  An important topic in the autonomous driving research is the development of
maneuver planning systems. Vehicles have to interact and negotiate with each
other so that optimal choices, in terms of time and safety, are taken. For this
purpose, we present a maneuver planning module able to negotiate the entering
in busy roundabouts. The proposed module is based on a neural network trained
to predict when and how entering the roundabout throughout the whole duration
of the maneuver. Our model is trained with a novel implementation of A3C, which
we will call Delayed A3C (D-A3C), in a synthetic environment where vehicles
move in a realistic manner with interaction capabilities. In addition, the
system is trained such that agents feature a unique tunable behavior, emulating
real world scenarios where drivers have their own driving styles. Similarly,
the maneuver can be performed using different aggressiveness levels, which is
particularly useful to manage busy scenarios where conservative rule-based
policies would result in undefined waits.
