Skip to main content
Log in

Trajectory Planning Algorithm Using Gauss Pseudo-Spectral Method Based on Vehicle-Infrastructure Cooperative System

  • Published:
International Journal of Automotive Technology Aims and scope Submit manuscript

Abstract

Vehicle-infrastructure cooperative systems can potentially enhance both traffic safety and efficiency by conducting coordinated control through the interactive strategy between the vehicles and the infrastructure. In this study, the interactive strategy of a vehicle infrastructure cooperative system is designed. Lane change maneuver is a conventional behavior in driving. Thus, this paper proposes a trajectory planning algorithm based on a Gauss pseudo-spectral method that is applied to the intelligent vehicle-infrastructure cooperative system in the lane change scenario. A road side unit calculates the planning trajectory using collected vehicle information and sensor data and then sends the trajectory planning advice to the designated vehicle. The Gauss pseudo-spectral method is used to obtain the planning trajectory, which effectively helps solve the discontinuous optimization problems in partial conditions. It transforms the optimal control problem of dynamic systems into a nonlinear programming problem using the orthogonal collocation method to discretize the objective function and various constraints of the optimization problem. Furthermore, the ssuential quadratic programming method is used to solve the problem numerically. The effectiveness of the proposed method and interactive strategy are demonstrated through simulations and experimental results.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Blincoe, L., Miller, T. R., Zaloshnja, E. and Lawrence, B. A. (2015). The economic and societal impact of motor vehicle crashes. National Highway Traffic Safety Administration 66, 2, 194–196.

    Google Scholar 

  • Darby, C. L., Hager, W. W. and Rao, A. V. (2011). An hp-adaptive pseudospectral method for solving optimal control problems. Optimal Control Applications & Methods 32, 4, 476–502.

    Article  MathSciNet  Google Scholar 

  • Donahue, J., Hendricks, L. A., Rohrbach, M., Venugopalan, S., Guadarrama, S., Saenko, K. and Darrell, T. (2017). Long-term recurrent convolutional networks for visual recognition and description. IEEE Trans. Pattern Analysis & Machine Intelligence 39, 4, 677–691.

    Article  Google Scholar 

  • Gill, P. E. and Saunders, M. M. A. (2005). SNOPT: An SQP algorithm for large-scale constrained optimization. SIAM Review 47, 1, 99–131.

    Article  MathSciNet  Google Scholar 

  • Gill, P. E. and Saunders, M. M. A (2012). tUser’ Guide for SNOPT Version 7: Software for Large-scale Nonlinear Programming. Stanford University.

    Google Scholar 

  • Gong, Q., Fahroo, F. and Ross, I. M. (2008). Spectral algorithm for pseudospectral methods in optimal control. J. Guidance, Control, and Dynamics 31, 3, 460–471.

    Article  Google Scholar 

  • Habel, L. C. and Schreckenberg, M. (2014). Asymmetric lane change rules for a microscopic highway traffic model. Lecture Notes in Computer Science, 8751, 620–629.

    Article  Google Scholar 

  • Han, P., Shan, J. Y. and Meng, X. Y. (2013). Re-entry trajectory optimization using an hp-adaptive radau pseudospectral method. Beijing Institute of Technology 22, 1, 1623–1636.

    Google Scholar 

  • Hong, B. X. and Wang, Q. (2012). Trajectory optimization of solid launch vehicle based on hp-adaptive pseudospectral method. Aerospace Control 30, 4, 18–31.

    Google Scholar 

  • Hou, H., Hager, W. and Rao, A. (2015). Convergence of a gauss pseudospectral method for optimal control. AIAA Guidance, Navigation, & Control Conf, Minneapolis, Minnesota, USA.

    Google Scholar 

  • Lee, K., Ahn, K. and Yoo, J. (2016). A novel P-norm correction method for lightweight topology optimization under maximum stress constraints. Computers & Structures, 171, 18–30.

