Kontrol Hierarkis Pengereman Sistem AEB berdasarkan Deteksi Halangan menggunakan Deep Learning untuk Mobil Otonom

Witjaksono, Kian (2025) Kontrol Hierarkis Pengereman Sistem AEB berdasarkan Deteksi Halangan menggunakan Deep Learning untuk Mobil Otonom. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Pengadopsian mobil otonom telah merevolusi industri transportasi dengan meningkatkan keselamatan, efisiensi, dan kenyamanan. Penelitian ini mengusulkan metode deteksi halangan pada mobil otonom menggunakan algoritma deteksi objek YOLOv7 berbasis deep learning. Metode ini difokuskan pada deteksi halangan tanpa pengenalan objek dan diuji menggunakan dataset khusus, menghasilkan F1-score sebesar 0.899. Estimasi jarak diperoleh dari perbandingan ukuran bounding box dengan frame gambar, menghasilkan MAPE sebesar 8.71%. Sistem pengereman Autonomous Emergency Braking (AEB) yang diuji menggunakan kontrol hierarkis menunjukkan MSE kecil dalam tracking kecepatan, yaitu sebesar 0.037.
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The adoption of autonomous vehicles has revolutionized the transportation industry by enhancing safety, efficiency, and convenience. This research proposes a method for obstacle detection in autonomous vehicles using the YOLOv7 object detection algorithm based on deep learning. The method focuses on obstacle detection without object recognition and is tested using a specialized dataset, achieving an F1-score of 0.899. Distance estimation is performed by comparing bounding box sizes within the image frame, yielding a mean absolute percentage error (MAPE) of 8.71%. The Autonomous Emergency Braking (AEB) system, implemented with a hierarchical control approach, demonstrates effectiveness in speed tracking, with a mean squared error (MSE) of 0.037.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Mobil Otonom, Deep Learning, Algoritma YOLO, Kontrol Hierarkis, Autonomous Emergency Braking, Autonomous Car, YOLO Algorithm, Hierarchical Control
Subjects:	T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL152.8 Vehicles, Remotely piloted. Autonomous vehicles.
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20201-(S1) Undergraduate Thesis
Depositing User:	Kian Witjaksono
Date Deposited:	14 May 2025 03:17
Last Modified:	14 May 2025 03:17
URI:	http://repository.its.ac.id/id/eprint/119056

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