Signal Modelling and Hidden Markov Models for Driving Manoeuvre Recognition and Driver Fault Diagnosis in an urban road scenario
Paper i 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.

Data analysis

System testing

Stochastic processes

Artificial neural networks

Hidden Markov models

Fault diagnosis

Signal analysis


System analysis and design

Road transportation


Pinar Boyraz Baykas

Olycksanalys och prevention

Memis Acar

Loughborough University

David Kerr

Loughborough University

IEEE Intelligent Vehicles Symposium, Proceedings

2007 IEEE Intelligent Vehicles Symposium
Istanbul, Turkey,


Informations- och kommunikationsteknik



Transportteknik och logistik


Sannolikhetsteori och statistik



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