Cooperative adaptive cruise control system with improved variable spacing strategy

To further improve the safety, tracking, comfort, fuel economy, and platoon fluctuation of the cooperative adaptive cruise control (CACC) system, and alleviate traffic congestion, an improved model predictive control (MPC) algorithm considering multi-objective optimization is designed. An error compensation prediction constant time headway spacing strategy considering relative velocity, relative acceleration, and preceding vehicle distance error is proposed. The spacing strategy is introduced into the prediction model of MPC to optimize the prediction accuracy, improve the response-ability of the rear vehicle to the change of the lead state, and better coordinate the conflicting multiple objectives. The asymptotic stability of the CACC system under the improved MPC algorithm is proved by the Lyapunov stability theory, and the evaluation index is established to quantify the comprehensive performance of the CACC system. The numerical simulation is carried out under rapid acceleration and deceleration conditions, and the results show that the improved model predictive control algorithm can improve the safety, tracking, comfort, fuel economy, and road capacity of the CACC system. To simulate real traffic scenarios, co-simulation is carried out under the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) condition, which further verifies the rationality and effectiveness of the algorithm.