To this end, understanding passenger intents fromspoken interactions and vehicle vision systems is a crucial component fordeveloping contextual and visually grounded conversational agents for AV.Towards this goal, we explore AMIE (Automated-vehicle Multimodal In-cabinExperience), the in-cabin agent responsible for handling multimodalpassenger-vehicle interactions. In this work, we discuss the benefits of amultimodal understanding of in-cabin utterances by incorporatingverbal/language input together with the non-verbal/acoustic and visual cluesfrom inside and outside the vehicle. Our experimental results outperformedtext-only baselines as we achieved improved performances for intent detectionwith a multimodal approach.

Audio-Visual Understanding of Passenger Intents for In-Cabin Conversational Agents