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Deep Learning for Autonomous Vehicle Control [electronic resource] : Algorithms, State-of-the-Art, and Future Prospects / by Sampo Kuutti, Saber Fallah, Richard Bowden, Phil Barber.

By: Kuutti, Sampo [author.].
Contributor(s): Fallah, Saber [author.] | Bowden, Richard [author.] | Barber, Phil [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Advances in Automotive Technology: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2019Edition: 1st ed. 2019.Description: XIV, 70 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031015021.Subject(s): Electrical engineering | Mechanical engineering | Automotive engineering | Transportation engineering | Traffic engineering | Electrical and Electronic Engineering | Mechanical Engineering | Automotive Engineering | Transportation Technology and Traffic EngineeringAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 621.3 Online resources: Click here to access online
Contents:
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.
In: Springer Nature eBookSummary: 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.
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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.

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.

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