7,357 research outputs found

    Professional Driver Training and Driver Stress: Effects on Simulated Driving Performance

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    Book chapter from Traffic and Transport Psychology, edited by G. Underwood, published by Elsevier, 2005

    Using Machine Learning to Determine the Motorist Somnolence

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    Traffic accidents pose an increasing threat to society, and researchers are dedicated to preventing accidents and reducing fatalities, as highlighted by the World Health Organ-ization. One significant cause of accidents is drowsy driving, which often leads to severe injuries and loss of life. The objective of this research is to create a fatigue detection sys-tem that can effectively minimize accidents associated with exhaustion. The system uti-lizes facial recognition technology to identify drowsy drivers by analyzing eye patterns through video processing. When the level of fatigue surpasses a predetermined thresh-old, the system alerts the driver and adjusts the vehicle's acceleration accordingly. The implementation of OpenCv libraries, such as Haar-cascade, along with Raspberry Pi fa-cilitates seamless integration of the system. This dissertation evaluates advancements in computational engineering for the development of a fatigue detection system to miti-gate accidents caused by drowsiness. It offers valuable insights and recommendations to enhance comprehension and optimize the system's effectiveness, ultimately leading to safer road travel

    A Comparative Emotions-detection Review for Non-intrusive Vision-Based Facial Expression Recognition

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    Affective computing advocates for the development of systems and devices that can recognize, interpret, process, and simulate human emotion. In computing, the field seeks to enhance the user experience by finding less intrusive automated solutions. However, initiatives in this area focus on solitary emotions that limit the scalability of the approaches. Further reviews conducted in this area have also focused on solitary emotions, presenting challenges to future researchers when adopting these recommendations. This review aims at highlighting gaps in the application areas of Facial Expression Recognition Techniques by conducting a comparative analysis of various emotion detection datasets, algorithms, and results provided in existing studies. The systematic review adopted the PRISMA model and analyzed eighty-three publications. Findings from the review show that different emotions call for different Facial Expression Recognition techniques, which should be analyzed when conducting Facial Expression Recognition. Keywords: Facial Expression Recognition, Emotion Detection, Image Processing, Computer Visio

    Detecting Worker Attention Lapses in Human-Robot Interaction: An Eye Tracking and Multimodal Sensing Study

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    The advent of industrial robotics and autonomous systems endow human-robot collaboration in a massive scale. However, current industrial robots are restrained in co-working with human in close proximity due to inability of interpreting human agents' attention. Human attention study is non-trivial since it involves multiple aspects of the mind: perception, memory, problem solving, and consciousness. Human attention lapses are particularly problematic and potentially catastrophic in industrial workplace, from assembling electronics to operating machines. Attention is indeed complex and cannot be easily measured with single-modality sensors. Eye state, head pose, posture, and manifold environment stimulus could all play a part in attention lapses. To this end, we propose a pipeline to annotate multimodal dataset of human attention tracking, including eye tracking, fixation detection, third-person surveillance camera, and sound. We produce a pilot dataset containing two fully annotated phone assembly sequences in a realistic manufacturing environment. We evaluate existing fatigue and drowsiness prediction methods for attention lapse detection. Experimental results show that human attention lapses in production scenarios are more subtle and imperceptible than well-studied fatigue and drowsiness.Comment: 6 page

    Computing driver tiredness and fatigue in automobile via eye tracking and body movements

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    The aim of this paper is to classify the driver tiredness and fatigue in automobile via eye tracking and body movements using deep learning based Convolutional Neural Network (CNN) algorithm. Vehicle driver face localization serves as one of the most widely used real-world applications in fields like toll control, traffic accident scene analysis, and suspected vehicle tracking. The research proposed a CNN classifier for simultaneously localizing the region of human face and eye positioning. The classifier, rather than bounding rectangles, gives bounding quadrilaterals, which gives a more precise indication for vehicle driver face localization. The adjusted regions are preprocessed to remove noise and passed to the CNN classifier for real time processing. The preprocessing of the face features extracts connected components, filters them by size, and groups them into face expressions. The employed CNN is the well-known technology for human face recognition. One we aim to extract the facial landmarks from the frames, we will then leverage classification models and deep learning based convolutional neural networks that predict the state of the driver as 'Alert' or 'Drowsy' for each of the frames extracted. The CNN model could predict the output state labels (Alert/Drowsy) for each frame, but we wanted to take care of sequential image frames as that is extremely important while predicting the state of an individual. The process completes, if all regions have a sufficiently high score or a fixed number of retries are exhausted. The output consists of the detected human face type, the list of regions including the extracted mouth and eyes with recognition reliability through CNN with an accuracy of 98.57% with 100 epochs of training and testing

