AN IN-DEPTH STUDY OF LANE DETECTION FOR AUTONOMOUS CARS USING COMPUTER VISION TECHNIQUES

Abstract

The article discusses the importance of selfdriving cars to improve road safety and reduce the number of accidents caused by human error. Self-driving cars not only reduce human error but also help reduce driver fatigue. We further explore the use of computer vision in autonomous cars, with previous research relying on deep learning algorithms with LiDAR sensors which can be expensive. The authors propose a more cost-effective approach using simple computer vision algorithms such as color space transformation, Canny edge detection, and Hough line transformation to detect lane lines and steer the car accordingly. This approach requires less operational hardware and can be implemented using affordable boards like Raspberry Pi and Nvidia Jetson Nano. The article also highlights the reconstruction of a remote-controlled car that had a 95% accuracy using a certain set of parameters was a tool for understanding autonomous cars better.