國立虎尾科技大學 |

Deep learning for autonomous vehicle control : = algorithms, state-of-the-art, and future prospects /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Deep learning for autonomous vehicle control :/ Sampo Kuutti, Saber Fallah, Richard Bowden, Phil Barber.
Reminder of title:	algorithms, state-of-the-art, and future prospects /
Author:	Kuutti, Sampo,
other author:	Fallah, Saber,
Description:	1 online resource (82 p.)
Subject:	Automobiles - Automatic control. -
Online resource:	https://portal.igpublish.com/iglibrary/search/MCPB0006488.html
ISBN:	9781681736075

Deep learning for autonomous vehicle control : = algorithms, state-of-the-art, and future prospects /
Kuutti, Sampo,

Deep learning for autonomous vehicle control :algorithms, state-of-the-art, and future prospects /Sampo Kuutti, Saber Fallah, Richard Bowden, Phil Barber. - 1st ed. - 1 online resource (82 p.) - Synthesis lectures on advances in automotive technology ;8. - Synthesis lectures on advances in automotive technology ;#10..

Includes bibliographical references and index.

The next generation of autonomous vehicles will provide major improvements in traffic flow, fuel efficiency, and vehicle safety. Several challenges currently prevent the deployment of autonomous vehicles, one aspect of which is robust and adaptable vehicle control. Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and nonlinear control problems, but also in generalizing previously learned rules to new scenarios. For these reasons, the use of deep neural networks for vehicle control has gained significant interest. In this book, we introduce relevant deep learning techniques, discuss recent algorithms applied to autonomous vehicle control, identify strengths and limitations of available methods, discuss research challenges in the field, and provide insights into the future trends in this rapidly evolving field.

Mode of access: World Wide Web.

ISBN: 9781681736075Subjects--Topical Terms:

671346
Automobiles
--Automatic control.Index Terms--Genre/Form:

554714
Electronic books.

LC Class. No.: TL152.8

Dewey Class. No.: 629.2220285

Deep learning for autonomous vehicle control : = algorithms, state-of-the-art, and future prospects /
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