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Personalized driving assistance algorithms: Case study of federated learning based forward collision warning

https://doi.org/10.1016/j.aap.2022.106609

Yu, Rongjie ; Zhang, Ruici ; Ai, Haoan ; Wang, Liqiang ; Zou, Zihang ( April 2022 , Accident Analysis & Prevention)

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Anti-Neuron Watermarking: Protecting Personal Data Against Unauthorized Neural Networks

Zou, Zihang ; Gong, Boqing ; Wang, Liqiang ( January 2022 , European Conference on Computer Vision (ECCV))

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Convolutional neural networks with refined loss functions for the real-time crash risk analysis

https://doi.org/10.1016/j.trc.2020.102740

Yu, Rongjie ; Wang, Yiyun ; Zou, Zihang ; Wang, Liqiang ( October 2020 , Transportation Research Part C: Emerging Technologies)
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The United States COVID-19 Forecast Hub dataset

https://doi.org/10.1038/s41597-022-01517-w

Cramer, Estee Y. ; Huang, Yuxin ; Wang, Yijin ; Ray, Evan L. ; Cornell, Matthew ; Bracher, Johannes ; Brennen, Andrea ; Rivadeneira, Alvaro J. ; Gerding, Aaron ; House, Katie ; et al ( December 2022 , Scientific Data)

Abstract Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.
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