Lane Detection System for Driver Assistance in Vehicles

TL;DR

This paper develops a real-time lane detection system using traditional computer vision techniques to assist driver and autonomous vehicle navigation, demonstrating robustness across various conditions.

Contribution

The work introduces a comprehensive image processing pipeline for lane detection that operates effectively in real-time under diverse environmental conditions.

Findings

Effective lane detection in different lighting and road conditions

Robust performance with traditional vision techniques

Identified challenges in extreme scenarios like shadows and sharp curves

Abstract

This work presents the development of a lane detection system aimed at assisting the driving of conventional and autonomous vehicles. The system was implemented using traditional computer vision techniques, focusing on robustness and efficiency to operate in real-time, even under adverse conditions such as worn-out lanes and weather variations. The methodology employs an image processing pipeline that includes camera calibration, distortion correction, perspective transformation, and binary image generation. Lane detection is performed using sliding window techniques and segmentation based on gradients and color channels, enabling the precise identification of lanes in various road scenarios. The results indicate that the system can effectively detect and track lanes, performing well under different lighting conditions and road surfaces. However, challenges were identified in extreme situations, such as intense shadows and sharp curves. It is concluded that, despite its limitations, the traditional computer vision approach shows significant potential for application in driver assistance systems and autonomous navigation, with room for future improvements.