Automative Video Compression for Remote Driving via Safety Considerations

Dan Peled, Armin Shmilovici, Ofer Hadar

Abstract: Remote driving serves as a viable solution in situations where fully autonomous vehicles encounter critical events, such as sensor failures. However, implementing remote driving poses certain technical challenges, including the need to ensure high-quality video transmission to the remote driver. Additionally, in scenarios involving poor road conditions, multiple autonomous vehicles may simultaneously require remote driving assistance at specific locations, straining the communication infrastructure. To address these challenges, we propose a novel approach that involves compression of the driving video using a driving safety model. This model intelligently prioritizes key objects within the frame, resulting in improved compression quality. An initial experiment demonstrated that 60% of the required bitrate can be reduced while retaining 90% of the perceived quality.