Deep learning for driverless vehicles

Cameron Hodges, Senjian An, Hossein Rahmani, Mohammed Bennamoun

Research output: Chapter in Book/Conference paperChapter

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationSmart Innovation, Systems and Technologies
PublisherSpringer Science + Business Media
Pages83-99
Number of pages17
Volume136
DOIs
Publication statusPublished - 1 Jan 2019

Publication series

NameSmart Innovation, Systems and Technologies
Volume136
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

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  • Cite this

    Hodges, C., An, S., Rahmani, H., & Bennamoun, M. (2019). Deep learning for driverless vehicles. In Smart Innovation, Systems and Technologies (Vol. 136, pp. 83-99). (Smart Innovation, Systems and Technologies; Vol. 136). Springer Science + Business Media. https://doi.org/10.1007/978-3-030-11479-4_4