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Principles of transparency for autonomous vehicles: first results of an experiment with an augmented reality human–machine interface

  • Raissa PokamEmail author
  • Serge Debernard
  • Christine Chauvin
  • Sabine Langlois
Original Article
  • 29 Downloads

Abstract

Highly automated driving allows the driver to temporarily delegate the driving task to the autonomous vehicle. The challenge is to define the information that needs to be displayed to the driver in this mode, to let him be able to take over properly. This study investigates the automation transparency to ensure a meta-cooperation between the driver and the automation and the way to convey information to the driver using Augmented Reality according to some transparency principles. Therefore, among 45 participants, we evaluated five human–machine interface (HMI) in which some or all of the following functions were integrated: information acquisition, information analysis, decision-making and action execution. To validate our transparency principles, we assessed Situation Awareness, discomfort feeling, and the participants’ preferences. Even though there is no convergence in the first results, it appears clearly that no transparency in the HMI does not help to understand the environment. Additionally, it appears that “information acquisition” and “action execution” functions are quite necessary. Furthermore, the HMI with the high level of transparency was preferred by the participants. However, more analysis is required to obtain final results.

Keywords

Driverless vehicles Human–machine interface Cognitive work analysis Human–machine cooperation Transparency Augmented reality Simulation 

Notes

Acknowledgements

This work receives support from the French government in accordance with the PIA (French acronym for Program of Future Investments) within the IRT (French acronym for Technology Research Institute) SystemX.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Raissa Pokam
    • 1
    • 2
    Email author
  • Serge Debernard
    • 2
  • Christine Chauvin
    • 3
  • Sabine Langlois
    • 1
    • 4
  1. 1.IRT SystemX, Centre d’Intégration nano-INNOVPalaiseauFrance
  2. 2.Université Polytechnique Hauts-de-France, CNRS, UMR 8201 - LAMIHValenciennesFrance
  3. 3.Lab-STICC IHSEV TeamUMR CNRS 6285, Université Bretagne SudLorientFrance
  4. 4.Renault, Technocentre-Human FactorGuyancourtFrance

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