@inproceedings{f9d70adef2af4c9db272f1ca401e56ef,
title = "Content adaptive video compression for autonomous vehicle remote driving",
abstract = "It is anticipated that in some extreme situations, autonomous cars will benefit from the intervention of a {"}Remote Driver{"}. The vehicle computer may discover a failure and decide to request remote assistance for safe roadside parking. In a more extreme scenario, the vehicle may require a complete remote-driver takeover due to malfunctions or an inability to resolve unknown decision logic. In such cases, the remote driver will need a sufficiently good quality real-time video stream of the vehicle cameras to respond quickly and accurately enough to the situation at hand. Relaying such a video stream to the remote Command and Control (C&C) center is especially challenging when considering the varying wireless channel bandwidths expected in these scenarios. This paper proposes an innovative end-to-end content-sensitive video compression scheme to allow efficient and satisfactory video transmission from autonomous vehicles to the remote C&C center.",
keywords = "Autonomous cars, Driving Simulator, HEVC, Photorealistic, Region of Interest, Remote Driver, Video Compression",
author = "Itai Dror and Raz Birman and Oren Solomon and Tomer Zehavi and Lior Taib and Amit Doran and Roee Ezra and Noui Rengenzad and Ofer Hadar",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; Applications of Digital Image Processing XLIV 2021 ; Conference date: 01-08-2021 Through 05-08-2021",
year = "2021",
month = jan,
day = "1",
doi = "https://doi.org/10.1117/12.2595863",
language = "American English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Tescher, {Andrew G.} and Touradj Ebrahimi",
booktitle = "Applications of Digital Image Processing XLIV",
address = "United States",
}