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