  In the typical autonomous driving stack, planning and control systems
represent two of the most crucial components in which data retrieved by sensors
and processed by perception algorithms are used to implement a safe and
comfortable self-driving behavior. In particular, the planning module predicts
the path the autonomous car should follow taking the correct high-level
maneuver, while control systems perform a sequence of low-level actions,
controlling steering angle, throttle and brake. In this work, we propose a
model-free Deep Reinforcement Learning Planner training a neural network that
predicts both acceleration and steering angle, thus obtaining a single module
able to drive the vehicle using the data processed by localization and
perception algorithms on board of the self-driving car. In particular, the
system that was fully trained in simulation is able to drive smoothly and
safely in obstacle-free environments both in simulation and in a real-world
urban area of the city of Parma, proving that the system features good
generalization capabilities also driving in those parts outside the training
scenarios. Moreover, in order to deploy the system on board of the real
self-driving car and to reduce the gap between simulated and real-world
performances, we also develop a module represented by a tiny neural network
able to reproduce the real vehicle dynamic behavior during the training in
simulation.
