Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2025
Abstract
No abstract has been registered
Authors
David Chludil Curt Almqvist Mats Berlin Arne Steffenrem Steven E. McKeand Jiří Korecký Jan Stejskal Jaroslav Čepl Fikret Isik Debojyoti Chakraborty Silvio Schueler Torsten Pook Christi Sagariya Milan LstibůrekAbstract
No abstract has been registered
Authors
Sean P Healey Zhiqiang Yang Angela M Erb Ryan Bright Grant M Domke Tracey S Frescino Crystal B SchaafAbstract
No abstract has been registered
Abstract
Accurately predicting whether pedestrians will cross in front of an autonomous vehicle is essential for ensuring safe and comfortable maneuvers. However, developing models for this task remains challenging due to the limited availability of diverse datasets containing both crossing (C) and non-crossing (NC) scenarios. Therefore, we propose a procedure that leverages synthetic videos with C/NC labels and an untrained model whose architecture is designed for C/NC prediction to automatically produce C/NC labels for a set of real-world videos. Thus, this procedure performs a synth-to-real unsupervised domain adaptation for C/NC prediction, so we term it S2R-UDA-CP. To assess the effectiveness of S2R-UDA-CP in self-labeling, we utilize two state-of-the-art models, PedGNN and ST-CrossingPose, and we rely on the publicly-available PedSynth dataset, which consists of synthetic videos with C/NC labels. Notably, once the real-world videos are self-labeled, they can be used to train models different from those used in S2R-UDA-CP. These models are designed to operate onboard a vehicle, whereas S2R-UDA-CP is an offline procedure. To evaluate the quality of the C/NC labels generated by S2R-UDA-CP, we also employ PedGraph+ (another literature referent) as it is not used in S2R-UDA-CP. Overall, the results show that training models to predict C/NC using videos labeled by S2R-UDA-CP achieves performance even better than models trained on human-labeled data. Our study also highlights different discrepancies between automatic and human labeling. To the best of our knowledge, this is the first study to evaluate synth-to-real self-labeling for C/NC prediction.
Authors
Daniel Moreno-Fernández Johannes Breidenbach Isabel Cañellas Gherardo Chirici Giovanni D’amico Marco Ferretti Francesca Giannetti Stefano Puliti Sebastian Schnell Ross Shackleton Mitja Skudnik Iciar AlberdiAbstract
No abstract has been registered
Authors
Shaohui Zhang Poul Erik Lærke Mathias Neumann Andersen Junxiang Peng Esben Øster Mortensen Johannes Wilhelmus Maria Pullens Sheng Wang Klaus Steenberg Larsen Davide Cammarano Uffe Jørgensen Kiril ManevskiAbstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Katharina HobrakAbstract
No abstract has been registered
Authors
Arne Verstraeten Andreas Schmitz Bernd Ahrends Nicholas Clarke Wim de Vries Karin Hansen Char Hilgers Carmen Iacoban Tamara Jakovljevic Per Erik Karlsson Till Kirchner Aldo Marchetto Henning Meesenburg Gunilla Pihl Karlsson Anne-Katrin Prescher Anne Thimonier Peter WaldnerAbstract
No abstract has been registered
Authors
Aline Roma Tomaz William Ramos da Silva Thiago Inagaki Emylaine Oliveira Santos Giselle Gomes Monteiro Fracetto Felipe José Cury Fracetto Vitor da Silveira Freitas Diego Victor Babos Débora Milori Abelardo Antônio de Assunção Montenegro Ademir De Oliveira FerreiraAbstract
No abstract has been registered