Lane Detection in Autonomous Cars using Deep Learning Algorithms

Addressing the problem of environmental perception is the first step in the development of lane detection and autonomous driving systems. The safety and dependability of autonomous driving systems are directly impacted by lane detection accuracy, highlighting the importance of the environmental perception component. With the goal of reducing the influence of external elements (such as changes in light, road degradation, and shadows) on the accuracy of detection findings, deep learning technology’s rapid development has made it a preferred approach for lane detecting tasks. This research investigates the potential usefulness of deep learning-based systems in quickly changing driving settings and tackles lane detection as an image segmentation problem. For the purpose of segmenting lane line images, this study suggests a two-branch neural network model built using the Python programming language. A semantic segmentation branch and a clustering branch are the two separate branches that make up the model. While the latter improves the processing of discrete lane line feature points through clustering analysis, the former is in charge of pixel-level lane line segmentation. In order to improve the detection process’ accuracy, feature points in the image were aligned and the matching fitting parameters were extracted using linear and curve fitting approaches. The model’s performance was then assessed after it was trained and validated using TUSimple and additional publically accessible datasets. In conclusion, this work shows how deep learning may improve lane recognition accuracy in challenging driving situations and effectively promote the advancement of autonomous driving systems technically. This opens the door for