  Connected Autonomous Vehicle (CAV) Network can be defined as a collection of
CAVs operating at different locations on a multilane corridor, which provides a
platform to facilitate the dissemination of operational information as well as
control instructions. Cooperation is crucial in CAV operating systems since it
can greatly enhance operation in terms of safety and mobility, and high-level
cooperation between CAVs can be expected by jointly plan and control within CAV
network. However, due to the highly dynamic and combinatory nature such as
dynamic number of agents (CAVs) and exponentially growing joint action space in
a multiagent driving task, achieving cooperative control is NP hard and cannot
be governed by any simple rule-based methods. In addition, existing literature
contains abundant information on autonomous driving's sensing technology and
control logic but relatively little guidance on how to fuse the information
acquired from collaborative sensing and build decision processor on top of
fused information. In this paper, a novel Deep Reinforcement Learning (DRL)
based approach combining Graphic Convolution Neural Network (GCN) and Deep Q
Network (DQN), namely Graphic Convolution Q network (GCQ) is proposed as the
information fusion module and decision processor. The proposed model can
aggregate the information acquired from collaborative sensing and output safe
and cooperative lane changing decisions for multiple CAVs so that individual
intention can be satisfied even under a highly dynamic and partially observed
mixed traffic. The proposed algorithm can be deployed on centralized control
infrastructures such as road-side units (RSU) or cloud platforms to improve the
CAV operation.
