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 200 ms even when the network data rate is below 500 kbit/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. 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：远程操作被视为快速部署自动驾驶车辆的关键技术。目前，通过流媒体图像远程控制联网车辆需要可靠且高吞吐量的网络连接，而在依赖公共网络基础设施的实际远程操作部署中，这种条件可能受到限制。本文研究了如何借助计算机视觉辅助的语义通信来规避传统图像压缩技术带来的数据丢失与损坏问题。通过将检测到的道路使用者分割信息编码为低分辨率灰度图像中的彩色高亮标记，该方法可将所需数据速率较传统技术降低50%，同时保持视觉清晰度。即便在网络数据速率低于500 kbit/s的条件下，仍能实现低于200毫秒的中位端到端延迟，同时清晰勾勒出显著道路使用者轮廓，以增强远程操作员的态势感知能力。该方法在采用自动化末端配送车辆的4G移动通信波动区域得到验证。结果表明，即使在常受约束的公共4G/5G移动网络上，大规模部署远程操作自动驾驶车辆亦具可行性，这为加速自动驾驶车辆的全国性推广提供了潜在可能。