Speed Control of An Autonomous Electric Vehicle Using Fuzzy Logic With Computer Vision-Based Input
##plugins.themes.bootstrap3.article.main##
Keywords
fuzzy logic controller, speed control system, autonomous electric vehicle, steering angle, distance
Abstract
A robust speed control mechanism ensures safety in an autonomous electric vehicle system. Such a system must dynamically adjust the vehicle's speed based on its surrounding environment. This research employs computer vision for object and road detection to measure the distance between the car and nearby objects. Fuzzy logic methods—specifically Mamdani and Sugeno—are utilized to automatically and stably regulate the speed of autonomous electric vehicles from their starting point to their destination. The control system considers various road conditions, including left-slanting, straight, and right-slanting roads, and the real-time presence or absence of objects. Testing is conducted across three real-world scenarios using distance and steering angle inputs. The servo angle represents the output, which ranges from 0 to 1800 and corresponds to the vehicle's speed. The results indicate that the Mamdani method provides greater speed control accuracy than the Sugeno method, which relies on a singleton output. For conditions involving left-slanting, straight, and right-slanting roads with objects within a 10-meter range, the Mamdani method produced outputs of 1370, 1800, and 1370, respectively, aligning well with predefined speed control rules. In contrast, the Sugeno method yielded 880, 1470, and 650 outputs for the same conditions, which did not adhere to the predefined rules for slow, medium, and fast speeds. In conclusion, the Mamdani method demonstrates superior accuracy and suitability for speed control in autonomous electric vehicles compared to the Sugeno method.
References
[2] Q. Yao, Y. Tian, Q. Wang, and S. Wang, “Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges,” IEEE Access, vol. 8, pp. 161211–161222, 2020, doi: 10.1109/ACCESS.2020.3020075.
[3] J. Guo, Y. Luo, and K. Li, “An adaptive hierarchical trajectory following control approach of autonomous four-wheel independent drive electric vehicles,” IEEE Trans. Intell. Transp. Syst., vol. 19, no. 8, pp. 2482–2492, 2018, doi: 10.1109/TITS.2017.2749416.
[4] D. Phan, A. Bab-Hadiashar, M. Fayyazi, R. Hoseinnezhad, R. N. Jazar, and H. Khayyam, “Interval Type 2 Fuzzy Logic Control for Energy Management of Hybrid Electric Autonomous Vehicles,” IEEE Trans. Intell. Veh., vol. 6, no. 2, pp. 210–220, 2021, doi: 10.1109/TIV.2020.3011954.
[5] T. Tiwari, S. Agarwal, and A. Etar, “Controller design for autonomous vehicle,” Proc. 2021 1st Int. Conf. Adv. Electr. Comput. Commun. Sustain. Technol. ICAECT 2021, 2021, doi: 10.1109/ICAECT49130.2021.9392498.
[6] G. Quoc, B. Tran, and E. Costa, “Comfort-oriented Adaptive Cruise Control of an Autonomous Vehicle Ecole Nationale Sup e l ’ Eau et l ’ Environnement Adaptive Speed Control of an Autonomous Vehicle with a Comfort Objective,” no. June, 2020.
[7] B. Rouzier, M. Hazaz, T. Murakami, and W. Xu, “Application of active driving assist to remotely controlled car in collision avoidance,” IEEJ J. Ind. Appl., vol. 7, no. 4, pp. 289–297, 2018, doi: 10.1541/ieejjia.7.289.
[8] Y. Kebbati, N. Ait-oufroukh, V. Vigneron, D. Ichalal, and D. Gruyer, “Optimized self-adaptive PID speed control for autonomous vehicles To cite this version : HAL Id : hal-03442081 Optimized self-adaptive PID speed control for autonomous vehicles,” Int. Conf. Autom. Comput., pp. 1–6, 2021.
[9] A. D. O. D. S. Dantas, A. F. O. D. A. Dantas, J. T. L. S. Campos, D. L. De Almeida Neto, and C. E. T. Dórea, “PID Control for Electric Vehicles Subject to Control and Speed Signal Constraints,” J. Control Sci. Eng., vol. 2018, 2018, doi: 10.1155/2018/6259049.
[10] S. H. HosseinNia, I. Tejado, B. M. Vinagre, V. Milanés, and J. Villagrá, “Low speed control of an autonomous vehicle using a hybrid fractional order controller,” Proc. - 2011 2nd Int. Conf. Control. Instrum. Autom. ICCIA 2011, pp. 116–121, 2011, doi: 10.1109/ICCIAutom.2011.6356641.
[11] T. A. Tutunji, M. Salah-Eddin, and H. Abdalqader, “Unmanned Ground Vehicle Control using IoT,” 2020 21st Int. Conf. Res. Educ. Mechatronics, REM 2020, no. December 2020, 2020, doi: 10.1109/REM49740.2020.9313890.
