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This paper presents a Model Predictive Control (MPC) based path tracking algorithm for autonomous vehicles, addressing the critical need for robust planning and control in this rapidly evolving field. The algorithm optimizes control inputs over a prediction horizon using a vehicle dynamic model and reference path, incorporating obstacle avoidance by integrating obstacle locations from a 3D LiDAR-generated occupancy grid map into the cost function. Simulation and real-world experiments demonstrate the algorithm's ability to effectively follow the reference path while avoiding obstacles.
MPC-based path tracking for autonomous vehicles can achieve reliable navigation and obstacle avoidance in real-world settings.
In recent years, there have been countless studies on autonomous vehicles. And this field is growing. Considering this growth, the issue of planning and control, which has an important place in autonomous vehicles, comes to the fore. In this study, a path tracking algorithm based on Model Predictive Control (MPC) is developed for autonomous vehicle control. MPC is basically to predict the future behavior of a generated cost function to be minimized by optimization methods. In the proposed algorithm, control inputs are calculated over a prediction horizon using the vehicle dynamic model and the reference path to optimize the vehicle progression. In order to add the obstacle avoidance mechanism to the system, obstacle locations are detected from an occupancy grid map generated with three-dimensional LiDAR and added to the cost function. Simulation and real-world tests have shown that the MPC algorithm can optimally follow the reference path while avoiding obstacles.
