Lane Detection for Self-Driving Cars
Asst. Prof. M.N.V.Viswanadh M.Tech1, Prof. Dr. Bomma Rama Krishna Ph.D2, Naga Venkata Raviteja Ungarala3, Manimela Anakammarao4, Manne Lokesh Satya Tejas5, Pothumanchili Siva Mukesh6, Kondaveti Reshi Charan7
1Assistant Professor, Artificial Intelligence and Machine Learning & Swarnandhra College of Engg. and Technology
2Professor, Artificial Intelligence and Machine Learning & Swarnandhra College of Engineering and Technology
3Artificial Intelligence and Machine Learning & Swarnandhra College of Engineering and Technology
4Artificial Intelligence and Machine Learning & Swarnandhra College of Engineering and Technology
5Artificial Intelligence and Machine Learning & Swarnandhra College of Engineering and Technology
6Artificial Intelligence and Machine Learning & Swarnandhra College of Engineering and Technology
7Artificial Intelligence and Machine Learning & Swarnandhra College of Engineering and Technology
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Abstract - The project focuses on developing a real-time vehicle detection and lane analysis system using computer vision techniques. The system is designed to process video inputs, detect vehicles, analyze lane curvature, and provide safety suggestions to drivers. The application leverages YOLO (You Only Look Once) for vehicle detection, optical flow for speed estimation, and Hough Transform for lane detection. The system is implemented as a Flask-based web application, allowing users to upload video files, process them, and view the results with overlays indicating vehicle distances, lane status, and safety suggestions. The project aims to enhance road safety by providing real-time feedback to drivers about their surroundings.
Keywords: Real-time Vehicle Detection, Lane Detection, YOLO, Optical Flow, Hough Transform, Road Safety, Computer Vision, Video Processing, Lane Curvature Analysis, ADAS
