DocumentCode :
3268621
Title :
Signal Modelling and Hidden Markov Models for Driving Manoeuvre Recognition and Driver Fault Diagnosis in an urban road scenario
Author :
Boyraz, Pinar ; Acar, Memis ; Kerr, David
Author_Institution :
Loughborough Univ., Leicester
fYear :
2007
fDate :
13-15 June 2007
Firstpage :
987
Lastpage :
992
Abstract :
Hidden Markov models (HMM) are used to identify a vehicle´s manoeuvre sequence and its appropriateness for a given urban road driving situation. One of the novel aspects of this work has been the development of an efficient signal modelling approach to form a context-aware, flexible system which proved to respond well in urban road scenarios, especially in situations where the driver is likely to have an accident due to impaired performance. Another contribution has been to clarify how HMMs can be used not just to recognize vehicle manoeuvres but also to distinguish an impaired driver from a normal one in complex driving contexts. The system has worked well on simulator data and is about to be implemented in the real conditions of an urban trajectory.
Keywords :
fault diagnosis; hidden Markov models; road vehicles; traffic engineering computing; ubiquitous computing; context-aware flexible system; driver fault diagnosis; driving manoeuvre recognition; hidden Markov models; manoeuvre sequence; signal modelling; urban road scenario; Artificial neural networks; Data analysis; Fault diagnosis; Hidden Markov models; Road transportation; Safety; Signal analysis; Stochastic processes; System analysis and design; System testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Vehicles Symposium, 2007 IEEE
Conference_Location :
Istanbul
ISSN :
1931-0587
Print_ISBN :
1-4244-1067-3
Electronic_ISBN :
1931-0587
Type :
conf
DOI :
10.1109/IVS.2007.4290245
Filename :
4290245
Link To Document :
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