Smooth and Low Latency Video Streaming for Autonomous Cars during Handover

Oussama El Marai, Tarik Taleb*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

Self-driving vehicles are expected to bring many benefits among which are enhancing traffic efficiency and reliability, and reducing fuel consumption which would have a great economic and environmental impact. The success of this technology heavily relies on the full situational awareness of its surrounding entities. This is achievable only when everything is networked, including vehicles, users and infrastructure, and exchange the sensed data among the nearby objects to increase their awareness. Nevertheless, human intervention is still needed in the loop anyway to deal with unseen situations or compensate for inaccurate or improper vehicle's decisions. For such cases, video feed, in addition to other data such as LiDAR, is considered essential to provide humans with the real picture of what is happening to eventually take the right decision. However, if the video is not delivered in a timely fashion, it becomes useless or likely produces catastrophic outcomes. Additionally, any disruption in the streamed video, for instance during handover operation while traversing inter-countries cross borders, is very annoying to the user and possibly cause damage as well. in this article, we start by describing two important use cases, namely Remote Driving and Platooning, where the timely delivery of video is of extreme importance [1]. Thereafter, we detail our implemented solution to accommodate the aforementioned use cases for self-driving vehicles. Through extensive experiments in local and LTE networks, we show that our solution ensures a very low endto- end latency. Also, we show that our solution keeps the video outage as low as possible during handover operation.

Original languageEnglish
Article number9136585
Pages (from-to)302-309
Number of pages8
JournalIEEE NETWORK
Volume34
Issue number6
DOIs
Publication statusPublished - 1 Nov 2020
MoE publication typeA1 Journal article-refereed

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