The PMDI project aims at radically improving the safety of urban mobility by extending STEP, an automotive data management and analytics platform, to support real-time and near-real-time use cases, particularly focusing on dangerous crossings at urban intersections. Such capabilities will be achieved by deploying STEP on Multi-access Edge Computing (MEC) hardware modules, and integrating within the platform fast AI video and image analytics as well as danger detection algorithms taking as inputs V2X messages from a variety of sources, including (virtual) on-board units and infrastructural sensors. To ensure that dangerous conditions are correctly learnt by AI algorithms, digital twins of the road sections under examination will be built leveraging domain specific language technologies designed to ease the integration.
PMDI: An AI-Enabled Ecosystem for Cooperative Urban Mobility / Fornaciari, W.; Agosta, G.; Fioravanti, M.; Giuseppetti, P.; Solinas, A.; Gallo, L.; Pernigotto, M.; Pedol, M.; Pro, F.; Amerini, I.; Papa, L.; Maiano, L.; Trovini, G.; Di Giamberardino, M.; Satta, P.. - 15227:(2025), pp. 231-246. ( 24th International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2024 Greece ) [10.1007/978-3-031-78380-7_19].
PMDI: An AI-Enabled Ecosystem for Cooperative Urban Mobility
Pro F.;Amerini I.;Papa L.;Maiano L.;
2025
Abstract
The PMDI project aims at radically improving the safety of urban mobility by extending STEP, an automotive data management and analytics platform, to support real-time and near-real-time use cases, particularly focusing on dangerous crossings at urban intersections. Such capabilities will be achieved by deploying STEP on Multi-access Edge Computing (MEC) hardware modules, and integrating within the platform fast AI video and image analytics as well as danger detection algorithms taking as inputs V2X messages from a variety of sources, including (virtual) on-board units and infrastructural sensors. To ensure that dangerous conditions are correctly learnt by AI algorithms, digital twins of the road sections under examination will be built leveraging domain specific language technologies designed to ease the integration.| File | Dimensione | Formato | |
|---|---|---|---|
|
Fornaciari_PMDI_2025.pdf
solo gestori archivio
Tipologia:
Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
6.11 MB
Formato
Adobe PDF
|
6.11 MB | Adobe PDF | Contatta l'autore |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
