Remote Operation is touted as being key to the rapid deployment of automated vehicles. Streaming imagery to control connected vehicles remotely currently requires a reliable, high throughput network connection, which can be limited in real-world remote operation deployments relying on public network infrastructure. This paper investigates how the application of computer vision assisted semantic communication can be used to circumvent data loss and corruption associated with traditional image compression techniques. By encoding the segmentations of detected road users into colour coded highlights within low resolution greyscale imagery, the required data rate can be reduced by 50 \% compared with conventional techniques, while maintaining visual clarity. This enables a median glass-to-glass latency of below 200ms even when the network data rate is below 500kbit/s, while clearly outlining salient road users to enhance situational awareness of the remote operator. The approach is demonstrated in an area of variable 4G mobile connectivity using an automated last-mile delivery vehicle. With this technique, the results indicate that large-scale deployment of remotely operated automated vehicles could be possible even on the often constrained public 4G/5G mobile network, providing the potential to expedite the nationwide roll-out of automated vehicles.

翻译：远程操控被视为实现自动驾驶车辆快速部署的关键。当前，远程操控联网车辆需要依赖可靠的高吞吐量网络连接来传输图像流，这在依赖公共网络基础设施的实际远程操控部署中往往受到限制。本文研究了如何应用计算机视觉辅助的语义通信来规避传统图像压缩技术所伴随的数据丢失与损坏问题。通过将检测到的道路使用者分割结果编码为低分辨率灰度图像中的颜色编码高亮区域，所需数据速率可比传统技术降低50%，同时保持视觉清晰度。即使在网络数据速率低于500kbit/s的情况下，该方法仍能实现低于200毫秒的中值端到端延迟，并清晰勾勒出关键道路使用者以增强远程操作员的情境感知能力。该方案在一辆自动驾驶末端配送车辆上，于4G移动网络信号多变的区域进行了验证。结果表明，采用此项技术后，即使在使用常受限制的公共4G/5G移动网络时，大规模部署远程操控自动驾驶车辆仍具有可行性，这为加速自动驾驶车辆在全国范围的推广提供了可能。