Safety-Assisted Driving Technology Based on Artificial Intelligence and Machine Learning for Moving Vehicles in Vietnam

• Autorzy: Vu Hong-Son , Nguyen Van-Hien

Identyfikatory

DOI

10.15439/2022R05

• Języki publikacji: EN

Abstrakty

EN

ADAS (Advanced Driver Assistance Systems) plays an important role in building a safe and modern traffic system. For these systems, precise detection performance and response speed are critical. However, the detection of mobile vehicles is facing many difficulties due to the density of vehicles, the complex background scene in the city, etc. In addition, the detection and identification requirements respond in real time is also a challenge for current systems. This paper proposes a model using deep learning algorithms and artificial intelligence to increase accuracy and improve response speed for intelligent driving assistance systems. Accordingly, this paper proposes the YOLO (You Only Look One) model together with a sample data set collected and classified separately suitable for Vietnam traffic and our training algorithm. The experimental results were then performed on an NVIDIA Jetson TX2 embedded computer. The experimental results show that, the proposed method has increased the speed by at least 1.5 times with the detection rate reaching 79\% for the static camera system; and speed up at least 1.5x with a detection rate of 89\% for the dynamic camera system at 1280x720px high resolution images.

Słowa kluczowe

EN

ADAS; Advanced Driver Assistance Systems; YOLO model; Deep learning algorithm; Artificial Intelligence

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 33
• Strony: 279--284
• Opis fizyczny: Bibliogr. 12 poz., rys., tab., wykr.

Twórcy

autor

Vu Hong-Son

• Faculty of Electrical and Electronics Engineering, Hung Yen University of Technology and Education

autor

Nguyen Van-Hien

• Faculty of Electrical and Electronics Engineering, Hung Yen University of Technology and Education

Bibliografia

• [1] A. F. Agarap, Deep Learning using Rectified Linear Units (ReLU), https://arxiv.org/abs/1803.08375, 2018.
• [2] G. S. W. Luger, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, ISBN 978-0-8053-4780-7, 26 July 2020.
• [3] M. Galvani, History and future of driver assistance,” IEEE Instrumentation Measurement Magazine, ISSN 1941-0123, 2019.
• [4] Ultralytics, “YOLOv5 Documentation,” [Trực tuyến]. Available: https://docs.ultralytics.com/.
• [5] S. D. R. G. A. F. Joseph Redmon, You Only Look Once: Unified, Real-Time Object Detection, https://arxiv.org/abs/1506.02640 [cs.CV], 8 Jun 2015.
• [6] M. Schumann, A Book about Colab: (and related activities), ISBN 978-0-89439-085-2, 2015.
• [7] GeeksforGeeks, “Python Virtual Environment | Introduction,” 2020. [Trực tuyến]. Available: https://www.geeksforgeeks.
• [8] C. H. Thuc, “Precision, Recall và F1-score là gì?,” 23 02 2020. [Trực tuyến]. Available: https://caihuuthuc.wordpress.com/2020/02/23/precision-recall-va-f1-score-la-gi/.
• [9] D. Thuan, Evolution of YOLO Algorithm and YOLOv5: The State-of-the-art Object Detection, Bachelor thesis (3.092Mt), Spring 2021.
• [10] H.-S. Vu, J.-X. Guo, K.-H. Chen, S.-J. Hsieh và D.-S. Chen, A real-time moving objects detection and classification approach for static cameras, IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), 2016.
• [11] V. T. D. T. D. N. Hong Son Vu, MỘT PHƯƠNG PHÁP PHÁT HIỆN ĐIỂM MÙ VỚI ĐỘ TIN CẬY CAO VÀ THỜI GIAN THỰC CHO CÁC HỆ THỐNG HỖ TRỢ LÁI XE THÔNG MINH, MOET B2020-SKH-02, October 2020.
• [12] V. H. Son, A high dynamic range imaging algorithm: implementation and evaluation, Engineering and Technology - Research article, Aug 7, 2019.

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).