TY - CHAP A1 - Behret, Valentino A1 - Kushtanova, Regina A1 - Fadl, Islam A1 - Weber, Simon A1 - Helmer, Thomas A1 - Palme, Frank ED - Bashford-Rogers, Thomas ED - Meneveaux, Daniel ED - Ammi, Mehdi ED - Ziat, Mounia ED - Jänicke, Stefan ED - Purchase, Helen ED - Radeva, Petia ED - Furnari, Antonino ED - Bouatouch, Kadi ED - Sousa, A. Augusto T1 - Sensor Calibration and Data Analysis of the MuFoRa Dataset T2 - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - (Volume 2) N2 - Autonomous driving sensors face significant challenges under adverse weather conditions such as fog and rain, which can seriously degrade their performance and reliability. Existing datasets often lack the reproducible and measurable data needed to adequately quantify these effects. To address this gap, a new multimodal dataset (MuFoRa) has been collected under controlled adverse weather conditions at the CARISSMA facility, using a stereo camera and two solid-state LiDAR sensors. This dataset is used to quantitatively assess sensor degradation by measuring the entropy for images and the number of inliers for point clouds on a spherical target. These metrics are used to evaluate the impact on performance under varying conditions of fog (5 to 150 m visibility) and rain (20 to 100 mm/h intensity) at different distances (5 to 50 m). Additionally, two calibration target detection approaches - Deep-learning and Hough-based - are evaluated to achieve accurate sensor alignment. The contributions include the introduction of a new dataset focused on fog and rain, the evaluation of sensor degradation, and an improved calibration approach. This dataset is intended to support the development of more robust sensor fusion and object detection algorithms for autonomous driving. UR - https://doi.org/10.5220/0013310400003912 Y1 - 2025 UR - https://doi.org/10.5220/0013310400003912 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58088 SN - 978-989-758-728-3 SP - 622 EP - 631 PB - SciTePress CY - Setúbal ER -