A Study on Current Trends in Deep Learning for Autonomous Driving

Sivapriya Rajan , Dr.Rahul Shajan

Index Terms: Deep learning, GAN, reinforcement, perception.

Abstract: Recent developments in autonomous driving technology have been largely driven by deep learning. Deep neural networks are now the preferred approach for tackling challenging problems in autonomous driving, such as vision, control, and decision-making, thanks to the quick rise in processing power and the accessibility of vast amounts of data. The creation of end-to-end deep learning frameworks, which allow the optimization of the complete system in a single training procedure, is one of the very latest trends in deep learning for autonomous driving. The systems' accuracy and robustness have increased as a result. The adoption of reinforcement learning methods, which enable autonomous cars to learn from their own mistakes and improve their decision-making over time, is another development. The use of generative adversarial networks (GANs) for various autonomous driving tasks, such as picture synthesis and domain adaption, has also seen a substantial growth in research. Powerful generative models known as GANs can be trained to produce new data that closely matches existing data. In general, deep learning continues to be essential to the creation of autonomous vehicle systems. It is anticipated that deep learning will continue to drive innovation in this field and result in more advanced and secure autonomous driving systems in the future as more data becomes available and computing power increases. (Abstract)

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"A Study on Current Trends in Deep Learning for Autonomous Driving", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.8, Issue 3, page no.1370 - 1373, March-2023, Available :https://ijsdr.org/papers/IJSDR2303230.pdf

Volume 8 Issue 3, March-2023

Pages : 1370 - 1373

Paper Reg. ID: IJSDR_204958

Published Paper Id: IJSDR2303230

Downloads: 000347444

Research Area: Engineering

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Published Paper PDF: https://ijsdr.org/papers/IJSDR2303230

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2303230

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

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