Journal of Electrical Engineering and Automation
IRO Journals
Volume 4 — Issue 4 — December 2022

Dynamic Vehicle Modelling and Controlling Techniques for Autonomous Vehicle Systems

https://doi.org/10.36548/jeea.2022.4.007
R. Sushma
Department of Electronics and Instrumentation Engineering, Dayananda Sagar College of Engineering, Bangalore, Karnataka, India
J. Satheesh Kumar
Associate Professor, Department of Electronics and Instrumentation Engineering, Dayananda Sagar College of Engineering, Bangalore, Karnataka, India
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How to Cite

Sushma, R., and J. Satheesh Kumar. 2023. “Dynamic Vehicle Modelling and Controlling Techniques for Autonomous Vehicle Systems”. Journal of Electrical Engineering and Automation 4 (4): 307-15. https://doi.org/10.36548/jeea.2022.4.007.
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Keywords

— Autonomous vehicle
— model predictive control
— highway lane
— visualization
Published: 09-01-2023

Abstract

The driving scenario of an automated vehicle is the crucial technology in the design of autonomous cars. This suggested approach aims to address the shortcomings of autonomous cars, such as their poor real- time performance and low control precision. The process for building a virtual simulation environment for autonomous vehicle testing and validation is described in this study. Model Predictive Control and Proportional Integral and Derivative Control are used in MATLAB simulation to build three car models. These are related to the 2D and 3D animation used in collision detection and visualization. The virtual engine visualization is included throughout the model. A variety of test circumstances are used to validate the simulation model, and the model’s performance is assessed in the presence of various barriers. The simulation's findings demonstrate that the autonomous vehicle has a strong potential for self-adaptation even in challenging and complex working environments. No instances of car sideslip or track departure have been noted. It is discovered that this autonomous car performs remarkably well overall when compared to other autonomous vehicles. The suggested approach is essential for enhancing autonomous vehicle driving safety, maintaining vehicle control in challenging situations, and improving the advancement of intelligent vehicle driving assistance.

References

  1. Max Schwenzer, Muzaffer Ay, Thomas Bergs & Dirk Abel, “Review on model predictive control: an engineering perspective”, The International Journal of Advanced Manufacturing Technology volume 117, pages1327–1349 (2021)
  2. Kai Zhang, Yeqi Lu, Xiaoyue Huang, ‘‘Design of Adaptive Cruise Control considering multi scene application’’, IEEE, DOI: 10.1109/CAC53003.2021.9728110, China Automation Congress (CAC), 2021.
  3. Z. Wang, G. Li, H. Jiang, Q. Chen, and H. Zhang, ‘‘Collision-free navigation of autonomous vehicles using convex quadratic programming-based model predictive control,’’ IEEE/ASME Trans. Mechatronics, vol. 23, no. 3, pp. 1103–1113, Jun. 2018.
  4. C. Liu, S. Lee, S. Varnhagen, and H. E. Tseng, ‘‘Path planning for autonomous vehicles using model predictive control’’ in Proc. IEEE Intell. Vehicles Symp. (IV), Beach, CA, USA, Jun. 2017, pp. 174–179.
  5. B. Zhang, C. Zong, G. Chen, and B. Zhang, ‘‘Electrical vehicle path tracking based model predictive control with a Laguerre function and exponential weight’’ IEEE Access, vol. 7, pp. 17082–17097, 2019.
  6. J. Funke, M. Brown, S. M. Erlien, and J. C. Gerdes, ‘‘Collision avoidance and stabilization for autonomous vehicles in emergency scenarios,’’ IEEE Trans. Control Syst. Technol., vol. 25, no. 4, pp. 1204–1216, Jul. 2017.
  7. M.Park, S.Lee, andW.Han, ‘‘Development of steering control system for autonomous vehicle using geometry-based path tracking algorithm’’ ETRI J., vol. 37, no. 3, pp. 617– 625, Jun. 2015.
  8. P. R. Ouyang, J. Acob, and V. Pano, ‘‘PID with sliding mode control for trajectory tracking of robotic system,’’ Robot. Comput.-Integr. Manuf., vol. 30, no. 2, pp. 189–200, Apr. 2014.
  9. H.Yoshida, N.D.A.o.J. Department of Mechanical Systems Engineering, M. Omae, T. Wada, ‘‘Toward next active safety technology of intelligent vehicle,’’ J. Robot. Mechtron., vol. 27, no. 6, pp. 610–616, Dec. 2015.
  10. K.D.Kim, ‘‘Collision free autonomous ground traffic: A model predictive control approach,’’ in Proc. ACM/IEEE 4th Int. Conf. Cyber-Phys. Syst. ICCPS, Philadelphia, PA, USA, Apr. 2013, pp. 51–60.
  11. D. Soudbakhsh and A. Eskandarian, ‘‘A collision avoidance steering controller using linear quadratic regulator,’’ SAE Tech. Paper 2010-01-0459, Detroit, MI, USA, Apr. 2010.
  12. Yimin Zhou; Gang Wang; Guoqing Xu; Guoqiang Fu, “Safety driving assistance system design in intelligent vehicles”, IEEE International Conference on Robotics and Biomimetics (ROBIO), DOI: 10.1109/ROBIO.2014.7090740, 2014.
  13. Loi Tran, Michael Hennessey, John Abraham, “Simulation and Visualization of Dynamic Systems Using MATLAB, Simulink, Simulink 3D Animation, and SolidWorks”, https://doi.org/10.1115/IMECE2011-62704, August 1, 2012.