Complex Trajectory Tracking Using PID Control for Autonomous Driving

  • Wael FaragEmail author


In this paper, a Proportional-Integral-Differential (PID) controller that facilitates track maneuvering for self-driving cars is proposed. Three different design approaches are used to find and tune the controller hyper-parameters. One of them is “WAF-Tune”, which is an ad hoc trial-and-error based technique that is specifically proposed in this paper for this specific application. The proposed controller uses only the Cross-Track-Error (CTE) as an input to the controller, whereas the output is the steering command. Extensive simulation studies in complex tracks with many sharp turns have been carried out to evaluate the performance of the proposed controller at different speeds. The analysis shows that the proposed technique outperforms the other ones. The usefulness and the shortcomings of the proposed tuning mechanism are also discussed in details.


PID control Self-driving Car Autonomous driving PID tuning 



  1. 1.
    Mansour, K., Farag, W.:, AiroDiag: a sophisticated tool that diagnoses and updates vehicles software over air, IEEE Intern. Electric Vehicle Conference (IEVC), TD Convention Center Greenville, SC, USA, March 4, 2012, ISBN: 978-1-4673-1562-3Google Scholar
  2. 2.
    Farag, W., Saleh, Z, Traffic Signs Identification by Deep Learning for Autonomous Driving, Smart Cities Symposium (SCS'18), Bahrain, 22–23 April 2018Google Scholar
  3. 3.
    Farag, W: CANTrack: enhancing automotive CAN bus security using intuitive encryption algorithms, 7th Inter. Conf. on Modeling, Simulation, and Applied Optimization (ICMSAO), UAE, March 2017Google Scholar
  4. 4.
    Farag, W., Saleh, Z.: Road lane-lines detection in real-time for advanced driving assistance systems", Intern. Conf. On Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT'18), Bahrain, 18–20 Nov. 2018Google Scholar
  5. 5.
    Farag, W., Recognition of traffic signs by convolutional neural nets for self-driving vehicles, International Journal of Knowledge-based and Intelligent Engineering Systems, IOS Press, Vol: 22, No: 3, pp. 205–214, 2018Google Scholar
  6. 6.
    Farag, W., Saleh, Z.: Behavior Cloning for Autonomous Driving Using Convolutional Neural Networks”, Intern. Conf. on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT'18), Bahrain, 18–20 Nov. 2018Google Scholar
  7. 7.
    Khattak, A.J., & Wali, B.: Analysis of volatility in driving regimes extracted from basic safety messages transmitted between connected vehicles, Transportation research part C: emerging technologies, 84, 48–73, (2017)Google Scholar
  8. 8.
    Alonso, L., Pérez-Oria, J., Al-Hadithi, B.M., Jiménez, A.: Self-tuning PID controller for autonomous car tracking in urban traffic, in 17th Inter. Conf. on Sys. Theory, Control, and Computing (ICSTCC), Sinaia, Romania, 11–13 Oct. 2013Google Scholar
  9. 9.
    Zhao, P., Chen, J., Song, Y., Tao, X., Xu, T., Mei, T.: Design of a Control System for an autonomous vehicle based on adaptive-PID. Int. J. Adv. Robot. Syst. 9(2), 44 (2012)CrossRefGoogle Scholar
  10. 10.
    Farag, W., Saleh, Z: Tuning of PID track followers for autonomous driving, Intern. Conf. On Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT'18), Bahrain, 18–20 Nov. 2018Google Scholar
  11. 11.
    Chebly, A., Talj, R., and Charara, A.: Coupled longitudinal and lateral control for an autonomous vehicle dynamics modeled using a robotics formalism, IFAC PapersOnLine, Vol 50, Issue 1, July 2017, Pages 12526–12532,
  12. 12.
    Attia, R., Orjuela, R. and Bassent, M.: Longitudinal control for automated vehicle guidance, Workshop on Engine and Powertrain Control, Simulation and Modeling, IFAC, Rueil-Malmaison, France, October 23-25, 2012Google Scholar
  13. 13.
    Filho, C., Wolf, D., Grassi, V. Jr, Osório, F.: Longitudinal and lateral control for autonomous ground vehicles, IEEE Intelligent Vehicles Symposium, Dearborn, MI, USA, 8–11 June 2014Google Scholar
  14. 14.
