Signal Modelling and Hidden Markov Models for Driving Manoeuvre Recognition and Driver Fault Diagnosis in an urban road scenario
Paper in proceeding, 2007
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.
Hidden Markov models
Artificial neural networks
Signal analysis
Fault diagnosis
Data analysis
Stochastic processes
Road transportation
System testing
Safety
System analysis and design
Author
Pinar Boyraz Baykas
Loughborough University
Memis Acar
Loughborough University
David Kerr
Loughborough University
IEEE Intelligent Vehicles Symposium, Proceedings
987-992 4290245
978-142441068-2 (ISBN)
2007 IEEE Intelligent Vehicles Symposium, IV 2007
Istanbul, Turkey,
Istanbul, Turkey,
Areas of Advance
Information and Communication Technology
Transport
Subject Categories
Transport Systems and Logistics
Vehicle Engineering
Probability Theory and Statistics
DOI
10.1109/IVS.2007.4290245