Design of Predictive Control System for Lane Change in Autonomous Vehicle

  • Shervind Maharani Mega Permatasari Engineering Physics, Industrial Technology and Systems Engineering, Institut Teknologi Sepuluh Nopember, Surabaya
  • Bambang Lelono Widjiantoro Engineering Physics, Industrial Technology and Systems Engineering, Institut Teknologi Sepuluh Nopember, Surabaya
  • Katherin Indriawati Engineering Physics, Industrial Technology and Systems Engineering, Institut Teknologi Sepuluh Nopember, Surabaya
https://doi.org/10.25139/ijair.v6i1.8141
Abstract views: 184 , PDF downloads: 131
Keywords: Autonomous Vehicle, Lane Change, Model Predictive Control

Abstract

The automobile business is introducing a lot of autonomous vehicles in the modern day. Lane changes are one of the most complicated urban scenarios in which autonomous vehicles are used. Self-driving automobiles must thus interact with human-driven vehicles in a certain way. In this work, we concentrate on the autonomous vehicle's lane-changing control system for obstacle avoidance. This study employs a predictive control system as its methodology. The vehicle's next movements can be predicted by this control system. The vehicle's position, which is adjusted by the steering angle, is the controllable variable.  The vehicle's position, which is adjusted by the steering angle, is the controllable variable. It is clear from the numerical simulation results that the predictive control system executes control actions on lane changes correctly, avoiding collisions with the running vehicle obstacles. RMSE (Root-Mean Square Error) is a performance metric that is derived from the difference between the vehicle's lateral position and the reference trajectory value. The RMSE of the planned predictive control is 0.9681.

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References

S. Thrun et al., "Stanley : The Robot that Won the DARPA Grand Challenge," vol. 23, no. June, pp. 661–692, 2006.W.-K. Chen, Linear Networks and Systems (Book style). Belmont, CA: Wadsworth (1993) 123–135.

C. Urmson et al., "Autonomous Driving in Urban Environments : Boss and the Urban Challenge," vol. 25, no. February, pp. 425–466, 2008..

M. Almaged and A. Mahmood, "Autonomous Model Vehicles : Signal Conditioning and Digital Control Design," no. September 2018, 2019.

J. Liu, P. Jayakumar, J. L. Stein, and T. Ersal, "An MPC algorithm with combined speed and steering control for obstacle avoidance in autonomous ground vehicles," ASME 2015 Dyn. Syst. Control Conf. DSCC 2015, vol. 3, 2015.

Arfianti, Rida Ayu (2024) Perancangan Sistem Kontrol untuk Obstacle Avoidance pada Autonomous Vehicle dengan Kontrol Prediktif Berbasis Robot Operating System (ROS). Masters thesis, Institut Teknologi Sepuluh Nopember.

Permatasari, Shervind Maharani (2023) Perancangan Sistem Trajectory Generation Pada Autonomous Vehicle Menggunakan A* Algorithm. Other thesis, Institut Teknologi Sepuluh Nopember.

Hu, X., Chen, L., Tang, B., Cao, D., & He, H. (2018). Dynamic path planning for autonomous driving on various roads with avoidance of static and moving obstacles. Mechanical Systems and Signal Processing, 100, 482–500. https://doi.org/10.1016/j.ymssp.2017.07.019

Li, X., Sun, Z., Cao, D., He, Z., & Zhu, Q. (2016). Real-time trajectory planning for autonomous urban driving: Framework, algorithms, and verifications. IEEE/ASME Transactions on Mechatronics, 21(2), 740–753. https://doi.org/10.1109/TMECH.2015.2493980

Liu, J., Jayakumar, P., Stein, J. L., & Ersal, T. (2015). An MPC algorithm with combined speed and steering control for obstacle avoidance in autonomous ground vehicles. ASME 2015 Dynamic Systems and Control Conference, DSCC 2015, 3. https://doi.org/10.1115/DSCC2015-9747

Liu, J., Jayakumar, P., Stein, J. L., & Ersal, T. (2019). Improving the robustness of an MPC-based obstacle avoidance algorithm to parametric uncertainty using worst-case scenarios. Vehicle System Dynamics, 57(6), 874–913. https://doi.org/10.1080/00423114.2018.1492141

S. Brown, M. Lee, dan C. Wilson, "Trajectory Planning for Autonomous Vehicles Using the Kinematic Bicycle Model," IEEE Transactions on Intelligent Transportation Systems,

C. Huang, F. Naghdy and H. Du, "Model predictive control-based lane change control system for an autonomous vehicle," 2016 IEEE Region 10 Conference (TENCON), Singapore, 2016, pp. 3349-3354, doi: 10.1109/TENCON.2016.7848673. keywords: {Wheels;Actuators;Radio frequency;Feedforward neural networks;Lane change control;model predictive control;vehicle safety;autonomous vehicle},

Published
2024-05-31
How to Cite
Mega Permatasari, S. M., Widjiantoro, B. L., & Indriawati, K. (2024). Design of Predictive Control System for Lane Change in Autonomous Vehicle. International Journal of Artificial Intelligence & Robotics (IJAIR), 6(1), 8-18. https://doi.org/10.25139/ijair.v6i1.8141
Issue
Vol. 6 No. 1 (2024): May 2024
Section
Articles

Copyright (c) 2024 Shervind Maharani Mega Permatasari, Bambang Lelono Widjiantoro, Katherin Indriawati

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