  Cooperative perception (CP) is a key technology to facilitate consistent and
accurate situational awareness for connected and autonomous vehicles (CAVs). To
tackle the network resource inefficiency issue in traditional broadcast-based
CP, unicast-based CP has been proposed to associate CAV pairs for cooperative
perception via vehicle-to-vehicle transmission. In this paper, we investigate
unicast-based CP among CAV pairs. With the consideration of dynamic perception
workloads and channel conditions due to vehicle mobility and dynamic radio
resource availability, we propose an adaptive cooperative perception scheme for
CAV pairs in a mixed-traffic autonomous driving scenario with both CAVs and
human-driven vehicles. We aim to determine when to switch between cooperative
perception and stand-alone perception for each CAV pair, and allocate
communication and computing resources to cooperative CAV pairs for maximizing
the computing efficiency gain under perception task delay requirements. A
model-assisted multi-agent reinforcement learning (MARL) solution is developed,
which integrates MARL for an adaptive CAV cooperation decision and an
optimization model for communication and computing resource allocation.
Simulation results demonstrate the effectiveness of the proposed scheme in
achieving high computing efficiency gain, as compared with benchmark schemes.
