Topological sequence recognition mechanism of dynamic connected cars using the Connected Mobile Virtual Fence (CMVF) system for connected car technology
- Authors
- Eom; Y.H.; Hwang; G.; Lee; M.; Choi; Y.G.; Cho; S.; Kim, Robert Young Chul; R.Y.C.; Jeon; B.
- Issue Date
- 2020
- Publisher
- MDPI AG
- Keywords
- Autonomous vehicle; Broadcast storm; Connected car; Connected mobile virtual fence (CMVF) system; Topological sequence recognition mechanism; Vehicle to everything (V2X)
- Citation
- Applied Sciences (Switzerland), v.10, no.12
- Journal Title
- Applied Sciences (Switzerland)
- Volume
- 10
- Number
- 12
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/12508
- DOI
- 10.3390/app10124347
- ISSN
- 2076-3417
- Abstract
- To prevent traffic accidents, even autonomous vehicles, as well as connected cars, need to know the driving situation of other vehicles in the vicinity. In particular, in emergency situations, messages' transmission among vehicles can face many problems such as the broadcast storm, message flooding, or message contention. Therefore, this paper proposes a topological sequence recognition mechanism that calculates the driving direction of vehicles, the geographical location and relative position associated with the driving direction, and the relative safety distance for each vehicle in connected subgroups of connected cars using the Connected Mobile Virtual Fence (CMVF) system. Thus, the proposed mechanism can alleviate issues with message dissemination as a vehicle will know the driving situations of other nearby vehicles. In addition, the proposed mechanism is found to be very effective, particularly in preventing secondary accidents due to traffic accidents in front of the vehicle so that emergency messages can be disseminated to the trailing vehicles. Finally, it is expected that the proposed mechanism will be reflected in the technology of connected cars and autonomous vehicles. © 2020 by the authors.
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Collections - Graduate School > Software and Communications Engineering > 1. Journal Articles
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