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Deep Learning for Driverless Vehicles

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter (peer-reviewed) › peer-review

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Deep Learning for Driverless Vehicles. / Hodges , Cameron; An, Senjian; Rahmani, Hossein et al.
Handbook of Deep Learning Applications. ed. / V. Balas; S. Roy; D. Sharma; P. Samui. Cham: Springer, 2019. p. 83-99 ( Smart Innovation, Systems and Technologies; Vol. 136).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter (peer-reviewed) › peer-review

Harvard

Hodges , C, An, S, Rahmani, H & Bennamoun, M 2019, Deep Learning for Driverless Vehicles. in V Balas, S Roy, D Sharma & P Samui (eds), Handbook of Deep Learning Applications. Smart Innovation, Systems and Technologies, vol. 136, Springer, Cham, pp. 83-99. https://doi.org/10.1007/978-3-030-11479-4_4

APA

Hodges , C., An, S., Rahmani, H., & Bennamoun, M. (2019). Deep Learning for Driverless Vehicles. In V. Balas, S. Roy, D. Sharma, & P. Samui (Eds.), Handbook of Deep Learning Applications (pp. 83-99). ( Smart Innovation, Systems and Technologies; Vol. 136). Springer. https://doi.org/10.1007/978-3-030-11479-4_4

Vancouver

Hodges C, An S, Rahmani H, Bennamoun M. Deep Learning for Driverless Vehicles. In Balas V, Roy S, Sharma D, Samui P, editors, Handbook of Deep Learning Applications. Cham: Springer. 2019. p. 83-99. ( Smart Innovation, Systems and Technologies). Epub 2019 Feb 26. doi: 10.1007/978-3-030-11479-4_4

Author

Hodges , Cameron ; An, Senjian ; Rahmani, Hossein et al. / Deep Learning for Driverless Vehicles. Handbook of Deep Learning Applications. editor / V. Balas ; S. Roy ; D. Sharma ; P. Samui. Cham : Springer, 2019. pp. 83-99 ( Smart Innovation, Systems and Technologies).

Bibtex

@inbook{ebca7c203a934b049942985d0ff645f6,
title = "Deep Learning for Driverless Vehicles",
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.",
author = "Cameron Hodges and Senjian An and Hossein Rahmani and Mohammed Bennamoun",
year = "2019",
month = mar,
day = "1",
doi = "10.1007/978-3-030-11479-4_4",
language = "English",
isbn = "9783030114787",
series = " Smart Innovation, Systems and Technologies",
publisher = "Springer",
pages = "83--99",
editor = "V. Balas and Roy, {S. } and D. Sharma and Samui, {P. }",
booktitle = "Handbook of Deep Learning Applications",

}

RIS

TY - CHAP

T1 - Deep Learning for Driverless Vehicles

AU - Hodges , Cameron

AU - An, Senjian

AU - Rahmani, Hossein

AU - Bennamoun, Mohammed

PY - 2019/3/1

Y1 - 2019/3/1

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-030-11479-4_4

DO - 10.1007/978-3-030-11479-4_4

M3 - Chapter (peer-reviewed)

SN - 9783030114787

T3 - Smart Innovation, Systems and Technologies

SP - 83

EP - 99

BT - Handbook of Deep Learning Applications

A2 - Balas, V.

A2 - Roy, S.

A2 - Sharma, D.

A2 - Samui, P.

PB - Springer

CY - Cham

ER -