Edición N°15 – Artículo 03
Prototipo de sistema de frenado inteligente para vehículos monitoreado con IoT para minimizar accidentes o emergencias en carreteras
Prototype of an intelligent braking system for vehicles monitored with iot to minimize accidents or emergencies on roads
Autores: Benjamín Azanza Díaz, Ariel Nicolas Bravo Jurado, David Alejandro Cruz Palacios, Daniel Alejandro Urbina Artega (Colegio Técnico Salesiano Don Bosco)
Resumen
El proyecto consiste en el diseño de un prototipo de frenado automático que realiza sus funciones leyendo sensores electrónicos, estudiando los factores de movimiento del entorno, y el estado de los bombeos por minuto (BPM) del usuario. En primera instancia, se van a leer los datos obtenidos de los sensores de distancia, revoluciones por minuto (RPM) y BPM, para que el sistema pueda detectar si el usuario se encuentra apto para conducir, ubicados de manera estratégica para acercarse lo más posible a los valores de la realidad, el momento en el cual el conductor se encuentra en el vehículo.
Luego de obtener las primeras lecturas de los sensores, el algoritmo generado interpretará la información y actuará en consecuencia a ello. Cuando esto suceda, el prototipo seleccionara alguno de los 2 tipos de frenado disponibles; freno de emergencia o de servicio. Adicionalmente, también se dispone del sistema antibloqueo de frenos (ABS).
El sistema contiene un microcontrolador con gran disponibilidad entradas. Estos valores serán mostrados al usuario mediante el uso de IoT, siendo este capaz de observar las mediciones del sistema en tiempo real.
Se concluirá comprobando la eficiencia del prototipo creado en simulaciones reales por separado en un vehículo a escala, por lo que no se usarán entornos para comprobar el funcionamiento, sino que todo se hará de forma física.
Palabras clave: frenado automático, ARDUINO, BPM, RPM, ABS, IoT, sensores electrónicos.
Abstract
The project consists of designing a prototype automatic braking system that functions by reading electronic sensors, studying environmental motion factors, and monitoring the user’s beats per minute (BPM). Initially, data will be gathered from distance sensors, revolutions per minute (RPM), and BPM sensors to allow the system to detect if the user is fit to drive. These sensors are strategically placed to get as close as possible to real-world values when the driver is inside the vehicle.
After obtaining the initial sensor readings, the generated algorithm will interpret the information and act accordingly. When this occurs, the prototype will select one of two available braking types: emergency brake or service brake. Additionally, the system includes an Anti-lock Braking System (ABS).
The system contains a microcontroller with a large number of input ports. These values will be displayed to the user through IoT technology, enabling real-time monitoring of the system’s measurements.
The project will conclude by verifying the efficiency of the prototype through separate real simulations on a scale model vehicle. Therefore, no virtual environments will be used to test functionality; all tests will be conducted physically.
Keywords: automatic braking, ARDUINO, BPM, RPM, ABS, IoT, electronic sensors.
Edición N°13
Fecha de publicación: 11 diciembre del 2023.
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- [1] S. Anbalagan, P. Srividya, B. Thilaksurya, S. G. Senthivel, G. Suganeshwari, y G. Raja, «Vision-Based Ingenious Lane Departure Warning System for Autonomous Vehicles», Sustainability, vol. 15, n.o 4, Art. n.o 4, ene. 2023, doi:10.3390/su15043535.
- [2] A. Ziębiński, R. Cupek, D. Grzechca, y L. Chruszczyk, «Review of advanced driver assistance systems (ADAS)», presentado en AIP Conference Proceedings, nov. 2017, p. 120002. doi:10.1063/1.5012394.
- [3] Kaspersky, «¿Qué es la Internet de las cosas? Definición y explicación», latam.kaspersky.com. [En línea]. Disponible en: https://shorturl.at/ajpsM
- [4] K. Hiramatsu, «Design Principles for Advanced Driver Assistance System: Keeping Drivers In the-Loop», International Harmonized Research Activities (IHRA), 2010.
