Data-Driven Cellular Network Selector for Vehicle Teleoperations
This work addresses the challenge of reliable cellular connectivity for remote operators of autonomous vehicles, representing an incremental improvement over existing commercial systems.
The paper tackled the problem of optimizing video transmission for vehicle teleoperations by developing a machine learning algorithm, Active Network Selector (ANS), which significantly outperformed a baseline non-learning method in reducing packet loss and latency.
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.