TY - GEN
T1 - Human performance profilingwhile driving a sidestick-controlled car
AU - Mercep, Ljubo
AU - Spiegelberg, Gernot
AU - Knoll, Alois
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2015.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84945571146&partnerID=8YFLogxK
U2 - 10.1007/978-3-662-44983-7_40
DO - 10.1007/978-3-662-44983-7_40
M3 - Conference contribution
AN - SCOPUS:84945571146
SN - 9783662449820
T3 - Studies in Classification, Data Analysis, and Knowledge Organization
SP - 455
EP - 463
BT - Data Science, Learning by Latent Structures, and Knowledge Discovery
A2 - Bohmer, Matthias
A2 - Krolak-Schwerdt, Sabine
A2 - Lausen, Berthold
PB - Kluwer Academic Publishers
T2 - 1st European Conference on Data Analysis, ECDA 2013
Y2 - 10 July 2013 through 12 July 2013
ER -