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Puthon A.-S.,MINES ParisTech | Nashashibi F.,MINES ParisTech | Bradai B.,The Driving Center
IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC | Year: 2010

Speed limit determination is a complex task that may be solved by fusing data from GIS (Geographical Information System) and camera sensor. Among the existing data fusion models the Dempster-Shafer Belief Theory is found to be the most appropriate in this application. A confidence measure weights each source output, namely speed limit present on road sign and driving situation. Using the discounting scheme of Dempster-Shafer, we propose a new way of computing the navigation confidence measure by taking into account the reliability of the GIS. Preliminary tests showed that our method achieves promising results and solves conflicts between vision-and navigation-based system. ©2010 IEEE. Source


Maxwell H.G.,St Josephs Care Group | Dubois S.,St Josephs Care Group | Weaver B.,Lakehead University | Bedard M.,The Driving Center
Canadian Journal of Public Health | Year: 2010

Objectives: To examine the relationship between the combination of alcohol and benzodiazepines and the risk of committing an unsafe driver action. Methods: We used data from the Fatality Analysis Reporting System (1993-2006) on drivers aged 20 or older who were tested for both alcohol and drugs. Using a case-control design, we compared drivers who had at least one unsafe driver action (UDA; e.g., weaving) recorded in relation to the crash (cases) to drivers who did not (controls). Results: Drivers who tested positive for intermediate- and long-acting benzodiazepines in combination with alcohol had significantly greater odds of a UDA compared to those under the influence of alcohol alone, up to blood alcohol concentrations (BACs) of 0.08 and 0.05 g/100 ml, respectively. The odds of a UDA with short-acting benzodiazepines combined with alcohol were no different than for alcohol alone. Conclusions: This study demonstrates that the combination of alcohol and benzodiazepines can have detrimental effects on driving beyond those of alcohol alone. By describing these combined effects in terms of BAC equivalencies, this study also allows for the extrapolation of simple, concrete concepts that communicate risk to the average benzodiazepine user. © Canadian Public Health Association, 2010. All rights reserved. Source


Rekik A.,University of Sfax | Ben-Hamadou A.,The Driving Center | Mahdi W.,University of Sfax | Mahdi W.,Taif University
Multimedia Tools and Applications | Year: 2015

Lip-reading (LR) systems play an important role for automatic speech recognition when acoustic information is corrupted or unavailable. This article proposes an adaptive LR system for speech segment recognition using image and depth data. In addition to 2D images, the proposed system handles depth data that are very informative about 3D lips’ deformations when uttering and present a certain robustness against the variation of mouth skin color and texture. The proposed system is based on two main steps. In the first step, the mouth thumbnails are extracted based on a 3D face pose tracking. Then, appearance and motion descriptors are computed and combined in a final feature vector describing the uttered speech. The accuracy of 3D face tracking module is evaluated on the BIWI Kinect Head Pose database. The obtained results show that our method is competitive comparing to other state-of-the-art methods combining image and depth data (i.e., 2.26 mm and 3.86∘ for mean position error and mean orientation error). Additionally, the overall LR system is evaluated using three public LR datasets (i.e., MIRACL-VC1, OuluVS, and CUAVE). The obtained results demonstrate that data are complementary to 2D image data and reduce the speaker dependency problem in LR. The OuluVS and CUAVE datasets containing 2D images only are used to evaluate the proposed system when depth data are unavailable and to compare it to recent state-of-the art LR systems. The obtained results show very competitive recognition rates (up to 96 % for MIRACL-VC1, 93.2 % for OuluVS, and 90 % for CUAVE). © 2015 Springer Science+Business Media New York Source


Rekik A.,University of Sfax | Ben-Hamadou A.,The Driving Center | Mahdi W.,University of Sfax
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

Visual speech recognition remains a challenging topic due to various speaking characteristics. This paper proposes a new approach for lipreading to recognize isolated speech segments (words, digits, phrases, etc.) using both of 2D image and depth data. The process of the proposed system is divided into three consecutive steps, namely, mouth region tracking and extraction, motion and appearance descriptors (HOG and MBH) computing, and classification using the Support Vector Machine (SVM) method. To evaluate the proposed approach, three public databases (MIRALC, Ouluvs, and CUAVE) were used. Speaker dependent and speaker independent settings were considered in the evaluation experiments. The obtained recognition results demonstrate that lipreading can be performed effectively, and the proposed approach outperforms recent works in the literature for the speaker dependent setting while being competitive for the speaker independent setting. © Springer International Publishing Switzerland 2014. Source


Ruscio D.,The Driving Center | Ciceri M.R.,Catholic University of the Sacred Heart | Ciceri M.R.,University of Milan | Biassoni F.,Catholic University of the Sacred Heart
Accident Analysis and Prevention | Year: 2015

Brake Reaction Time (BRT) is an important parameter for road safety. Previous research has shown that drivers' expectations can impact RT when facing hazardous situations, but driving with advanced driver assistance systems, can change the way BRT are considered. The interaction with a collision warning system can help faster more efficient responses, but at the same time can require a monitoring task and evaluation process that may lead to automation complacency. The aims of the present study are to test in a real-life setting whether automation compliancy can be generated by a collision warning system and what component of expectancy can impact the different tasks involved in an assisted BRT process. More specifically four component of expectancy were investigated: presence/absence of anticipatory information, previous direct experience, reliability of the device, and predictability of the hazard determined by repeated use of the warning system. Results supply indication on perception time and mental elaboration of the collision warning system alerts. In particular reliable warning quickened the decision making process, misleading warnings generated automation complacency slowing visual search for hazard detection, lack of directed experienced slowed the overall response while unexpected failure of the device lead to inattentional blindness and potential pseudo-accidents with surprise obstacle intrusion. © 2015 Elsevier Ltd All rights reserved. Source

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