Deep Learning for Driverless Vehicles

Hodges, Cameron and An, Senjian and Rahmani, Hossein and Bennamoun, Mohammed (2019) Deep Learning for Driverless Vehicles. In: Handbook of Deep Learning Applications. Smart Innovation, Systems and Technologies . Springer, Cham, pp. 83-99. ISBN 9783030114787

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Automation is becoming a large component of many industries in the 21st century, in areas ranging from manufacturing, communications and transportation. Automation has offered promised returns of improvements in safety, productivity and reduced costs. Many industry leaders are specifically working on the application of autonomous technology in transportation to produce “driverless” or fully autonomous vehicles. A key technology that has the potential to drive the future development of these vehicles is deep learning. Deep learning has been an area of interest in machine learning for decades now but has only come into widespread application in recent years. While traditional analytical control systems and computer vision techniques have in the past been adequate for the fundamental proof of concept of autonomous vehicles, this review of current and emerging technologies demonstrates these short comings and the road map for overcoming them with deep learning.

Item Type: Contribution in Book/Report/Proceedings
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 131836
Deposited By: ep_importer_pure
Deposited On: 11 Mar 2019 14:40
Refereed?: Yes
Published?: Published
Last Modified: 21 Feb 2020 05:58

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