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Powered Two-Wheelers Critical Events Detection and Recognition Using Data-Driven Approaches

Authors
  • ATTAL, Ferhat
  • BOUBEZOUL, Abderrahmane
  • SAME, Allou
  • OUKHELLOU, Latifa
  • ESPIE, Stéphane
Publication Date
Jan 01, 2018
Identifiers
DOI: 10.1109/TITS.2018.2797065
OAI: oai:cadic-ifsttar-oai.fr:DOC00029621
Source
Portail Documentaire MADIS
Keywords
License
Unknown
External links

Abstract

Driving errors are considered to be the greatest contributory cause in all road accidents and an important contributory cause of most fatal accidents. This is particularly the case for the users of powered two-wheeled vehicles (PTWs), perhaps because PTW riders play a greater role in the control of their vehicles' stability than four-wheeled vehicle drivers. Thus, observing and analyzing the evolution of riders' behavior in a real-life context is an important step in the identification of the road environment characteristics that constitute a risk factor for PTW riders. A relevant research issue in naturalistic studies is related to the detection and identification of critical riding events from among the vast amount of data recorded during the experiment. In this paper, two approaches were used to automatically detect such critical riding events. First, we formalized this problem in terms of detecting changes in the mean and variance of the signals generated by the acceleration and angular velocity sensors. For this purpose, two steps were performed: 1) a data segmentation and feature extraction step in which the multidimensional time series of accelerometer and angular velocity data were segmented and modeled using a Gaussian mixture model with quadratic logistic proportions; and 2) a classification step in which each detected segment was assigned to the appropriate riding sequence, whether 'naturalistic' or 'critical' (i.e., fall or near fall), using the k-nearest neighbor algorithm. The second approach was based on online fall detection. This methodology used control charts (a multivariate cumulative sum), an approach that has been traditionally employed for sequential detection. These two algorithms were applied to a database composed of data from a real-life driving experiment. The obtained results show the effectiveness of both methodologies. / Driving errors are considered to be the greatest contributory cause in all road accidents and an important contributory cause of most fatal accidents. This is particularly the case for the users of powered two-wheeled vehicles (PTWs), perhaps because PTW riders play a greater role in the control of their vehicles' stability than four-wheeled vehicle drivers. Thus, observing and analyzing the evolution of riders' behavior in a real-life context is an important step in the identification of the road environment characteristics that constitute a risk factor for PTW riders. A relevant research issue in naturalistic studies is related to the detection and identification of critical riding events from among the vast amount of data recorded during the experiment. In this paper, two approaches were used to automatically detect such critical riding events. First, we formalized this problem in terms of detecting changes in the mean and variance of the signals generated by the acceleration and angular velocity sensors. For this purpose, two steps were performed: 1) a data segmentation and feature extraction step in which the multidimensional time series of accelerometer and angular velocity data were segmented and modeled using a Gaussian mixture model with quadratic logistic proportions; and 2) a classification step in which each detected segment was assigned to the appropriate riding sequence, whether 'naturalistic' or 'critical' (i.e., fall or near fall), using the k-nearest neighbor algorithm. The second approach was based on online fall detection. This methodology used control charts (a multivariate cumulative sum), an approach that has been traditionally employed for sequential detection. These two algorithms were applied to a database composed of data from a real-life driving experiment. The obtained results show the effectiveness of both methodologies.

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