Mostrar el registro sencillo del ítem
dc.contributor.author | Girbés, Vicent | es_ES |
dc.contributor.author | Hernández, Daniel | es_ES |
dc.contributor.author | Armesto, Leopoldo | es_ES |
dc.contributor.author | Dols Ruiz, Juan Francisco | es_ES |
dc.contributor.author | Sala, Antonio | es_ES |
dc.date.accessioned | 2021-02-20T04:31:15Z | |
dc.date.available | 2021-02-20T04:31:15Z | |
dc.date.issued | 2019-08-11 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/161981 | |
dc.description.abstract | [EN] Modelling the dynamic behaviour of heavy vehicles, such as buses or trucks, can be very useful for driving simulation and training, autonomous driving, crash analysis, etc. However, dynamic modelling of a vehicle is a difficult task because there are many subsystems and signals that affect its behaviour. In addition, it might be hard to combine data because available signals come at different rates, or even some samples might be missed due to disturbances or communication issues. In this paper, we propose a non-invasive data acquisition hardware/software setup to carry out several experiments with an urban bus, in order to collect data from one of the internal communication networks and other embedded systems. Subsequently, non-conventional sampling data fusion using a Kalman filter has been implemented to fuse data gathered from different sources, connected through a wireless network (the vehicle¿s internal CAN bus messages, IMU, GPS, and other sensors placed in pedals). Our results show that the proposed combination of experimental data gathering and multi-rate filtering algorithm allows useful signal estimation for vehicle identification and modelling, even when data samples are missing. | es_ES |
dc.description.sponsorship | This research was funded by the Spanish Ministry of Economy and European Union, grant DPI2016-81002-R, by Generalitat Valenciana, grant APOSTD/2017/055, and by the European Union too through the European Social Fund (ESF). The authors want to acknowledge public transport operators Autos Vallduxense S.L. and EMT Valencia for their support to the project by lending their vehicles and bus drivers. The authors are also thankful to Instituto de Diseño y Fabricación and SAFETRANS project (DPI2013-42302-R) research teams, for lending most of the electronic devices and sensors used in the experimentation. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation.ispartof | Sensors | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Sensor fusion | es_ES |
dc.subject | Sampled-data | es_ES |
dc.subject | Kalman filter | es_ES |
dc.subject | Dynamic systems | es_ES |
dc.subject | Parameter identification | es_ES |
dc.subject | Heavy vehicles | es_ES |
dc.subject | CAN bus | es_ES |
dc.subject | SAE J1939 | es_ES |
dc.subject.classification | INGENIERIA MECANICA | es_ES |
dc.subject.classification | INGENIERIA DE SISTEMAS Y AUTOMATICA | es_ES |
dc.title | Drive Force and Longitudinal Dynamics Estimation in Heavy-Duty Vehicles | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.3390/s19163515 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//DPI2016-81002-R/ES/CONTROL AVANZADO Y APRENDIZAJE DE ROBOTS EN OPERACIONES DE TRANSPORTE/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//DPI2013-42302-R/ES/SISTEMAS DE CONDUCCION SEGURA DE VEHICULOS DE TRANSPORTE DE PASAJEROS Y MATERIALES CON ASISTENCIA HAPTICA%2FAUDIOVISUAL E INTERFACES BIOMEDICAS/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/GVA//APOSTD%2F2017%2F055/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Ingeniería Mecánica y de Materiales - Departament d'Enginyeria Mecànica i de Materials | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Ingeniería de Sistemas y Automática - Departament d'Enginyeria de Sistemes i Automàtica | es_ES |
dc.description.bibliographicCitation | Girbés, V.; Hernández, D.; Armesto, L.; Dols Ruiz, JF.; Sala, A. (2019). Drive Force and Longitudinal Dynamics Estimation in Heavy-Duty Vehicles. Sensors. 19(16):1-19. https://doi.org/10.3390/s19163515 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.3390/s19163515 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 19 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 19 | es_ES |
dc.description.issue | 16 | es_ES |
dc.identifier.eissn | 1424-8220 | es_ES |
dc.identifier.pmid | 31405235 | es_ES |
dc.identifier.pmcid | PMC6719239 | es_ES |
dc.relation.pasarela | S\392225 | es_ES |
dc.contributor.funder | European Social Fund | es_ES |
dc.contributor.funder | Generalitat Valenciana | es_ES |
dc.contributor.funder | Ministerio de Economía y Competitividad | es_ES |
dc.description.references | Bloom, T., & Friedman, H. (2013). Classifying dogs’ (Canis familiaris) facial expressions from photographs. Behavioural Processes, 96, 1-10. doi:10.1016/j.beproc.2013.02.010 | es_ES |
