Focusing on autonomous driving algorithm development, this paper proposes a novel real-
time trajectory planner formulated as a Nonlinear Model Predictive Control (NMPC) …

This paper presents a trajectory planner for autonomous driving based on a Nonlinear
Model Predictive Control (NMPC) algorithm that accounts for Pacejka's nonlinear lateral tyre …

Several studies have demonstrated that car dynamics computation is essential for
autonomous car motion planning. One of the most promising techniques for motion planning …

The aim of this study was to develop trajectory planning that would allow an autonomous
racing car to be driven as close as possible to what a driver would do, defining the most …

Model predictive controller (MPC) has demonstrated its competency in controlling
autonomous vehicles. But to apply the current MPC-based schemes, it has to be formulated …

Motion trajectory planning is one crucial aspect for automated vehicles, as it governs the
own future behavior in a dynamically changing environment. A good utilization of a vehicle's …

Motion planning by considering it as an optimal problem is an effective and widely
applicable method. Its comprehensive performance greatly depends on the vehicle …

A huge research effort is being spent worldwide by automotive companies and academic
institutions for developing vehicles with high levels of autonomy, ranging from advanced …

Model predictive control (MPC) is often used for controlling the autonomous vehicle tracking
the target path. But to apply MPC schemes, the nonlinear model of vehicle kinematics needs …

Abstract<? Pub Inc> With the advent of faster computer processors and better optimization
algorithms, Model Predictive Control (MPC) systems are more readily used for real-time …