  This paper explores the capability of deep neural networks to capture key
characteristics of vehicle dynamics, and their ability to perform coupled
longitudinal and lateral control of a vehicle. To this extent, two different
artificial neural networks are trained to compute vehicle controls
corresponding to a reference trajectory, using a dataset based on high-fidelity
simulations of vehicle dynamics. In this study, control inputs are chosen as
the steering angle of the front wheels, and the applied torque on each wheel.
The performance of both models, namely a Multi-Layer Perceptron (MLP) and a
Convolutional Neural Network (CNN), is evaluated based on their ability to
drive the vehicle on a challenging test track, shifting between long straight
lines and tight curves. A comparison to conventional decoupled controllers on
the same track is also provided.
