Lane Detection using Computer Vision and Machine Learning for Self-Driving Cars

Abstract

Self-Driving Cars are not only a reality today but a glimpse into how advanced and complex technology is going to be in the next century. The best and the brightest have been brought together by industry and academia around the world in this race to develop the best Autonomous vehicles possible. There's one important question which remains a topic of debate; which techniques should one use? Some researchers believe that traditional computer vision approaches are the answer, while several others have been utilizing deep learning-based approaches. With this research, we compare which of these methods proves to be better in the case of lane detection, one of the most fundamental aspects of self-driving cars. The paper builds on previous research done in comparing the two approaches for various fields, and listing out the pros and cons of both approaches. The readers would have an in-depth understanding of the state-of-the-art techniques utilized for lane detection, giving them the ability to make an unbiased choice for their specific use case.