Automative Video Compression for Remote Driving via Safety Considerations

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publication2023 IEEE Conference on Standards for Communications and Networking, CSCN 2023
PublisherInstitute of Electrical and Electronics Engineers
Pages54-58
Number of pages5
ISBN (Electronic)9798350395389
DOIs
StatePublished - 1 Jan 2023
Event2023 IEEE Conference on Standards for Communications and Networking, CSCN 2023 - Munich, Germany
Duration: 6 Nov 20238 Nov 2023

Publication series

Name2023 IEEE Conference on Standards for Communications and Networking, CSCN 2023

Conference

Conference2023 IEEE Conference on Standards for Communications and Networking, CSCN 2023
Country/TerritoryGermany
CityMunich
Period6/11/238/11/23

Keywords

  • Autonomous Cars
  • Region of Interest
  • Remote Driver
  • Safety
  • Video Compression

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Safety, Risk, Reliability and Quality

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