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

By: Kuutti, Sampo [author.].

Contributor(s): Fallah, Saber [author.] | Bowden, Richard (Ph. D. in computer vision) [author.] | Barber, Phil (Ph. D. in automotive fuel injection system dynamics) [author.].

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BookSeries: Synthesis digital library of engineering and computer science: ; Synthesis lectures on advances in automotive technology: #8.Publisher: [San Rafael, California] : Morgan & Claypool, [2019]Description: 1 PDF (xiii, 66 pages) : illustrations (some color).Content type: text Media type: electronic Carrier type: online resourceISBN: 9781681736082.Subject(s): Automobiles -- Automatic control | Machine learning | artificial intelligence | machine learning | deep learning | neural networks | computer vision | autonomous vehicles | intelligent transportation systems | advanced driver assistance systems | vehicle control | interpretability | safety validationDDC classification: 629.2220285 Online resources: Abstract with links to full text | Abstract with links to resource Also available in print.

Contents:

1. Introduction -- 2. Deep learning -- 2.1. Neural network architectures -- 2.2. Supervised learning -- 2.3. Reinforcement learning -- 2.4. Further reading

3. Deep learning for vehicle control -- 3.1. Autonomous vehicle control -- 3.2. Research challenges -- 3.3. Summary

4. Safety validation of neural networks -- 4.1. Validation techniques -- 4.2. Discussion -- 4.3. Summary -- 5. Concluding remarks.

Summary: 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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Item type	Current location	Call number	Status	Date due	Barcode	Item holds
E books	PK Kelkar Library, IIT Kanpur		Available		EBKE928

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Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

Part of: Synthesis digital library of engineering and computer science.

Includes bibliographical references (pages 45-64).

1. Introduction -- 2. Deep learning -- 2.1. Neural network architectures -- 2.2. Supervised learning -- 2.3. Reinforcement learning -- 2.4. Further reading

3. Deep learning for vehicle control -- 3.1. Autonomous vehicle control -- 3.2. Research challenges -- 3.3. Summary

4. Safety validation of neural networks -- 4.1. Validation techniques -- 4.2. Discussion -- 4.3. Summary -- 5. Concluding remarks.

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

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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.

Also available in print.

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

By: Kuutti, Sampo [author.].

Contributor(s): Fallah, Saber [author.] | Bowden, Richard (Ph. D. in computer vision) [author.] | Barber, Phil (Ph. D. in automotive fuel injection system dynamics) [author.].