| Fast LIDAR-based road detection using fully convolutional neural networks L Caltagirone, S Scheidegger, L Svensson, M Wahde 2017 ieee intelligent vehicles symposium (iv), 1019-1024, 2017 | 276 | 2017 |
| Mono-camera 3d multi-object tracking using deep learning detections and pmbm filtering S Scheidegger, J Benjaminsson, E Rosenberg, A Krishnan, K Granström 2018 IEEE Intelligent Vehicles Symposium (IV), 433-440, 2018 | 162 | 2018 |
| climateBUG: A data-driven framework for analyzing bank reporting through a climate lens Y Yu, S Scheidegger, J Elliott, Å Löfgren Expert Systems with Applications 239, 122162, 2024 | 1 | 2024 |
| Separable convolutional eigen-filters (SCEF): Building efficient CNNs using redundancy analysis S Scheidegger, Y Yu, T McKelvey arXiv e-prints, arXiv: 1910.09359, 2019 | 1 | 2019 |
| Monocular simultaneous localisation and mapping for road vehicles M Ernst, S Scheidegger | 1 | 2015 |
| A Pre-study on Data Processing Pipelines for Roadside Object Detection Systems Towards Safer Road Infrastructure Y Yu, S Scheidegger, JF Grönvall, M Palm, E Svanberg, JA Wennerby, ... arXiv preprint arXiv:2205.01783, 2022 | | 2022 |
| Qually: a quality validation toolbox for automotive perception data towards trustworthy AI Y Yu, S Scheidegger, J Bakker | | 2022 |
| Safety-driven data labelling platform to enable safe and responsible AI Y Yu, S Scheidegger, J Bakker | | 2021 |
| Building Efficient CNNs Using Depthwise Convolutional Eigen-Filters (DeCEF) Y Yu, S Scheidegger, T McKelvey arXiv preprint arXiv:1910.09359, 2019 | | 2019 |
| Halvledarreläer M Ernst, S Scheidegger | | 2013 |
Yinan YuChalmers University of TechnologyVerified email at chalmers.se
