000 04511nam a2200745 i 4500
001 8794726
003 IEEE
005 20200413152933.0
006 m eo d
007 cr cn |||m|||a
008 190827s2019 caua ob 000 0 eng d
020 _a9781681736082
_qelectronic
020 _z9781681736167
_qhardcover
020 _z9781681736075
_qpaperback
024 7 _a10.2200/S00932ED1V01Y201906AAT008
_2doi
035 _a(CaBNVSL)thg00979392
035 _a(OCoLC)1113932953
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
_dCaBNVSL
050 4 _aTL152.8
_b.K888 2019eb
082 0 4 _a629.2220285
_223
100 1 _aKuutti, Sampo,
_eauthor.
245 1 0 _aDeep learning for autonomous vehicle control :
_balgorithms, state-of-the-art, and future prospects /
_cSampo Kuutti, Saber Fallah, Richard Bowden, Phil Barber.
264 1 _a[San Rafael, California] :
_bMorgan & Claypool,
_c[2019]
300 _a1 PDF (xiii, 66 pages) :
_billustrations (some color).
336 _atext
_2rdacontent
337 _aelectronic
_2isbdmedia
338 _aonline resource
_2rdacarrier
490 1 _aSynthesis lectures on advances in automotive technology,
_x2576-8131 ;
_v#8
538 _aMode of access: World Wide Web.
538 _aSystem requirements: Adobe Acrobat Reader.
500 _aPart of: Synthesis digital library of engineering and computer science.
504 _aIncludes bibliographical references (pages 45-64).
505 0 _a1. Introduction -- 2. Deep learning -- 2.1. Neural network architectures -- 2.2. Supervised learning -- 2.3. Reinforcement learning -- 2.4. Further reading
505 8 _a3. Deep learning for vehicle control -- 3.1. Autonomous vehicle control -- 3.2. Research challenges -- 3.3. Summary
505 8 _a4. Safety validation of neural networks -- 4.1. Validation techniques -- 4.2. Discussion -- 4.3. Summary -- 5. Concluding remarks.
506 _aAbstract freely available; full-text restricted to subscribers or individual document purchasers.
510 0 _aCompendex
510 0 _aINSPEC
510 0 _aGoogle scholar
510 0 _aGoogle book search
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.
530 _aAlso available in print.
588 _aTitle from PDF title page (viewed on June 26, 2019).
650 0 _aAutomobiles
_xAutomatic control.
650 0 _aMachine learning.
653 _aartificial intelligence
653 _amachine learning
653 _adeep learning
653 _aneural networks
653 _acomputer vision
653 _aautonomous vehicles
653 _aintelligent transportation systems
653 _aadvanced driver assistance systems
653 _avehicle control
653 _ainterpretability
653 _asafety validation
700 1 _aFallah, Saber,
_eauthor.
700 1 _aBowden, Richard
_c(Ph. D. in computer vision),
_eauthor.
700 1 _aBarber, Phil
_c(Ph. D. in automotive fuel injection system dynamics),
_eauthor.
776 0 8 _iPrint version:
_z9781681736075
_z9781681736167
830 0 _aSynthesis digital library of engineering and computer science.
830 0 _aSynthesis lectures on advances in automotive technology ;
_v#8.
856 4 0 _3Abstract with links to full text
_uhttps://doi.org/10.2200/S00932ED1V01Y201906AAT008
856 4 2 _3Abstract with links to resource
_uhttps://ieeexplore.ieee.org/servlet/opac?bknumber=8794726
999 _c562428
_d562428