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

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

Advanced Virtual Reality Technology in the Field of Education

The main objective of this paper is to improvise the VR framework in the field of education in which we can add additional features that help the students to learn subjects in a more interesting and interacting way, which makes learning more effective for the students and they experience the subject in a virtual world

Attention Based Spatial-Temporal GCN with Kalman filter for Traffic Flow Prediction

Intelligent Transportation Systems (ITS) are becoming increasingly important as traditional traffic management systems struggle to handle the rapid growth of vehicles on the road. Accurate traffic prediction is a critical component of ITS, as it can help improve traffic management, avoid congested roads, and allocate resources more efficiently for connected vehicles. However, modeling traffic in a large and interconnected road network is challenging because of its complex spatio-temporal data. While classical statistics and machine learning methods have been used for traffic prediction, they have limited ability to handle complex traffic data, leading to unsatisfactory accuracy. In recent years, deep learning methods, such as Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), have shown superior capabilities for traffic prediction. However, most CNN-based models are built for Euclidean grid-structured data, while traffic road network data are irregular and better formatted as graph-structured data. Graph Convolutional Neural Networks (GCNs) have emerged to extend convolution operations to more general graph-structured data. This paper reviews recent developments in traffic prediction using deep learning, focusing on GCNs as a promising technique for handling irregular, graph-structured traffic data. We also propose a novel GCN-based method that leverages attention mechanisms to capture both local and long-range dependencies in traffic data with Kalman Filter, and we demonstrate its effectiveness through experiments on real-world datasets where the model achieved around 5% higher accuracy compared to the original model

IOT SYSTEMS FOR TRAVEL TIME ESTIMATION

This research introduces new approach for vehicle re-identification by computing Relative Entropy and Pearson correlation between ILD signatures, and then estimating TT based on the highest correlated signatures. To clear measure noise, TT for vehicles is assumed to follow the same pattern within a certain time frame. Thus, TT values are arranged in time series groups before applying a spike detection algorithm to determine the TT range with the highest number of vehicles. A data spike is considered for estimating TT. Given that the number of vehicles within the spike is greater than number of vehicles in all other data groups, TT will be the mean value of TT within the spike