Steering measurement decomposition for vehicle lane keeping: a study of driver behaviour

Zhang, Yu and Gordon, Timothy and Martinez, Miguel and Bingham, Chris (2018) Steering measurement decomposition for vehicle lane keeping: a study of driver behaviour. Measurement, 121 . pp. 26-38. ISSN 0263-2241

MEAS-D-16-00810.pdf - Whole Document

Item Type:Article
Item Status:Live Archive


Steering control for vehicle lane keeping has attracted significant attention from both automotive industries and researchers. To describe intermittent pulse-like qualities imparted by drivers that are seen in real-world steering measurements, a pulse control model (PCM) is presented for vehicle lane keeping. Inspired by the PCM, a steering angle measurement is decomposed into a combination of trend, integrated sine components (ISCs) and sine components (SCs), where trend corresponds to the path curvature, ISCs to the heading angles, and SCs to the lateral positions. Trends are extracted through the use of empirical mode decomposition (EMD) and principal component analysis (PCA), with singular spectral analysis (SSA) and Fourier curve-fitting (FCF) being employed to determine the ISCs and SCs in the main pulses. Through statistical pattern analysis on experimental measurements of drivers’ steering performance, it is revealed that (1) the pulse steering behaviour from real drivers shows the benefit of the proposed PCM for steering control during lane keeping, and (2) classification of pulse steering characteristics can be used for normal driver state identification and highlight abnormal driving behaviour, leading to the prospect of identifying driving characteristics typical of impaired concentration, substance misuse or tiredness, for instance.

Keywords:Pulse control model, empirical model decomposition, principal component analysis, singular spectral analysis, Fourier curve-fitting, statistical pattern analysis
Subjects:G Mathematical and Computer Sciences > G500 Information Systems
H Engineering > H330 Automotive Engineering
Divisions:College of Science > School of Engineering
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ID Code:31166
Deposited On:27 Feb 2018 10:28

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