Title :
Qualitative Analysis of Inter-Vehicle Relationship for Scenario Parsing
Author :
Dai, Wuyang ; Zhang, Hao ; Meng, Huadong ; Wang, Xiqin
Author_Institution :
Tsinghua Univ., Beijing
fDate :
Sept. 30 2007-Oct. 3 2007
Abstract :
Currently, the prevalent frontal collision warning systems (FCWS) are mainly based on quantitative ways. Their warning algorithms usually do prediction and assessment in the quantitative level which can not supply a universal quality under different traffic scenarios. The lack of cognition to surroundings may probably mislead the threat assessment. Besides, it is not the quantitative method but qualitative way in which people make judgments. So the scenario parsing together with qualitative methods was proposed. From this view, a qualitative analysis of inter-vehicles is presented as a step forward along the scenario parsing roadmap. The real-data experiments illustrate persuasive results.
Keywords :
Gaussian processes; alarm systems; data mining; driver information systems; expert systems; road accidents; road safety; road traffic; road vehicles; string matching; trees (mathematics); GMM method; Gaussian mixture model; driver assistance; expert systems; frontal collision warning systems; inter-vehicle relationship; rule mining; scenario parsing; substring tree methods; threat assessment; traffic accident avoidance; Acceleration; Alarm systems; Cognition; Expert systems; Intelligent transportation systems; Prediction algorithms; Road accidents; Traffic control; USA Councils; Vehicle driving;
Conference_Titel :
Intelligent Transportation Systems Conference, 2007. ITSC 2007. IEEE
Conference_Location :
Seattle, WA
Print_ISBN :
978-1-4244-1396-6
Electronic_ISBN :
978-1-4244-1396-6
DOI :
10.1109/ITSC.2007.4357792