Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2025
Sammendrag
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Sammendrag
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Forfattere
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 LstibůrekSammendrag
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Forfattere
Sean P Healey Zhiqiang Yang Angela M Erb Ryan Bright Grant M Domke Tracey S Frescino Crystal B SchaafSammendrag
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Sammendrag
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.
Forfattere
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 AlberdiSammendrag
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Forfattere
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 ManevskiSammendrag
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Sammendrag
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Forfattere
Siri Svendgård-Stokke Eva Solbjørg Flo Heggem Anne B. Nilsen Svein Olav Krøgli Sebastian Eiter Henrik Forsberg Mathiesen Jonathan Rizzi Torgeir Tajet Ole Einar Ellingbø TveitoSammendrag
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