Deep Learning for Autonomous Vehicle Control: Algorithms, State-of-the-Art, and Future Prospects

— —

Sampo Kuutti Saber Fallah

Deep Learning for Autonomous Vehicle Control

Algorithms, State-of-the-Art, and Future Prospects

Sampo Kuutti, Saber Fallah, Richard Bowden , Phil Barber

$84.95 $72.23

Paperback

Not in-store but you can order this
How long will it take?

Availability Information

We source books from suppliers in Australia and overseas. For books we don't currently have in stock, the time it takes to get them from our suppliers can vary widely - from a few days to a few months - so we check each book with each supplier to determine the expected time it will take to be supplied to us.

We then advise you accordingly. If the time taken to get any book is too long for you, you can let us know and we will cancel or adjust your order, and refund as required.

To find out the anticipated arrival time for specific items prior to ordering, please contact us by phone or email:

Phone +61 2 9264 3111, or 1800 4 BOOKS (1800 4 26657) if outside Sydney:
option 1 Abbey's Bookshop (Crime, History, Science, Kids & more) • info@abbeys.com.au
option 2 Language Book Centre (ESL & Foreign Languages) • language@abbeys.com.au
option 3 Galaxy Bookshop (Sci-fi, Fantasy, Romance, Graphic Novels) • sf@galaxybooks.com.au

QTY:

English

Springer International Publishing AG
08 August 2019

Mechanical engineering; Electrical engineering; Highway & traffic engineering; Automotive technology & trades

Series: Synthesis Lectures on Advances in Automotive Technology

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.

By: Sampo Kuutti, Saber Fallah, Richard Bowden, Phil Barber
Imprint: Springer International Publishing AG
Country of Publication: Switzerland
Dimensions: Height: 235mm, Width: 191mm,
Weight: 179g
ISBN: 9783031003745
ISBN 10: 3031003748
Series: Synthesis Lectures on Advances in Automotive Technology
Pages: 70
Publication Date: 08 August 2019
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

List of Figures.- List of Tables.- Preface.- Introduction.- Deep Learning.- Deep Learning for Vehicle Control.- Safety Validation of Neural Networks.- Concluding Remarks.- Bibliography.- Authors' Biographies.

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.