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008 220601s2019 sz | s |||| 0|eng d
020 _a9783031015021
_9978-3-031-01502-1
024 7 _a10.1007/978-3-031-01502-1
_2doi
050 4 _aTK1-9971
072 7 _aTHR
_2bicssc
072 7 _aTEC007000
_2bisacsh
072 7 _aTHR
_2thema
082 0 4 _a621.3
_223
100 1 _aKuutti, Sampo.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978950
245 1 0 _aDeep Learning for Autonomous Vehicle Control
_h[electronic resource] :
_bAlgorithms, State-of-the-Art, and Future Prospects /
_cby Sampo Kuutti, Saber Fallah, Richard Bowden, Phil Barber.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXIV, 70 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.
650 0 _aElectrical engineering.
_978951
650 0 _aMechanical engineering.
_95856
650 0 _aAutomotive engineering.
_978952
650 0 _aTransportation engineering.
_93560
650 0 _aTraffic engineering.
_915334
650 1 4 _aElectrical and Electronic Engineering.
_978953
650 2 4 _aMechanical Engineering.
_95856
650 2 4 _aAutomotive Engineering.
_978954
650 2 4 _aTransportation Technology and Traffic Engineering.
_932448
700 1 _aFallah, Saber.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978955
700 1 _aBowden, Richard.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978956
700 1 _aBarber, Phil.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978957
710 2 _aSpringerLink (Online service)
_978958
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031000072
776 0 8 _iPrinted edition:
_z9783031003745
776 0 8 _iPrinted edition:
_z9783031026300
830 0 _aSynthesis Lectures on Advances in Automotive Technology,
_x2576-8131
_978959
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01502-1
912 _aZDB-2-SXSC
942 _cEBK
999 _c84687
_d84687