[12] S. Wang, X. Yin, P. Li, M. Zhang, and X. Wang, “Trajectory Tracking Control for Mobile Robots Using Reinforcement Learning and PID,” Iran. J. Sci. Technol. - Trans. Electr. Eng., vol. 44, no. 3, pp. 1059–1068, 2020, doi: 10.1007/s40998-019-00286-4.
[13] J. E. Naranjo, F. Serradilla, and F. Nashashibi, “Speed control optimization for autonomous vehicles with metaheuristics,” Electron., vol. 9, no. 4, pp. 1–15, 2020, doi: 10.3390/electronics9040551.
[14] H. A. Almusawi, M. Al-Jabali, A. M. Khaled, K. Péter, and H. Géza, “Self-Driving robotic car utilizing image processing and machine learning,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1256, no. 1, p. 012024, 2022, doi: 10.1088/1757-899x/1256/1/012024.
[15] S. Yang, W. Wang, C. Liu, and W. Deng, “Scene understanding in deep learning-based end-to-end controllers for autonomous vehicles,” IEEE Trans. Syst. Man, Cybern. Syst., vol. 49, no. 1, pp. 53–63, 2019, doi: 10.1109/TSMC.2018.2868372.
[16] and V. D. Kocić, Jelena, Nenad Jovičić, “An End-to-End Deep Neural Network for Autonomous Driving Designed for Embedded Automotive Platforms,” Sensors, vol. 19, no. 9, p. 2064, 2019, [Online]. Available: https://arxiv.org/abs/1912.05440%0Ahttps://proceedings.mlr.press/v15/ross11a/ross11a.pdf%0Ahttps://doi.org/10.1016/j.array.2021.100057
[17] T. Hossain, H. Habibullah, and R. Islam, “Steering and Speed Control System Design for Autonomous Vehicles by Developing an Optimal Hybrid Controller to Track Reference Trajectory,” Machines, vol. 10, no. 6, 2022, doi: 10.3390/machines10060420.
[18] X. Han, X. Zhang, Y. Du, and G. Cheng, “Design of Autonomous Vehicle Controller Based on BP-PID,” IOP Conf. Ser. Earth Environ. Sci., vol. 234, no. 1, 2019, doi: 10.1088/1755-1315/234/1/012097.
[19] B. Korkmaz, U. B. Etlik, A. Beke, and T. Kumbasar, “Fuzzy logic based self-driving racing car control system,” 2018 6th Int. Conf. Control Eng. Inf. Technol. CEIT 2018, no. October, pp. 1–6, 2018, doi: 10.1109/CEIT.2018.8751900.
[20] S. ping Chen, G. ming Xiong, H. yan Chen, and D. Negrut, “MPC-based path tracking with PID speed control for high-speed autonomous vehicles considering time-optimal travel,” J. Cent. South Univ., vol. 27, no. 12, pp. 3702–3720, 2020, doi: 10.1007/s11771-020-4561-1.
[21] S. Chen and H. Chen, “MPC-based path tracking with PID speed control for autonomous vehicles,” IOP Conf. Ser. Mater. Sci. Eng., vol. 892, no. 1, 2020, doi: 10.1088/1757-899X/892/1/012034.
[22] J. F. Wang and H. Zhao, “Speed control of tracked vehicle autonomous driving system using fuzzy self-tuning PID,” Proc. - 2019 4th Int. Conf. Mech. Control Comput. Eng. ICMCCE 2019, pp. 305–308, 2019, doi: 10.1109/ICMCCE48743.2019.00075.
[23] B. Y. Suprapto, S. Dwijayanti, D. M. A. Hafiz, and F. A. Ardandy, “Designing an Autonomous Vehicle Using Sensor Fusion Based on Path Planning and Deep Learning Algorithms,” vol. 115, no. September, pp. 86–98, 2024.
[24] E. Aridhi, D. Popescu, and A. Mami, “FPGA based co-design of a speed fuzzy logic controller applied to an autonomous car,” Int. J. Reconfigurable Embed. Syst., vol. 10, no. 3, pp. 195–211, 2021, doi: 10.11591/IJRES.V10.I3.PP195-211.
[25] H. Xue, Z. Zhang, M. Wu, and P. Chen, “Fuzzy controller for autonomous vehicle based on rough sets,” IEEE Access, vol. 7, pp. 147350–147361, 2019, doi: 10.1109/ACCESS.2019.2946663
##plugins.themes.bootstrap3.article.sidebar##
##plugins.themes.bootstrap3.article.details##
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
The authors declare that:
1. This paper has not been published in the same form elsewhere.
2. It will not be submitted anywhere else for publication prior to acceptance/rejection by this Journal.
3. A copyright permission is obtained for materials published elsewhere and which require this permission for reproduction.