부산대학교 도서관

Lane Detection is a critical component of an autonomous driving system that can be integrated alongside with High-definition (HD) map to improve accuracy and reliability of the system. Typically, lane detection is achieved using computer vision algorithms such as edge detection and Hough transform, deep learning-based algorithms, or motion-based algorithms to detect and track the lanes on the road. However, these approaches can contain incorrectly detected line segments with outliers. To address these issues, we proposed a vanishing point aided lane detection method that utilizes both camera and LiDAR sensors, and then employs a RANSAC-based post-processing method to remove potential outliers to improve the accuracy of the detected lanes. We evaluated this method on four datasets provided from the KITTI Benchmark Suite and achieved a total precision of 87%.