  The recent advancements in wireless technology enable connected autonomous
vehicles (CAVs) to gather data via vehicle-to-vehicle (V2V) communication, such
as processed LIDAR and camera data from other vehicles. In this work, we design
an integrated information sharing and safe multi-agent reinforcement learning
(MARL) framework for CAVs, to take advantage of the extra information when
making decisions to improve traffic efficiency and safety. We first use weight
pruned convolutional neural networks (CNN) to process the raw image and point
cloud LIDAR data locally at each autonomous vehicle, and share CNN-output data
with neighboring CAVs. We then design a safe actor-critic algorithm that
utilizes both a vehicle's local observation and the information received via
V2V communication to explore an efficient behavior planning policy with safety
guarantees. Using the CARLA simulator for experiments, we show that our
approach improves the CAV system's efficiency in terms of average velocity and
comfort under different CAV ratios and different traffic densities. We also
show that our approach avoids the execution of unsafe actions and always
maintains a safe distance from other vehicles. We construct an
obstacle-at-corner scenario to show that the shared vision can help CAVs to
observe obstacles earlier and take action to avoid traffic jams.
