Nonlinear Model Predictive Control for Autonomous Vehicle Overtaking with Bezier Curve Path Implementing Autonomous Car Overtaking with A Real-Time Path Planning Approach Using Bezier Curves
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Abstract
Rapid advancements in autonomous vehicle technology have significantly improved driving safety, yet formulating safe and efficient overtaking strategies remains a key challenge. This study proposes an algorithmic framework for autonomous vehicles to perform overtaking maneuvers, validated through comprehensive simulations. The framework utilizes Nonlinear Model Predictive Control (NMPC) to manage complex vehicle dynamics, processing real-time velocity and steering angle inputs. Real-time feedback from LiDAR sensors enables dynamic trajectory generation using Bezier curve formulations, which provide reference inputs for the pure pursuit algorithm to guide the NMPC during overtaking. Once the vehicle aligns with the obstacle, the Bezier curve is adjusted to smoothly return the vehicle to its original path. The approach was thoroughly evaluated using the Robot Operating System (ROS) and Gazebo, ensuring realistic visualization and robust performance under dynamic conditions.
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