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

CARLA Real Traffic Scenarios -- novel training ground and benchmark for autonomous driving