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Title: Learning for Vehicle-to-Vehicle Cooperative Perception Under Lossy Communication
Award ID(s):
2127881
PAR ID:
10543742
Author(s) / Creator(s):
; ; ; ; ; ;
Publisher / Repository:
IEEE
Date Published:
Journal Name:
IEEE Transactions on Intelligent Vehicles
Volume:
8
Issue:
4
ISSN:
2379-8858
Page Range / eLocation ID:
2650 to 2660
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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  1. Cities around the world are increasingly promoting electric vehicles (EV) to reduce and ultimately eliminate greenhouse gas emissions. A huge number of EVs will put unprecedented stress on the power grid. To efficiently serve the increased charging load, these EVs need to be charged in a coordinated fashion. One promising coordination strategy is vehicle-to-vehicle (V2V) charging coordination, enabling EVs to sell their surplus energy in an ad-hoc, peer to peer manner. This paper introduces an Information Centric Networking (ICN)-based protocol to support ad-hoc V2V charging coordination (V2V-CC). Our evaluations demonstrate that V2V-CC can provide added flexibility, fault tolerance, and reduced communication latency than a conventional centralized cloud based approach. We show that V2V-CC can achieve a 93% reduction in protocol completion time compared to a conventional approach. We also show that V2V-CC also works well under extreme packet loss, making it ideal for V2V charging coordination. 
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