A Model-Based Control of Self-Driving Car Trajectory for Lanes Change Maneuver in a Smart City-Reference-Cited by-同舟云学术

A Model-Based Control of Self-Driving Car Trajectory for Lanes Change Maneuver in a Smart City

Published:2023-10-27 Issue: Volume:18 Page:346-353
ISSN:2224-2856
Container-title:WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL
language:en
Short-container-title:

Author:

Astrov Igor¹

Affiliation:

1. Department of Software Science, Tallinn University of Technology, Akadeemia tee 15a, 12618, Tallinn, ESTONIA

Abstract

High-quality computer control of autonomous vehicles in various environments is a priority for cyber-physical systems (CPS), Industry 4.0, and the global economy as a whole. The paper discusses the linearized control model of a Self-Driving Car (SDC) with a weight of 1160 kg. For safe maneuvering with obstacle avoidance, we employ an optimal control by Linear Quadratic Regulator (LQR) using a Simulink/MATLAB environment that is capable to demonstrate the satisfiability of LQR control for this maneuver using a 3D simulation environment under changing urban conditions in a smart city. This controller is easy for engineering implementation.

Publisher

World Scientific and Engineering Academy and Society (WSEAS)

Subject

Artificial Intelligence,General Mathematics,Control and Systems Engineering

Reference16 articles.

1. V. Tsiatsis, S. Karnouskos, J. Holler, D. Boyle, and C. Mulligan, “Autonomous vehicles and systems of cyber-physical systems,” in Internet of Things: Technologies and Applications for a New Age of Intelligence, London: Academic Press, 2019, pp. 209–305.

2. N. A. Stanton, R. Stewart, D. Harris, and R. J. Houghton, “Distributed situation awareness in dynamic systems: theoretical development and application of an ergonomics methodology,” Ergonomics, vol. 49, October 2006, pp. 1287–1311.

3. R. Sell, M. Leier, A. Rassõlkin, and J.-P. Ernits, “Autonomous last mile shuttle ISEAUTO for education and research,” International Journal of Artificial Intelligence and Machine Learning, vol. 10, June 2020, pp. 18–30.

4. K. Kalda, R. Sell, and R.-M. Soe, “Selfdriving shuttle bus use case in city of Tallinn”, Proc. IOP Conference Series: Materials Science and Engineering/ Modern Materials and Manufacturing, MMM 2021, pp. 1–6, 2021.

5. I. Astrov, Neural Network Control of Vehicles: Modeling and Simulation. Hauppauge, New York, USA: Nova Science Publishers, 2022.