    Low-cost vehicle driver assistance system for fatigue and distraction detection

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    In recent years, the automotive industry is equipping vehicles with sophisticated, and often, expensive systems for driving assistance. However, this vehicular technology is more focused on facilitating the driving and not in monitoring the driver. This paper presents a low-cost vehicle driver assistance system for monitoring the drivers activity that intends to prevent an accident. The system consists of 4 sensors that monitor physical parameters and driver position. From these values, the system generates a series of acoustic signals to alert the vehicle driver and avoiding an accident. Finally the system is tested to verify its proper operation.This work has been partially supported by the “Programa para la Formación de Personal Investigador–(FPI-2015-S2-884)” by the “Universitat Politècnica de València”.Sendra, S.; García-García, L.; Jimenez, JM.; Lloret, J. (2017). Low-cost vehicle driver assistance system for fatigue and distraction detection. En Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Verlag. 69-78. doi:10.1007/978-3-319-51207-5_7S6978Mapfre Foundation. (Online Article) Seguridad activa y pasiva. www.seguridadvialenlaempresa.com/seguridad-empresas/actualidad/noticias/seguridad-vial-activa-y-pasiva-2.jsp . Accessed 25 Aug 2016Dirección general de tráfico, Ministerio del Interior, Spanish Government. (Online Article) Las principales cifras de la siniestralidad vial. España 2014, p. 21 (2014). http://www.dgt.es/es/seguridad-vial/estadisticas-e-indicadores/publicaciones/ . Accessed 25 Aug 2016Fukuhara, H.: Vehicle collision alert system. US Patent 5355118 A, 11 Oct 1994Dirección general de tráfico, Ministerio del Interior, Spanish Government. (Online Article) Anuario estadístico de accidentes 2014, p. 10 (2014). http://www.dgt.es/es/seguridad-vial/estadisticas-e-indicadores/publicaciones/anuario-estadistico-general/ . Accessed 25 Aug 2016Dirección general de tráfico, Ministerio del Interior, Spanish Government. (Online Article) Otros factores de riesgo: La fatiga. http://www.dgt.es/PEVI/documentos/catalogo_recursos/didacticos/did_adultas/fatiga.pdf . Accessed 25 Aug 2016Seeing machines web page. https://www.seeingmachines.com/ . Accessed 25 Aug 2016Sigari, M.H., Pourshahabi, M.R., Soryani, M., Fathy, M.: A review on driver face monitoring systems for fatigue and distraction detection. Int. J. Adv. Sci. Technol. 64, 73–100 (2014). http://dx.doi.org/10.14257/ijast.2014.64.07Kutila, M., Jokela, M., Markkula, G., Romera Rue, M.: Driver distraction detection with a camera vision system. In: 14th IEEE International Conference on Image Processing (ICIP 2007), San Antonio, TX, USA, 16–19 September 2007. doi: 10.1109/ICIP.2007.4379556Rezaei, M., Klette, R.: 3D cascade of classifiers for open and closed eye detection in driver distraction monitoring. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds.) CAIP 2011. LNCS, vol. 6855, pp. 171–179. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-23678-5_19Mbouna, R.O., Kong, S.G., Chun, M.G.: Visual analysis of eye state and head pose for driver alertness monitoring. IEEE Trans. Intell. Transp. Syst. 14(3), 1462–1469 (2013). doi: 10.1109/TITS.2013.2262098Wahlstrom, E., Masoud, O., Papanikolopoulos, N.: Vision-based methods for driver monitoring. In: Proceedings of the International Conference on Intelligent Transportation Systems, vol. 2, pp. 903–908 (2003)Cherrat, L., Ezziyyani, M., El Mouden, A., Hassar, M.: Security and surveillance system for drivers based on user profile and learning systems for face recognition. Netw. Protoc. Algorithms 7(1), 98–118 (2015). doi: http://dx.doi.org/10.5296/npa.v7i1.7151Dong, Y., Hu, Z., Uchimura, K., Murayama, N.: Driver inattention monitoring system for intelligent vehicles: a review. IEEE Trans. Intell. Transp. Syst. 12(2), 596–614 (2011). doi: 10.1109/TITS.2010.2092770Force Sensitive Resistor features. http://www.trossenrobotics.com/productdocs/2010-10-26-DataSheet-FSR402-Layout2.pdf . Accessed 25 Aug 2016Louiza, M., Samira, M.: A new framework for request-driven data harvesting in vehicular sensor networks. Netw. Protoc. Algorithms 5(4), 1–18 (2013)Yao, H., Si, P., Yang, R., Zhang, Y.: Dynamic spectrum management with movement prediction in vehicular ad hoc networks. Ad Hoc Sens. Wirel. Netw. 32(1), 79–97 (2016
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