  An efficient and reliable multi-agent decision-making system is highly
demanded for the safe and efficient operation of connected autonomous vehicles
in intelligent transportation systems. Current researches mainly focus on the
Deep Reinforcement Learning (DRL) methods. However, utilizing DRL methods in
interactive traffic scenarios is hard to represent the mutual effects between
different vehicles and model the dynamic traffic environments due to the lack
of interactive information in the representation of the environments, which
results in low accuracy of cooperative decisions generation. To tackle these
difficulties, this research proposes a framework to enable different Graph
Reinforcement Learning (GRL) methods for decision-making, and compares their
performance in interactive driving scenarios. GRL methods combinate the Graph
Neural Network (GNN) and DRL to achieve the better decisions generation in
interactive scenarios of autonomous vehicles, where the features of interactive
scenarios are extracted by the GNN, and cooperative behaviors are generated by
DRL framework. Several GRL approaches are summarized and implemented in the
proposed framework. To evaluate the performance of the proposed GRL methods, an
interactive driving scenarios on highway with two ramps is constructed, and
simulated experiment in the SUMO platform is carried out to evaluate the
performance of different GRL approaches. Finally, results are analyzed in
multiple perspectives and dimensions to compare the characteristic of different
GRL approaches in intelligent transportation scenarios. Results show that the
implementation of GNN can well represents the interaction between vehicles, and
the combination of GNN and DRL is able to improve the performance of the
generation of lane-change behaviors. The source code of our work can be found
at https://github.com/Jacklinkk/TorchGRL.
