CCPAV: Centralized cooperative perception for autonomous vehicles using CV2X

Abstract

Cooperative perception improves awareness for various traffic situations by connecting autonomous vehicles to each other as well as to the surrounding environment. Overcoming the line-of-sight challenge due to the limitations of vehicle's local sensors is of great importance. However, inefficient solutions can quickly lead to depletion of the limited communication resources. This is critical, especially that the dominant CV2X (Cellular Vehicle to Everything) technology witnesses unprecedented growth in the number and density of connected smart devices. To this end, this paper provides a new centralized cooperative perception approach using CV2X. The system uses vehicle trajectories to prioritize messages communicated messages. Through the basestation (BS), the system prioritizes message requests while being constrained by the available network resources. This system is then implemented and evaluated using 1000 different traffic scenarios. These scenarios are generated using both SUMO (Simulation of Urban MObility) traffic simulator and a new implemented camera simulator used to represent the vehicle's sensors’ abilities to perceive the surrounding environment. Results show that the system, on average, can execute at least 95% of the total message values using only 100 physical resource blocks for CV2X regardless of the autonomous vehicle densities. This is very robust especially for congested networks as the number of messages requests executed varies between 65% to 95% given the different autonomous vehicle densities. To the best of our knowledge, this work is the first to use vehicle trajectories to jointly select messages for transmission and allocate RBs (Resource Blocks).