Integrating Heterogeneous Sources for Learned Prediction of Vehicular Data Consumption
Integrating Heterogeneous Sources for Learned Prediction of Vehicular Data Consumption
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DOI：
10.1109/mdm55031.2022.00029
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
Andi Zang;Xiaofeng Zhu;Ce Li;Fan Zhou;Goce Trajcevski
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文献类型：
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作者：
Andi Zang;Xiaofeng Zhu;Ce Li;Fan Zhou;Goce Trajcevski
除了多个传感器来测量可用于提高安全性和效率的参数之外，现代车辆还收集关于外部数据的信息（例如，交通状况、天气），如果使用得当，可以进一步改善整体旅行体验。具体而言，当涉及到导航时，可以提供增强的上下文感知（尤其是对于自动驾驶）的一个来源是高清（HD）地图，其最近在车辆技术和使用中的普及性大幅增长。由于它们限于特定的地理区域，因此在整个给定的行程中，需要在多个场合下载（和处理）不同的部分，连同来自其他内部和外部源的其他数据一起沿着。在本文中，我们提供了一种有效的深度学习方法，用于解决最近引入的预测给定行程未来时刻的地图数据消耗（PMDC）问题。我们提出了一种新的方法，集成了多个数据源（道路网络，交通，历史行程，高清地图），并为一个给定的行程，使预测的地图数据消费。我们的实验观察证明了所提出的方法在候选基线上的好处。
In addition to the multiple sensors to measure parameters that can be used to improve both safety and efficiency, modern vehicles also gather information about external data (e.g., traffic conditions, weather) which, if properly used, could further improve the overall trip experience. Specifically, when it comes to navigation, one source that can provide increased 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, different portions need to be downloaded (and processed) on multiple occasions throughout a given trip, along with the other data from other internal and external sources. In this paper, we provide an effective deep learning approach for the recently introduced problem of Predicting Map Data Consumption (PMDC) in the future time instants for a given trip. We propose a novel methodology that integrates multiple data sources (road network, traffic, historic trips, HD maps) and, for a given trip, enables prediction of the map data consumption. Our experimental observations demonstrate the benefits of the proposed approach over the candidate baselines.