Pujitha Gunaratne

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— Naturalistic driving studies (NDS) capture huge amounts of drive data, that is analyzed for critical information about driver behavior, driving characteristics etc. Moreover, NDS involve data collected from a wide range of sensing technologies in cars and this makes the analysis of this data a challenging task. In this paper, we propose a multimodal(More)
— Naturalistic driving studies (NDS) provide critical information about driving behaviors and characteristics that could lead to crashes and near-crashes. Such studies involve analysis of large volumes of data from multiple sensors and detection and extraction of critical events is an important step in NDS. This paper introduces techniques that analyze the(More)
— Analysis of naturalistic driving data provides a rich set of semantics which can be used to determine the driving characteristics that could lead to crashes and near-crashes. In this paper, we introduce " drive quality " analysis as part of the drive analysis process of naturalistic driving studies (NDSs) that we have previously introduced in [1]. In this(More)
Problem Large volumes of data from multiple sensors are captured in Naturalistic Driving Studies (NDS) such as in the Strategic Highway Research Program 2 (SHRP2). In order to extract and characterize distraction events leading to crashes and near-crashes, visual data from multiple cameras coupled with other sensory data are analyzed by human data(More)
In this paper we present an extended geometric approach , that detects and eliminate the specular points in a texture image of shiny objects, with the help of corresponding range data. The approach neither count on spectral variations of the texture image nor employs rigid constrains on illumination sources, such as point light source limitations. It rather(More)
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