dc.description.references | Armesto, L., Arnal, L., Dols, J., Girbés, V., & Peris, J. C. (2016). Proyecto SAFEBUS: Sistemas Avanzados de Seguridad Integral en Autobuses. Revista Iberoamericana de Automática e Informática Industrial RIAI, 13(1), 103-114. doi:10.1016/j.riai.2015.04.006 | es_ES |
dc.description.references | Girbes, V., Armesto, L., Dols, J., & Tornero, J. (2017). An Active Safety System for Low-Speed Bus Braking Assistance. IEEE Transactions on Intelligent Transportation Systems, 18(2), 377-387. doi:10.1109/tits.2016.2573921 | es_ES |
dc.description.references | Balsa‐Barreiro, J., Valero‐Mora, P. M., Pareja Montoro, I., & Sánchez García, M. (2013). Geo‐referencing naturalistic driving data using a novel method based on vehicle speed. IET Intelligent Transport Systems, 7(2), 190-197. doi:10.1049/iet-its.2012.0152 | es_ES |
dc.description.references | Törnros, J. (1998). Driving behaviour in a real and a simulated road tunnel—a validation study. Accident Analysis & Prevention, 30(4), 497-503. doi:10.1016/s0001-4575(97)00099-7 | es_ES |
dc.description.references | Bella, F. (2008). Driving simulator for speed research on two-lane rural roads. Accident Analysis & Prevention, 40(3), 1078-1087. doi:10.1016/j.aap.2007.10.015 | es_ES |
dc.description.references | Kemeny, A., & Panerai, F. (2003). Evaluating perception in driving simulation experiments. Trends in Cognitive Sciences, 7(1), 31-37. doi:10.1016/s1364-6613(02)00011-6 | es_ES |
dc.description.references | Girbés, V., Armesto, L., & Tornero, J. (2014). Path following hybrid control for vehicle stability applied to industrial forklifts. Robotics and Autonomous Systems, 62(6), 910-922. doi:10.1016/j.robot.2014.01.004 | es_ES |
dc.description.references | Yan, X., Abdel-Aty, M., Radwan, E., Wang, X., & Chilakapati, P. (2008). Validating a driving simulator using surrogate safety measures. Accident Analysis & Prevention, 40(1), 274-288. doi:10.1016/j.aap.2007.06.007 | es_ES |
dc.description.references | Hidalgo, C. E., Marcano, M., Fernández, G., & Pérez, J. M. (2020). Maniobras cooperativas aplicadas a vehículos automatizados en entornos virtuales y reales. Revista Iberoamericana de Automática e Informática industrial, 17(1), 56. doi:10.4995/riai.2019.11155 | es_ES |
dc.description.references | Hsu, L.-Y., & Chen, T.-L. (2012). Vehicle Dynamic Prediction Systems with On-Line Identification of Vehicle Parameters and Road Conditions. Sensors, 12(11), 15778-15800. doi:10.3390/s121115778 | es_ES |
dc.description.references | Sun, R., Cheng, Q., Xue, D., Wang, G., & Ochieng, W. (2017). GNSS/Electronic Compass/Road Segment Information Fusion for Vehicle-to-Vehicle Collision Avoidance Application. Sensors, 17(12), 2724. doi:10.3390/s17122724 | es_ES |
dc.description.references | Jeong, Y., Son, S., Jeong, E., & Lee, B. (2018). An Integrated Self-Diagnosis System for an Autonomous Vehicle Based on an IoT Gateway and Deep Learning. Applied Sciences, 8(7), 1164. doi:10.3390/app8071164 | es_ES |
dc.description.references | Yang Jiansen, Guo Konghui, Ding Haitao, Zhang Jianwei, & Xiang Bin. (2010). The application of SAE J1939 protocol in Automobile Smart and Integrated Control System. 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering. doi:10.1109/cmce.2010.5610301 | es_ES |
dc.description.references | Turk, E., & Challenger, M. (2018). An android-based IoT system for vehicle monitoring and diagnostic. 2018 26th Signal Processing and Communications Applications Conference (SIU). doi:10.1109/siu.2018.8404378 | es_ES |
dc.description.references | Ozguner, U., Redmill, K. A., & Broggi, A. (s. f.). Team terramax and the DARPA grand challenge: a general overview. IEEE Intelligent Vehicles Symposium, 2004. doi:10.1109/ivs.2004.1336387 | es_ES |
dc.description.references | Li, Y., & Ji, X. (2013). Controller Design for ISG Hybrid Electric Vehicle Based on SAE J1939 Protocol. Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013). doi:10.2991/iccsee.2013.647 | es_ES |
dc.description.references | Wang Dafang, Nan Jinrui, & Sun Fengchun. (2008). The application of CAN communication in distributed control system of electric city bus. 2008 IEEE Vehicle Power and Propulsion Conference. doi:10.1109/vppc.2008.4677533 | es_ES |
dc.description.references | Hu, J., Li, G., Yu, X., & Liu, S. (2007). Design and Application of SAE J1939 Communication Database in City-Bus Information Integrated Control System Development. 2007 International Conference on Mechatronics and Automation. doi:10.1109/icma.2007.4304114 | es_ES |
dc.description.references | Tomero, J., & Armesto, L. (s. f.). A general formulation for generating multi-rate models. Proceedings of the 2003 American Control Conference, 2003. doi:10.1109/acc.2003.1239742 | es_ES |
dc.description.references | Tan, H., Shen, B., Liu, Y., Alsaedi, A., & Ahmad, B. (2017). Event-triggered multi-rate fusion estimation for uncertain system with stochastic nonlinearities and colored measurement noises. Information Fusion, 36, 313-320. doi:10.1016/j.inffus.2016.12.003 | es_ES |