Vehicle Adaptive Cruise Control Based on Model Predictive Algorithm

With the development of the information age and electric vehicles, there is an increasing demand for highly safe and comfortable driving experiences. Autonomous driving, based on adaptive cruise systems, has emerged in response to these needs. As one of the most critical technologies in vehicle ADAS, adaptive cruise systems have always been a focal point in the research of autonomous driving technology. These systems can control vehicle speed, distance, and lane-changing operations through various sensors and decision-making facilities during long-distance driving, thereby reducing driver fatigue and enhancing driving comfort. At the same time, adaptive cruise systems carry out safer operations by precisely perceiving the environment and performing accurate calculations, meeting the demand for a safe and comfortable driving environment. The paper predicts vehicle behavior using the MPC (Model Predictive Control) algorithm based on the principles of adaptive cruise systems. The intelligent decision-making and optimization instructions of the adaptive cruise system module are then used to control the vehicle’s power, braking, and other systems to execute the commands. A simulation platform was built using Matlab/Simulink software to simulate working conditions such as distance control. The experimental results show that the MPC algorithm enhances the precision of the vehicle’s following movement in the cruise system, demonstrating better practical effects. It adapts to various traffic environments and ensures greater stability and safety during driving.