Neuware -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 non-linear 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 84 pp. Englisch.

Bestandsnummer des Verk�ufers 9783031003745

Verk�ufer kontaktieren

Diesen Artikel melden

Bibliografische Details

Titel: Deep Learning for Autonomous Vehicle Control
Verlag: Springer International Publishing, Springer Nature Switzerland Aug 2019
Erscheinungsjahr: 2019
Sprache: Englisch
ISBN-10: 3031003748
ISBN-13: 9783031003745
Einband: Taschenbuch
Zustand: Neu

�ber diesen Titel

Inhaltsangabe

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 non-linear 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.

�ber die Autorin bzw. den Autor

Sampo Kuutti received his MEng degree in mechanical engineering in 2017 from University of Surrey, Guildford, U.K., where he is currently pursuing a Ph.D. in automotive engineering with the Connected Autonomous Vehicles Lab within the Centre for Automotive Engineering. His research interests include deep learning applied to autonomous vehicles, functional safety validation, and safety and interpretability in machine learning systems.Saber Fallah is a Senior Lecturer (Associate Professor) in Vehicle and Mechatronic Systems at the University of Surrey and the Director of Connected Autonomous Vehicle Lab (CAVLAB) within the Centre for Automotive Engineering, where he leads several research activities funded by the UK and European governments (e.g., EPSRC, Innovate UK, H2020) in collaboration with major companies active in autonomous vehicle technologies. His research interests include reinforced deep learning, advanced control, optimization, and estimation and their applications to connected autonomous vehicles.Richard Bowden is Professor of computer vision and machine learning at the University of Surrey where he leads the Cognitive Vision Group within the Centre for Vision, Speech and Signal Processing. His research centers on the use of computer vision to locate, track, and understand humans. He is anassociate editor for the journals Image and Vision Computing and IEEE TPAMI. In 2013 he was awarded a Royal Society Leverhulme Trust Senior Research Fellowship and is a fellow of the Higher Education Academy, a senior member of the IEEE, and a Fellow of the International Association of Pattern Recognition (IAPR).Phil Barber was formerly Principal Technical Specialist in Capability Research at Jaguar Land Rover. For over 30 years in the automotive industry he has witnessed the introduction of computer controlled by-wire technology and been part of the debate over the safety issues involved in the implementation of real-time vehicle control.

��ber diesen Titel� kann sich auf eine andere Ausgabe dieses Titels beziehen.

Anbieterinformationen

Impressum Thorsten Retsch Buchversand Mimpf2000 Ober�lschnitz 16 95517 Emtmannsberg Deutschland Telefon: 09209-2023188 Email: mimpf2000@online.de USt-ID-Nr.: DE 235096871 Wir f�hren gebrauchte B�cher aus allen Sparten der Literatur

Zur Homepage des Verk�ufers

Unternehmensdaten des Verk�ufers

buchversandmimpf2000
Germany

Gesch�ftsbedingungen und Versandinformationen

Verkaufsbedingungen

Widerrufsbelehrung/ Muster-Widerrufsformular/
Allgemeine Gesch�ftsbedingungen und Kundeninformationen/ Datenschutzerkl�rung

Widerrufsrecht f�r Verbraucher
(Verbraucher ist jede nat�rliche Person, die ein Rechtsgesch�ft zu Zwecken abschlie�t, die �berwiegend weder ihrer gewerblichen noch ihrer selbstst�ndigen beruflichen T�tigkeit zugerechnet werden k�nnen.)

Widerrufsbelehrung

Widerrufsrecht
Sie haben das Recht, binnen 14 Tagen ohne Angabe von Gr�nden diesen Vertrag zu widerrufen.
Die Widerrufsfr...

Mehr Information

Versandbedingungen

Soweit in der Artikelbeschreibung keine andere Frist angegeben ist, erfolgt die Lieferung der Ware innerhalb von 3-5 Werktagen nach Vertragsschluss, bei Vorauszahlung erst nach Eingang des vollst�ndigen Kaufpreises und der Versandkosten. Alle Preise inkl. MwSt.

Impressum & Info zum Verk�ufer