  Testing self-driving cars in different areas requires surrounding cars with
accordingly different driving styles such as aggressive or conservative styles.
A method of numerically measuring and differentiating human driving styles to
create a virtual driver with a certain driving style is in demand. However,
most methods for measuring human driving styles require thresholds or labels to
classify the driving styles, and some require additional questionnaires for
drivers about their driving attitude. These limitations are not suitable for
creating a large virtual testing environment. Driving models (DMs) simulate
human driving styles. Calibrating a DM makes the simulated driving behavior
closer to human-driving behavior, and enable the simulation of human-driving
cars. Conventional DM-calibrating methods do not take into account that the
parameters in a DM vary while driving. These "fixed" calibrating methods cannot
reflect an actual interactive driving scenario. In this paper, we propose a
DM-calibration method for measuring human driving styles to reproduce real
car-following behavior more accurately. The method includes 1) an objective
entropy weight method for measuring and clustering human driving styles, and 2)
online adaption of DM parameters based on deep learning by combining Bayesian
optimization (BO) and a gated recurrent unit neural network. We conducted
experiments to evaluate the proposed method, and the results indicate that it
can be easily used to measure human driver styles. The experiments also showed
that we can calibrate a corresponding DM in a virtual testing environment with
up to 26% more accuracy than with fixed calibration methods.
