  The control for aggressive driving of autonomous cars is challenging due to
the presence of significant tyre slip. Data-driven and mechanism-based methods
for the modeling and control of autonomous cars under aggressive driving
conditions are limited in data efficiency and adaptability respectively. This
paper is an attempt toward the fusion of the two classes of methods. By means
of a modular design that is consisted of mechanism-based and data-driven
components, and aware of the two-timescale phenomenon in the car model, our
approach effectively improves over previous methods in terms of data
efficiency, ability of transfer and final performance. The hybrid
mechanism-and-data-driven approach is verified on TORCS (The Open Racing Car
Simulator). Experiment results demonstrate the benefit of our approach over
purely mechanism-based and purely data-driven methods.
