Feature-based Vehicle Identification Framework for Optimization of Collective Perception Messages in Vehicular Networks

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2023-02-01
Major/Subject
Mcode
Degree programme
Language
en
Pages
11
1-11
Series
IEEE Transactions on Vehicular Technology
Abstract
The world is moving towards a fully connected digital world, where objects produce and consume data, at a sultry pace. Autonomous vehicles will play a key role in bolstering the digitization of the world. These connected vehicles must communicate timely data with their surrounding objects and road participants to fully and accurately understand their environments and eventually operate smoothly. As a result, the hugely exchanged data would scramble the network traffic that, at a given point, would no longer increase the awareness level of the vehicle. In this paper, we propose a vision-based approach to identify connected vehicles and use it to optimize the exchange of collective perception messages (CPMs), in terms of both the CPM generation frequency and the number of generated CPMs. To validate our proposed approach, we created a <sc>Cartery</sc> framework that integrates SUMO, Carla, and OMNeT++. We also compared our solution with both baselines and European Telecommunications Standards Institute solutions, considering three main KPIs: the channel busy ratio, environmental awareness, and the CPM generation frequency. Simulation results show that our proposed solution exhibits the best trade-off between the network load and situational awareness.
Description
Publisher Copyright: Author
Keywords
Cams, Carla, Collective perception message optimization, Connected vehicles, Intelligent transportation system, Object recognition, OMNeT++, Roads, Simulation, SUMO, Vehicle dynamics, Vehicle identification, Vehicle to everything communication, Vehicle-to-everything
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Citation
Masuda , H , Marai , O E , Tsukada , M , Taleb , T & Esaki , H 2023 , ' Feature-based Vehicle Identification Framework for Optimization of Collective Perception Messages in Vehicular Networks ' , IEEE Transactions on Vehicular Technology , vol. 72 , no. 2 , pp. 2120-2129 . https://doi.org/10.1109/TVT.2022.3211852