Abstract
Aiming at the problems of small parking space and poor parking effect for existing parallel parking, this paper proposes a parallel parking path planning based on improved arctangent function optimization. Firstly, the vehicle parking kinematics model is established, and the vehicle parameters are determined according to the three classical models. Secondly, the parking space model is established, and the most reasonable parking space parameters are selected according to the minimum parking space. Then, aiming at the problems of abrupt curvature of the designed arc-line-arc initial path, unsmooth path, large yaw angle at the end and large parking space, an improved arctangent function model is proposed, the parking constraints are established, the absolute value of vehicle yaw angle is taken as the objective function, and the parameters are optimized by genetic algorithm. Finally, it is verified by simulation experiments. The results show that the method can achieve smoother path, smaller parking space and more ideal parking posture, meet the requirements of stability, safety and comfort in the process of parallel parking, and improve the ability of parallel parking. Therefore, this method can provide a theoretical reference for path planning in automatic parking technology.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Beijing Jianzhu University (2015). Garage Building Design Code JGJ100-2015. China Architecture and Building Press. Beijing, China.
Cai, L., Guan, H., Zhang, H. L., Jia, X. and Zhan, J. (2022). Multi-maneuver vertical parking path planning and control in a narrow space. Robotics and Autonomous Systems, 149, 103964.
Chen, W., Fang, Y. and Wei, Z. (2017). Path planning of two-direction vertical parking based on optimization with genetic algorithm. Qiche Gongcheng/Automotive Engineering 39, 11, 1325–1332.
Dreyfuss, M., Shaki, Y. Y. and Yechiali, U. (2022). The double-space parking problem. OR Spectrum, 1–17.
Han, G. L. (2021). Automatic parking path planning based on ant colony optimization and the grid method. J. Sensors, 2021, 8592558.
Huang, J., Yang, Y., Ding, D., Li, Y. and He, Y. (2022). Automatic parking paths planning research based on scattering points six-degree polynomial and easement curve. Proc. Institution of Mechanical Engineers, Part D: J. Automobile Engineering, 09544070221076594.
Jiang, H. B., Shen, Z. N., Shidian, M. A. and Chen, L. (2017). Intelligent identification of automatic parking system based on information fusion. J. Mechanical Engineering 53, 22, 125–133.
Jiang, W., Xin, X., Chen, W. and Cui, W. (2021) Multi-condition parking space recognition based on information fusion and decision planning of automatic parking system. J. Mechanical Engineering 57, 6, 131–141.
Kim, D., Chung, W. and Park, S. (2010). Practical motion planning for car-parking control in narrow environment. IET Control Theory & Applications 4, 1, 129–139.
Lee, S., Lim, W. and Sunwoo, M. (2020). Robust parking path planning with error-adaptive sampling under perception uncertainty. Sensors 20, 12, 3560.
Li, C., Jiang, H., Ma, S., Jiang, S. and Li, Y. (2020). Automatic parking path planning and tracking control research for intelligent vehicles. Applied Sciences 10, 24, 9100.
Li, H., Guo, K. H., Song, X. L. and Li, F. (2013). Trajectory planning of automatic parallel parking with multi-constraints based on Matlab. J. Central South University (Science and Technology) 44, 1, 102.
Li, M., Zhou, P., He, X., Lv, H. and Zhang, H. (2021). Parallel parking path planning and tracking control based on adaptive algorithms. Int. J. Automotive Technology 22, 4, 949–965.
Lin, Z. Z., Li, Q., Liang, Y. J. and Cheng, D. (2012). Parallel parking algorithm based on autonomous path planning. Application Research of Computers 29, 5, 1713–1715.
Ma, S., Jiang, H., Han, M., Xie, J. and Li, C. (2017). Research on automatic parking systems based on parking scene recognition. IEEE Access, 5, 21901–21917.
Tang, C., Yang, J., Wang, K. and Dalian, M. (2019). Concept of elevated city and three layer model for urban space. Chinese J. Underground Space and Engineering 15, 3, 637–642.w
Wang, D., Liang, H., Mei, T. and Zhu, H. (2011). Research on self-parking path planning algorithms. IEEE Int. Conf. Vehicular Electronics and Safety (ICVES), Beijing, China.
Yu, L., Wang, X., Hou, Z., Du, Z., Zeng, Y. and Mu, Z. (2021). Path planning optimization for driverless vehicle in parallel parking integrating radial basis function neural network. Applied Sciences 11, 17, 8178.
Zhang, B., Li, Z., Ni, Y. and Li, Y. (2022). Research on path planning and tracking control of automatic parking system. World Electric Vehicle J., 13, 1, 14.
Acknowledgement
This research was supported by the National Natural Science Foundation of China (Grant No. 52162044), the Key Research Program of Jiangxi Province (Grant No. 20212BBE51014), the Nanchang Key Laboratory of Intelligent and Connected New Energy Vehicles (Grant No. 2020-NCZDSY-006), and the High-end Talents Project of Science and Technology Innovation of Jiangxi Province (Grant No. JXSQ 2019201119).
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
Chen, Q., Gan, L., Chen, B. et al. Parallel Parking Path Planning Based on Improved Arctangent Function Optimization. Int.J Automot. Technol. 24, 23–33 (2023). https://doi.org/10.1007/s12239-023-0003-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12239-023-0003-z