用机器学习动态选最优蜂窝网络，提升远程驾驶视频质量
Data-Driven Cellular Network Selector for Vehicle Teleoperations
- 基于时间序列模型实时分析多网络状态，动态选择最佳传输路径
- 相比现有商用非学习算法，丢包率与延迟显著降低
- 适合自动驾驶远程操控系统开发者及车联网优化研究者
远程操控机器人系统（即远程驾驶）对自动驾驶技术发展至关重要，使远程操作员能够实时查看车辆摄像头画面并作出决策。基于视频的远程操控系统性能高度依赖蜂窝网络质量，特别是数据包丢失率和延迟。为优化这些参数，自动驾驶车辆可连接多个蜂窝网络，并实时决定每个视频数据包通过哪个网络传输。本文提出一种名为主动网络选择器（Active Network Selector, ANS）的算法，采用时间序列机器学习方法解决该问题。我们对比了ANS与当前商业系统中使用的非学习基线算法，结果表明，在降低数据包丢失率和延迟方面，ANS表现更优。
原文摘要 · Abstract (English)
Remote control of robotic systems, also known as teleoperation, is crucial for the development of autonomous vehicle (AV) technology. It allows a remote operator to view live video from AVs and, in some cases, to make real-time decisions. The effectiveness of video-based teleoperation systems is heavily influenced by the quality of the cellular network and, in particular, its packet loss rate and latency. To optimize these parameters, an AV can be connected to multiple cellular networks and determine in real time over which cellular network each video packet will be transmitted. We present an algorithm, called Active Network Selector (ANS), which uses a time series machine learning approach for solving this problem. We compare ANS to a baseline non-learning algorithm, which is used today in commercial systems, and show that ANS performs much better, with respect to both packet loss and packet latency.
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