Cloud predictive control of connected vehicle platoon with quantized signals

The intelligent and connected vehicle platoon control system requires supercomputing capabilities and scalability to process and store data for complex tasks in face of increasingly complex information. Traditional platoon control platforms are inadequate to handle enormous computational requirements. In this paper, we propose a cloud predictive control (CPC) architecture to achieve cooperative control in vehicle platoon system. Firstly, the CPC architecture provides remote control, effective monitoring, and platoon data collection through cloud technology and vehicle communications. This integration enhances the coordination and operation of platoon by centralizing control and information flow. Secondly, due to the hard constraints for vehicles in actual platoon system, this paper introduces a cloud predictive method for designing controller. Additionally, since a large amount of data needs to be processed by the cloud, quantization techniques are utilized to reduce the transmission cost. The stability of individual vehicle and overall stability of platoon are analyzed in this paper. Finally, the proposed method is validated through simulation experiments.