The University of Southampton
University of Southampton Institutional Repository

Contrasting models of driver behaviour in emergencies using retrospective verbalisations and network analysis

Contrasting models of driver behaviour in emergencies using retrospective verbalisations and network analysis
Contrasting models of driver behaviour in emergencies using retrospective verbalisations and network analysis
Automated assistance in driving emergencies aims to improve the safety of our roads by avoiding or mitigating the effects of accidents. However, the behavioural implications of such systems remain unknown. This paper introduces the driver decision-making in emergencies (DDMiEs) framework to investigate how the level and type of automation may affect driver decision-making and subsequent responses to critical braking events using network analysis to interrogate retrospective verbalisations. Four DDMiE models were constructed to represent different levels of automation within the driving task and its effects on driver decision-making. Findings suggest that whilst automation does not alter the decision-making pathway (e.g. the processes between hazard detection and response remain similar), it does appear to significantly weaken the links between information-processing nodes. This reflects an unintended yet emergent property within the task network that could mean that we may not be improving safety in the way we expect.

Practitioner Summary: This paper contrasts models of driver decision-making in emergencies at varying levels of automation using the Southampton University Driving Simulator. Network analysis of retrospective verbalisations indicates that increasing the level of automation in driving emergencies weakens the link between information-processing nodes essential for effective decision-making.
1366-5847
1337-1346
Banks, Victoria
0dbdcad0-c654-4b87-a804-6a7548d0196d
Stanton, Neville
351a44ab-09a0-422a-a738-01df1fe0fadd
Banks, Victoria
0dbdcad0-c654-4b87-a804-6a7548d0196d
Stanton, Neville
351a44ab-09a0-422a-a738-01df1fe0fadd

Banks, Victoria and Stanton, Neville (2015) Contrasting models of driver behaviour in emergencies using retrospective verbalisations and network analysis. Ergonomics, 58 (8), 1337-1346. (doi:10.1080/00140139.2015.1005175).

Record type: Article

Abstract

Automated assistance in driving emergencies aims to improve the safety of our roads by avoiding or mitigating the effects of accidents. However, the behavioural implications of such systems remain unknown. This paper introduces the driver decision-making in emergencies (DDMiEs) framework to investigate how the level and type of automation may affect driver decision-making and subsequent responses to critical braking events using network analysis to interrogate retrospective verbalisations. Four DDMiE models were constructed to represent different levels of automation within the driving task and its effects on driver decision-making. Findings suggest that whilst automation does not alter the decision-making pathway (e.g. the processes between hazard detection and response remain similar), it does appear to significantly weaken the links between information-processing nodes. This reflects an unintended yet emergent property within the task network that could mean that we may not be improving safety in the way we expect.

Practitioner Summary: This paper contrasts models of driver decision-making in emergencies at varying levels of automation using the Southampton University Driving Simulator. Network analysis of retrospective verbalisations indicates that increasing the level of automation in driving emergencies weakens the link between information-processing nodes essential for effective decision-making.

Full text not available from this repository.

More information

Accepted/In Press date: 24 December 2014
Published date: 2 February 2015

Identifiers

Local EPrints ID: 413239
URI: http://eprints.soton.ac.uk/id/eprint/413239
ISSN: 1366-5847
PURE UUID: c80dc8c8-7ef7-4b20-8fd1-0ef09a14e837
ORCID for Neville Stanton: ORCID iD orcid.org/0000-0002-8562-3279

Catalogue record

Date deposited: 17 Aug 2017 16:31
Last modified: 05 Nov 2019 01:43

Export record

Altmetrics

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.

×