Application of Data-Driven Model Predictive Control for Autonomous Vehicle Steering

TL;DR

This paper applies a data-driven model predictive control method to autonomous vehicle steering, reducing modeling complexity and computational cost while maintaining accurate trajectory tracking, validated through simulations.

Contribution

It introduces a data-driven MPC approach for vehicle steering that bypasses complex modeling, offering improved efficiency and accuracy over traditional methods.

Findings

Effective trajectory tracking with low computational time

Outperforms PID and vehicle kinematics MPC in simulations

Validated in CarSim-Simulink environment

Abstract

With the development of autonomous driving technology, there are increasing demands for vehicle control, and MPC has become a widely researched topic in both industry and academia. Existing MPC control methods based on vehicle kinematics or dynamics have challenges such as difficult modeling, numerous parameters, strong nonlinearity, and high computational cost. To address these issues, this paper adapts an existing Data-driven MPC control method and applies it to autonomous vehicle steering control. This method avoids the need for complex vehicle system modeling and achieves trajectory tracking with relatively low computational time and small errors. We validate the control effectiveness of the algorithm in specific scenario through CarSim-Simulink simulation and perform comparative analysis with PID and vehicle kinematics MPC, confirming the feasibility and superiority of it for vehicle steering control.