Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
research-article

Adaptive path planning and tracking system based on model predictive control for autonomous vehicle local obstacle avoidance

Published: 10 December 2023 Publication History

Abstract

Path planning and tracking control are the key technologies of autonomous vehicle. The planned path and tracking results affect driving stability and safety directly. In this paper, an improved local path planning method based on model predictive control is proposed to match the variation of vehicle speed and road adhesion coefficient. A two‐layer model predictive control (MPC) path planning and tracking system is further designed to validate the method and the simulation results show that the proposed solution solves the problem of excessive avoidance and reduces the lateral deviation with the reference path.

References

[1]
A. Rupp and M. Stolz, Survey on control schemes for automated driving on highways, In Automated driving: safer and more efficient future driving, Springer, Cham, 2017, 43–69.
[2]
Y. Kebbati, N. Ait‐Oufroukh, D. Ichalal, and V. Vigneron, Lateral control for autonomous wheeled vehicles: a technical review, Asian J. Control 25 (2023), no. 4, 2539–2563.
[3]
G. Chen, J. Yao, H. Hu, Z. Gao, L. He, and X. Zheng, Design and experimental evaluation of an efficient MPC‐based lateral motion controller considering path preview for autonomous vehicles, Control Eng. Pract. 123 (2022), 105164.
[4]
H. Wang, T. Zhang, W. Quan, and Q. Li, Observer‐based path following control for autonomous vehicles with localization errors and tire slip effects, Asian J. Control 25 (2023), no. 2, 1526–1541.
[5]
H. Nam, W. Choi, and C. Ahn, Model predictive control for evasive steering of an autonomous vehicle, Int. J. Automot Technol. 20 (2019), 1033–1042.
[6]
L. Tang, F. Yan, B. Zou, K. Wang, and C. Lv, An improved kinematic model predictive control for high‐speed path tracking of autonomous vehicles, IEEE Access 8 (2020), 51400–51413.
[7]
F. Bounini, D. Gingras, H. Pollart and D. Gruyer, Modified artificial potential field method for online path planning applications, 2017 IEEE Intelligent Vehicles Symposium (IV), Los Angeles, CA, USA, 11–14 June (2017), 180–185.
[8]
X. Fan, Y. Guo, H. Liu, B. Wei, and W. Lyu, Improved artificial potential field method applied for AUV path planning, Math. Probl. Eng. 2020 (2020), 1–21.
[9]
J. Zhou, Research on intelligent vehicle lane‐changing trajectory planning and replanning methods in multi‐vehicle traffic environment. Ms Thesis, Jilin University, China, 2020.
[10]
Y. Yan, C. S. Li, and F. Tang, Autonomous vehicle lane‐changing trajectory planning based on quintuple polynomial model, J. Mach. Des. 358 (2019), 46–51.
[11]
M. Luo, X. Hou, and J. Yang, Surface optimal path planning using an extended Dijkstra algorithm, IEEE Access 8 (2020), 147827–147838.
[12]
P. Wang, Y. Liu, W. Yao, and Y. Yu, Improved A‐star algorithm based on multivariate fusion heuristic function for autonomous driving path planning, Proc. Inst. Mech. Eng. C.: J. Autom. Eng. 237 (2023), no. 7, 1527–1542.
[13]
R. Chen, J. Hu, and W. Xu, An RRT‐Dijkstra‐based path planning strategy for autonomous vehicles, Appl. Sci. 12 (2022), 11982.
[14]
X. Zhang, T. Zhu, Y. Xu, H. Liu, and F. Liu, Local path planning of the autonomous vehicle based on adaptive improved RRT algorithm in certain lane environments, Actuators 11 (2022), no. 4, 109.
[15]
S. Erke, D. Bin, N. Yiming, Z. Qi, X. Liang, and Z. Dawei, An improved A‐star based path planning algorithm for autonomous land vehicles, Int. J. Adv. Robot. Syst. 17 (2020), no. 5, 1729881420962263.
[16]
H. Zhang, Y. Wang, J. Zheng, and J. Yu, Path planning of industrial robot based on improved RRT algorithm in complex environments, IEEE Access 6 (2018), 53296–53306.
[17]
C. Liu, S. Lee, S. Varnhagen, H.E. Tseng, Path planning for autonomous vehicles using model predictive control, 2017 IEEE Intelligent Vehicles Symposium (IV), Los Angeles, CA, USA, 11–14 June (2017), 174–179.
[18]
Z. Zuo, X. Yang, Z. Zhang and Y. Wang, Lane‐associated MPC path planning for autonomous vehicles, 2019 Chinese control conference (CCC), Guangzhou, China, 27–30 July (2019), 335–340.
[19]
Q. Li, Z. Wang, W. Wang, Z. Liu, Y. Chen, X. Ng, and M. H. Ang Jr., A model predictive obstacle avoidance method based on dynamic motion primitives and a Kalman filter, Asian J. Control 25 (2023), no. 2, 1510–1525.
[20]
H. Chen and X. Zhang, Path planning for intelligent vehicle collision avoidance of dynamic pedestrian using Att‐LSTM, MSFM, and MPC at unsignalized crosswalk, IEEE Trans. Ind. Electron. 69 (2021), 4285–4295.
[21]
J. Hu, Y. Zhang, and S. Rakheja, Path planning and tracking for autonomous vehicle collision avoidance with consideration of tire‐road friction coefficient, IFAC‐PapersOnLine 53 (2020), no. 2, 15524–15529.
[22]
J. Gon, K. Liu, and J. Qi, Model predictive control for unmanned vehicles, 2nd ed., Beijing Institute of Technology Press, China, 2020.
[23]
Y. Li, J. Fan, Y. Liu, J. He, Z. Li, and S. Pan, Path planning and path tracking control for autonomous vehicle based on MPC with adaptive dual‐horizon‐parameters, J. Automot. Saf. Energy 12 (2021), no. 4, 528–539.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image Asian Journal of Control
Asian Journal of Control  Volume 26, Issue 3
May 2024
549 pages
EISSN:1934-6093
DOI:10.1002/asjc.v26.3
Issue’s Table of Contents

Publisher

John Wiley & Sons, Inc.

United States

Publication History

Published: 10 December 2023

Author Tags

  1. autonomous vehicle
  2. local obstacles avoidance
  3. model predictive control
  4. path planning
  5. tracking control

Qualifiers

  • Research-article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 0
    Total Downloads
  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 15 Oct 2024

Other Metrics

Citations

View Options

View options

Get Access

Login options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media