Delay-aware Cooperative Perception with Deep Reinforcement Learning in Vehicular Networks

Cooperative perception is an advanced strategy within traffic systems designed to enhance the environmental perception capabilities of vehicles, where participants exchange cooperative perception messages (CPMs) through Vehicle-to-Everything (V2X) technology. However, most existing cooperative perception methods may ignore the communication bandwidth constraints of the system, potentially resulting in connected autonomous vehicles (CAVs) receiving outdated CPMs. In this paper, we propose a novel cooperative perception framework that enhances the accuracy of CAVs perception while reducing the transmission data size to meet the transmission delay requirements of CPMs under limited bandwidth. Furthermore, we propose a strategy for selecting cooperative partners and CPMs based on the Double Deep Q-Network (DDQN) algorithm. Additionally, an invalid action masking approach is presented to address the dynamic changes in the action space over time and reduce the size of the DDQN action space. Simulation results demonstrate that the proposed cooperative perception method consumes less data compared to some existing methods. Moreover, under limited communication bandwidth constraints, it achieves higher perception accuracy due to its ability to avoid transmission delay.