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Safe Fuzzy Longitudinal and Lateral Controller Design for High-Speed Lane-Change Maneuvers in Autonomous Vehicles Using the Pacejka Tire Model | ||
| Iranian Journal of Fuzzy Systems | ||
| دوره 23، شماره 1، فروردین و اردیبهشت 2026، صفحه 125-145 اصل مقاله (1.17 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22111/ijfs.2026.52694.9307 | ||
| نویسندگان | ||
| Mojtaba Nouri Manzar1؛ Mohammad Reza Kamali Ardakani2؛ Mahdi Pourgholi* 3 | ||
| 1Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran | ||
| 2Faculty of automation engineering, university of Bologna, Italy, Bologna | ||
| 3Electrical Engineering Dept. Shahid Beheshti University | ||
| چکیده | ||
| High-speed lane-change maneuvers in autonomous vehicles require control strategies that remain safe and well-damped under strongly nonlinear tire–vehicle dynamics and uncertain interactions with surrounding traffic. This paper proposes a stability-aware fuzzy control framework built on a nonlinear single-track (bicycle) vehicle model augmented with an enhanced semi-empirical Pacejka tire formulation to capture the coupled longitudinal–lateral–yaw behavior under realistic tire-condition effects. Two coordinated Mamdani type-1 fuzzy controllers are designed: one generates the steering command for lateral–yaw regulation and trajectory tracking, and the other produces the longitudinal acceleration command for speed adaptation and headway preservation. All membership-function parameters and rule weights are jointly tuned via constrained simulation-based optimization using the Slime Mould Algorithm (SMA), while actuator bounds and an explicit hard collision-avoidance constraint are enforced throughout the maneuver horizon. A Lyapunov-inspired penalty is embedded in the objective to suppress oscillations and promote well-damped responses. Simulations of high-speed lane changes (up to 72 km/h) show rapid convergence of lateral and longitudinal errors (≈2-3 s over a ≈450 m path), smooth control actions, and collision-free behavior. Compared with an unoptimized fuzzy baseline, the tuned design reduces peak lateral deviation by ≈35% and yaw-rate oscillations by ≈40%. Monte-Carlo trials with parametric uncertainty, noise measurement, and actuator delays further confirm repeatable closed-loop performance and robust safety margins in mixed-traffic scenarios. | ||
| کلیدواژهها | ||
| Autonomous vehicles؛ lane change؛ fuzzy logic control؛ metaheuristic optimization؛ Pacejka tire model؛ robustness؛ safety constraints | ||
| مراجع | ||
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[1] M. Ardelt, C. Coester, N. Kaempchen, Highly automated driving on freeways in real traffic using a probabilistic framework, IEEE Transactions on Intelligent Transportation Systems, 13(4) (2012), 1576-1585. https://doi.org/ 10.1109/TITS.2012.2196273 [2] Q. H. Do, H. Tehrani, S. Mita, M. Egawa, Human drivers based active-passive model for automated lane change, IEEE Intelligent Transportation Systems Magazine, 9(1) (2017), 42-56. https://doi.org/10.1109/MITS.2016. 2613913 [3] M. H. Haider, H. Ali, A. Aman Khan, H. Zheng, Autonomous mobile robot navigation using adaptive neuro fuzzy inference system, 2022 International Conference on Innovations and Development of Information Technologies and Robotics (IDITR), (2022), 93-99. https://doi.org/10.1109/IDITR54676.2022.9796495 [4] A. Hentout, A. Maoudj, A. Kouider, Shortest path planning and efficient fuzzy logic control of mobile robots in indoor static and dynamic environments, Romanian Journal of Information Science and Technology, 27(1) (2024), 21-36. https://doi.org/10.59277/ROMJIST.2024.1.02 [5] P. Hidas, Modelling lane changing and merging in microscopic traffic simulation, Transportation Research Part C: Emerging Technologies, 10(5-6) (2002), 351-371. https://doi.org/10.1016/S0968-090X(02)00026-8 [6] F. Kamil, et al., Fuzzy logic-based