DNN-Controlled Multi-Technology Platooning

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.

Authors

Institutions

Publication Details

Published
2025-06-02
DOI
https://doi.org/10.1109/vnc64509.2025.11054110
Primary Topic
Software-Defined Networks and 5G
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

DNN-Controlled Multi-Technology Platooning

Christian Quadri, Manuel Dileo, Marco Ajmone Marsan, Vincenzo Mancuso
Software-Defined Networks and 5G
article

DNN-Controlled Multi-Technology Platooning

Christian Quadri, Manuel Dileo, Marco Ajmone Marsan, Vincenzo Mancuso
article en

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.

University of Milan (IT), IMDEA Networks (ES)
Openalex Percentile: Top 83%
Software-Defined Networks and 5G
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

DNN-Controlled Multi-Technology Platooning — Christian Quadri, Manuel Dileo, et al. · (2025) | TGRS Research Map | TGRS