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003			ES-MaUEC
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007			cr nn 008mamaa
008			220601s2019 sz | o |||| 0|eng d
020			_a9783031015021
024	7		_a10.1007/978-3-031-01502-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTL152.8 _b2019 EB
100	1		_aKuutti, Sampo _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686095
245	1	0	_aDeep Learning for Autonomous Vehicle Control : _bAlgorithms, State-of-the-Art, and Future Prospects _cby Sampo Kuutti, Saber Fallah, Richard Bowden, Phil Barber
250			_a1st edition 2019
264		1	_aCham _bSpringer International Publishing _c2019
300			_a1 recurso en línea (XIV, 70 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Advances in Automotive Technology _x2576-8131
505	0		_aList of Figures -- List of Tables -- Preface -- Introduction -- Deep Learning -- Deep Learning for Vehicle Control -- Safety Validation of Neural Networks -- Concluding Remarks -- Bibliography -- Authors' Biographies.
520			_aThe 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.
988			_aSynthesis Collection of Technology_2019
650		7	_2embne _9673285 _aAutomóviles _xControl automático
650		7	_2embne _9405815 _aSistemas inteligentes de transporte
700	1		_aFallah, Saber _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686096
700	1		_aBowden, Richard _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686097
700	1		_aBarber, Phil _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686098
776	0	8	_iPrinted edition: _z9783031000072
776	0	8	_iPrinted edition: _z9783031003745
776	0	8	_iPrinted edition: _z9783031026300
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01502-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2023 _dz _eb _zSI