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

Hidetaka Masuda, Oussama El Marai, Manabu Tsukada, Tarik Taleb, Hiroshi Esaki

Research output: Contribution to journalArticleScientificpeer-review

8 Citations (Scopus)
45 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)2120-2129
Number of pages10
JournalIEEE Transactions on Vehicular Technology
Issue number2
Early online date4 Oct 2022
Publication statusPublished - 1 Feb 2023
MoE publication typeA1 Journal article-refereed


  • 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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