DOI: https://doi.org/10.14710/teknik.v43i3.47582

Autonomous Car Mechanical Performance Analysis with Region of Interest Method Using Raspberry Pi 4 and Arduino Nano

Analisis Performa Mekanik Autonomous Car Dengan Metode Region of Interest Menggunakan Raspberry Pi 4 dan Arduino Nano

*Martinus Hendra Dewantara  -  Pawiyatan Luhur Sel. IV No.1, Bendan Duwur, Kec. Gajahmungkur, Kota Semarang, Jawa Tengah 50234, Indonesia
Leonardus Heru Pratomo  -  , Indonesia
Slamet Riyadi  -  , Indonesia
Open Access Copyright (c) 2022 TEKNIK

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Abstract

The development of artificial intelligence science and technology has now begun to penetrate the automotive sector. Autonomous car is one of them that is now starting to take on the role with several capabilities such as lidar sensors, GPS systems, and image reading through the camera. In this paper, an autonomous car uses an image reading system using a camera as an optical sensor. Object detection is needed in real time because the autonomous car continues to move along the track. Researchers use HSV color classification with the Region of Interest method, which has the ability to mark certain areas so that it can be used to optimize system performance to detect and classify trajectories quickly and precisely. This autonomous car uses a 4WD system with a DC motor as the main driver, Raspberry Pi 4 and Arduino nano as the mainboard for operation. The test method in this study includes testing image processing using the Region of Interest, this test includes road detection that has been designed and mechanical testing of the propulsion used in this autonomous car. In this study, trials have been carried out and this prototype successfully works according to the algorithm that has been made. In this trial, the AGV using the ROI method has very accurate reading and movement accuracy. In trials and hardware implementations carried out in this autonomous car laboratory with artificial intelligence, it can work according to the algorithm created with a success rate of 90%.

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Keywords: Raspberry Pi 4; Arduino Nano; HSV System; Region of Interest; Autonomous Car

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Section: Artikel
Language : EN
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  1. Ab Wahab, M. N., Nazir, A., Ren, A. T. Z., Noor, M. H. M., Akbar, M. F., & Mohamed, A. S. A. (2021). Efficientnet-lite and hybrid CNN-KNN implementation for facial expression recognition on raspberry pi. IEEE Access, 9, 134065-134080
  2. Amat, R., Sari, J. Y., & Ningrum, I. P. (2017). Implementasi metode local binary patterns untuk pengenalan pola huruf hiragana dan katakana pada smartphone. JUTI J. Ilm. Teknol. Inf, 15(2), 152
  3. Choi, J. H., Chun, Y. D., Han, P. W., Kim, M. J., Koo, D. H., Lee, J., & Chun, J. S. (2010). Design of high power permanent magnet motor with segment rectangular copper wire and closed slot opening on electric vehicles. IEEE Transactions on Magnetics, 46(6), 2070-2073
  4. Deshpande, R. R., Madhavi, C. R., & Bhatt, M. R. (2021). 3d image generation from single image using color filtered aperture and 2.1 d sketch-a computational 3d imaging system and qualitative analysis. IEEE Access, 9, 93580-93592
  5. Dewangan, D. K., & Sahu, S. P. (2020). Deep learning-based speed bump detection model for intelligent vehicle system using raspberry Pi. IEEE sensors journal, 21(3), 3570-3578
  6. Gandhi, G. M. (2019, March). Artificial intelligence integrated blockchain for training autonomous cars. In 2019 Fifth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) (Vol. 1, pp. 157-161). IEEE
  7. Ikhlayel, M., Iswara, A. J., Kurniawan, A., Zaini, A., & Yuniarno, E. M. (2020, November). Traffic Sign Detection for Navigation of Autonomous Car Prototype using Convolutional Neural Network. In 2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM) (pp. 205-210). IEEE
  8. Jo, K., Lee, M., Lim, W., & Sunwoo, M. (2019). Hybrid local route generation combining perception and a precise map for autonomous cars. IEEE Access, 7, 120128-120140
  9. Kafadar, Ö. (2020). RaspMI: Raspberry Pi assisted embedded system for monitoring and recording of seismic ambient noise. IEEE Sensors Journal, 21(5), 6306-6313.
  10. Koike, A., & Sueda, Y. (2019, September). Contents delivery for autonomous driving cars in conjunction with car navigation system. In 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS) (pp. 1-4). IEEE
  11. Kojima, A., & Nose, Y. (2018, December). Development of an autonomous driving robot car using FPGA. In 2018 International Conference on Field-Programmable Technology (FPT) (pp. 411-414). IEEE
  12. Li, N., Li, J. S. J., & Randhawa, S. (2017). Color filter array demosaicking based on the distribution of directional color differences. IEEE Signal Processing Letters, 24(5), 604-608.
  13. Luu, D. L., Lupu, C., & Chirita, D. (2019, June). Design and development of smart cars model for autonomous vehicles in a platooning. In 2019 15th International Conference on Engineering of Modern Electric Systems (EMES) (pp. 21-24). IEEE
  14. Mík, A. J., & Bouchner, B. P. (2020, June). Safety of crews of autonomous cars. In 2020 Smart City Symposium Prague (SCSP) (pp. 1-5). IEEE.
  15. Nakamoto, N., & Kobayashi, H. (2019, October). Development of an Open-source Educational and Research Platform for Autonomous Cars. In IECON 2019-45th Annual Conference of the IEEE Industrial Electronics Society (Vol. 1, pp. 6871-6876). IEEE
  16. Okuyama, T., Gonsalves, T., & Upadhay, J. (2018, March). Autonomous driving system based on deep q learnig. In 2018 International conference on intelligent autonomous systems (ICoIAS) (pp. 201-205). IEEE
  17. Padmaja, B., Rao, P. N., Bala, M. M., & Patro, E. K. R. (2018, August). A novel design of autonomous cars using IoT and visual features. In 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), 2018 2nd International Conference on (pp. 18-21). IEEE
  18. Pratomo, A. H., Kaswidjanti, W., & Mu’arifah, S. (2020). Implementasi Algoritma Region of Interest (ROI) Untuk Meningkatkan Performa Algoritma Deteksi Dan Klasifikasi Kendaraan. J. Teknol. Inf. dan Ilmu Komput, 7(1), 155-162.
  19. Rodríguez, R. A., Cammarano, P., Giulianelli, D. A., Vera, P. M., Trigueros, A., & Albornoz, L. J. (2018). Using Raspberry Pi to create a solution for accessing educative questionnaires from mobile devices. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 13(4), 144-151.
  20. Widodo, A. S., & Prasetyaningrum, P. T. (2018, October). Perancangan Aplikasi Internet of Thing (IoT) Autonomous Pada Mobil. In Seminar Multimedia & Artificial Intelligence (Vol. 1, pp. 35-38).
  21. Yapriono, D. H., & Dewanto, J. (2015). Perancangan Spion Elektrik Tipe Tanduk Pada Bus Pariwisata Berukuran Besar. Jurnal Teknik Mesin Universitas Kristen Petra 16(1):1–8. doi: 10.9744/jtm.16.1.9-16

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