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Light Virtual Reality systems for the training of conditionally automated vehicle drivers

Abstract : In conditionally automated vehicles, drivers can engage in secondary activities while traveling to their destination. However, drivers are required to appropriately respond, in a limited amount of time, to a takeover request when the system reaches its functional boundaries. In this context, Virtual Reality systems represent a promising training and learning tool to properly familiarize drivers with the automated vehicle and allow them to interact with the novel equipment involved. In this study, the effectiveness of an Head-Mounted display (HMD)-based training program for acquiring interaction skills in automated cars was compared to a user manual and a fixed-base simulator. Results show that the training system affects the takeover performances evaluated in a test drive in a high-end driving simula-tor. Moreover, self-reported measures indicate that the HMD-based training is preferred with respect to the other systems.
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Contributor : Alexis Paljic <>
Submitted on : Wednesday, May 9, 2018 - 11:36:05 AM
Last modification on : Thursday, September 24, 2020 - 5:04:02 PM
Long-term archiving on: : Monday, September 24, 2018 - 3:57:42 PM

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  • HAL Id : hal-01788614, version 1
  • ARXIV : 1803.04968

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Daniele Sportillo, Alexis Paljic, Luciano Ojeda, Philippe Fuchs, Vincent Roussarie. Light Virtual Reality systems for the training of conditionally automated vehicle drivers. IEEE Virtual Reality, Mar 2018, Reutlingen, Germany. ⟨hal-01788614⟩

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