H. Masuda, O. E. Marai, M. Tsukada, T. Taleb and H. Esaki, "Feature-Based Vehicle Identification Framework for Optimization of Collective Perception Messages in Vehicular Networks," in IEEE Transactions on Vehicular Technology, vol. 72, no. 2, pp. 2120-2129, Feb. 2023, doi: 10.1109/TVT.2022.3211852
Feature-based vehicle identification framework for optimization of collective perception messages in vehicular networks
|Author:||Masuda, Hidetaka1; Marai, Oussama El2; Tsukada, Manabu1;|
1Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan
2Department of Communications and Networking, School of Electrical Engineering, Aalto University, Espoo, Finland
3Faculty of Information Technology and Electrical Engineering, Oulu University, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 3.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2023050239909
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2023-05-02
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 Cartery 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.
IEEE transactions on vehicular technology
|Pages:||2120 - 2129|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported in part by the European Union’s Horizon 2020 Research and Innovation Programme through CHARITY Project under Grant Agreement 101016509, in part by the Academy of Finland Project 6Genesis under Grant Agreement 318927, and in part by JSPS KAKENHI under Grants 19KK0281 and 21H03423.
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
© The Author(s) 2022. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0.