We look at platooning applications that leverage two wireless technologies for the coordination of vehicle movements. The first technology is DSRC (dedicated short-range communications), based on IEEE 802.11p, which allows a distributed implementation of the platoon coordination. The second technology is 5G (and beyond). In the latter case, the platoon control algorithm is centralized in an edge computing facility close to the platoon (or in the cloud, if latency allows). Trying to maximize platoon performance and overcome the unpredictability of the radio channel, both radio access technologies are simultaneously active, and a deep neural network (DNN) is used to decide which of the two should be relied on for platoon control, at each point in time. The proposed platooning architecture is compared against previously proposed alternatives, investigating performance with detailed simulation tools. Results show significant advantages in terms of accuracy and safety in inter-vehicle distance for all vehicles within the platoon.

DNN-Controlled Multi-Technology Platooning / C. Quadri, M. Dileo, V. Mancuso, M.A. Marsan (IEEE VEHICULAR NETWORKING CONFERENCE). - In: 2025 IEEE Vehicular Networking Conference (VNC)[s.l] : IEEE, 2025 Jun. - ISBN 979-8-3315-2437-1. - pp. 1-8 (( Intervento presentato al 16. convegno 16th IEEE Vehicular Networking Conference, VNC 2025 tenutosi a Porto nel 2025 [10.1109/vnc64509.2025.11054110].

DNN-Controlled Multi-Technology Platooning

C. Quadri
Primo
;
M. Dileo
Secondo
;
2025

Abstract

We look at platooning applications that leverage two wireless technologies for the coordination of vehicle movements. The first technology is DSRC (dedicated short-range communications), based on IEEE 802.11p, which allows a distributed implementation of the platoon coordination. The second technology is 5G (and beyond). In the latter case, the platoon control algorithm is centralized in an edge computing facility close to the platoon (or in the cloud, if latency allows). Trying to maximize platoon performance and overcome the unpredictability of the radio channel, both radio access technologies are simultaneously active, and a deep neural network (DNN) is used to decide which of the two should be relied on for platoon control, at each point in time. The proposed platooning architecture is compared against previously proposed alternatives, investigating performance with detailed simulation tools. Results show significant advantages in terms of accuracy and safety in inter-vehicle distance for all vehicles within the platoon.
DNN; DSRC: 5G; Platooning; Simulation
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   MINISTERO DELL'UNIVERSITA' E DELLA RICERCA
   codice identificativo PE00000014
giu-2025
IEEE Communications Society (ComSoc)
IEEE Intelligent Transportation Systems Society (ITSS)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1185479
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