Abstract
According to the position relationship between the vehicle and the obstacle, a new obstacle avoidance path planner was designed to solve the limitation of traditional local obstacle avoidance path planner in excessive obstacle avoidance. In order to improve the control accuracy of the path tracking controller and ensure the stability of the vehicle, a comprehensive evaluation index of path tracking performance considering control accuracy and driving stability was established. The optimal prediction time domain and control time domain parameters at different vehicle speeds were obtained, and an adaptive dual time domain parameter path tracking controller was designed. Based on the joint-simulation platform, the integrated structure of the planning layer and the control layer was simulated and verified. Simulation results show that the new obstacle avoidance function can avoid excessive obstacle avoidance while ensuring real-time performance, and improve the driving stability of the vehicle. The adaptive time-domain parameter path tracking controller has better comprehensive control performance and can improve driving safety under extreme conditions. The integrated structure of local obstacle avoidance path planning and path tracking control are beneficial for the vehicle to plan and accurately track the local obstacle avoidance path in multiple static obstacle scenes.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Amer, N. H., Zamzuri, H., Hudha, K. and Kadir, Z. A. (2017). Modelling and control strategies in path tracking control for autonomous ground vehicles: A review of state of the art and challenges. J. Intelligent & Robotic Systems 86, 2, 225–254.
Chen, W., Liu, X., Huang, H. and Yu, H. (2012). Research on side slip angle dynamic boundary control for vehicle stability control considering the impact of road surface. J. Mechanical Engineering 48, 14, 112–118.
Chu, K., Lee, M. and Sunwoo, M. (2012). Local path planning for off-road autonomous driving with avoidance of static obstacles. IEEE Trans. Intelligent Transportation Systems 13, 4, 1599–1616.
Corno, M., Panzani, G., Roselli, F., Giorelli, M., Azzolini, D. and Savaresi, S. M. (2021). An LPV approach to autonomous vehicle path tracking in the presence of steering actuation nonlinearities. IEEE Trans. Control Systems Technology 29, 4, 1766–1774.
Dolgov, D., Thrun, S., Montemerlo, M. and Diebel, J. (2010). Path planning for autonomous vehicles in unknown semi-structured environments. Int. J. Robotics Research 29, 5, 485–501.
Gong, J., Liu, K. and Qi, J. (2020). Model Predictive Control for Self-driving Vehicles. 2nd edn. Beijing Institute of Technology Press, Beijing, China.
Hang, P., Luo, F., Fang, S. and Chen, X. (2017). Path tracking control of a four-wheel-independent-steering electric vehicle based on model predictive control. 36th Chinese Control Conf. (CCC), Dalian, China.
Huang, Y., Ding, H., Zhang, Y., Wang, H., Cao, D., Xu, N. and Hu, C. (2020). A motion planning and tracking framework for autonomous vehicles based on artificial potential field elaborated resistance network approach. IEEE Trans. Industrial Electronics 67, 2, 1376–1386.
Jeon, J. H., Cowlagi, R. V., Peters, S. C., Karaman, S., Frazzoli, E., Tsiotras, P. and Iagnemma, K. (2013). Optimal motion planning with the half-car dynamical model for autonomous high-speed driving. IEEE American Control Conf. (ACC), 188–193.
Li, X., Sun, Z., Cao, D., Liu, D. and He, H. (2017). Development of a new integrated local trajectory planning and tracking control framework for autonomous ground vehicles. Mechanical Systems and Signal Processing, 87, 118–137.
Li, Y., Liu, Y., Feng, Q., Nan, Y., He, J. and Fan, J. (2020). Path tracking control for an intelligent commercial vehicle based on optimal preview and model predictive. J. Automotive Safety and Energy 11, 4, 462–169.
Liu, J., Yang, J., Liu, H., Tian, X. and Gao, M. (2017). An improved ant colony algorithm for robot path planning. Soft Computing 21, 19, 5829–5839.
Lu, H., Xing, Y. and Zhuo, G. (2020). Review on motion control of autonomous vehicles. J. Mechanical Engineering 56, 10, 127–143.
Wang, Z., Li, G., Jiang, H., Chen, Q. and Zhang, H. (2018). Collision-free navigation of autonomous vehicles using convex quadratic programming-based model predictive control. IEEE/ASME Trans. Mechatronics 23, 3, 1103–1113.
Zhang, X. and Zhu, X. (2019). Autonomous path tracking control of intelligent electric vehicles based on lane detection and optimal preview method. Expert Systems with Applications, 121, 38–48.
Acknowledgement
This work was supported by National Natural Science Foundation of China (51207012) and Natural Science Foundation of Shaanxi Province (2021JM-163).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Li, Y., Fan, J., Liu, Y. et al. Path Planning and Path Tracking for Autonomous Vehicle Based on MPC with Adaptive Dual-Horizon-Parameters. Int.J Automot. Technol. 23, 1239–1253 (2022). https://doi.org/10.1007/s12239-022-0109-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12239-022-0109-8