DOI QR코드

DOI QR Code

An Edge AI Device based Intelligent Transportation System

  • Jeong, Youngwoo (Department of Electronic Engineering, Seoul National University of Science and Technology) ;
  • Oh, Hyun Woo (Department of Electronic Engineering, Seoul National University of Science and Technology) ;
  • Kim, Soohee (Department of Electronic Engineering, Seoul National University of Science and Technology) ;
  • Lee, Seung Eun (Department of Electronic Engineering, Seoul National University of Science and Technology)
  • Received : 2022.04.14
  • Accepted : 2022.07.07
  • Published : 2022.09.30

Abstract

Recently, studies have been conducted on intelligent transportation systems (ITS) that provide safety and convenience to humans. Systems that compose the ITS adopt architectures that applied the cloud computing which consists of a high-performance general-purpose processor or graphics processing unit. However, an architecture that only used the cloud computing requires a high network bandwidth and consumes much power. Therefore, applying edge computing to ITS is essential for solving these problems. In this paper, we propose an edge artificial intelligence (AI) device based ITS. Edge AI which is applicable to various systems in ITS has been applied to license plate recognition. We implemented edge AI on a field-programmable gate array (FPGA). The accuracy of the edge AI for license plate recognition was 0.94. Finally, we synthesized the edge AI logic with Magnachip/Hynix 180nm CMOS technology and the power consumption measured using the Synopsys's design compiler tool was 482.583mW.

Keywords

Acknowledgement

This study was supported by the SeoulTech (Seoul National University of Science and Technology).

References

  1. K. Gharehbaghi and M. Myers, "Intelligent system intricacies: Safety, security and risk management apprehensions of ITS," in 8th International Conference on Industrial Technology and Management (ICITM), Cambridge, United Kingdom, pp. 37-40, 2019. DOI: 10.1109/ICITM.2019.8710708.
  2. B. Chang and J. Chiou, "Cloud computing-based analyses to predict vehicle driving shockwave for active safe driving in intelligent transportation system," IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 2, pp. 852-866, Feb. 2020. DOI: 10.1109/TITS.2019.2902529.
  3. D. Li, L. Deng, Z. Cai, and X. Yao, "Notice of retraction: intelligent transportation system in macao based on deep self-coding learning," IEEE Transactions on Industrial Informatics, vol. 14, no. 7, pp. 3253-3260, Jul. 2018. DOI: 10.1109/TII.2018.2810291.
  4. A. Moubayed, A. Shami, P. Heidari, A. Larabi, and R. Brunner, "Edge-enabled V2X service placement for intelligent transportation systems," IEEE Transactions on Mobile Computing, vol. 20, no. 4, pp. 1380-1392, 2021. DOI: 10.1109/TMC.2020.2965929.
  5. C. Chen, B. Liu, S. Wan, P. Qiao, and Q. Pei, "An edge traffic flow detection scheme based on deep learning in an intelligent transportation system," IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 3, pp. 1840-1852, Mar. 2021. DOI: 10.1109/TITS.2020.3025687.
  6. M. Gusev and S. Dustdar, "Going back to the roots-The evolution of edge computing, an iot perspective," IEEE Internet Computing, vol. 22, no. 2, pp. 5-15, Mar./Apr. 2018. DOI: 10.1109/MIC.2018.022021657.
  7. D. H. Hwang, C. Y. Han, H. W. Oh, and S. E. Lee, "ASimOV: A framework for simulation and optimization of an embedded AI accelerator," Micromachines, vol. 12, vol. 7, pp. 838, Jul. 2021. DOI: 10.3390/mi12070838.
  8. J. -Y. Sung, S. -B. Yu, and S. -h. P, "Real-time automatic license plate recognition system using YOLOv4," in IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia), Seoul, Korea, pp. 1-3, 2020. DOI: 10.1109/ICCE-Asia49877.2020.9277050.
  9. Y. Jing, B. Youssefi, M. Mirhassani, and R. Muscedere, "An efficient FPGA implementation of optical character recognition for license plate recognition," in IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), Windsor: ON, Canda, pp. 1-4, 2017. DOI: 10.1109/CCECE.2017.7946734.
  10. Y. Caid, X. Lin, H. Qian, and P. Lu, "FPGA accelerator design for license plate recognition based on 1BIT convolutional neural network," in Journal of Physics: Conference Series, Hangzhou, China, vol. 1621, no. 1, 2020. DOI: 10.1088/1742-6596/1621/1/012022.
  11. D. M. F. Izidio, A. P. A. Ferreira, H. R. Medeiros, and E. N. d. S. Barros, "An embedded automatic license plate recognition system using deep learning," Design Automation for Embedded System, vol. 24, no. 1, pp. 23-43, Nov. 2019. DOI:10.1007/s10617-019-09230-5.
  12. I. V. Pustokhina, D. A. Pustokhin, J. J. P. C. Rodrigues, D. Gupta, A. Khanna, K. Shankar, C. Seo, G. P. Joshi, "Automatic vehicle license plate recognition using optimal K-means with convolutional neural network for intelligent transportation Systems," IEEE Access, vol. 8, pp. 92907-92917, May. 2020. DOI: 10.1109/ACCESS.2020.2993008.
  13. W. Riaz, A. Azeem, G. Chenqiang, Z. Yuxi, Saifullah, and W. Khalid, "YOLO based recognition method for automatic license plate recognition," in IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA), Dalian, China, pp. 87-90, 2020. DOI: 10.1109/AEECA49918.2020.9213506.
  14. Y. H. Yoon, D. H. Hwang, J. H. Yang, and S. E. Lee, "Intellino: Processor for embedded artificial intelligence," Electronics, vol. 9, no. 7, pp. 1-12, Jul. 2020. DOI: 10.3390/electronics9071169.