Skip to main content
SpringerLink
Log in

Optimal car-following control for intelligent vehicles using online road-slope approximation method

  • Research Paper
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

The design of a car-following control system is a multiobjective optimization problem that involves issues in rider safety, ride comfort, and fuel economy. This study proposes a hierarchical design of optimal car-following control where the system is intuitively split into two subsystems with different dynamic properties. Specifically, the high-level subsystem is a linear car-following system with a measurable disturbance of the preceding vehicle’s acceleration, while the low-level subsystem is a nonlinear acceleration-tracking system with an unmeasurable road slope. In the design of optimal car-following control, the measurable disturbance of the preceding vehicle’s acceleration is considered from a theoretical perspective, and the unmeasurable road slope is estimated by a novel engineering-oriented approximation method to reduce the influence of driveline oscillation. The performance of the proposed optimal control scheme is evaluated through simulation and real-vehicle experiments, which show that the proposed control algorithm provides a satisfactory road-slope approximation accuracy and that the car-following performance of the proposed optimal control system is better than that of a factory-installed adaptive cruise controller.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Ganji B, Kouzani A Z, Khoo S Y, et al. Adaptive cruise control of a HEV using sliding mode control. Expert Syst Appl, 2014, 41: 607–615

    Article  Google Scholar 

  2. Nilsson P, Hussien O, Balkan A, et al. Correct-by-construction adaptive cruise control: two approaches. IEEE Trans Contr Syst Technol, 2016, 24: 1294–1307

    Article  Google Scholar 

  3. Yi K, Lee S, Kwon Y D. An investigation of intelligent cruise control laws for passenger vehicles. Proc Inst Mech Eng Part D J Automobile Eng, 2001, 215: 159–169

    Article  Google Scholar 

  4. Li S E, Li K, Wang J. Economy-oriented vehicle adaptive cruise control with coordinating multiple objectives function. Vehicle Syst Dyn, 2013, 51: 1–17

    Article  Google Scholar 

  5. Chu H, Guo L, Gao B, et al. Predictive cruise control using high-definition map and real vehicle implementation. IEEE Trans Veh Technol, 2018, 67: 11377–11389

    Article  Google Scholar 

  6. Li S, Li K, Rajamani R, et al. Model predictive multi-objective vehicular adaptive cruise control. IEEE Trans Contr Syst Technol, 2011, 19: 556–566

    Article  Google Scholar 

  7. Marzbanrad J, Moghaddam I T. Self-tuning control algorithm design for vehicle adaptive cruise control system through real-time estimation of vehicle parameters and road grade. Vehicle Syst Dyn, 2016, 54: 1291–1316

    Article  Google Scholar 

  8. Moon S, Yi K. Human driving data-based design of a vehicle adaptive cruise control algorithm. Vehicle Syst Dyn, 2008, 46: 661–690

    Article  Google Scholar 

  9. Darbha S, Konduri S, Pagilla P R. Benefits of V2V communication for autonomous and connected vehicles. IEEE Trans Intell Transp Syst, 2019, 20: 1954–1963

    Article  Google Scholar 

  10. Wang Z, Wu G, Barth M J. A review on cooperative adaptive cruise control (CACC) systems: architectures, controls, and applications. In: Proceedings of 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 2018. 2884–2891

  11. Cheng H T, Shan H, Zhuang W. Infotainment and road safety service support in vehicular networking: from a communication perspective. Mech Syst Signal Process, 2011, 25: 2020–2038

    Article  Google Scholar 

  12. Li D Y, Liu M, Zhao F, et al. Challenges and countermeasures of interaction in autonomous vehicles. Sci China Inf Sci, 2019, 62: 050201

    Article  Google Scholar 

  13. Li K, Tan H S, Misener J, et al. Digital map as a virtual sensor-dynamic road curve reconstruction for a curve speed assistant. Veh Syst Dyn, 2008, 46: 1141–1158

