Abstract
A path planning and tracking framework based on model predictive control to avoid obstacles is proposed for autonomous vehicles. Firstly, a vehicle in road coordinate system is established to describe the relationship between the vehicle and the reference path. Secondly, to deal with multi obstacles, an efficient search-based method along the reference path is used to build collision-free driving corridors as state constraints. Then, a multi-constrained model predictive controller based on vehicle kinematic and dynamic model is employed to compute the optimal steering angle. Finally, the simulation results show that the proposed path planning and control framework approach are effective for various driving scenarios.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
González, D., Pérez, J., Milanés, V., et al.: A review of motion planning techniques for automated vehicles. IEEE Trans. Intell. Transp. Syst. 17(4), 1135–1145 (2016)
Ji, J., Khajepour, A., Melek, W.W., et al.: Path planning and tracking for vehicle collision avoidance based on model predictive control with multiconstraints. IEEE Trans. Veh. Technol. 66(2), 952–964 (2017)
Wang, Q., Müller, S.: A hierarchical controller for path planning and path following based on model predictive control. In: Proceedings of the 13th International Symposium on Advanced Vehicle Control (AVEC 2016), Munich, Germany, 13–16 September 2016 (2016)
Wang, Z., Li, G., Jiang, H., et al.: Collision-free navigation of autonomous vehicles using convex quadratic programming-based model predictive control. IEEE/ASME Trans. Mechatron. PP(99), 1 (2018)
Huang, Y., et al.: A motion planning and tracking framework for autonomous vehicles based on artificial potential field elaborated resistance network approach. IEEE Trans. Industr. Electron. 67(2), 1376–1386 (2019)
Elmi, Z., Efe, M.Ö.: Path planning using model predictive controller based on potential field for autonomous vehicles. In: IECON 2018–44th Annual Conference of the IEEE Industrial Electronics Society, pp. 2613–2618 (2018)
Acknowledgement
The work is supported by the Fundamental Research Funds for the Central Universities (Grant no. 22120190205).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Xiong, L., Fu, Z., Zeng, D., Qian, Z., Leng, B. (2022). A Path Planning and Tracking Framework Based on Model Predictive Control for Autonomous Vehicle Obstacle Avoidance. In: Orlova, A., Cole, D. (eds) Advances in Dynamics of Vehicles on Roads and Tracks II. IAVSD 2021. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-07305-2_105
Download citation
DOI: https://doi.org/10.1007/978-3-031-07305-2_105
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-07304-5
Online ISBN: 978-3-031-07305-2
eBook Packages: EngineeringEngineering (R0)