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Proper functioning of connected and automated vehicles (CAVs) is crucial forthe safety and efficiency of future intelligent transport systems. Meanwhile,transitioning to fully autonomous driving requires a long period of mixedautonomy traffic, including both CAVs and human-driven vehicles. Thus,collaboration decision-making for CAVs is essential to generate appropriatedriving behaviors to enhance the safety and efficiency of mixed autonomytraffic. In recent years, deep reinforcement learning (DRL) has been widelyused in solving decision-making problems. However, the existing DRL-basedmethods have been mainly focused on solving the decision-making of a singleCAV. Using the existing DRL-based methods in mixed autonomy traffic cannotaccurately represent the mutual effects of vehicles and model dynamic trafficenvironments. To address these shortcomings, this article proposes a graphreinforcement learning (GRL) approach for multi-agent decision-making of CAVsin mixed autonomy traffic. First, a generic and modular GRL framework isdesigned. Then, a systematic review of DRL and GRL methods is presented,focusing on the problems addressed in recent research. Moreover, a comparativestudy on different GRL methods is further proposed based on the designedframework to verify the effectiveness of GRL methods. Results show that the GRLmethods can well optimize the performance of multi-agent decision-making forCAVs in mixed autonomy traffic compared to the DRL methods. Finally, challengesand future research directions are summarized. This study can provide avaluable research reference for solving the multi-agent decision-makingproblems of CAVs in mixed autonomy traffic and can promote the implementationof GRL-based methods into intelligent transportation systems. The source codeof our work can be found at https://github.com/Jacklinkk/Graph_CAVs.

Graph Reinforcement Learning Application to Co-operative Decision-Making in Mixed Autonomy Traffic: Framework, Survey, and Challenges