Real-time Lane Detection Techniques Using Optimal Estimator and Deep Learning-based Lane Segmentation for Self-driving Vehicles

Road lane-line detection systems are crucial for assisting human drivers in various driving scenes. Nowadays, these systems have become more critical for autonomous cars. In recent years, many advanced lane detection and tracking methods have been studied. However, most approaches focus on detecting lanes by performing conventional image processing on a single frame. Often, the lack of distinguishing features and the presence of extreme weather conditions such as dense rain or snow makes these algorithms lose their accuracy. In this work, deep learning is merged as a tool to address these issues. A modified version of the LaneNet algorithm was designed as a road lane detector, in which the front image was passed as input to the encoder model. Consequently, the road lanes could be robustly detected even if they were discrete or unclear. For lane curvature estimation, a Kalman filter was utilized, which took the results obtained from the LaneNet detection module as input. The detection results were compared with those obtained using the conventional Hough transformation. The obtained results demonstrate the effectiveness of our approach.