Using Model Predictive Control for Collision Avoidance During Lane Change Maneuvers in Autonomous Vehicles

Abstract

This paper proposes a novel approach for collision avoidance during lane changes using Model Predictive Control (MPC). The proposed method integrates real-time trajectory planning with dynamic vehicle modeling to predict and optimize the vehicle's motion over a finite time horizon. The paper presents the fundamental principles of MPC, its integration with vehicle dynamics, and its application to real-time control. Simulation results demonstrate the effectiveness of MPC in optimizing trajectory planning and ensuring safety under various traffic scenarios. This paper provides a comprehensive comparison of MPC with other control models such as Proportional-Integral-Derivative (PID) control, Rule-Based Control (RBC), and Reinforcement Learning (RL)-based approaches. Simulation results demonstrate the effectiveness of the proposed method in a variety of traffic scenarios, including high-density and mixed-traffic environments. Experimental results highlight the relative performance of these models under simulated environments in MATLAB.