Sistem Deteksi Dan Manajemen Prioritas Adaptif Kendaraan Darurat Untuk Pengendalianlampu Lalu Lintas
Abstract
Keterlambatan kendaraan darurat di persimpangan lalu lintas dapat berdampak serius terhadap keselamatan publik, khususnya di lingkungan perkotaan yang padat. Untuk mengatasi tantangan ini, makalah ini mengusulkan sistem kontrol lalu lintas berbasis edge secara real-time yang secara dinamis memprioritaskan kendaraan darurat melalui deteksi multimodal. Sistem ini mengintegrasikan pengenalan sirene berbasis audio dan deteksi objek visual menggunakan algoritma YOLOv5, yang diimplementasikan pada platform komputasi edge Raspberry Pi. Mikrofon arah dan modul kamera digunakan untuk menangkap data lingkungan, yang kemudian diproses secara paralel untuk mengidentifikasi keberadaan serta arah kedatangan kendaraan darurat. Berdasarkan hasil deteksi, sistem akan menyesuaikan waktu sinyal lalu lintas normal dengan mengaktifkan fase lampu hijau pada lajur yang sesuai, sehingga memastikan kendaraan darurat dapat melintas segera. Evaluasi eksperimental yang dilakukan pada prototipe skala 1:50 menunjukkan akurasi deteksi yang tinggi dan latensi yang rendah. Subsistem audio mencapai waktu respons di bawah 50 ms dengan deteksi yang konsisten dari berbagai arah. Modul visual menghasilkan skor kepercayaan rata-rata di atas 0,87 dan berhasil mengklasifikasikan semua kendaraan darurat maupun non-darurat dengan benar. Sistem ini juga menunjukkan kinerja komputasi yang stabil di bawah beban kerja berkelanjutan. Temuan ini membuktikan kelayakan penerapan mekanisme kontrol sinyal lalu lintas yang ringan, responsif, dan tidak bergantung pada infrastruktur, sejalan dengan tujuan kota cerdas dalam mengoptimalkan respons terhadap keadaan darurat.
Kata Kunci—deteksi kendaraan darurat, kontrol lampu lalu lintas, komputasi edge, YOLOv5, sensor multimodal, Raspberry Pi, sistem waktu nyata, kota cerdas
References
G. Karmakar, A. Chowdhury, J. Kamruzzaman and I. Gondal, "A Smart Priority-Based Traffic Control System for Emergency Vehicles," in IEEE Sensors Journal, vol. 21, no. 14, pp. 15849-15858, 15 July15, 2021, doi: 10.1109/JSEN.2020.3023149.
V. -T. Tran and W. -H. Tsai, "Acoustic-Based Emergency Vehicle Detection Using Convolutional Neural Networks," in IEEE Access, vol. 8, pp. 75702-75713, 2020, doi: 10.1109/ACCESS.2020.2988986.
S. Deepajothi, D. P. Rajan, P. Karthikeyan and S. Velliangiri, "Intelligent Traffic Management for Emergency Vehicles using Convolutional Neural Network," 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2021, pp. 853-857, doi: 10.1109/ICACCS51430.2021.9441929.
J. P. Byrne, N. C. Mann, M. Dai, S. A. Mason, P. Karanicolas, S. Rizoli, and A. B. Nathens, "Association Between Emergency Medical Service Response Time and Motor Vehicle Crash Mortality in the United States," JAMA Surg., vol. 154, no. 4, pp. 286-293, Apr. 2019, doi: 10.1001/jamasurg.2018.5097.
V. -T. Tran and W. -H. Tsai, "Audio-Vision Emergency Vehicle Detection," in IEEE Sensors Journal, vol. 21, no. 24, pp. 27905-27917, 15 Dec.15, 2021, doi: 10.1109/JSEN.2021.3127893.
N. A. Syauqi, "Lampu Lalu Lintas Adaptif untuk Prioritas Kendaraan Ambulans," e-Proceeding of Engineering, vol. 11, no. 6, Dec. 2024.
P. Rosayyan, J. Paul, S. Subramaniam, and S. I. Ganesan, "An optimal control strategy for emergency vehicle priority system in smart cities using edge computing and IOT sensors," Measurement: Sensors, vol. 26, 2023, Art. no. 100697, doi: 10.1016/j.measen.2023.100697.
M. Zohaib, M. Asim, and M. ELAffendi, "Enhancing Emergency Vehicle Detection: A Deep Learning Approach with Multimodal Fusion," Mathematics, vol. 12, no. 10, p. 1514, 2024, doi: 10.3390/math12101514.
B. Fatimah, A. Preethi, V. Hrushikesh, A. Singh B. and H. R. Kotion, "An automatic siren detection algorithm using Fourier Decomposition Method and MFCC," 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kharagpur, India, 2020, pp. 1-6, doi: 10.1109/ICCCNT49239.2020.9225414.
W. N. S. F. Wan Ariffin, C. S. Keat, T. P. A. L. Suriyan, N. A. Mohamad Nore, M. I. S. Mohd Lazim, H. L. Zakaria, N. B. Mohd Hashim, and A. S. Md Zain, "Real-time Dynamic Traffic Light Control System with Emergency Vehicle Priority," J. Phys.: Conf. Ser., vol. 1878, p. 012063, 2021, doi: 10.1088/1742-6596/1878/1/012063.
H. Razalli, R. Ramli and M. H. Alkawaz, "Emergency Vehicle Recognition and Classification Method Using HSV Color Segmentation," 2020 16th IEEE International Colloquium on Signal Processing & Its Applications (CSPA), Langkawi, Malaysia, 2020, pp. 284-289, doi: 10.1109/CSPA48992.2020.9068695.
A. Chowdhury, S. Kaisar, M. E. Khoda, R. Naha, M. A. Khoshkholghi, and M. Aiash, "IoT-Based Emergency Vehicle Services in Intelligent Transportation System," Sensors, vol. 23, no. 11, p. 5324, 2023, doi: 10.3390/s23115324.
M. Humayun, M. F. Almufareh, and N. Z. Jhanjhi, "Autonomous Traffic System for Emergency Vehicles," Electronics, vol. 11, no. 4, p. 510, 2022, doi: 10.3390/electronics11040510.
R. R. Rout, S. Vemireddy, S. K. Raul, and D. V. L. N. Somayajulu, "Fuzzy logic-based emergency vehicle routing: An IoT system development for smart city applications," Comput. Electr. Eng., vol. 88, p. 106839, 2020, doi: 10.1016/j.compeleceng.2020.106839.
H. Sun, X. Liu, K. Xu, J. Miao, and Q. Luo, "Emergency Vehicles Audio Detection and Localization in Autonomous Driving," arXiv preprint arXiv:2109.14797, 2021. [Online]. Available: https://arxiv.org/abs/2109.14797
S. Boddu and A. Mukherjee, "Efficient Edge Deployment of Quantized YOLOv4-Tiny for Aerial Emergency Object Detection on Raspberry Pi 5," arXiv preprint arXiv:2506.09300, 2025. [Online]. Available: https://arxiv.org/abs/2506.09300
