Adaptive Video Configuration and Bitrate Allocation for Teleoperated Vehicles
This addresses the problem of maintaining reliable video quality for human operators in teleoperated driving systems, which is incremental as it builds on existing video streaming techniques.
The paper tackles the challenge of transmitting multiple camera video streams for teleoperated vehicles by proposing an adaptive video streaming framework that dynamically reconfigures streams and allocates bitrates based on transmission quality predictions, resulting in improved visual quality as demonstrated on an actual system.
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.