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

ABEONA monitored traffic: VANET-assisted cooperative traffic congestion forecasting

The existing mechanisms to monitor vehicular traffic, such as the use of induction loops and cameras, are expensive to deploy and maintain. Vehicular communications opens up a new world of optimization opportunities as each vehicle can be used as a sensor to measure the fundamental variables defining the traffic state (flow, density, and speed). In this article, we propose ABEONA, a beacon-based traffic congestion algorithm and also the name of the Roman goddess of journey, which captures the current and recent-past traffic trends to forecast the near-future road conditions. Compared to the existing monitoring approaches, ABEONA allows for the estimation of the vehicular density and reduces installation and maintenance costs. ABEONA's algorithm incurs low overhead and enables drivers to use forecast traffic congestion events to replan their route accordingly.Publicad

Personal vehicle sharing services in North America

Over the past three decades, carsharing has grown from a collection of local grassroots organizations into a worldwide industry. Traditional carsharing, though expanding, has a limited network of vehicles and locations. The next generation of shared-use vehicle services could overcome such expansion barriers as capital costs and land use by incorporating new concepts like personal vehicle sharing.Personal vehicle sharing provides short-term access to privately-owned vehicles. As of May 2012, there were 33 personal vehicle sharing operators worldwide, with 10 active or in pilot phase, three planned, and four defunct in North America. Due to operator non-disclosure, personal vehicle sharing member numbers are currently unknown. The authors investigated personal vehicle sharing in North America by conducting 34 expert interviews. This research explores the development of personal vehicle sharing including business models, market opportunities, and service barriers to assess its early viability as a sustainable transportation mode and to provide a foundation for future research on the topic. Personal vehicle sharing has the potential to impact the transportation sector by increasing the availability and interconnectivity among modes and providing greater alternatives to vehicle ownership in more geographic locations

From Algorithmic Computing to Autonomic Computing

In algorithmic computing, the program follows a predefined set of rules – the algorithm. The analyst/designer of the program analyzes the intended tasks of the program, defines the rules for its expected behaviour and programs the implementation. The creators of algorithmic software must therefore foresee, identify and implement all possible cases for its behaviour in the future application! However, what if the problem is not fully defined? Or the environment is uncertain? What if situations are too complex to be predicted? Or the environment is changing dynamically? In many such cases algorithmic computing fails. In such situations, the software needs an additional degree of freedom: Autonomy! Autonomy allows software to adapt to partially defined problems, to uncertain or dynamically changing environments and to situations that are too complex to be predicted. As more and more applications – such as autonomous cars and planes, adaptive power grid management, survivable networks, and many more – fall into this category, a gradual switch from algorithmic computing to autonomic computing takes place. Autonomic computing has become an important software engineering discipline with a rich literature, an active research community, and a growing number of applications.:Introduction 5 1 A Process Data Based Autonomic Optimization of Energy Efficiency in Manufacturing Processes, Daniel Höschele 9 2 Eine autonome Optimierung der Stabilität von Produktionsprozessen auf Basis von Prozessdaten, Richard Horn 25 3 Assuring Safety in Autonomous Systems, Christian Rose 41 4 MAPE-K in der Praxis - Grundlage für eine mögliche automatische Ressourcenzuweisung, in der Cloud Michael Schneider 5

Laser-Based Detection and Tracking of Moving Obstacles to Improve Perception of Unmanned Ground Vehicles

El objetivo de esta tesis es desarrollar un sistema que mejore la etapa de percepción de vehículos terrestres no tripulados (UGVs) heterogéneos, consiguiendo con ello una navegación robusta en términos de seguridad y ahorro energético en diferentes entornos reales, tanto interiores como exteriores. La percepción debe tratar con obstáculos estáticos y dinámicos empleando sensores heterogéneos, tales como, odometría, sensor de distancia láser (LIDAR), unidad de medida inercial (IMU) y sistema de posicionamiento global (GPS), para obtener la información del entorno con la precisión más alta, permitiendo mejorar las etapas de planificación y evitación de obstáculos. Para conseguir este objetivo, se propone una etapa de mapeado de obstáculos dinámicos (DOMap) que contiene la información de los obstáculos estáticos y dinámicos. La propuesta se basa en una extensión del filtro de ocupación bayesiana (BOF) incluyendo velocidades no discretizadas. La detección de velocidades se obtiene con Flujo Óptico sobre una rejilla de medidas LIDAR discretizadas. Además, se gestionan las oclusiones entre obstáculos y se añade una etapa de seguimiento multi-hipótesis, mejorando la robustez de la propuesta (iDOMap). La propuesta ha sido probada en entornos simulados y reales con diferentes plataformas robóticas, incluyendo plataformas comerciales y la plataforma (PROPINA) desarrollada en esta tesis para mejorar la colaboración entre equipos de humanos y robots dentro del proyecto ABSYNTHE. Finalmente, se han propuesto métodos para calibrar la posición del LIDAR y mejorar la odometría con una IMU