Human performance profilingwhile driving a sidestick-controlled car

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

Abstract

We have established a metric for measuring human performance while operating a sidestick-controlled car and have used it in conjunction with a known environment type to identify unusual steering trends. We focused on the analysis of the vehicle’s offset from the lane center in the time domain and identified a set of this signal’s features shared by all test drivers. The distribution of these features identifies a specific driving environment type and represents the essence of the proposed metric. We assumed that the driver performance, while operating a sidestick-controlled car, is determined by the environment type on one side and the driver’s own mental state on the other. The goal is to detect the mismatch of the assumed driving environment, gained from the introduced metric, and a ground truth about the actual environmental type, which can be obtained through map and GPS data, in order to identify unusual steering trend possibly caused by a change in driver fitness.

OriginalspracheEnglisch
TitelData Science, Learning by Latent Structures, and Knowledge Discovery
Redakteure/-innenMatthias Bohmer, Sabine Krolak-Schwerdt, Berthold Lausen
Herausgeber (Verlag)Kluwer Academic Publishers
Seiten455-463
Seitenumfang9
ISBN (Print)9783662449820
DOIs
PublikationsstatusVeröffentlicht - 2015
Veranstaltung1st European Conference on Data Analysis, ECDA 2013 - Walferdange, Luxemburg
Dauer: 10 Juli 201312 Juli 2013

Publikationsreihe

NameStudies in Classification, Data Analysis, and Knowledge Organization
Band48
ISSN (Print)1431-8814

Konferenz

Konferenz1st European Conference on Data Analysis, ECDA 2013
Land/GebietLuxemburg
OrtWalferdange
Zeitraum10/07/1312/07/13

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