Abstract
The research on autonomous vehicles was started nearly a century ago but the major parts of significant advance have been done over the past few decades. In this paper, designing and simulating of a Model Predictive Controller (MPC) and comparing it to a classical feedback controller, which was designed and implemented on a car for Lane Keeping Assist (LKA) and Adaptive Cruise Control (ACC) systems, are presented. The goal of the control system is to follow linear trajectories and stay in the lanes by correcting the lateral deviation to reach the destination point. By regulating the longitudinal and lateral accelerations of the vehicle, it is possible to provide hands-free driving experience only on highways as it satisfies the level 2 autonomy according to the SAE automation levels. In the Adaptive Cruise Control (ACC) system, the ego car follows the velocity set by driver until it maintains a safe distance from the lead car. If the space between the lead car and the ego car is less than the safe distance, the ego car reduces the velocity and does not follow the driver set velocity until it has reached a safe distance with the lead car. The frontal or rear-end collisions and traffic congestions can be reduced by maintaining the safe distance using the spacing control by Adaptive Cruise Control (ACC) system. Model Predictive Control with the linear time invariant system with input, output and state variables uses the feed-forward and the disturbance models are used for ACC and Lane keeping Assist systems.
Highlights
The technologies have been growing rapidly with the increase in the driver safety features like Intelligent Driver’s Assist Systems (IDAS) and Advanced Driver Assist Systems (ADAS) which try to help the driver to travel safely
According to the National Highway Traffic Safety Administration (NHTSA) under the Fatality Analysis Reporting System (FARS) the number of deaths occurred in United States of America is 37,461 in 2016
This paper presents development of the Model Predictive Control (MPC) for steering of an autonomous vehicle
Summary
The technologies have been growing rapidly with the increase in the driver safety features like Intelligent Driver’s Assist Systems (IDAS) and Advanced Driver Assist Systems (ADAS) which try to help the driver to travel safely. This paper presents development of the Model Predictive Control (MPC) for steering of an autonomous vehicle. This paper mainly concentrates on the Model Predictive Control (MPC) for steering and Adaptive Cruise Control systems for an autonomous vehicle. A Model Predictive Controller has been designed for the steering control Such that the vehicle stays within the lanes follows the desired path as close as possible. Based on the optimization problem we considered the linear time invariant model predictive control:. The lateral movement of the vehicle in the lanes is controlled using the non-linear time invariant system. Yaw moment inertia of the vehicle is calculated and the longitudinal distance from the center of gravity to the front and rear tires is measured. The steering angle projection is characterized by the nonlinear single-track augmented model (Shengbo Li, 2011):
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