Haptic icons: a hands-on approach to haptic HMI in automated vehicles

This paper examines the potential integration of haptic feedback on steering wheels for automated driving applications, with a particular focus on transitions between automated and manualmodes, takeover requests, and warnings. An iterative, three-phase methodology was employed: (1) The initial set o...

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Bibliographic Details
Authors: Sarabia, J. (Joseba)|||/items/7b18ab0d-fde8-4929-9e8b-08eaa49cbdae, Vaca, M. (Myriam)|||/items/215728b8-ff54-433d-a379-7d0965350974, Marcano, M. (Mauricio)|||/items/e3b25788-2216-4408-90c5-67ab0f9328be, Diaz, S. (Sergio)|||/items/538dfa5f-ca93-42ec-b01a-22c2a1000ac0, Pérez-Rastelli, J. (Joshué)|||/items/04553204-375c-44c9-afe0-30ca1810fd40, Zubizarreta, A. (Asier)|||/items/6b26b887-170a-4613-9537-7b8c2c9d69bf
Format: article
Publication Date:2025
Country:España
Institution:Universidad de Navarra
Repository:Dadun. Depósito Académico Digital de la Universidad de Navarra
Language:English
OAI Identifier:oai:dadun.unav.edu:10171/120512
Online Access:https://hdl.handle.net/10171/120512
Access Level:Open access
Keyword:Haptic HMI
Automated driving
User study
Driving simulator
Description
Summary:This paper examines the potential integration of haptic feedback on steering wheels for automated driving applications, with a particular focus on transitions between automated and manualmodes, takeover requests, and warnings. An iterative, three-phase methodology was employed: (1) The initial set of haptic notifications was designed based on input from the literature review, (2) These notifications were then tested in a driving simulator to identify the most effective options, and (3) The selected notifications were evaluated in a dynamic simulator under realistic conditions, including noise, vibration, and harshness (NVH). User studies were conducted at each phase to gather subjective metrics and validate the usability of the haptic feedback. The results demonstrate that specific haptic patterns enhance driver situational awareness and improve transitions between driving modes compared to conventional auditory signals, contributing to safer human-machine interaction in automated vehicles.