Title: Detection of steering events based on vehicle logging data using hidden Markov models

Authors: Roza Maghsood; Pär Johannesson

Addresses: Mathematical Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden ' SP Technical Research Institute of Sweden, P.O. Box 24036, SE-400 22 Gothenburg, Sweden

Abstract: In vehicle design it is desirable to model the loads by describing load environment, customer usage and vehicle dynamics. In this study a method will be proposed for detection of steering events such as curves and manoeuvring using on-board logging signals available on trucks. The method is based on hidden Markov models (HMMs), which are probabilistic models that can be used to recognise patterns in time series data. In an HMM, 'hidden' refers to a Markov chain where the states are not observable. However, observations depending on the hidden Markov chain can be observed. The idea here is to consider the current driving event as the hidden state, while the on-board logging signals generate the observed sequence. Examples of curve detection are presented for both simulated and measured data on a truck. The classification results indicate that the method can recognise left and right turns with small misclassification errors.

Keywords: HMMs; hidden Markov models; Markov chain; Viterbi algorithm; Baum-Welch algorithm; steering events; event classification; on-board logging signals; lateral acceleration; vehicle logging data; vehicle design; vehicle dynamics; modelling; curve detection; simulation; trucks; truck steering; steering wheel turns; vehicle turning.

DOI: 10.1504/IJVD.2016.075780

International Journal of Vehicle Design, 2016 Vol.70 No.3, pp.278 - 295

Available online: 04 Apr 2016 *

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