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All Journal Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Pasundan Informatika
Kevin Anggara Putra
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Engineering

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Penghindaran Rintangan Otomatis Pada Agen Otonom Berbasis End-to-End Deep Imitation Learning Supeno, Handoko; Muhammad Tirta Mulia; Mellia Liyanthy; Kevin Anggara Putra; Fauzan Nursalma Mawardi
TEMATIK Vol. 10 No. 2 (2023): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Desember 2023
Publisher : LPPM POLITEKNIK LP3I BANDUNG

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Abstract

This research addresses the issue of automatic obstacle avoidance during navigation by autonomous agents. Designing a traditional, programmed automatic obstacle avoidance system would be difficult and expensive. Therefore, a neural network-based approach is proposed, known as end-to-end deep imitation learning, where the approach is data-driven and thus relatively easier and more cost-effective compared to traditional methods. The research also proposes the architecture of a convolutional neural network design and image processing techniques for effective and efficient machine learning training. Testing is conducted on a path with randomly placed obstacles in the Webots simulator. Gradual performance evaluations demonstrate that the proposed architecture successfully trains autonomous agents to maneuver when encountering dynamic obstacles with a relatively small training dataset.
Pengembangan Automatic Emergency Braking (AEB) pada Autonomous Driving Menggunakan Deep Q – Learning dengan PER Kevin Anggara Putra; Handoko Supeno
JURNAL PASUNDAN INFORMATIKA Vol. 4 No. 1 (2025): Pasinformatik Edisi 01, Januari 2025
Publisher : Program Studi Teknik Informatika Universitas Pasundan

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Abstract

Penelitian ini membahas tentang kemampuan kendaraan otonom untuk menghindari rintangan, yang merupakan salah satu fitur penting dari sistem navigasi kendaraan otonom. Perancangan sistem pengereman darurat otomatis secara tradisional akan sulit karena membutuhkan biaya yang besar, kompleksitas, dan pertimbangan keamanan. Reinforcement Learning (RL) adalah salah satu metode kecerdasan buatan atau pembelajaran mesin yang dapat belajar langsung dari lingkungan. Penelitian ini menggunakan algoritma RL dengan metode deep q – learning (DQN) dan dioptimalkan dengan prioritized experience replay untuk menghindari rintangan. Simulasi dilakukan pada jalur dengan rintangan yang diposisikan satu jalan dengan model pada simulator CARLA.
Co-Authors Fauzan Nursalma Mawardi Liyanthy, Mellia Muhammad Tirta Mulia Supeno, Handoko