Immersive audio techniques can create realistic sound environments in the same way humans perceive sound in a natural one. This capability can be applied to car cabin environment to enhance the user's experience and improve the vehicle-driver interaction. In this scenario, our study is focused on the effects these systems have on the driver's experience in terms of emotional responsiveness and degree of immersion. The spatial audio system is realized with a modified version of Recursive Ambiophonic Crosstalk Cancellation and a deep analysis is performed with a real-time monitoring achieved by the implementation of a multimodal approach that exploits deep learning and data fusion techniques to ensure a comprehensive evaluation of the driver's status. Several experimental results are reported by means of a driving simulator equipped with a camera-based driver monitoring system and a physiological acquisition system.

Vehicle Sound Interaction: A Preliminary Study on Driver's Experience Affected by Immersive Sound Reproduction / Bruschi, V.; Generosi, A.; Dourou, N. A.; Spinsante, S.; Mengoni, M.; Cecchi, S.. - (2025). ( 2025 Immersive and 3D Audio: from Architecture to Automotive, I3DA 2025 Bologna, Italy 10-12 September 2025) [10.1109/I3DA65421.2025.11202078].

Vehicle Sound Interaction: A Preliminary Study on Driver's Experience Affected by Immersive Sound Reproduction

Bruschi V.;Generosi A.;Dourou N. A.;Spinsante S.;Mengoni M.;Cecchi S.
2025-01-01

Abstract

Immersive audio techniques can create realistic sound environments in the same way humans perceive sound in a natural one. This capability can be applied to car cabin environment to enhance the user's experience and improve the vehicle-driver interaction. In this scenario, our study is focused on the effects these systems have on the driver's experience in terms of emotional responsiveness and degree of immersion. The spatial audio system is realized with a modified version of Recursive Ambiophonic Crosstalk Cancellation and a deep analysis is performed with a real-time monitoring achieved by the implementation of a multimodal approach that exploits deep learning and data fusion techniques to ensure a comprehensive evaluation of the driver's status. Several experimental results are reported by means of a driving simulator equipped with a camera-based driver monitoring system and a physiological acquisition system.
2025
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/350473
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