Distributed and Coordinated Model Predictive Control for Channel Resource Allocation in Cooperative Vehicle Safety Systems

Cooperative vehicle safety systems rely on periodic broadcasts of beacons to track positions and movements of concerned vehicles. In vehicular networking, vehicle driving environment is changing rapidly. This unique characteristic can cause dynamic network topology and heavy traffic conditions. In scenarios where traffic density is high, a large number of beacons could cause channel congestion, and the tracking performance of safety applications can thus be seriously impacted. To maintain high-tracking accuracy for each node under varying traffic situations, this article presents a distributed and coordinated channel access control strategy based on multiagent model predictive control theory. First, we propose a multidimensional and hybrid Petri net model to characterize the interactions among multiple vehicles. The interaction model describes the possibility of collisions among vehicles. We then propose an application-dependent utility function that incorporates intervehicle collision behavior. A model predictive control problem for beaconing rate adaption is formulated based on the function. Next, a distributed and coordinated decision-making scheme is designed. In this scheme, each node is treated as an agent. Each agent uses a model predictive control controller and coordinates with its neighboring agents to take channel access control actions. Simulation results validate that it improves channel resource utilization and tracking accuracy under dynamic driving situations.