  Navigating through intersections is one of the main challenging tasks for an
autonomous vehicle. However, for the majority of intersections regulated by
traffic lights, the problem could be solved by a simple rule-based method in
which the autonomous vehicle behavior is closely related to the traffic light
states. In this work, we focus on the implementation of a system able to
navigate through intersections where only traffic signs are provided. We
propose a multi-agent system using a continuous, model-free Deep Reinforcement
Learning algorithm used to train a neural network for predicting both the
acceleration and the steering angle at each time step. We demonstrate that
agents learn both the basic rules needed to handle intersections by
understanding the priorities of other learners inside the environment, and to
drive safely along their paths. Moreover, a comparison between our system and a
rule-based method proves that our model achieves better results especially with
dense traffic conditions. Finally, we test our system on real world scenarios
using real recorded traffic data, proving that our module is able to generalize
both to unseen environments and to different traffic conditions.