    Article  Google Scholar 

  • Li, Y. F., Zhang, L. and Song, Y. (2016). A vehicular collision warning algorithm based on the time-to-collision estimation under connected environment. Proc. 14th Int. Conf. Control Automation Robotics and Vision (ICARCV), Phuket, Thailand.

    Google Scholar 

  • Liu, N. and Han, J. (2018). A deep spatial contextual long-term recurrent convolutional network for saliency detection. IEEE Trans. Image Processing 27, 7, 3264–3274.

    Article  MathSciNet  Google Scholar 

  • Mishra, P. K., Nath, S. K., Kosec, G and Sen, M. K. (2017). An improved radial basis-pseudospectral method with hybrid Gaussian-cubic kernels. Engineering Analysis with Boundary Elements, 80, 162–171.

    Article  MathSciNet  Google Scholar 

  • Ran, B., Cheng, Y., Li, S., Ding, F., Jin, J., Chen, X and Zhang, Z. (2018). Connected Automated Vehicle Highway Systems and Methods. US Patent Application No. 15/628, 331.

    Google Scholar 

  • Ran, B., Cheng, Y., Li, S., Zhang, Z., Ding, F., Tan, H., Wu, Y., Dong, S., Ye, L., Li, X., Chen, T., Shi, K., Jin, J. and Chen, X. (2019). Intelligent Road Infrastructure System (IRIS): Systems and Methods. US Patent Application No. 16/135, 916.

    Google Scholar 

  • Rodemerk, C., Habenicht, S., Weitzel, A., Winner, H. and Schmitt, T. (2012). Development of a general criticality criterion for the risk estimation of driving situations and its application to a maneuver-based lane change assistance system. Proc. Intelligent Vehicles Symp., Alcala de Henares, Spain.

    Google Scholar 

  • Ruiming, W. (2018). Research on Trajectory Planning of Wheeled Mobile Robot. M. S. Thesis. Harbin University of Science and Technology. Harbin, China.

    Google Scholar 

  • Snider, J. M. (2009). Automatic Steering Methods for Autonomous Automobile Path Tracking. Ph. D. Dissertation. Carnegie Mellon University. Pittsburgh, Pennsylvania, USA.

    Google Scholar 

  • Yang, G., Zhang, D. H., Li, G. Q. and Luo, Y. G (2017). Cooperative same-direction automated lane-changing based on vehicle-to-vehicle communication. J. Highway and Transportation Research and Development 34, 1, 120–129.

    Google Scholar 

  • You, F., Zhang, R. H., Lie, G., Wang, H. W, Wen, H. and Xu, J. (2015). Trajectory planning and tracking control for autonomous lane change maneuver based on the cooperative vehicle infrastructure system. Expert Systems with Applications 42, 14, 5932–5946.

    Article  Google Scholar 

  • Wang, N., Er, M. J., Sun, J. C. and Liu, Y. C. (2015). Adaptive robust online constructive fuzzy control of a complex surface vehicle system. IEEE Trans. Cybernetics 46, 7, 1511–1523.

    Article  Google Scholar 

  • Zhang, W., Zhang, Y., Li, W. and Wang, Y. (2016). Path planning for rapid large-angle maneuver of satellites based on the gauss pseudospectral method. Mathematical Problems in Engineering, 2016, Article ID 1081267.

    Google Scholar 

  • Zichao, H., Duanfeng, C., Chaozhong, W. and Yi, H. (2018). Path planning and cooperative control for automated vehicle platoon using hybrid automata. IEEE Trans. Intelligent Transportation Systems 20, 3, 959–974.

    Google Scholar 

Download references

Acknowledgement

This research was funded by the Key-Area Research and Development Program of Guangdong Province [2019B090912001]

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kegang Zhao.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhu, Y., Zhao, K., Li, H. et al. Trajectory Planning Algorithm Using Gauss Pseudo-Spectral Method Based on Vehicle-Infrastructure Cooperative System. Int.J Automot. Technol. 21, 889–901 (2020). https://doi.org/10.1007/s12239-020-0086-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12239-020-0086-8

Keywords

Navigation