  This work introduces interactive traffic scenarios in the CARLA simulator,
which are based on real-world traffic. We concentrate on tactical tasks lasting
several seconds, which are especially challenging for current control methods.
The CARLA Real Traffic Scenarios (CRTS) is intended to be a training and
testing ground for autonomous driving systems. To this end, we open-source the
code under a permissive license and present a set of baseline policies. CRTS
combines the realism of traffic scenarios and the flexibility of simulation. We
use it to train agents using a reinforcement learning algorithm. We show how to
obtain competitive polices and evaluate experimentally how observation types
and reward schemes affect the training process and the resulting agent's
behavior.
