  The improvement of traffic efficiency at urban intersections receives strong
research interest in the field of automated intersection management. So far,
mostly non-learning algorithms like reservation or optimization-based ones were
proposed to solve the underlying multi-agent planning problem. At the same
time, automated driving functions for a single ego vehicle are increasingly
implemented using machine learning methods. In this work, we build upon a
previously presented graph-based scene representation and graph neural network
to approach the problem using reinforcement learning. The scene representation
is improved in key aspects by using edge features in addition to the existing
node features for the vehicles. This leads to an increased representation
quality that is leveraged by an updated network architecture. The paper
provides an in-depth evaluation of the proposed method against baselines that
are commonly used in automatic intersection management. Compared to a
traditional signalized intersection and an enhanced first-in-first-out scheme,
a significant reduction of induced delay is observed at varying traffic
densities. Finally, the generalization capability of the graph-based
representation is evaluated by testing the policy on intersection layouts not
seen during training. The model generalizes virtually without restrictions to
smaller intersection layouts and within certain limits to larger ones.
