Integrating MPC and RL for Efficient Control of Autonomous Vehicles

Abstract

Autonomous vehicles offer significant potential for improving traffic efficiency and reducing fuel consumption, with Model Predictive Control (MPC) being widely used due to its ability to guarantee constraint satisfaction and safety while providing optimal control performance. However, car models traditionally used in MPC approaches for vehicle control often overlooks discrete dynamics like gear changes, which are critical for optimizing vehicle fuel consumption. Advancements have incorporated these discrete dynamics into MPC, resulting in a hybrid model that considers both continuous and discrete dynamics. The incorporation of the fuel model, along with these discrete dynamics, significantly increases the computational complexity of the MPC problem, making real-time implementation challenging. To address this issue, Reinforcement Learning (RL) can be leveraged to simplify the optimization problem by learning policies that determine key discrete components, such as gear selection. This allows the MPC controller to handle a simpler optimization problem, thereby reducing the computational burden and enabling real-time control. This research aims to propose a new approach to integrate RL and MPC for vehicle control, where RL is used to manage gear transitions and MPC controls the overall vehicle dynamics, offering a computationally efficient solution, while achieving near optimal performance comparable to the conventional MPC approach.