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

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	629.2220285
書名/作者:	Deep learning for autonomous vehicle control : : algorithms, state-of-the-art, and future prospects // Sampo Kuutti, Saber Fallah, Richard Bowden, Phil Barber.
作者:	Kuutti, Sampo,
其他作者:	Fallah, Saber,
面頁冊數:	1 online resource (82 p.)
標題:	Automobiles - Automatic control.
標題:	Machine learning.
ISBN:	9781681736075
ISBN:	9781681736082
ISBN:	9781681736167
書目註:	Includes bibliographical references and index.
摘要、提要註:	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.
電子資源:	https://portal.igpublish.com/iglibrary/search/MCPB0006488.html

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 ;8..

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:

466245
Automobiles
--Automatic control.Index Terms--Genre/Form:

336502
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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