MUMBAI, India, June 30 -- Intellectual Property India has published a patent application (202641054584 A) filed by The Principal, Mepco Schlenk Engineering College on April 29, 2026, for Real Time Lane Detection Traffic Sign Recognition And Driver Alert Assistance System.
Inventors include Dr. V. Karthikeyan; V. Kiran Kumar; and R. Mithun Raj.
The application for the patent was published on June 26, 2026, under issue no. 26/2026.
Abstract: The current invention discloses a system for real-time lane detection, traffic sign recognition, and driver alert assistance. It aims to provide an accurate, non-invasive, and automated road scene analysis system for use in intelligent transportation and vehicular safety applications. The system uses a digital image acquisition component to capture real-time road scene images and video frames from a forward-facing camera. The frames are acquired under practical road and lighting conditions to ensure robustness and adaptability to environmental variations. The frames are processed to reduce noise, normalize pixel values, and prepare the input for intelligent analysis. The system analyzes the frames using advanced deep learning methodologies, including lane segmentation with attention mechanisms and object detection for traffic sign recognition. The extracted visual information is evaluated using trained deep learning models to identify lane regions and detect important traffic signs such as Stop, Speed Limit, and Child-Pedestrian Crossing. The detection results are displayed through a visual output interface with lane overlays, bounding boxes, and warning banners. The system further generates audio and voice alerts to notify the driver about lane departure and detected traffic signs in real time. The system has the ability to monitor vehicle alignment within the lane while also recognizing critical roadside signs without physical contact or destructive inspection. The system can be integrated with real-time driver assistance platforms, intelligent vehicular systems, and safety monitoring applications. The system also has the ability to store detection outputs, alert events, and processed results for further analysis. The proposed system has minimal dependence on continuous human observation. It increases response speed and reduces errors caused by fatigue or delayed reaction. The system has improved stability against road and illumination variations due to the use of deep learning-based image analysis. The current invention presents an intelligent system for automated lane monitoring, traffic sign detection, and alert assistance. It leads to improved driver awareness, enhanced road safety, and better intelligent transportation performance in modern vehicular environments.
Disclaimer: Curated by HT Syndication.
