This article introduces a new method for tracking vehicle lanes using sophisticated visual recognition methods. The swift advancement of self-driving technology makes precise lane detection and tracking essential for secure and dependable vehicle navigation. The method suggested involves using different computer vision algorithms such as edge detection, color space transformation, and Hough Transform to precisely locate and follow lane markings under different road conditions. Furthermore, machine learning algorithms are incorporated to improve the system’s ability to manage obstacles like occlusions, changing lighting conditions, and complicated road structures. The system is intended for use in modern autonomous vehicles, operating in real-time. Thorough testing in various settings proves the efficiency of the method, indicating notable enhancements in both accuracy and processing speed when compared to traditional techniques. This research work helps advance safer and more dependable self-driving systems with a new approach for detecting lanes and guiding vehicles.
A Novel Approach towards Vehicle Lane Tracing using Auto Vision Techniques
2024-11-07
304342 byte
Conference paper
Electronic Resource
English
Vision-based approach towards lane line detection and vehicle localization
British Library Online Contents | 2016
|Road lane monitoring using artificial vision techniques
Automotive engineering | 1995
|Road lane monitoring using artificial vision techniques
British Library Conference Proceedings | 1995
|