Enhanced Lane Recognition for Automated Warning Sign Placement Using Canny Edge Detection

Real-time lane recognition is critical for intelligent roadside warning sign vehicles that provide post-accident precautions.However, lane interference, uneven lighting, and night time dimness can impair automatic cruise control.This paper presents an enhanced lane recognition methodology using Canny edge detection to enable automated placement of roadside warning signs.Image preprocessing via multi-directional Sobel filtering improves edge detection across varied lighting.Experiments demonstrated up to 85% lane recognition accuracy using the proposed techniques, outperforming prior thresholding approaches.An optimized PID controller enabled precise vehicle control based on lane data.The system was implemented on a K210 controller, validating real-time embedded performance.By overcoming lane detection deficiencies, this research enables reliable automated warning sign deployment to enhance road safety.