Complex Trajectory Tracking Using PID Control for Autonomous Driving

Abstract

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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

References

  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: 978-1-4673-1562-3

  2. 2.

    Farag, W., Saleh, Z, Traffic Signs Identification by Deep Learning for Autonomous Driving, Smart Cities Symposium (SCS'18), Bahrain, 22–23 April 2018

  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 2017

  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. 2018

  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, 2018

  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. 2018

  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)

  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. 2013

  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)

    Article  Google 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. 2018

  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, https://doi.org/10.1016/j.ifacol.2017.08.2190

  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, 2012

  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 2014

  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, 2004

  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, 2005

  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)

    Article  Google 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)

    Article  Google Scholar 

  19. 19.

    Hessburg, T.: Fuzzy logic control for lateral vehicle guidance. IEEE Control Syst Mag. 14, 55–63 (1994)

    Article  Google 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)

    Article  Google 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, USA

  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 1-85233-138-0

    Google 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)

  27. 27.

    By TimmmyK - Own work, CC0, https://commons.wikimedia.org/w/index.php?curid=36662015. 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 2015

  29. 29.

    By Skorkmaz [Public domain], from Wikimedia Commons, https://commons.wikimedia.org/wiki/File:Change_with_Ki.png. 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 1942

  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)

    Article  Google 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 2018

  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)

    Article  Google Scholar 

  35. 35.

    SPMD Data, https://catalog.data.gov/dataset?tags=safety-pilot-model-deployment. Accessed 12 Mar 2019

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Wael Farag.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Farag, W. Complex Trajectory Tracking Using PID Control for Autonomous Driving. Int. J. ITS Res. 18, 356–366 (2020). https://doi.org/10.1007/s13177-019-00204-2

Download citation

Keywords

  • PID control
  • Self-driving Car
  • Autonomous driving
  • PID tuning