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All Journal International Journal of Technology and Modeling
Kaya, Emre
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Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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Next-Generation Autonomous Vehicles Enhancing Safety and Efficiency with Deep Learning Yılmaz, Mehmet; Demir, Ayşe; Kaya, Emre; Çelik, Zeynep; Özkan, Burak; Şahin, Elif
International Journal of Technology and Modeling Vol. 2 No. 1 (2023)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v2i1.110

Abstract

The rapid advancement of deep learning has significantly transformed the development of next-generation autonomous vehicles, enhancing both safety and efficiency. This paper explores the integration of deep learning techniques, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and reinforcement learning, in perception, decision-making, and control systems of autonomous vehicles. By leveraging vast datasets and real-time processing, deep learning enables precise object detection, path planning, and adaptive driving strategies. Furthermore, the implementation of sensor fusion techniques combining LiDAR, radar, and cameras enhances situational awareness, reducing the risk of accidents. Despite these advancements, challenges such as computational complexity, adversarial robustness, and ethical considerations remain key research areas. This study provides an overview of the current state-of-the-art deep learning applications in autonomous vehicles and discusses future directions toward fully autonomous, safer, and more efficient transportation systems.
Co-Authors Çelik, Zeynep Demir, Ayşe Özkan, Burak Şahin, Elif Yılmaz, Mehmet