Model Predictive Power Control for Cooperative Vehicle Safety Systems

Abstract

In vehicular networking, the heavy traffic can cause channel congestion and hence, degrade the tracking accuracy of cooperative vehicle safety systems. To overcome this problem, a dynamic packet reception model that integrates the packets reception rate and the vehicle density is proposed. Then, a traffic-flow-based vehicle density estimation method is designed. This estimation method is capable of predicting the vehicle density in the scenario, where there exist strong interactions among the vehicles. Based on the vehicle density method, a dynamical transmission power control strategy is developed. This transmission power control strategy employs model predictive control to make the optimal control decisions based on the estimated vehicle density. Experimental analyses demonstrate that the dynamical power control strategy can greatly enhance the vehicle tracking performance of cooperative vehicle safety systems under dynamical traffic situation.