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_c120637 _d120637 |
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| 001 | 120637 | ||
| 003 | ES-MaUEC | ||
| 005 | 20230102114052.0 | ||
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| 007 | cr nn nnnaamaa | ||
| 008 | 200425s2020 gw a o |||| 0|eng d | ||
| 020 | _a9783030459055 | ||
| 024 | 7 |
_a10.1007/978-3-030-45905-5 _2doi |
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| 040 |
_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC |
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| 050 | 4 |
_aTL272.57 _b2020 EB |
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| 100 | 1 |
_aArtuñedo, Antonio _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9674671 |
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| 245 | 1 | 0 |
_aDecision-making Strategies for Automated Driving in Urban Environments _cby Antonio Artuñedo. |
| 250 | _aFirst edition | ||
| 264 | 1 |
_aCham _bSpringer International Publishing : _bImprint: Springer _c2020 |
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| 300 |
_a1 recurso en línea (XVIII, 195 páginas) _b117 ilustraciones, 108 ilustraciones a color |
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| 336 |
_2rdacontent _aTexto _btxt |
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| 337 |
_2rdamedia _aelectrónico _bc |
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| 338 |
_2rdacarrier _arecurso electrónico _bcr |
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| 347 |
_aArchivo de texto _bPDF |
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| 490 | 0 |
_aSpringer Theses Recognizing Outstanding Ph.D. Research _x2190-5053 |
|
| 490 | 0 | _aIntelligent Technologies and Robotics (Springer-42732) | |
| 505 | 0 | _aIntroduction -- Literature Overview -- Decision Making Architecture -- Global Planning and Mapping -- Motion Prediction and Manoeuvre Planning -- Optimal Trajectory Generation -- Integration and Demonstrations. | |
| 520 | 3 | _aThis book describes an effective decision-making and planning architecture for enhancing the navigation capabilities of automated vehicles in the presence of non-detailed, open-source maps. The system involves dynamically obtaining road corridors from map information and utilizing a camera-based lane detection system to update and enhance the navigable space in order to address the issues of intrinsic uncertainty and low-fidelity. An efficient and human-like local planner then determines, within a probabilistic framework, a safe motion trajectory, ensuring the continuity of the path curvature and limiting longitudinal and lateral accelerations. LiDAR-based perception is then used to identify the driving scenario, and subsequently re-plan the trajectory, leading in some cases to adjustment of the high-level route to reach the given destination. The method has been validated through extensive theoretical and experimental analyses, which are reported here in detail. | |
| 988 | _aSpringer_Robotics_23062020 | ||
| 650 | 7 |
_2embne _9671208 _aSistemas de asistencia a la conducción |
|
| 710 | 2 |
_aSpringerLink (Online service) _0http://id.loc.gov/authorities/names/no2005046756 _1http://viaf.org/viaf/148105729 |
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| 776 | 0 | 8 |
_iPrinted edition: _z9783030459048 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030459062 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030459079 |
| 856 | 4 | 0 |
_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-45905-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid) |
| 942 |
_2lcc _cLE |
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| 998 |
_b07/2020 _dz _eIG _zSI |
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