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Rolling out the red (and green) carpet: supporting driver decision making in automation-to-manual transitions

Rolling out the red (and green) carpet: supporting driver decision making in automation-to-manual transitions
Rolling out the red (and green) carpet: supporting driver decision making in automation-to-manual transitions

This paper assessed four types of human-machine interfaces (HMIs), classified according to the stages of automation proposed by Parasuraman et al. ['A model for types and levels of human interaction with automation,' IEEE Trans. Syst. Man, Cybern. A, Syst. Humans, vol. 30, no. 3, pp. 286-297, May 2000]. We hypothesized that drivers would implement decisions (lane changing or braking) faster and more correctly when receiving support at a higher automation stage during transitions from conditionally automated driving to manual driving. In total, 25 participants with a mean age of 25.7 years (range 19-36 years) drove four trials in a driving simulator, experiencing four HMIs having the following different stages of automation: baseline (information acquisition - low), sphere (information acquisition - high), carpet (information analysis), and arrow (decision selection), presented as visual overlays on the surroundings. The HMIs provided information during two scenarios, namely a lane change and a braking scenario. Results showed that the HMIs did not significantly affect the drivers' initial reaction to the take-over request. Improvements were found, however, in the decision-making process: When drivers experienced the carpet or arrow interface, an improvement in correct decisions (i.e., to brake or change lane) occurred. It is concluded that visual HMIs can assist drivers in making a correct braking or lane change maneuver in a take-over scenario. Future research could be directed toward misuse, disuse, errors of omission, and errors of commission.

Augmented reality, automated driving, driver support systems, human factors, human performance, transitions of control
2168-2291
20-31
Eriksson, Alexander
75015c12-48a6-41ac-8fc4-15b1d71237f3
Petermeijer, Sebastiaan M.
26629bf3-2808-4a39-9852-9955a5988ab3
Zimmermann, Markus
4017da2c-f428-48ae-aed8-0f951deefd98
De Winter, Joost C.F.
59ebe174-7c3e-4b83-937e-f36a9a9c106a
Bengler, Klaus J.
a99aab2e-820c-4e56-a927-9589e259ee0b
Stanton, Neville A.
351a44ab-09a0-422a-a738-01df1fe0fadd
Eriksson, Alexander
75015c12-48a6-41ac-8fc4-15b1d71237f3
Petermeijer, Sebastiaan M.
26629bf3-2808-4a39-9852-9955a5988ab3
Zimmermann, Markus
4017da2c-f428-48ae-aed8-0f951deefd98
De Winter, Joost C.F.
59ebe174-7c3e-4b83-937e-f36a9a9c106a
Bengler, Klaus J.
a99aab2e-820c-4e56-a927-9589e259ee0b
Stanton, Neville A.
351a44ab-09a0-422a-a738-01df1fe0fadd

Eriksson, Alexander, Petermeijer, Sebastiaan M., Zimmermann, Markus, De Winter, Joost C.F., Bengler, Klaus J. and Stanton, Neville A. (2019) Rolling out the red (and green) carpet: supporting driver decision making in automation-to-manual transitions. IEEE Transactions on Human-Machine Systems, 49 (1), 20-31. (doi:10.1109/THMS.2018.2883862).

Record type: Article

Abstract

This paper assessed four types of human-machine interfaces (HMIs), classified according to the stages of automation proposed by Parasuraman et al. ['A model for types and levels of human interaction with automation,' IEEE Trans. Syst. Man, Cybern. A, Syst. Humans, vol. 30, no. 3, pp. 286-297, May 2000]. We hypothesized that drivers would implement decisions (lane changing or braking) faster and more correctly when receiving support at a higher automation stage during transitions from conditionally automated driving to manual driving. In total, 25 participants with a mean age of 25.7 years (range 19-36 years) drove four trials in a driving simulator, experiencing four HMIs having the following different stages of automation: baseline (information acquisition - low), sphere (information acquisition - high), carpet (information analysis), and arrow (decision selection), presented as visual overlays on the surroundings. The HMIs provided information during two scenarios, namely a lane change and a braking scenario. Results showed that the HMIs did not significantly affect the drivers' initial reaction to the take-over request. Improvements were found, however, in the decision-making process: When drivers experienced the carpet or arrow interface, an improvement in correct decisions (i.e., to brake or change lane) occurred. It is concluded that visual HMIs can assist drivers in making a correct braking or lane change maneuver in a take-over scenario. Future research could be directed toward misuse, disuse, errors of omission, and errors of commission.

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ErikssonEtAl_RollingOutTheCarpet_unmarked(1) - Accepted Manuscript
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More information

Accepted/In Press date: 20 October 2018
e-pub ahead of print date: 28 December 2018
Published date: 1 February 2019
Keywords: Augmented reality, automated driving, driver support systems, human factors, human performance, transitions of control

Identifiers

Local EPrints ID: 428188
URI: https://eprints.soton.ac.uk/id/eprint/428188
ISSN: 2168-2291
PURE UUID: 93781919-9948-48e8-801d-ac2450e047c1
ORCID for Alexander Eriksson: ORCID iD orcid.org/0000-0003-1549-1327
ORCID for Neville A. Stanton: ORCID iD orcid.org/0000-0002-8562-3279

Catalogue record

Date deposited: 14 Feb 2019 17:30
Last modified: 15 Aug 2019 00:40

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