Adaptive cruise control based on fuzzy logic for collision avoidance in autonomous vehicles
DOI:
https://doi.org/10.15282/ijame.23.3.2026.1.1037Keywords:
Autonomous vehicle, Collision avoidance, Fuzzy control, Intelligent Adaptive Cruise Control, Safety controlAbstract
Intelligent adaptive cruise control (IACC) systems have become a key technology for enhancing the safety and reliability of autonomous vehicles operating in dynamic traffic environments. This study proposes a fuzzy logic-based IACC architecture for collision avoidance that integrates longitudinal speed control, lateral steering control, obstacle detection, safety distance assessment, rollover prevention, and emergency braking into a unified decision-making framework. Given the high cost and complexity associated with full-scale vehicle experiments, the proposed system was implemented and validated within a MATLAB®/Simulink® environment, utilizing a dynamic model based on the real-world characteristics of a commercial passenger vehicle. The controller continuously estimates the distance to surrounding obstacles, assesses the vehicle's dynamic state, and autonomously selects the safest maneuver—coordinating lane-change and emergency braking actions while accounting for vehicle stability and passenger comfort. Three representative driving scenarios, featuring obstacles of varying sizes and positions, were designed to evaluate the controller under realistic operating conditions. Simulation results demonstrated a 99.9% success rate in collision avoidance, smooth trajectory tracking, stable speed regulation, effective rollover prevention via lateral acceleration monitoring, and reliable emergency braking in critical situations. These results demonstrate that the proposed fuzzy logic-based IACC architecture offers a robust and effective solution for autonomous collision avoidance by integrating multiple safety-oriented control strategies into a single intelligent framework. Furthermore, the proposed approach provides a promising foundation for future experimental validation and real-world implementation in autonomous vehicles.
References
[1] W. Wu, D. Zou, J. Ou, and L. Hu, “Adaptive cruise control strategy design with optimized active braking control algorithm,” Mathematic Problems Engineering, vol. 2020, no. 1, p. 8382734, 2020, https://doi.org/10.1155/2020/8382734.
[2] N. C. Basjaruddin, Kuspriyanto, D. Saefudin, and I. K. Nugraha, “Developing adaptive cruise control based on fuzzy logic using hardware simulation,” International Journal of Electrical and Computer Engineering, vol. 4, no. 6, pp. 944-951, 2014, https://doi.org/10.11591/ijece.v4i6.6734.
[3] G. O. Burnham, J. Seo, and G. A. Bekey, “Identification of human driver models in car following,” IEEE transactions on Automatic Control, vol. 19, no. 6, pp. 911-915, 1974, https://doi.org/10.1109/TAC.1974.1100740.
[4] J. E. Naranjo, C. Gonzaález, J. Reviejo, R. Garciía, and T. de Pedro, “Adaptive fuzzy control for inter-vehicle gap keeping,” IEEE Transactions on Intelligent Transportation Systems, vol. 4, no. 3, pp. 132-142, 2003, https://doi.org/10.1109/TITS.2003.821294.
[5] J. J. Martinez and C. Canudas-de-Wit, “A safe longitudinal control for adaptive cruise control and stop-and-go scenarios,” IEEE Transactions on Control Systems Technology, vol. 15, no. 2, pp. 246-258, 2007, https://doi.org/10.1109/TCST.2006.886432.
[6] T. Stanger and L. Del Re, “A model predictive cooperative adaptive cruise control approach,” in Proceedings of the American Control Conference, 2013, pp. 1374-1379. https://doi.org/10.1109/acc.2013.6580028.
[7] C. Piao, J. Gao, Q. Yang, and J. Shi, “Adaptive cruise control method based on hierarchical control and multi-objective optimization,” Transactions of the Institute of Measurement and Control, vol. 45, no. 7, pp. 1298-1312, 2023, https://doi.org/10.1177/01423312221137508.
[8] J. Chen, F. Tao, Z. Fu, and N. Wang, “Vehicle-following control based on continuous synthesis variable time headway model,” International Journal of Fuzzy Systems, vol. 27, no. 3, pp. 912-930, 2025, https://doi.org/10.1007/s40815-024-01814-z.
[9] S. Abdallaoui, H. Ikaouassen, A. Kribèche, A. Chaibet, and E. Aglzim, “Advancing autonomous vehicle control systems: An in‐depth overview of decision‐making and manoeuvre execution state of the art,” The Journal of Engineering, vol. 2023, no. 11, p. e12333, 2023, https://doi.org/10.1049/tje2.12333.
[10] F. Kamil, F. H. Gburi, M. A. Kadhom, and B. A. Kalaf, “Fuzzy logic-based control for intelligent vehicles: A survey,” in AIP Conference Proceedings, 2024, vol. 3092, no. 1, p. 040017, https://doi.org/10.1063/5.0199602.
[11] R. Li, S. Deng, and Y. Hu, “Autonomous vehicle modeling and velocity control based on decomposed fuzzy PID,” International Journal of Fuzzy Systems, vol. 24, no. 5, pp. 2354-2362, 2022, https://doi.org/10.1007/s40815-022-01279-y.
[12] H. Ekanayake, P. Abejeewa, W. A. L. Priyankara, and A. Induranga, “Overview of use of PID, fuzzy logic, and model predictive control in autonomous vehicle systems,” International Journal of Research and Scientific Innovation, vol. 12, no. 10, pp. 3685-3697, 2025, https://doi.org/10.51244/ijrsi.2025.1210000318.