    Le-Anh, T., Koster, M.B. De: A review of design and control of automated guided vehicle systems, Erasmus Research Institute of Management (ERIM), Report Series No. 2004–03-LIS, 2004Google Scholar
  15. 15.
    Cheein, F.A.A., Cruz, C., Bastos, T.F., Carelli, R.: SLAM-based cross-a-door solution approach for a robotic wheelchair. Int. J. Adv. Robot. Syst. 7(2), 155–164 (2010)Google Scholar
  16. 16.
    Lenain, R., Thuilot, B., Cariou, C. and Martinet, P.: Model predictive control for vehicle guidance in presence of sliding: application to farm vehicles’ path tracking, IEEE Conf. on robotics and automation, pp. 885–890, 2005Google Scholar
  17. 17.
    Byrne, R.H.: Design of a Model Reference Adaptive Controller for vehicle road following. Math. Comput. Model. 22(4-7), 343–354 (1995)CrossRefzbMATHGoogle Scholar
  18. 18.
    Li, Z., Chen, W., Liu, H.: Robust control of wheeled Mobile manipulators using hybrid joints. Int. J. Adv. Robot. Syst. 5(1), 83–90 (2008)CrossRefGoogle Scholar
  19. 19.
    Hessburg, T.: Fuzzy logic control for lateral vehicle guidance. IEEE Control Syst Mag. 14, 55–63 (1994)CrossRefGoogle Scholar
  20. 20.
    Choomuang, R., Afzulpurkar, N.: Hybrid Kalman filter/fuzzy logic based position control of autonomous Mobile robot. Int J Adv Robotic Syst. 2(3), 197–208 (2005)Google Scholar
  21. 21.
    Wang, W., Kenzo, N., Yuta, O.: Model reference sliding mode control of small helicopter X.R.B based on vision. Int J Adv Robotic Syst. 5(3), 233–242 (2006)Google Scholar
  22. 22.
    Shumeet, B.: Evolution of an artificial neural network based autonomous land vehicle controller. IEEE Trans Syst Man Cybern. 26(3), 450–463 (1996)CrossRefGoogle Scholar
  23. 23.
    Lee, Y., Lee, Y.H., Na, S.G., Kwon, O.S., Heo, H.: The improvement of the PID response using support vector regression, The SPRING 8th International Conference on Computing, Communications and Control Technologies: CCCT 2010, April, 2010, Orlando, Florida, USAGoogle Scholar
  24. 24.
    Zhuang, D.: The vehicle directional control based on fractional order PDμ controller. Journal of Shanghai Jiaotong. 41(2), 278–283 (2007)Google Scholar
  25. 25.
    Tan, K.K., Qing-Guo, W., Chieh, H.C.: Advances in PID Control. Springer-Verlag, London (1999) ISBN 1-85233-138-0Google Scholar
  26. 26.
    Crenganis, M., Bologa, O.: Implementing PID controller for a dc motor actuated mini milling machine. Academic J Manuf. Eng. 14(2), (2016)Google Scholar
  27. 27.
    By TimmmyK - Own work, CC0, Accessed 12 Mar 2019
  28. 28.
    Kong, J., Pfeiffer, M., Schildbach, G. and Borrelli, F., Kinematic and dynamic vehicle models for autonomous driving, in IEEE Intelligent Vehicles Symposium (IV), Seoul, South Korea, 28 June 2015Google Scholar
  29. 29.
    By Skorkmaz [Public domain], from Wikimedia Commons, Accessed 12 Mar 2019
  30. 30.
    Thrun, S.: CS373: Artificial Intelligence for Robotics. Udacity, San Francisco, California (2018)Google Scholar
  31. 31.
    Ziegler, J., Nichols, N.B., and Rochester N.Y.: Optimum settings for automatic controllers, Transactions Of The A.S.M.E., pp. 759–765, November 1942Google Scholar
  32. 32.
    Liu, P., Yu, H., Cang, S.: Optimized adaptive tracking control for an underactuated vibro-driven capsule system. Nonlinear Dyn. 94(3), 1803–1817 (2018)CrossRefGoogle Scholar
  33. 33.
    Liu, P., Neumann, G., Fu, Q., Pearson, S., and Yu, H., Energy-Efficient Design and Control of a Vibro-Driven Robot, in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 1464-1469), October 2018Google Scholar
  34. 34.
    Liu, P., Yu, H., Cang, S.: Trajectory synthesis and optimization of an underactuated microrobotic system with dynamic constraints and couplings. Int. J. Control. Autom. Syst. 16(5), 2373–2383 (2018)CrossRefGoogle Scholar
  35. 35.

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Engineering & TechnologyAmerican University of the Middle EastEgailaKuwait
  2. 2.Electrical Power Engineering DepartmentCairo UniversityGizaEgypt

Personalised recommendations