In this paper, driver-in-the-loop simulation system is designed to acquire multiple sets of driving data and driving skill level classification is achieved through wavelet analysis, employing singularity as evaluation criterion. Based on FSAE race car model, longitudinal-lateral combined driver model is proposed through co-simulation of Carsim® and NI-PXI apparatus. Two-point visual preview theory is employed to construct lateral model, which is determined by static gains, correction time and physical delay. These three parameters are achieved by fitting driving data test with least square method. Dynamic velocity controller is added to longitudinal model in order to obtain maximum speed in tangential direction of desired trajectory. Finally, the racing driver model introduced in this paper is validated through simulation on circuit of FSAE, exhibiting good performance in path tracking. The result also shows the racing driver model performs better than typical driver model in lateral tracking control on the given circuit, with maximum lateral error of 0.497m. Meanwhile, the number of singularities of steering input, operated by racing driver model, is much smaller than typical driver model. That means racing driver model handles much smoother than typical driver. Thus, it reveals the different features between racing driver and typical driver distinctly, which is consistent with characteristics extracted from driving test data. The proposed driver model is significant for research in race car dynamics simulation as well.

Racing Driver Modeling Based on Driving Behavior

Paper Proceedings: https://asmedigitalcollection.asme.org/IDETC-CIE/IDETC-CIE2021/volume/85369

ASME 2021 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE2021)

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