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, William;Agosta, Giovanni;Fioravanti, Massimo;
2025-01-01

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.
2025
Embedded Computer Systems: Architectures, Modeling, and Simulation
9783031783791
9783031783807
Urban Mobility, V2X, Cooperative Cyber-physical Systems, AI video analytics
File in questo prodotto:
File Dimensione Formato  
SAMOS2024 printed PDMI paper.pdf

accesso aperto

Descrizione: final paper
: Publisher’s version
Dimensione 1.03 MB
Formato Adobe PDF
1.03 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1282112
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 1
social impact