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Using GOMS and the Thinking Aloud Technique to infer driver states

Käthner, David und Bühring, Julia und Ihme, Klas (2017) Using GOMS and the Thinking Aloud Technique to infer driver states. TeaP 2017, 2017-03-26 - 2017-03-29, Dresden.

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Kurzfassung

Modelling human drivers as discrete states is a frequently used approach to understand and analyse driver behaviour, but a great challenge lies in linking empirical data with proposed states (Toledo, 2003). We propose a modelling approach based on a fine granular analysis of the driving task, using the established GOMS methodology (e.g. John & Kieras, 1996). At its core, GOMS assumes goals whose fulfilment are the end point of any human action. To reach these goals, and transfer current states into goal states, operators are employed. Being mere constructs, goals of drivers while operating their vehicle in highly complex environments cannot be measured directly, but must be modelled. One possibility are task models using hierarchical abstractions of the driving task (Walker, Stanton & Salmon, 2015). However, unlike tasks in highly controlled environments, driving seems to be comprised of a multitude of parallel goals, with drivers enjoying a high degree of freedom in setting these goals in specific situations. In a simulator experiment with 22 subjects, we therefore explored a second possibility: To ask drivers what their concrete goals were, employing the Thinking Aloud Technique (e.g. Nielsen, Clemmensen & Yssing). Driving in either a highly controlled traffic scenario, or in highly complex traffic situation on a two lane highway, subjects were instructed to report their current goals, current and planned actions, as well as general thoughts throughout the drive. Both video and audio from these trials were recorded, and played back to the drivers after each drive, allowing to ask the subjects further specific questions about their goals and actions in specific situations. Categorising the goals from the recorded audio and video data then allowed us to use them to construct task models, based on the DriveGOMS-methodology (Käthner, Andrée, Drewitz & Ihme, 2016). The goals served as the endpoints of windows in which driving operators were applied, enabling us to construct discrete states directly based on empirical data.

elib-URL des Eintrags:https://elib.dlr.de/106521/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:Using GOMS and the Thinking Aloud Technique to infer driver states
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Käthner, Daviddavid.kaethner (at) dlr.dehttps://orcid.org/0000-0003-4168-2266NICHT SPEZIFIZIERT
Bühring, JuliaJulia.Buehring (at) st.ovgu.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Ihme, Klasklas.ihme (at) dlr.dehttps://orcid.org/0000-0002-7911-3512NICHT SPEZIFIZIERT
Datum:März 2017
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
Status:veröffentlicht
Stichwörter:driver modelling, task modelling, driver behaviour, driving task, GOMS, DriveGOMS, driver states
Veranstaltungstitel:TeaP 2017
Veranstaltungsort:Dresden
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:26 März 2017
Veranstaltungsende:29 März 2017
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Verkehr
HGF - Programmthema:Bodengebundener Verkehr (alt)
DLR - Schwerpunkt:Verkehr
DLR - Forschungsgebiet:V BF - Bodengebundene Fahrzeuge
DLR - Teilgebiet (Projekt, Vorhaben):V - Fahrzeugintelligenz (alt)
Standort: Braunschweig
Institute & Einrichtungen:Institut für Verkehrssystemtechnik
Hinterlegt von: Käthner, David
Hinterlegt am:02 Mai 2017 10:50
Letzte Änderung:24 Apr 2024 20:11

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