- [5] R. Jabbar, M. Shinoy, M. Kharbeche, K. Al-Khalifa, M. Krichen, y K. Barkaoui, «Urban Traffic Monitoring and Modeling System: An IoT Solution for Enhancing Road Safety», en 2019 International Conference on Internet of Things, Embedded Systems and Communications (IINTEC), dic. 2019, pp. 13-18. doi:10.1109/IINTEC48298.2019.9112118
- [6] CAR AND DRIVER, «Ken Block prueba el nuevo freno de mano electrónico del Focus RS», Car and Driver. [En línea]. Disponible en: https://shorturl.at/yMR03
- [7] W. Shi, M. B. Alawieh, X. Li, y H. Yu, «Algorithm and hardware implementation for visual perception system in autonomous vehicle: A survey», Integration, vol. 59, pp. 148-156, sep. 2017, doi:10.1016/j.vlsi.2017.07.007.
- [8] V. Kumar, P. Aravind, S. Pooja, S. Prathyush, S. AngelDeborah, y K. Chandran, «Driver Assistance System using Raspberry Pi and Haar Cascade Classifiers», may 2021, pp. 1729-1735. doi: 10.1109/
ICICCS51141.2021.9432361. - [9] S. Grubmüller, J. Plihal, y P. Nedoma, «Automated Driving from the View of Technical Standards», en Automated Driving: Safer and More Efficient Future Driving, D. Watzenig y M. Horn, Eds., Cham: Springer International Publishing, 2017, pp. 29-40. doi:10.1007/978-3-319-31895-0_3.
- [10] H. Thevendran, A. Nagendran, H. Hydher, A. Bandara, y U. Oruthota, «Deep Learning & Computer Vision for IoT based Intelligent Driver Assistant System», en 2021 10th International Conference on Information and Automation for Sustainability (ICIAfS), ago. 2021, pp. 340-345. doi:10.1109/ICIAfS52090.2021.9605823.
- [11] B. Varma, S. Sam, y L. Shine, «Vision Based Advanced Driver Assistance System Using Deep Learning», en 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), jul. 2019, pp. 1-5. doi:10.1109/ICCCNT45670.2019.8944842.
- [12] HowStuffWorks, «Could anti-lock brakes detect a flat? | HowStuffWorks». [En línea]. Disponible en: https://shorturl.at/beAT0
- [13] D. Yi, J. Su, C. Liu, M. Quddus, y W. H. Chen, «A machine learning based personalized system for driving state recognition», Transportation Research Part C: Emerging Technologies, vol. 105, pp.241-261, ago. 2019, doi:10.1016/j.trc.2019.05.042.
- [14] R. Albasrawi, F. F. Fadhil, y M. T. Ghazal, «Driver drowsiness monitoring system based on facial Landmark detection with convolutional neural network for prediction», Bulletin of Electrical Engineering and Informatics, vol. 11, n.o 5, Art. n.o 5, oct. 2022, doi:10.11591/eei.v11i5.3966.
- [15] K. Ferencz y J. Domokos, «Using Node-RED platform in an industrial environment», feb. 2020.
- [16] S. Hind, «Dashboard design and the ‘datafied’ driving experience», Big Data & Society, vol.8, n.o 2, p. 205395172110498, jul. 2021, doi:10.1177/20539517211049862.
- [17] M. Meena y V. Prakash, «Vehicle to Vehicle Communication for Collision Avoidance», Engineering, Technology and Applied Science Research, vol.6, pp. 1380-1386, may 2018.
- [18] S. Garethiya, L. Ujjainiya, y V. Dudhwadkar, «Predictive vehicle collision avoidance system using Raspberry – pi», ARPN Journal of Engineering and Applied Sciences, vol.10, pp. 3655-3659, ene. 2015.
- [19] Arduino Spain, «[GUÍA] Arduino sensor de pulso cardiaco ky-039 + código, conexión», Arduino Spain. [En línea]. Disponible en: https://shorturl.at/rwzQZ
- [20] L. Louis, «Working Principle of Arduino and Using it as a Tool for Study and Research», presentado en International Journal of Control, Automation, Communication and Systems, jul. 2018. doi:10.5121/ijcacs.2016.1203.
- [21] P.-Y. Hsiao, C.-W. Yeh, S.-S. Huang, y L. C. Fu, «A Portable Vision-Based Real-Time Lane Departure Warning System: Day and Night», Vehicular Technology, IEEE Transactions on, vol. 58, pp.2089-2094, jun. 2009, doi:10.1109/TVT.2008.2006618.
- [22] D. Parekh et al., «A Review on Autonomous Vehicles: Progress, Methods and Challenges», Electronics, vol. 11, n.o 14, Art. n.o 14, ene. 2022, doi:10.3390/electronics11142162.