[13] N. Awad, A. Lasheen, M. Elnaggar, and A. Kamel, “Model predictive control with fuzzy logic switching for path tracking of autonomous vehicles,” ISA transactions, vol. 129, pp. 193-205, 2022, https://doi.org/10.1016/j.isatra.2021.12.022.
[14] S. Hao, Y. Chen, J. Gao, H. He, and Y. Wang, “Fuzzy model predictive control for collision avoidance control of autonomous vehicles,” in Advances in Transdisciplinary Engineering, 2025, pp. 267-277, https://doi.org/10.3233/ATDE250427.
[15] I. Aliskan, “The optimization-based fuzzy logic controllers for autonomous ground vehicle path tracking,” Engineering Applications of Artificial Intelligence, vol. 151, p. 110642, 2025, https://doi.org/10.1016/j.engappai.2025.110642.
[16] Ambuj and R. Machavaram, “Intelligent path planning for autonomous ground vehicles in dynamic environments utilizing adaptive Neuro-Fuzzy control,” Engineering Applications of Artificial Intelligence, vol. 144, pp. 110119, 2025, https://doi.org/10.1016/j.engappai.2025.110119.
[17] W. Medina Medina, “Diseño de un sistema de control de tráiler autónomo,” Ingeniería Industrial, pp. 25-66, 2022, https://doi.org/10.26439/ing.ind2022.n.5799.
[18] B. Suprapto and S. Dwijayanti, “Comparative performance of fuzzy logic and PID steering control for improved swerve autonomous vehicles,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 10, pp. 516–527, 2026, https://doi.org/10.29207/resti.v10i2.7594.
[19] B. Wang, Y. Lei, Y. Fu, and X. Geng, “Autonomous vehicle trajectory tracking lateral control based on the terminal sliding mode control with radial basis function neural network and fuzzy logic algorithm,” Mechanical Sciences, vol. 13, no. 2, pp. 713-724, 2022, https://doi.org/10.5194/ms-13-713-2022.
[20] B. Arifin, B. Y. Suprapto, S. A. D. Prasetyowati, and Z. Nawawi, “Steering control in electric power steering autonomous vehicle using type-2 fuzzy logic control and PI control,” World Electric Vehicle Journal, vol. 13, no. 3, p. 53, 2022, https://doi.org/10.3390/wevj13030053.
[21] B. Y. Suprapto, S. Dwijayanti, and M. I. Fadillah, “Design and optimization of a type-2 fuzzy logic-based lateral control system for enhancing trajectory stability in autonomous vehicles,” Eastern-European Journal of Enterprise Technologies, vol. 3, no. 3, p. 86, 2025, https://doi.org/10.15587/1729-4061.2025.326193.
[22] S Devikala, “Development of fuzzy logic controller in automatic vehicle navigation using IoT,” Journal of Electrical Systems, vol. 20, no. 3s, pp. 114-121, 2024, https://doi.org/10.52783/jes.1254.
[23] S. Jain and I. Malhotra, “A fuzzy logic design for self-driving vehicle to avoid obstacles,” in Uncertainty in Computational Intelligence-Based Decision Making: A volume in Advanced Studies in Complex Systems, pp. 143-171, 2024, https://doi.org/10.1016/B978-0-443-21475-2.00015-1.
[24] Q. Liu, Z. Song, X. Xu, J. Wang, and J. P. Lazaro, “Research on vehicle obstacle avoidance control based on improved artificial potential field method and fuzzy model predictive control,” Vehicles, vol. 8, no. 4, p. 86, 2026, https://doi.org/10.3390/vehicles8040086.
[25] L. Wang, X. Deng, J. Gui, and S. Wan, “Fuzzy logic-based probabilistic forecasting of short-term trajectory for autonomous vehicles,” IEEE Transactions on Consumer Electronics, vol. 71, no. 2, pp. 7158-7169, 2025, https://doi.org/10.1109/TCE.2025.3541570.
[26] M. Sadaf, Z. Iqbal, Z. Anwar, U. Noor, M. Imran, and T. R. Gadekallu, “A novel framework for detection and prevention of denial of service attacks on autonomous vehicles using fuzzy logic,” Vehicular Communications, vol. 46, p. 100741, 2024, https://doi.org/10.1016/j.vehcom.2024.100741.
[27] S. Nahavandi, S. Mohamed, I. Hossain, et al., “Autonomous convoying: A survey on current research and development,” IEEE Access, vol. 10, pp. 13663-13683, 2022, https://doi.org/10.1109/ACCESS.2022.3147251.
[28] S. Magdici and M. Althoff, “Adaptive cruise control with safety guarantees for autonomous vehicles,” in IFAC-PapersOnLine, vol. 50, no. 1, pp. 5774-5781, 2017. https://doi.org/10.1016/j.ifacol.2017.08.418.
[29] K. W. Schmidt, “Cooperative adaptive cruise control for vehicle following during lane changes,” vol. 50, no. 1, pp. 12582-12587, 2017. https://doi.org/10.1016/j.ifacol.2017.08.2199.
[30] A. Ondoğan and H. S. Yavuz, “Fuzzy logic based adaptive cruise control for low-speed following,” in 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), 2019, pp. 1-5, https://doi.org/10.1109/ISMSIT.2019.8932776.
[31] M. U. Khan, S. A. A. Zaidi, A. Ishtiaq, S. U. R. Bukhari, S. Samer, and A. Farman, “A comparative survey of LiDAR-SLAM and LiDAR based sensor technologies,” in Proceedings of the 2021 Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021, 2021, pp. 1-8, https://doi.org/10.1109/MAJICC53071.2021.9526266.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 The Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
