VANISHING POINT AIDED LANE DETECTION USING A MULTI-SENSOR SYSTEM

• Published in: ISPRS annals of the photogrammetry, remote sensing and spatial information sciences Vol. X-1/W1-2023; pp. 635 - 641
• Main Authors: Zhang, Z., Kang, G., Ai, M., El-Sheimy, N.
• Format: Journal Article
• Language: English
• Published: Gottingen Copernicus GmbH 01.01.2023; Copernicus Publications
• Subjects: Algorithms; Computer vision; Coordinate transformations; Critical components; Deep learning; Digital cameras; Edge detection; High definition; Hough transformation; Information science; Kalman filters; Lidar; Machine learning; Neural networks; Outliers (statistics); Peer review; Photogrammetry; Remote sensing; Roads & highways; Sensors
• ISSN: 2194-9050; 2194-9042; 2196-6346; 2194-9050
• DOI: 10.5194/isprs-annals-X-1-W1-2023-635-2023

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%.