Weather4cast at NeurIPS 2022: Super-Resolution Rain Movie Prediction under Spatio-temporal Shifts
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Gruca, A.; Serva, F.; Lliso, L.; Pihrt, J.; Raevskiy, R.; Šimánek, P.
Rok
2023
Publikováno
Proceedings of the NeurIPS 2022 Competitions Track. Proceedings of Machine Learning Research, 2023. p. 292-312. Proceedings of Machine Learning Research. vol. 220. ISSN 2640-3498.
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Stať ve sborníku
Pracoviště
Anotace
Weather4cast again advanced modern algorithms in AI and machine learning through a highly topical interdisciplinary competition challenge: The prediction of hi-res rain radar movies from multi-band satellite sensors, requiring data fusion, multi-channel video frame prediction, and super-resolution. Accurate predictions of rain events are becoming ever more critical, with climate change increasing the frequency of unexpected rainfall. The resulting models will have a particular impact where costly weather radar is not available. We here present highlights and insights emerging from the thirty teams participating from over a dozen countries. To extract relevant patterns, models were challenged by spatio-temporal shifts. Geometric data augmentation and test-time ensemble models with a suitable smoother loss helped this transfer learning. Even though, in ablation, static information like geographical location and elevation was not linked to performance, the general success of models incorporating physics in this competition suggests that approaches combining machine learning with application domain knowledge seem a promising avenue for future research. Weather4cast will continue to explore the powerful benchmark reference data set introduced here, advancing competition tasks to quantitative predictions, and exploring the effects of metric choice on model performance and qualitative prediction properties.
Spatiotemporal Prediction of Vehicle Movement Using Artificial Neural Networks
Autoři
Rok
2022
Publikováno
Proceedings of 2022 IEEE Intelligent Vehicles Symposium (IV). Piscataway: IEEE, 2022. p. 734-739. ISSN 1931-0587. ISBN 978-1-6654-8821-1.
Typ
Stať ve sborníku
Anotace
Prediction of the movement of all traffic participants is a very important task in autonomous driving. Well-predicted behavior of other cars and actors is crucial for safety. A sequence of bird’s-eye view artificially rasterized frames are used as input to neural networks which are trained to predict the future behavior of the participants. The Lyft Motion Prediction for Autonomous Vehicles dataset is explored and adapted for this task. We developed and applied a novel approach where the prediction problem is viewed as a problem of spatiotemporal prediction and we use methods based on convolutional recurrent neural networks.