    Article  Google Scholar 

  14. Yang D G, Jiang K, Zhao D, et al. Intelligent and connected vehicles: current status and future perspectives. Sci China Tech Sci, 2018, 61: 1446–1471

    Article  Google Scholar 

  15. Jo K, Kim J, Sunwoo M. Real-time road-slope estimation based on integration of onboard sensors with GPS using an IMMPDA filter. IEEE Trans Intell Transp Syst, 2013, 14: 1718–1732

    Article  Google Scholar 

  16. Ohnishi H. A study on road slope estimation for automatic transmission control. JSAE Rev, 2000, 21: 235–240

    Article  Google Scholar 

  17. Nourmohammadi H, Keighobadi J. Fuzzy adaptive integration scheme for low-cost SINS/GPS navigation system. Mech Syst Signal Process, 2018, 99: 434–449

    Article  Google Scholar 

  18. Bae H S, Ryu J, Gerdes J C. Road grade and vehicle parameter estimation for longitudinal control using GPS. In: Proceedings of the IEEE Conference on Intelligent Transportation Systems, 2001. 25–29

  19. Sun Y, Li L, Yan B, et al. A hybrid algorithm combining EKF and RLS in synchronous estimation of road grade and vehicle’ mass for a hybrid electric bus. Mech Syst Signal Process, 2016, 68–69: 416–430

    Article  Google Scholar 

  20. Vahidi A, Stefanopoulou A, Peng H. Recursive least squares with forgetting for online estimation of vehicle mass and road grade: theory and experiments. Vehicle Syst Dyn, 2005, 43: 31–55

    Article  Google Scholar 

  21. Zhu M, Chen H, Xiong G. A model predictive speed tracking control approach for autonomous ground vehicles. Mech Syst Signal Process, 2017, 87: 138–152

    Article  Google Scholar 

  22. Li L, Wang X, Song J. Fuel consumption optimization for smart hybrid electric vehicle during a car-following process. Mech Syst Signal Process, 2017, 87: 17–29

    Article  Google Scholar 

  23. Darbha S, Konduri S, Pagilla P R. Benefits of V2V communication for autonomous and connected vehicles. IEEE Trans Intell Transp Syst, 2019, 20: 1954–1963

    Article  Google Scholar 

  24. Ogata K. Discrete-Time Control Systems. Englewood Cliffs: Prentice Hall, 1995

    Google Scholar 

  25. Bryson A E, Ho Y C, Siouris G M. Applied optimal control: optimization, estimation, and control. IEEE Trans Syst Man Cybern, 1979, 9: 366–367

    Article  Google Scholar 

  26. Chu H Q, Guo L L, Yan Y J, et al. Energy-efficient longitudinal driving strategy for intelligent vehicles on urban roads. Sci China Inf Sci, 2019, 62: 064201

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by China Automobile Industry Innovation and Development Joint Fund (Grant Nos. U1664257, U1864206), National Natural Science Foundation of China (Grant No. 61903153), and Postdoctoral Science Foundation of China (Grant No. 2018M641779). The authors would like to thank Yongjun YAN, Pengfei SUN, Liangchun ZHAO, Yuxiang ZHANG, Xin LI, and Ting QU for their help in the real-vehicle implementation of the proposed control scheme.

Author information

Authors and Affiliations

  1. State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, 130022, China

    Hongqing Chu, Hong Chen & Bingzhao Gao

  2. Center for Cyber-Physical systems, University of Georgia, Athens, Georgia, 30602, USA

    Lulu Guo

  3. Clean Energy Automotive Engineering Center, Tongji University, Shanghai, 201804, China

    Hong Chen

Authors
  1. Hongqing Chu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lulu Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bingzhao Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bingzhao Gao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chu, H., Guo, L., Chen, H. et al. Optimal car-following control for intelligent vehicles using online road-slope approximation method. Sci. China Inf. Sci. 64, 112201 (2021). https://doi.org/10.1007/s11432-019-2756-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-019-2756-3

Keywords

Search

Navigation