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Journals / Gazi University Journal of Science / 2026 / Volume: 34 Issue: 2

Particle Swarm Optimization Method Based Controller Tuning for Adaptive Cruise Control Application

Erhan Özkaya(AVL TURKEY RESEARCH AND ENGINEERING)0000-0001-6781-8614
Hikmet Arslan(İSTANBUL TEKNİK ÜNİVERSİTESİ, MAKİNE FAKÜLTESİ)0000-0002-4132-8235
Osman Taha Şen(İSTANBUL TEKNİK ÜNİVERSİTESİ, MAKİNE FAKÜLTESİ)0000-0002-8604-3962
Pages
—
Publication Date
2021-06-01
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In conclusion, the adaptive cruise control system requirements can be easily and accurately ensured by the use of particle swarm optimization algorithm.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Major developments in relevant technology make the advanced driver assistance systems and autonomous driving functions more attainable.
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Key Findings & Data:Although advanced driver assistance systems and autonomous driving functions have many advantages, such as enhanced driver convenience, increased comfort, and fuel economy; concerns related to safety still exist.
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Conclusion & Impact:In conclusion, the adaptive cruise control system requirements can be easily and accurately ensured by the use of particle swarm optimization algorithm.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:ADAS · ACC · PSO

Abstract

Major developments in relevant technology make the advanced driver assistance systems and autonomous driving functions more attainable. Thus, conventional practices being applied in vehicle production evolves towards highly automated, safer, and more comfortable vehicles. Although advanced driver assistance systems and autonomous driving functions have many advantages, such as enhanced driver convenience, increased comfort, and fuel economy; concerns related to safety still exist. For instance, failures related to sensors or algorithms being used can lead to critical problems. Therefore, controller algorithms should be more robust and well-optimized in order to eliminate these safety issues. This requires the implementation of automated optimization algorithms for the controller parameter tuning process. The main objective of this study is to optimize the designed controller for an adaptive cruise control system by using the particle swarm optimization method, which is a swarm intelligence optimization technique. Based on the results, it is observed that the use of automated optimization techniques for adaptive cruise control systems leads to better accuracy and safety for driving control. Furthermore, the time consumed for the controller parameter tuning process is also decreased significantly. In conclusion, the adaptive cruise control system requirements can be easily and accurately ensured by the use of particle swarm optimization algorithm.

Keywords
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Cite This Article

Özkaya, E., Arslan, H., Şen, O. T. (2021). Particle Swarm Optimization Method Based Controller Tuning for Adaptive Cruise Control Application. Gazi University Journal of Science, Volume: 34 Issue: 2. https://doi.org/10.35378/gujs.762103

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References (14)

  1. Yaprak, Ş., Akbulut, A. M., “Trafik kaza ve denetim istatistikleri”, Polis Akademisi Yayınları, Rapor No: 27, (2019).
  2. Xiao, L., Gao, F., “A comprehensive review of the development of adaptive cruise control systems”, Vehicle Systems Dynamics, 48(10): 1167-1192, (2010).
  3. Gürbüz, H., Buyruk, S., “Improvement of safe stopping distance and accident risk coefficient based on active driver sight field on real road conditions”, Intelligent Transport Systems, 13(12): 1843-1850, (2019).
  4. Vedam, N., Diaz-Rodriguez, I., Bhattacharyya, S.P., “A novel approach to the design of controllers in an automotive cruise-control system”, 40th Annual Conference of the IEEE Industrial Electronics Society, Dallas, Texas, USA, (2014).
  5. Jiang, J., Ding, F., Zhou, Y., Wu, J., Tan, H., “A personalized human drivers' risk sensitive characteristics depicting stochastic optimal control algorithm for adaptive cruise control”, IEEE Access, 8: 145056-145066, (2020).
  6. Rout, M. K., Sain, D., Swain, S. K., Mishra, S. K., “PID controller design for cruise control system using genetic algorithm”, International Conference on Electrical, Electronics and Optimization Techniques, Chennai, India, (2016).
  7. Abdulnabi, A. R., “PID controller design for cruise control system using particle swarm optimization”, Iraqi Journal for Computers and Informatics, 43(2): 29-34, (2017).
  8. Pradhan, R., Majhi, S. K., Pradhan, J. K., Pati, B. B., “Antlion optimizer tuned PID controller based on bode ideal transfer function for automobile cruise control system”, Journal of Industrial Information Integration, 9: 45-52, (2018).
  9. Luo, L. H., Liu, H., Li, P., Wang, H., “Model predictive control for adaptive cruise control with multi-objectives: comfort, fuel-economy, safety and car-following”, Journal of Zhejiang University, 11(3): 191-201, (2010).
  10. Jazar, R. N., “Vehicle dynamics: theory and application”, Springer, New York, NY, (2008).
  11. Erdoğmuş, P., “Particle swarm optimization with applications”, IntechOpen, London, (2018).
  12. Özsağlam, M. Y., Çunkaş, M., “Optimizasyon problemlerinin çözümü için parçacık sürü optimizasyonu algoritması”, Journal of Polytechnic, 11(4): 299-305, (2008).
  13. Anstalt für Verbrennungskraftmaschinen List. https://www.avl.com/-/avl-vsm-4-, 10.11.2019.
  14. BS ISO 15622:2018., “Intelligent transport systems – adaptive cruise control systems – performance requirements and test procedures”, (2018).