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Conference papers

Multimodal human interaction analysis in vehicle cockpit

Quentin Portes 1, 2, 3 Julien Pinquier 2 Frédéric Lerasle 3 José Mendes Carvalho 1 
3 LAAS-RAP - Équipe Robotique, Action et Perception
LAAS - Laboratoire d'analyse et d'architecture des systèmes
Abstract : Nowadays, every car maker is thinking about the future of mobility. Electric vehicles, autonomous vehicles and sharing vehicles are one of the most promising opportunities. The lack of authority in autonomous and sharing vehicles raises different issues from which one of the main issues is passenger safety. To ensure it, new systems able to understand interactions and possible conflicts between passengers have to be designed. They should be able to predict critical situations in the car cockpit, and alert remote controllers to act accordingly. In order to better understand the features of these insecure situations, we recorded an audio-video dataset in real vehicle context. Twenty-two participants playing three different scenarios (“curious”, “argued refusal” and “not argued refusal”) of interactions between a driver and a passenger were recorded. We propose a deep learning model to identify conflict situations in a car cockpit. Our approach achieves a balanced accuracy of 81%. Practically, we highlight the importance that combining multimodality namely video, audio and text as well as temporality are the keys to perform such accurate predictions in scenario recognitinn.
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Submitted on : Wednesday, November 24, 2021 - 8:02:53 AM
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Quentin Portes, Julien Pinquier, Frédéric Lerasle, José Mendes Carvalho. Multimodal human interaction analysis in vehicle cockpit. IEEE International Intelligent Transportation Systems Conference (ITSC 2021), Sep 2021, Indianapolis, United States. pp.2118-2124, ⟨10.1109/ITSC48978.2021.9564792⟩. ⟨hal-03445480⟩



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