  Urban intersections are prone to delays and inefficiencies due to static
precedence rules and occlusions limiting the view on prioritized traffic.
Existing approaches to improve traffic flow, widely known as automatic
intersection management systems, are mostly based on non-learning reservation
schemes or optimization algorithms. Machine learning-based techniques show
promising results in planning for a single ego vehicle. This work proposes to
leverage machine learning algorithms to optimize traffic flow at urban
intersections by jointly planning for multiple vehicles. Learning-based
behavior planning poses several challenges, demanding for a suited input and
output representation as well as large amounts of ground-truth data. We address
the former issue by using a flexible graph-based input representation
accompanied by a graph neural network. This allows to efficiently encode the
scene and inherently provide individual outputs for all involved vehicles. To
learn a sensible policy, without relying on the imitation of expert
demonstrations, the cooperative planning task is considered as a reinforcement
learning problem. We train and evaluate the proposed method in an open-source
simulation environment for decision making in automated driving. Compared to a
first-in-first-out scheme and traffic governed by static priority rules, the
learned planner shows a significant gain in flow rate, while reducing the
number of induced stops. In addition to synthetic simulations, the approach is
also evaluated based on real-world traffic data taken from the publicly
available inD dataset.
