Deep Learning-Based Speed Limit Sign Detection Using YOLOv11 Applied to Speed Regulation in Electric Vehicles for ADAS

Authors

  • Mohammed Chaman Laboratory of Electronic Systems Information Processing Mechanics and Energetics, Ibn Tofail University, Kenitra, Morocco
  • Anas El Maliki Laboratory of Electronic Systems Information Processing Mechanics and Energetics, Ibn Tofail University, Kenitra, Morocco https://orcid.org/0000-0002-4499-9181
  • Hamad Dahou Laboratory of Electronic Systems Information Processing Mechanics and Energetics, Ibn Tofail University, Kenitra, Morocco https://orcid.org/0000-0001-6515-640X
  • Rachid El Gouri Laboratory of Advanced Systems Engineering, Ibn Tofail University, Kenitra, Morocco https://orcid.org/0000-0001-6006-8486
  • Hlou Laamari Laboratory of Electronic Systems Information Processing Mechanics and Energetics, Ibn Tofail University, Kenitra, Morocco https://orcid.org/0000-0001-9611-8983
  • Abdelkader Hadjoudja Laboratory of Electronic Systems Information Processing Mechanics and Energetics, Ibn Tofail University, Kenitra, Morocco https://orcid.org/0009-0008-9217-7546
Volume: 15 | Issue: 4 | Pages: 25354-25362 | August 2025 | https://doi.org/10.48084/etasr.11320

Received: 8 April 2025 | Revised: 1 May 2025 and 6 May 2025 | Accepted: 10 May 2025 | Online: 2 August 2025


Corresponding author: Mohammed Chaman

Abstract

Road safety and energy efficiency remain critical challenges in modern Electric Vehicles (EVs), particularly when drivers fail to adhere to speed limits. This study presents an effective speed limit sign detection and automatic speed regulation system using YOLOv11 within an Advanced Driver Assistance System (ADAS) framework. By integrating rapid sign detection with vector control of Permanent Magnet Synchronous Motors (PMSM), the proposed system delivers real-time speed limit compliance and improved vehicle performance. The YOLOv11 model was trained on a dataset of 23,000 traffic sign images. Experimental results demonstrate high performance, with a mean Average Precision (mAP) of 99.6% (mAP@50) and 86.2% (mAP@50–95), alongside 99.2% precision and 98.5% recall, underscoring the model's effectiveness. This work concludes that combining deep learning–based traffic sign recognition with advanced motor control significantly enhances ADAS capabilities and paves the way for future research into integrated, high-accuracy solutions for sustainable transportation.

Keywords:

YOLOv11, speed limit sign detection, Advanced Driver Assistance Systems (ADAS), electric vehicles, speed regulation, deep learning

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How to Cite

[1]
M. Chaman, A. El Maliki, H. Dahou, R. El Gouri, H. Laamari, and A. Hadjoudja, “Deep Learning-Based Speed Limit Sign Detection Using YOLOv11 Applied to Speed Regulation in Electric Vehicles for ADAS”, Eng. Technol. Appl. Sci. Res., vol. 15, no. 4, pp. 25354–25362, Aug. 2025.

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Copyright (c) 2025 Mohammed Chaman, Anas El Maliki, Hamad Dahou, Rachid El Gouri, Hlou Laamari, Abdelkader Hadjoudja

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