LEADER 01958nam  2200445z- 450 
001 9910346772403321
005 20210211
010   $a1000053685
035   $a(CKB)4920000000100799
035   $a(oapen)https://directory.doabooks.org/handle/20.500.12854/47328
035   $a(oapen)doab47328
035   $a(EXLCZ)994920000000100799
100   $a20202102d2016      |y         0
101 0 $ager
135   $aurmn|---annan
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 00$aFahrerabsichtserkennung und Risikobewertung fu?r warnende Fahrerassistenzsysteme
210   $cKIT Scientific Publishing$d2016
215   $a1 online resource (XX, 159 p. p.)
225 1 $aSchriftenreihe / Institut für Mess- und Regelungstechnik, Karlsruher Institut für Technologie
311 08$a3-7315-0508-8 
330   $aTo avoid accidents, warning driver assistance systems require an on-line estimation of the current risk of collision. For that, a new method is proposed that - in principle - is able to deal with arbitrary traffic situations. This is achieved by the use of generative models to describe the expected driver behavior. Corresponding user studies in real traffic show promising results even when real time constraints are taken into account.
606   $aTechnology: general issues$2bicssc
610   $aDriver Model
610   $aDynamic Bayesian Network
610   $aDynamisches Bayes'sches NetzDriver Intent Inference
610   $aFahrerabsichtserkennung
610   $aFahrerverhaltensmodell
610   $aRisikobewertung
610   $aRisk Assessment
610   $aSituation Awareness
610   $aSituationsbewusstsein
615  7$aTechnology: general issues
700   $aLiebner$b Martin$4auth$01296612
906   $aBOOK
912   $a9910346772403321
996   $aFahrerabsichtserkennung und Risikobewertung für warnende Fahrerassistenzsysteme$93024131
997   $aUNINA