  We consider the challenging problem of high speed autonomous racing in a
realistic Formula One environment. DeepRacing is a novel end-to-end framework,
and a virtual testbed for training and evaluating algorithms for autonomous
racing. The virtual testbed is implemented using the realistic F1 series of
video games, developed by Codemasters, which many Formula One drivers use for
training. This virtual testbed is released under an open-source license both as
a standalone C++ API and as a binding to the popular Robot Operating System 2
(ROS2) framework. This open-source API allows anyone to use the high fidelity
physics and photo-realistic capabilities of the F1 game as a simulator, and
without hacking any game engine code. We use this framework to evaluate several
neural network methodologies for autonomous racing. Specifically, we consider
several fully end-to-end models that directly predict steering and acceleration
commands for an autonomous race car as well as a model that predicts a list of
waypoints to follow in the car's local coordinate system, with the task of
selecting a steering/throttle angle left to a classical control algorithm. We
also present a novel method of autonomous racing by training a deep neural
network to predict a parameterized representation of a trajectory rather than a
list of waypoints. We evaluate these models performance in our open-source
simulator and show that trajectory prediction far outperforms end-to-end
driving. Additionally, we show that open-loop performance for an end-to-end
model, i.e. root-mean-square error for a model's predicted control values, does
not necessarily correlate with increased driving performance in the closed-loop
sense, i.e. actual ability to race around a track. Finally, we show that our
proposed model of parameterized trajectory prediction outperforms both
end-to-end control and waypoint prediction.
