000 03596nam a22005175i 4500
001 978-3-658-05750-3
003 DE-He213
005 20200420220219.0
007 cr nn 008mamaa
008 140423s2014 gw | s |||| 0|eng d
020 _a9783658057503
_9978-3-658-05750-3
024 7 _a10.1007/978-3-658-05750-3
_2doi
050 4 _aTA329-348
050 4 _aTA640-643
072 7 _aTBJ
_2bicssc
072 7 _aMAT003000
_2bisacsh
082 0 4 _a519
_223
100 1 _aHuelsen, Michael.
_eauthor.
245 1 0 _aKnowledge-Based Driver Assistance Systems
_h[electronic resource] :
_bTraffic Situation Description and Situation Feature Relevance /
_cby Michael Huelsen.
264 1 _aWiesbaden :
_bSpringer Fachmedien Wiesbaden :
_bImprint: Springer Vieweg,
_c2014.
300 _aXVII, 176 p. 55 illus., 30 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aIntroduction -- The Research Domain of this Thesis and its State of the Art -- Theoretical Foundations Relevant to this Thesis -- Situation Feature Relevance on Measurement Data -- Knowledge-Based Traffic Situation Description -- Relevance by Mutual Information on Ontology Features -- Conclusion.
520 _aThe comprehension of a traffic situation plays a major role in driving a vehicle. Interpretable information forms a basis for future projection, decision making and action performing, such as navigating, maneuvering and driving control. Michael Huelsen provides an ontology-based generic traffic situation description capable of supplying various advanced driver assistance systems with relevant information about the current traffic situation of a vehicle and its environment. These systems are enabled to perform reasonable actions and approach visionary goals such as injury and accident free driving, substantial assistance in arbitrary situations up to even autonomous driving.  Content Situation Feature Relevance on Vehicle Measurement Data Relevance of Historical Measurement Values Knowledge-Based Traffic Situation Description and Simulation Relevance by Mutual Information on Ontology Features Target Groups Researchers, lecturers and students in the fields of automotive engineering, mechatronics, computer science and artificial intelligence Engineers and developers in the automotive industry, specifically areas of driver assistance systems, vehicle control and mechatronics The Author Michael Huelsen completed his doctoral thesis in a cooperation between the Karlsruhe Institute of Technology (KIT) and the Robert Bosch GmbH. After working in automotive development he is now working in a leading position in purchasing and value engineering at a renowned company manufacturing electrical traction systems.
650 0 _aEngineering.
650 0 _aData structures (Computer science).
650 0 _aApplied mathematics.
650 0 _aEngineering mathematics.
650 0 _aControl engineering.
650 0 _aRobotics.
650 0 _aMechatronics.
650 1 4 _aEngineering.
650 2 4 _aAppl.Mathematics/Computational Methods of Engineering.
650 2 4 _aData Structures, Cryptology and Information Theory.
650 2 4 _aControl, Robotics, Mechatronics.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783658057497
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-658-05750-3
912 _aZDB-2-ENG
942 _cEBK
999 _c51790
_d51790