DOI QR코드

DOI QR Code

A development of Intelligent Parking Control System Using Sensor-based on Arduino

  • Received : 2021.10.31
  • Accepted : 2021.12.05
  • Published : 2021.12.30

Abstract

In this paper, for efficient parking control, in an Arduino environment, an intelligent parking control prototype was implemented to provide parking control and parking guidance information using HC-SR2O4 and RC522. The main elements of intelligent parking control are vehicle recognition sensors, parking control facilities, and integrated operating software. Whether the vehicle is parked on the parking surface may be confirmed through sensor or intelligent camera image analysis. Parking control equipment products include parking guidance and parking available display devices, vehicle number recognition cameras, and intelligent parking assistance systems. This paper applies and implements ultrasonic sensors and RFID concepts based on Arduino, recognizes registered vehicles, and displays empty spaces. When a vehicle enters a parking space to handle this function, the automatic parking management system distinguishes the registered vehicle from the external vehicle through the RC522 sensor. In addition, after checking whether the parking slot is empty, the HC-SR204 sensor is displayed through the LED so that the driver can visually check it. RFID is designed to check the parking status of the server in real time and provide the driver with optimal route service to the parking slot.

Keywords

References

  1. Edin, Mujcic., & Una, DrakulicMerisa Skrgic. (2018). Smart Parking System Based on Arduino SD Card Ajax Web Server. Advanced Technologies, Systems, and Applications, II, 741-750.
  2. Faiz, Ibrahim S., Patrik, Nirnay. J., Saideep, Pradeep. B., Omkar Pradip. K., & Nikhilkumar Shardoor B. (2016). Smart parking system based on embedded system and sensor network. Int. J. Compute. Appl. (View 30.07.2015)
  3. Guo, T., Dong, J., Li, H., & Gao, Y. (2017). Simple convolutional neural network on image classification. IEEE 2nd International Conference on Big Data Analysis (ICBDA), 721-724.
  4. Halleman, B. (2003). Europe's space program (parking space, naturally). Traffic Technology International, February/March, pp. 46-49.
  5. Kong, Y. H., & Lee, W. C. (2017). Dynamic Obstacle Avoidance and Optimal Path Finding Algorithm for Mobile Robot Using Q-Learning. Journal of Korean Institute of Information Technology, 15(9), 57-62. https://doi.org/10.14801/jkiit.2017.15.9.57
  6. Li, Y., Zhang, J., Gao, P., Jiang, L., & Chen, M. (2018). Grab Cut Image Segmentation Based on Image Regi. on. IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), 311-315.
  7. Muftah, F., & Fernstrom M. (2016). Investigation of smart parking systems and their technologies. Thirty Seventh International Conference on Information Systems, Dublin 2016.
  8. Steela, K., Birdsong, W., & Reddy, B. Y. (2019). Image classification using Tensorflow. 16th International Conference on Information Technology-New Generations (ITNG 2019), 485-488.
  9. Wang, H., & He, W. (2011). A reservation-based smart parking system. Computer Communications Workshops (INFOCOM WKSHPS), 2011 IEEE Conference on, Shanghai, 690- 695.