Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM62184
Volume:
10 |
4 |
April 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Electronics and Communication Engineering
MAHARAJ VIJAYARAM GAJAPATHI RAJ COLLEGE OF ENGINEERING
Electronics and Communication Engineering
MAHARAJ VIJAYARAM GAJAPATHI RAJ COLLEGE OF ENGINEERING
Electronics and Communication Engineering
MAHARAJ VIJAYARAM GAJAPATHI RAJ COLLEGE OF ENGINEERING
Rapid increases in the number of vehicles in urban traffic require intelligent traffic management methods more than the fixed time method. The following work represents the design of real-time traffic management based on artificial intelligence technology with the use of computer vision, embedded systems control and wireless communication. Video input from a USB camera is processed by a graphical user interface (GUI), using the YOLOv8 model trained specifically for traffic vehicle detection and detection of emergency vehicles. ArUco markers are used to estimate lane localization and vehicle density. Control information received from the GUI is sent via serial communication to an ESP32 sender, then is relayed wirelessly to an ESP32 receiver using ESP-NOW and transferred via UART to an Arduino Mega for control of the traffic light. Experimental results prove high accuracy of the proposed algorithm and fast performance with mAP50 equal to 0.835 and inference speed of 3.9 ms per frame.
KEYWORDS
The designed system is capable of incorporating AI, IoT, and embedded system technology in order to deliver effective and adaptive traffic control. The system carries out real-time vehicle detection, determines the traffic density accurately, and adapts to change the signal timing depending on the actual situation. High accuracy was attained during real-time processing with an mAP50 of 0.835, while low latency is demonstrated by the system with an average inference time of 3.9 ms per image frame. In addition, the inclusion of the priority feature of emergency vehicles makes the proposed system more road-friendly, as it reduces the response time and minimizes any risk to road users. This was possible with the help of efficient wireless transmission carried out using the ESP32 module, together with robust control from the Arduino platform. Overall, this technique offers an efficient and cost-effective way of traffic management that can be deployed successfully in future.
1. Abhijeet Choudhary, Akash Gupta, Akshay Dhuri, Prof. Nilima Nikam, “Artificial Intelligence Based Smart Traffic Management System Using Video Processing”– International Research Journal of Engineering and Technology (IRJET), Vol. 5, Issue 3, March 2018. 2. Mohammed Fayaz, Pooja K, Pranitha P Reddy, Swathi T, “Density based Traffic Control System with Ambulance Detection”– IJERT, Vol. 7, Issue 08, Special Issue RTESIT 2019. 3. Siddaraj M G, Syeda Umeela, Pravalika Gandhi, Saniya Kousar, Shrivathsa S K, “Automated Traffic Light System using Raspberry Pi and Con volution Neural Network”–International Research Journal of Engineering and Technology (IRJET), Vol. 8, Issue 7, July 2021. 4. Dr. Ch. Rambabu, J. Divya Madhuri, M. Vamsi, D. Tharun Nayak, L. Pavan Prasad, “Intelligent Traffic Signal Management Using Raspberry Pi and OpenCV”– International Journal of Novel Research and Development (IJNRD) Volume 9, Issue 4, April 2024. 5. Aleem Mohammad, Thanmai Gaddami, Panduranga Reddy Sarikonda, Pavan Kumar Kammar, Muneshwari Aluganur, Satish Somarapu, V Mallikarjuna Reddy, Suresh Kumar, “Automated Traffic Density Optimization System”- MATEC Web of Conferences, ICMED 2024. 6. Prof. Eshwaraj, Dr. Ananthayya M B, Prof. Gowtham B, Prof. Pooja A, Prof. Jayshri, Prof. Naveena G N, “Smart Traffic Management System Using Artificial Intelligence”–International Research Journal on Advanced Science Hub (IRJASH), Vol. 07, Issue 03, March 2025. 7. Sushmitha Gowda K R, N P Prajwal, Pramod B M, Nishanth A, Rohith V , “Smart Traffic Management System Emergency Vehicle”- IJISRT Publications, Vol.10, Issue 5, May 2025. 8. Sumukh Arood V, Dhesurya Kella, Mervin Joe Thomas, “Comparative Evaluation of Communication Protocols with Vision-Based Localization”- International Conference on Robotics and Mechatronics (ICRM), IEEE, 2025.
Publication Date
5/1/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
3
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...