Design, Build and Test a Real Time Road Sign Detection and Recognition System for Autonomous Vehicles | ||||
| Advanced Sciences and Technology Journal | ||||
| Articles in Press, Accepted Manuscript, Available Online from 22 August 2025 | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/astj.2025.393571.1071 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
A.M .Elhady  1; Adham Hassan Sayed2; Athanasious Nashaat Rasmy3; Eithar Reda Ebrahim3; Gannah Mohamed Youssef3; Mohamed Ahmed Salah3; Mohamed Ahmed Mohamed3; Omar Mohamed Elsadawy3; Veronia Medhat Sabry3; Youssef Emad Salib3
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| 1Mechatronics , Engineering, ECU, cairo, Egypt | ||||
| 2Mechatronics ,Engineering ,ECU,Cairo | ||||
| 3Mechatronics ,Engineering ,ECU,Cairo, Egypt | ||||
| Abstract | ||||
| The paper discusses a project focused on creating a road sign recognition system to boost the effectiveness and safety of autonomous vehicles. This system is designed to leverage state-of-the-art image processing and deep learning techniques to detect and understand various road signs, including all categories of regulatory, warning, and informational signs, as well as other traffic-related indicators. These signs are critical for the decision-making process of self-driving systems, as they provide essential guidance for safe and lawful driving. Through the successful integration of advanced software and hardware technologies, this work aims to significantly improve the autonomous vehicle’s ability to navigate complex road environments while maintaining strict compliance with key traffic rules. The system enhances situational awareness and helps the vehicle respond correctly to changing road conditions and signage. Because of the physical obstacles and logistical constraints involved in conducting real-world road testing, a controlled testing environment has been built in the laboratory to verify the system’s workability and performance. Extensive experiments were carried out to evaluate the system under various simulated conditions. Test results show a good response and high accuracy in the detection and recognition of road signs, confirming the robustness and reliability of the proposed solution. By implementing this road sign recognition system, this paper aspires to increase both the safety and efficiency of self-driving cars, contributing to the broader vision of fully autonomous transportation systems. | ||||
| Keywords | ||||
| Computer Vision; Road Sign Recognition; Deep Learning; YOLO xx | ||||
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