Vehicles with autonomous driving capabilities are present on public streets. However, edge cases remain that still require a human in-vehicle driver. Assuming the vehicle manages to come to a safe state in an automated fashion, teleoperated driving technology enables a human to resolve the situation remotely by a control interface connected via a mobile network. While this is a promising solution, it also introduces technical challenges, one of them being the necessity to transmit video data of multiple cameras from the vehicle to the human operator. In this paper, an adaptive video streaming framework specifically designed for teleoperated vehicles is proposed and demonstrated. The framework enables automatic reconfiguration of the video streams of the multi-camera system at runtime. Predictions of variable transmission service quality are taken into account. With the objective to improve visual quality, the framework uses so-called rate-quality models to dynamically allocate bitrates and select resolution scaling factors. Results from deploying the proposed framework on an actual teleoperated driving system are presented.

翻译：具有自主驾驶能力的车辆在公共街道上存在,但边缘情况仍然存在,仍然需要机动车辆驾驶员。假设车辆能够以自动方式进入安全状态,远程操作驾驶技术使人能够通过移动网络连接的控制界面远程解决这种情况。虽然这是一个有希望的解决办法,但也带来了技术挑战,其中之一是必须将车辆多台相机的视频数据传送给人操作员。本文提出并展示了专门为遥控车辆设计的适应性视频流框架。该框架使得多摄像管系统运行时能够自动重新配置视频流。考虑到可变传输服务质量的预测。为了提高视觉质量,框架利用所谓的速率质量模型来动态地分配比特率和选择分辨率缩放因素。本文件介绍了在实际远程操作驾驶系统上部署拟议框架的结果。