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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Human Performance Profiling While Driving a Sidestick-Controlled Car
verfasst von : Ljubo Mercep, Gernot Spiegelberg, Alois Knoll
Erschienen in: Data Science, Learning by Latent Structures, and Knowledge Discovery
Verlag: Springer Berlin Heidelberg
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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.