The adoption of self-driving cars (SDC) will certainly revolutionize our lives, even though they may take more time to become fully autonomous than initially predicted. The first vehicles are already present in certain cities of the world, as part of experimental robot-taxi services. However, most existing studies focus on the navigation part of such vehicles. We currently miss methods, datasets, and studies to assess the in-cabin human component of the adoption of such technology in real-world conditions. This paper proposes an experimental framework to study the activities of occupants of SDCs using a multidisciplinary approach (computer vision associated with human and social sciences), particularly non-driving related activities. The framework is composed of an experimentation scenario and a data acquisition module. We firstly seek to capture real-world data about the usage of the vehicle in the nearest possible, real-world conditions, and secondly to create a dataset containing in-cabin human activities to foster the development and evaluation of computer vision algorithms. The acquisition module records multiple views of the front seats of the vehicle (Intel RGB-D and GoPro cameras), in addition to survey data about the internal states and attitudes of participants towards this type of vehicle before, during, and after the experimentation. We evaluated the proposed framework with the realization of real-world experimentation with 30 participants (1 hour each) to study the acceptance of SDCs of SAE level 4.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    AutoExp: A Multidisciplinary, Multi-Sensor Framework to Evaluate Human Activities in Self-Driving Cars




    Publication date :

    2023-09-24


    Size :

    4114225 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    SELF DRIVING CARS

    KIM SI HYEONG / HAN YURIM / YEON HEE LEE et al. | European Patent Office | 2022

    Free access

    SELF DRIVING CARS

    European Patent Office | 2022

    Free access

    Self-driving Cars

    Luntovskyy, Andriy / Guetter, Dietbert / Masiuk, Andrii | Springer Verlag | 2023


    Personalize self-driving cars

    HUAI XIAONING | European Patent Office | 2017

    Free access

    A verification framework for behavioral safety of self‐driving cars

    Huihui Wu / Deyun Lyu / Yanan Zhang et al. | DOAJ | 2022

    Free access