control for intelligent vehicles: A survey, In AIP Conference Proceedings, AIP Publishing LLC, (2024). https://doi.org/10.1063/5.0199602 [7] S. Li, et al., Slime mould algorithm: A new method for stochastic optimization, Future Generation Computer Systems, 111 (2020), 300-323. https://doi.org/10.1016/j.future.2020.03.055 [8] K. Liu, J. Gong, A. Kurt, H. Chen, Dynamic modeling and control of high-speed automated vehicles for lane change maneuver, IEEE Transactions on Intelligent Vehicles, 3(3) (2018), 329-339. https://doi.org/10.1109/ TIV.2018.2843177 [9] Y. Luo, Y. Xiang, K. Cao, K. Li, A dynamic automated lane change maneuver based on vehicle-to-vehicle communication, Transportation Research Part C: Emerging Technologies, 62 (2016), 87-102. https://doi.org/10. 1016/j.trc.2015.11.011 [10] J. E. Naranjo, C. Gonz´alez, R. Garc, T. D. Pedro, Lane-change fuzzy control in autonomous vehicles for the overtaking maneuver, IEEE Transactions on Intelligent Transportation Systems, 9(3) (2008), 438-450. https: //doi.org/10.1109/TITS.2008.922880 [11] J. Nilsson, et al., If, when, and how to perform lane change maneuvers on highways, IEEE Intelligent Transportation Systems Magazine, 8(4) (2016), 68-78. https://doi.org/10.1109/MITS.2016.2565718 [12] ¨U. ¨Ozg¨uner, T. Acarman, K. A. Redmill, Autonomous ground vehicles, Artech House, 2011.
[13] R. E. Precup, et al., Problem setting for trajectory planning and cruise control of a connected autonomous electric bus in intersection scenarios with human-driven vehicles to optimize energy, comfort and tracking, Romanian Journal of Information Science and Technology, 28 (2025), 299-312. https://doi.org/10.59277/ROMJIST.2025. 3.05 [14] O. Rodr´ıguez-Abreo, et al., Fuzzy logic controller for UAV with gains optimized via genetic algorithm, Heliyon, 10(4) (2024). https://doi.org/10.1016/j.heliyon.2024.e26363 [15] S. Samonto, et al., Best fit membership function for designing fuzzy logic controller aided intelligent overcurrent fault protection scheme, International Transactions on Electrical Energy Systems, 31(5) (2021), e12875. https: //doi.org/10.1002/2050-7038.12875 [16] O. Sharma, N. C. Sahoo, N. B. Puhan, Recent advances in motion and behavior planning techniques for software architecture of autonomous vehicles: A state-of-the-art survey, Engineering Applications of Artificial Intelligence, 101 (2021), 104211. https://doi.org/10.1016/j.engappai.2021.104211 [17] K. B. Singh, S. Sivaramakrishnan, Extended pacejka tire model for enhanced vehicle stability control, arXiv preprint, (2023), arXiv:2305.18422. https://doi.org/10.48550/arXiv.2305.18422 [18] J. Wen, F. Wang, Stable levitation of single-point levitation systems for maglev trains by improved cascade control, Romanian Journal of Information Science and Technology, 27(3-4) (2024), 348-361. https://doi.org/10.59277/ ROMJIST.2024.3-4.08 [19] H. Xue, et al., Fuzzy controller for autonomous vehicle based on rough sets, IEEE Access, 7 (2019), 147350-147361. https://doi.org/10.1109/ACCESS.2019.2946663 [20] D. Yang, et al., Modeling and analysis of the lane-changing execution in longitudinal direction, IEEE Transactions on Intelligent Transportation Systems, 17(10) (2016), 2984-2992. https://doi.org/10.1109/TITS.2016.2542109 [21] H. Yu, et al., Automated vehicle-involved traffic flow studies: A survey of assumptions, models, speculations, and perspectives, Transportation Research Part C: Emerging Technologies, 127 (2021), 103101. https://doi.org/10. 1016/j.trc.2021.103101 [22] L. A. Zadeh, Fuzzy sets, Information and Control, 8(3) (1965), 338-353. https://doi.org/10.1016/ S0019-9958(65)90241-X [23] J. Zhou, et al., Multiobjective optimization of lane-changing strategy for intelligent vehicles in complex driving environments, IEEE Transactions on Vehicular Technology, 69(2) (2020), 1291-1308. https://doi.org/10.1109/ TVT.2019.2956504 | ||
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