Adaptive Model Predictive Control (Adp_MPC) Utilized in Autonomous Vehicle (AV) Assistance Systems

This paper suggests using adaptive model predictive control (Apd_MPC) for autonomous vehicle driver assistance systems. The Apd_MPC modifies parameters instantly according to variations in system dynamics, improving steering precision in autonomous vehicles. The findings show that adaptive model predictive control dramatically enhances the autonomous vehicle’s capacity to navigate intricate driving situations accurately and flexibly. The system can adapt to abrupt environmental changes by modifying control parameters, including barriers or road conditions. This adaptive method enhances safety and optimizes the vehicle’s performance, resulting in smoother and more dependable autonomous driving experiences. Simulated situations at various velocities in MATLAB/SIMULINK demonstrate the efficacy of this method. The results indicate that Apd_MPC offers a promising solution for enhancing the capabilities of autonomous vehicles in complex driving scenarios. The implementation of Apd_MPC in autonomous vehicle driver assistance systems shows excellent potential for improving safety, performance, and overall driving experience.