Abstract :

Road Traffic Accidents Caused By Speeding And Failure To Recognize Traffic Signs Remain One Of The Leading Causes Of Injuries And Fatalities Worldwide. In Electric Vehicles (EVs), Maintaining Safe Driving Behavior While Ensuring Energy Efficiency Is A Major Challenge. Conventional Traffic Sign Recognition Systems Often Rely On Manual Driver Observation Or Traditional Image Processing Techniques, Which May Suffer From Low Accuracy Under Varying Lighting, Weather Conditions, Occlusions, And High-speed Driving Environments. Recent Advancements In Artificial Intelligence (AI), Deep Learning, Convolutional Neural Networks (CNNs), Computer Vision, OpenCV, And Autonomous Vehicle Technologies Have Enabled The Development Of Intelligent Traffic Sign Recognition Systems Capable Of Accurately Identifying Road Signs And Automatically Controlling Vehicle Speed In Real Time. This Project Presents A Deep LearningBased Traffic Sign Recognition And Autonomous Speed Control System For Electric Vehicle Applications. The Proposed Framework Integrates A Camera Module, OpenCV Image Preprocessing, CNN-based Traffic Sign Recognition Model, Speed Control Module, Electronic Control Unit (ECU), Electric Motor Controller, And Human Machine Interface (HMI) Into A Unified Intelligent Driving Assistance System. Initially, The Front-mounted Camera Continuously Captures Road Images, Which Are Preprocessed Using OpenCV Before Being Supplied To The Trained CNN Model. The Deep Learning Model Identifies Traffic Signs Such As Speed Limits, Stop Signs, School Zones, Pedestrian Crossings, And No-entry Signs With High Accuracy. Based On The Recognized Traffic Sign, The Autonomous Speed Control Module Automatically Adjusts The EV Motor Speed While Simultaneously Displaying The Detected Sign And Recommended Speed On The Driver Interface. Experimental Evaluation Demonstrates High Traffic Sign Recognition Accuracy, Rapid Inference Speed, Low Response Time, Reliable Autonomous Speed Adjustment, And Improved Driving Safety Under Different Environmental Conditions. The Proposed Intelligent System Significantly Enhances Road Safety, Reduces Driver Workload, Improves Traffic Regulation Compliance, Optimizes EV Energy Efficiency, And Provides An Effective Solution For Future Autonomous And Intelligent Transportation Systems. Keywords: Deep Learning, Traffic Sign Recognition, Autonomous Speed Control, Electric Vehicle, CNN, Computer Vision, OpenCV, Artificial Intelligence, Autonomous Driving, Driver Assistance System.

Published:

06-8-2026

Issue:

Vol. 26 No. 8 (2026)

Page Nos:

740 - 748

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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