Home // International Journal On Advances in Intelligent Systems, volume 13, numbers 1 and 2, 2020 // View article

Driver Emotional States & Trust: Interactions with Partial Automation On-Road

Authors:
Liza Dixon
William M. Megill
Karsten Nebe

Keywords: Facial Emotion Recognition; Driver Emotions; Advanced Driver Assistance Systems (ADAS); Human-Machine Interaction; Automation

Abstract:
Many vehicles on-road today are equipped with Advanced Driver Assistance Systems (ADAS) which enable a driver to handover primary driving tasks to the vehicle under specific conditions, provided the driver continues to supervise the system (Level 2 automation). Various tools and methods are used in the study of human-machine interaction with vehicle automation in order to assess a driver’s experience and interactions with a system. When used in-vehicle, Facial Emotion Recognition (FER) offers researchers the possibility of a quantitative reading of the driver’s changing emotional state in response to interactions with the system. This paper presents a method of correlating FER data post-drive with participants’ reported feelings of trust in the system. FER visualizations of the duration of the test drive sessions as well as visualizations of specific driving events are presented. Challenges in the use of FER in-vehicle, “in the wild” (on-road) are also discussed. Participants with a gain in trust post-drive and those with a loss in trust post-drive more frequently displayed the emotions happy and angry, respectively. Results indicate that trust increases after a user’s first experience with an ADAS and further that FER may be predictive of user trust in automation.

Pages: 95 to 108

Copyright: Copyright (c) to authors, 2020. Used with permission.

Publication date: June 30, 2020

Published in: journal

ISSN: 1942-2679