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- W3181128319 abstract "Combining in-car multiple sensors measuring parameters that can be used to improve both safety and efficiency with a plethora of external data sources (e.g., traffic conditions, weather) which, if properly used, can significantly improve the overall trip experience. One source that can help the navigation and provide context awareness, especially for autonomous driving, are the High Definition (HD) maps, which have recently witnessed a tremendous growth of popularity in vehicular technology and use. As they are limited to a particular geographic area with respect to a given point along a trip, different portions need to be downloaded (and processed) on multiple occasions throughout a given trip, along with the other data from internal and external sources. We take a first step towards formalizing the problem of Predicting Map Data Consumption (PMDC) in the future time instants for a given trip, based on a (time) window from its history, and investigate the use of Long Short-Term Memory (LSTM) networks - a special type of Recurrent Neural Networks (RNN). Significant efforts were focused on generating an appropriate dataset for this study, towards which we fused the information available in multiple heterogeneous data sources. We conducted experimental observations demonstrating the benefits of the proposed approach." @default.
- W3181128319 created "2021-07-19" @default.
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- W3181128319 date "2021-06-01" @default.
- W3181128319 modified "2023-09-22" @default.
- W3181128319 title "Towards Predicting Vehicular Data Consumption" @default.
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- W3181128319 doi "https://doi.org/10.1109/mdm52706.2021.00025" @default.
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