The growth in the number of connected vehicles in ad-hoc networks or VANETs enables the use ofincreasingly sophisticated services and applications. One emerging application in this scenario is Platooning, a stable stream of connected autonomous vehicles. This application improves safety, vehicle flow on the road, energy consumption, among other variables. However, vehicles using this technology need to share space with vehicles not connected or with other automation levels. Among the bottlenecks in scenarios like this, Platoons formation and splits stand out, as these maneuvers involve costs and risk. Therefore, our contribution is developing a new strategy for Platoons formationin a mixed universe of vehicles to bring some of these desired benefits. The results show that the proposed solution achieved an accuracy of 89% of the entire amount of platoons in the datasets. Ourexperiments demonstrate cases with collisions, and the proposed solution provides a solution to a more safe operation on roads. Finally, we conclude that our solution provides more determinism in the traffic conditions, as vehicles achieving stationary speeds with low variation.

Platoon Grouping Network Offloading Mechanism for VANETs / Kamoi, R. N.; Junior, L. A. P.; Verri, F. A. N.; Marcondes, C. A. C.; GOMES FERREIRA, CARLOS HENRIQUE; Meneguette, R. I.; Cunha, A. M. D.. - In: IEEE ACCESS. - ISSN 2169-3536. - STAMPA. - 9:(2021), pp. 53936-53951. [10.1109/ACCESS.2021.3071085]

Platoon Grouping Network Offloading Mechanism for VANETs

Carlos Henrique Gomes Ferreira.;
2021

Abstract

The growth in the number of connected vehicles in ad-hoc networks or VANETs enables the use ofincreasingly sophisticated services and applications. One emerging application in this scenario is Platooning, a stable stream of connected autonomous vehicles. This application improves safety, vehicle flow on the road, energy consumption, among other variables. However, vehicles using this technology need to share space with vehicles not connected or with other automation levels. Among the bottlenecks in scenarios like this, Platoons formation and splits stand out, as these maneuvers involve costs and risk. Therefore, our contribution is developing a new strategy for Platoons formationin a mixed universe of vehicles to bring some of these desired benefits. The results show that the proposed solution achieved an accuracy of 89% of the entire amount of platoons in the datasets. Ourexperiments demonstrate cases with collisions, and the proposed solution provides a solution to a more safe operation on roads. Finally, we conclude that our solution provides more determinism in the traffic conditions, as vehicles achieving stationary speeds with low variation.
2021
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2932658