Turing in the driver’s seat: can people distinguish between automated and manually driven vehicles?
Turing in the driver’s seat: can people distinguish between automated and manually driven vehicles?
As the number of automated vehicles is increasing on our roads, we wanted to know if people could detect if a car was being driven by a human driver or automation in a lane change task. This is particularly relevant, as most of the road collisions involve automated vehicles being struck from behind by manually driven vehicles. To address the detection of automated vehicles, an online survey presented videos of lane change maneuvers on multi-lane carriageways from behind the automated vehicle. We reasoned that, on such roads, the behavior of the vehicles in front would have more of an effect on drivers than those of the vehicles behind. To this end, an online survey was conducted with 769 people judging 60 video clips, classifying the lane change either being performed by Autopilot software or a human driver. Over 34,000 responses were recorded. It was found that automated and manual lane changes were virtually indistinguishable from the rear of the vehicle. The main conclusion of the research was that vehicles in automated mode should display this fact to other road users all around the vehicle as this may have an effect on other road users in anticipating the behavior of the other vehicle.
Turing test, automation, autopilot, road user interaction, self driving
418-425
Stanton, Neville
351a44ab-09a0-422a-a738-01df1fe0fadd
Eriksson, Alexander
e3b11748-5b8d-40b9-b79c-ca6a5968c6b3
Banks, Victoria
0dbdcad0-c654-4b87-a804-6a7548d0196d
Hancock, P.A.
74168dea-32d1-412d-beef-61d0b565833b
1 November 2020
Stanton, Neville
351a44ab-09a0-422a-a738-01df1fe0fadd
Eriksson, Alexander
e3b11748-5b8d-40b9-b79c-ca6a5968c6b3
Banks, Victoria
0dbdcad0-c654-4b87-a804-6a7548d0196d
Hancock, P.A.
74168dea-32d1-412d-beef-61d0b565833b
Stanton, Neville, Eriksson, Alexander, Banks, Victoria and Hancock, P.A.
(2020)
Turing in the driver’s seat: can people distinguish between automated and manually driven vehicles?
Human Factors: The Journal of Human Factors and Ergonomics Society, 30 (6), .
(doi:10.1002/hfm.20864).
Abstract
As the number of automated vehicles is increasing on our roads, we wanted to know if people could detect if a car was being driven by a human driver or automation in a lane change task. This is particularly relevant, as most of the road collisions involve automated vehicles being struck from behind by manually driven vehicles. To address the detection of automated vehicles, an online survey presented videos of lane change maneuvers on multi-lane carriageways from behind the automated vehicle. We reasoned that, on such roads, the behavior of the vehicles in front would have more of an effect on drivers than those of the vehicles behind. To this end, an online survey was conducted with 769 people judging 60 video clips, classifying the lane change either being performed by Autopilot software or a human driver. Over 34,000 responses were recorded. It was found that automated and manual lane changes were virtually indistinguishable from the rear of the vehicle. The main conclusion of the research was that vehicles in automated mode should display this fact to other road users all around the vehicle as this may have an effect on other road users in anticipating the behavior of the other vehicle.
Text
TURING IN THE DRIVERS SEAT 2020 FINAL VERSION
- Accepted Manuscript
More information
Accepted/In Press date: 4 August 2020
e-pub ahead of print date: 14 August 2020
Published date: 1 November 2020
Additional Information:
Funding Information:
This study was supported by Jaguar Land Rover and the UK-EPSRC grant EP/N011899/1 as part of the jointly funded Towards Autonomy: Smart and Connected Control (TASCC) Program.
Publisher Copyright:
© 2020 The Authors. Human Factors and Ergonomics in Manufacturing & Service Industries published by Wiley Periodicals LLC
Keywords:
Turing test, automation, autopilot, road user interaction, self driving
Identifiers
Local EPrints ID: 443749
URI: http://eprints.soton.ac.uk/id/eprint/443749
ISSN: 0018-7208
PURE UUID: cc59c141-b41c-471f-8dc5-430c2e3be593
Catalogue record
Date deposited: 10 Sep 2020 16:48
Last modified: 17 Mar 2024 03:17
Export record
Altmetrics
Contributors
Author:
Alexander Eriksson
Author:
P.A. Hancock
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
