The University of Southampton
University of Southampton Institutional Repository

Acclimatizing to automation: driver workload and stress during partially automated car following in real traffic

Acclimatizing to automation: driver workload and stress during partially automated car following in real traffic
Acclimatizing to automation: driver workload and stress during partially automated car following in real traffic

Automated driving systems are increasingly prevalent on public roads, but there is currently little knowledge on the level of workload and stress of drivers operating an automated vehicle in a real environment. The present study aimed to measure driver workload and stress during partially automated driving in real traffic. We recorded heart rate, heart rate variability, respiratory rate, and subjective responses of nine test drivers in the Tesla Model S with Autopilot. The participants, who were experienced with driver assistance systems but naïve to the Tesla, drove a 32 min motorway route back and forth while following a lead car in regular traffic. In one of the two drives, participants performed a heads-up detection task of bridges they went underneath. Averaged across the two drives, the participants’ mean self-reported overall workload score on the NASA Task Load Index was 19%. Moreover, the participants showed a reduction in heart rate and self-reported workload over time, suggesting that the participants became accustomed to the experiment and technology. The mean hit (i.e., pressing the button near a bridge) rate in the detection task was 88%. In conclusion, driving with the Tesla Autopilot on a motorway involved a low level of workload that decreased with time on task.

Automated driving, Object detection, On-road, Stress, Workload
1369-8478
503-517
Heikoop, Daniël D.
d4598c35-c8a9-4d0f-82d1-d956d5793d7d
de Winter, Joost C.F.
59ebe174-7c3e-4b83-937e-f36a9a9c106a
van Arem, Bart
95291aab-bdc1-40ba-8c2a-6149e4a7238a
Stanton, Neville A.
351a44ab-09a0-422a-a738-01df1fe0fadd
Heikoop, Daniël D.
d4598c35-c8a9-4d0f-82d1-d956d5793d7d
de Winter, Joost C.F.
59ebe174-7c3e-4b83-937e-f36a9a9c106a
van Arem, Bart
95291aab-bdc1-40ba-8c2a-6149e4a7238a
Stanton, Neville A.
351a44ab-09a0-422a-a738-01df1fe0fadd

Heikoop, Daniël D., de Winter, Joost C.F., van Arem, Bart and Stanton, Neville A. (2019) Acclimatizing to automation: driver workload and stress during partially automated car following in real traffic. Transportation Research Part F: Traffic Psychology and Behaviour, 65, 503-517. (doi:10.1016/j.trf.2019.07.024).

Record type: Article

Abstract

Automated driving systems are increasingly prevalent on public roads, but there is currently little knowledge on the level of workload and stress of drivers operating an automated vehicle in a real environment. The present study aimed to measure driver workload and stress during partially automated driving in real traffic. We recorded heart rate, heart rate variability, respiratory rate, and subjective responses of nine test drivers in the Tesla Model S with Autopilot. The participants, who were experienced with driver assistance systems but naïve to the Tesla, drove a 32 min motorway route back and forth while following a lead car in regular traffic. In one of the two drives, participants performed a heads-up detection task of bridges they went underneath. Averaged across the two drives, the participants’ mean self-reported overall workload score on the NASA Task Load Index was 19%. Moreover, the participants showed a reduction in heart rate and self-reported workload over time, suggesting that the participants became accustomed to the experiment and technology. The mean hit (i.e., pressing the button near a bridge) rate in the detection task was 88%. In conclusion, driving with the Tesla Autopilot on a motorway involved a low level of workload that decreased with time on task.

Text
Acclimatizingtoautomation..._ - Accepted Manuscript
Download (820kB)

More information

Accepted/In Press date: 30 July 2019
e-pub ahead of print date: 27 August 2019
Keywords: Automated driving, Object detection, On-road, Stress, Workload

Identifiers

Local EPrints ID: 434337
URI: http://eprints.soton.ac.uk/id/eprint/434337
ISSN: 1369-8478
PURE UUID: 78733b2b-f08d-49af-82a7-cf7ca7b0225b
ORCID for Neville A. Stanton: ORCID iD orcid.org/0000-0002-8562-3279

Catalogue record

Date deposited: 19 Sep 2019 16:30
Last modified: 17 Mar 2024 03:17

Export record

Altmetrics

Contributors

Author: Daniël D. Heikoop
Author: Joost C.F. de Winter
Author: Bart van Arem

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×