The Italian EvoRoads Living Lab is situated in the Piemonte Region, Italy, considering both urban and rural secondary roads. For the urban roads, the Living Lab will focus especially on Turin, the biggest city in Piemonte. The Piemonte Region was chosen due to its diverse road infrastructure and its willingness to integrate cutting-edge vehicle and digital technologies into road safety management.
The Living Lab involves close collaboration with the Local Road Authority of Piemonte and CSI Piemonte, a regional digital assets operator. The pilot includes the use of probe vehicles from the Piemonte Region equipped with advanced On-Board Units (OBU), GNSS, IMU, and dashcams. The pilot will also include Road Side Units (RSUs) equipped with camera and LiDAR to monitor the status of several relevant roads’ intersections. These data acquisition systems and other historical information about road traffic and accidents in Piemonte will be integrated with the region’s Road Infrastructure Digital Twin, allowing for dynamic assessment of road conditions, safety criteria, and predictive analytics for maintenance.
Objectives
The primary role of the Italian Living Lab in the EvoRoads project is to provide a tool for the development and validation of road safety diagnostics using digital technologies. The Living Lab will focus on three core interventions:
- Dynamic quantification of road safety criteria using vehicle sensing data and digital twins.
- Development of a road distress and maintenance diagnosis tool supported by predictive analytics.
- Dynamic evaluation of infrastructure readiness, particularly for autonomous and connected vehicles.
Dynamic quantification of road safety criteria using vehicle sensing data and digital twins. Development of a road distress and maintenance diagnosis tool supported by predictive analytics. Dynamic evaluation of infrastructure readiness, particularly for autonomous and connected vehicles.
Use Cases
Dynamic quantification of multidimensional road safety criteria using advanced vehicle sensing data, drones, regional databases, and digital twins
Crash prediction model exploring the relationship between crash frequency/severity and infrastructure and dynamic road attributes
Strategies for road infrastructure maintenance/risk warnings provisioning based on the augmented road infrastructure safety assessments and the results of the observed safety KPIs