  The transition from today's mostly human-driven traffic to a purely automated
one will be a gradual evolution, with the effect that we will likely experience
mixed traffic in the near future. Connected and automated vehicles can benefit
human-driven ones and the whole traffic system in different ways, for example
by improving collision avoidance and reducing traffic waves. Many studies have
been carried out to improve intersection management, a significant bottleneck
in traffic, with intelligent traffic signals or exclusively automated vehicles.
However, the problem of how to improve mixed traffic at unsignalized
intersections has received less attention. In this paper, we propose a novel
approach to optimizing traffic flow at intersections in mixed traffic
situations using deep reinforcement learning. Our reinforcement learning agent
learns a policy for a centralized controller to let connected autonomous
vehicles at unsignalized intersections give up their right of way and yield to
other vehicles to optimize traffic flow. We implemented our approach and tested
it in the traffic simulator SUMO based on simulated and real traffic data. The
experimental evaluation demonstrates that our method significantly improves
traffic flow through unsignalized intersections in mixed traffic settings and
also provides better performance on a wide range of traffic situations compared
to the state-of-the-art traffic signal controller for the corresponding
signalized intersection.
