A Comprehensive Analysis of Model Predictive Control for Lane Keeping Assist System

Abstract

Lane Keeping Assist System (LKAS) enhances comfort and safety while driving. It plays a significant role in the Advanced Driver Assistance System (ADAS) and future Automated Driving (AD). The LKAS solution aims to help the driver keep the vehicle within the road lines, preventing unintentional lane departure. Despite LKAS being an important solution for comfortable driving, robust LKAS steering control is still lacking, requiring constant driver intervention or premature LKAS deactivation. LKAS require optimal control solutions with real-time constraints. This paper comprehensively analyzes Model Predictive Control (MPC) for real-time LKAS applications. Classical and parameterized MPC schemes with distinct Quadratic Programming (QP) solvers are combined to evaluate LKAS closed-loop control performance and realtime constraints. A sideslip and lateral speed bicycle modes were used to evaluate classical, trivial, and exponential MPC schemes. Experimental results highlight the three MPC and QP-appropriate solutions with satisfactory reference tracking without steering command and real-time constraints violation. INDEX TERMS Advanced Driver Assistance Systems, Lane Keeping Assist System, Model Predictive Control, MPC parameterization, Quadratic Programming. I. INTRODUCTIONV EHICLE safety encompasses regulations, actions, and technological solutions designed to prevent traffic accidents [1]. Today, there is a global effort involving multiple sectors, including transport, health, education, etc., to address safety on roads [2]. Carmakers and governments worldwide are actively seeking innovative solutions to enhance the safety of all road users. Active safety systems such as Antilock Braking Systems (ABS), Electronic Stability Control (ESC), Autonomous Emergency Braking (AEBS), and Lane Assist Systems (LAS) are employed to prevent accidents from occurring altogether, sometimes actively assisting the driver in mitigating the impact. These Advanced Driver Assistance Systems (ADAS) are often implemented to be forgiving of human error.