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AutoExp: A multidisciplinary, multi-sensor framework to evaluate the acceptability of self-driving cars

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  • معلومة اضافية
    • Contributors:
      Extraction de Caractéristiques et Identification (imagine); Pôle informatique graphique et géométrie (IGG); Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS); Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL); Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL); Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon); Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL); Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS); Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS); Laboratoire Ergonomie et Sciences Cognitives pour les Transports (TS2-LESCOT); Université Gustave Eiffel; Laboratoire Aménagement Économie Transports (LAET); Université Lumière - Lyon 2 (UL2)-École Nationale des Travaux Publics de l'État (ENTPE)-Centre National de la Recherche Scientifique (CNRS); Laboratoire de Biomécanique et Mécanique des Chocs (LBMC UMR T9406); Université Claude Bernard Lyon 1 (UCBL); Université de Lyon-Université de Lyon-Université Gustave Eiffel; Project Region AURA AutoBehave 2019; LIRIS UMR 5205 CNRS/INSA de Lyon/Université Claude Bernard Lyon 1/Université Lumière Lyon 2/École Centrale de Lyon
    • بيانات النشر:
      CCSD
    • الموضوع:
      2022
    • Collection:
      Université de Lyon: HAL
    • نبذة مختصرة :
      The arrival of self-driving cars (SDC) in our daily lives is imminent. However, research on the adoption of this technology is still an ongoing topic. Existing work focuses on situations where the driver may be asked to take back control of the vehicle. Therefore, we currently lack tools to analyze the behaviors of the occupants of SDC beyond driving-related activities, and studies in close to real-world situations. We propose a multidisciplinary, multi-sensor framework to evaluate the changes in the internal states and behaviors of SDC's occupants. To test the proposed framework, we carried out a four-day long experiment in July 2021 using a Renault Zoe car (electric supermini urban model). The experiment took place in the parking of the campus of Ecole Centrale de Nantes (ECN) in France, and the vehicle was robotized by the LS2N-ARMEN laboratory to behave as a SDC of level four (high driving automation). We acquired a multidisciplinary, multi-sensor dataset composed of recordings of 29 people (18 men/11 women) carrying out a daily domicile-work travel. Recruited participants presented a variety of body sizes, human traits, ages, and education levels. We hope that the proposed framework and the acquired dataset will encourage interdisciplinary research between AI field and Human and Social Sciences on the study of the acceptance of SDC, and of the transformations that our society is undergoing.
    • الدخول الالكتروني :
      https://hal.science/hal-03825090
      https://hal.science/hal-03825090v1/document
      https://hal.science/hal-03825090v1/file/ExperienceAutoBehaveNantes_202210241527.pdf
    • Rights:
      https://about.hal.science/hal-authorisation-v1/ ; info:eu-repo/semantics/OpenAccess
    • الرقم المعرف:
      edsbas.700628C4