Improved Steering and Adaptive Cruise Control for Autonomous Vehicles Using Model Predictive Control

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.