000 03725nam a22005655i 4500
001 978-3-658-21954-3
003 DE-He213
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008 180419s2018 gw | s |||| 0|eng d
020 _a9783658219543
_9978-3-658-21954-3
024 7 _a10.1007/978-3-658-21954-3
_2doi
050 4 _aTJ212-225
050 4 _aTJ210.2-211.495
072 7 _aTJFM
_2bicssc
072 7 _aTEC037000
_2bisacsh
072 7 _aTJFM
_2thema
082 0 4 _a629.8
_223
100 1 _aHeinrich, Steffen.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_946637
245 1 0 _aPlanning Universal On-Road Driving Strategies for Automated Vehicles
_h[electronic resource] /
_cby Steffen Heinrich.
250 _a1st ed. 2018.
264 1 _aWiesbaden :
_bSpringer Fachmedien Wiesbaden :
_bImprint: Springer,
_c2018.
300 _aXV, 133 p. 59 illus., 25 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAutoUni – Schriftenreihe,
_x2512-1154 ;
_v119
505 0 _aA Framework for Universal Driving Strategy Planning -- Sampling-Based Planning in Phase Space -- A Universal Approach for Driving Strategies -- Modeling Ego Motion Uncertainty.
520 _aSteffen Heinrich describes a motion planning system for automated vehicles. The planning method is universally applicable to on-road scenarios and does not depend on a high-level maneuver selection automation for driving strategy guidance. The author presents a planning framework using graphics processing units (GPUs) for task parallelization. A method is introduced that solely uses a small set of rules and heuristics to generate driving strategies. It was possible to show that GPUs serve as an excellent enabler for real-time applications of trajectory planning methods. Like humans, computer-controlled vehicles have to be fully aware of their surroundings. Therefore, a contribution that maximizes scene knowledge through smart vehicle positioning is evaluated. A post-processing method for stochastic trajectory validation supports the search for longer-term trajectories which take ego-motion uncertainty into account. Contents A Framework for Universal Driving Strategy Planning Sampling-Based Planning in Phase Space A Universal Approach for Driving Strategies Modeling Ego Motion Uncertainty Target Groups Scientists and students in the field of robotics, computer science, mechanical engineering Engineers in the field of vehicle automation, intelligent systems and robotics About the Author Steffen Heinrich has a strong background in robotics and artificial intelligence. Since 2009 he has been developing algorithms and software components for self-driving systems in research facilities and for automakers in Germany and the US.
650 0 _aControl engineering.
_931970
650 0 _aRobotics.
_92393
650 0 _aAutomation.
_92392
650 0 _aArtificial intelligence.
_93407
650 0 _aNumerical analysis.
_94603
650 1 4 _aControl, Robotics, Automation.
_931971
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aNumerical Analysis.
_94603
710 2 _aSpringerLink (Online service)
_946638
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783658219536
776 0 8 _iPrinted edition:
_z9783658219550
830 0 _aAutoUni – Schriftenreihe,
_x2512-1154 ;
_v119
_946639
856 4 0 _uhttps://doi.org/10.1007/978-3-658-21954-3
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c77894
_d77894