AutoExp: A multidisciplinary, multi-sensor framework to evaluate human
  activities in self-driving cars
Carlos Crispim-Junior Romain Guesdon Christophe Jallais Florent Laroche Stephanie Souche-Le Corvec Laure Tougne Rodet
The adoption of self-driving cars 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 self-driving
cars 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 seek firstly 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.