Decentralized Model Predictive Control for Platooning: Enhancing Human--Driver Collaboration

Abstract

Recent advances in cooperative adaptive cruise control have demonstrated the potential for vehicle platooning to revolutionize road transportation through enhanced safety, reduced congestion, and improved energy efficiency. While autonomous vehicle technology continues to evolve rapidly, current regulatory frameworks and safety considerations necessitate human–driver supervision. This creates a unique challenge in developing control systems that can effectively balance autonomous operation with human intervention. To enhance the human–driver collaboration with autonomous vehicle platooning, in this article, we present a novel decentralized model predictive control framework that explicitly incorporates human–driver interaction while maintaining desired intervehicle distances and velocities in platoon formations. This framework employs a distributed architecture where each vehicle operates independently and exchanges local measurements through vehicle-to-vehicle communication. To overcome the inherent unreliability of wireless communications in real-world scenarios, we develop a robust distributed state estimation strategy. This approach enables each vehicle to combine local sensor measurements with received data to construct accurate estimates of the full platoon state. Based on these estimates, vehicles compute optimal control actions locally while achieving performance comparable to an ideal centralized controller with perfect communication. Through extensive Plexe simulations, we demonstrate that the proposed decentralized model predictive controller achieves comparable performance to the ideal centralized case, even under partial state information and communication constraints.