Vision-based curved lane keeping control for intelligent vehicle highway system

Abstract

This article addresses the coordinated longitudinal and lateral motion control for an intelligent vehicle highway system. The strategy of this work consists of defining the edges of the traveled lane using a vision sensor. According to the detected boundaries, a constrained path-following method is proposed to drive the longitudinal and the lateral vehicle’s motion. Error constraints of the intelligent vehicle highway system position are manipulated by including the function of barrier Lyapunov in designing the guidance algorithm for the intelligent vehicle highway system. To calculate the necessary forces that would steer the vehicle to the desired path, a control design is proposed that integrates the sign of the error for the compensation of the uncertain vehicle’s parameters. The Lyapunov function is later used to minimize the path-following errors and to guarantee a stable system. The efficiency of the developed approach is proved by numerical simulations.