Periodic Reporting for period 1 - EvoRoads (Evolutionary Solutions for Realising a Holistic Safe System Approach for All Road Users)
Période du rapport: 2024-05-01 au 2025-10-31
The EC-funded EvoRoads (Evolutionary Solutions for Realising a Holistic Safe System Approach for All Road Users) project is committed to achieving the EU's Vision Zero goal by developing a holistic framework of innovative models and services for data-driven safety assessments. This framework enables the data-driven evolution of safety assessment, dynamic monitoring of cyber-physical infrastructure, and proactive risk warning in both urban and rural environments, which historically have not received the focus of TEN-T or other strategic trunk roads.
The project involves developing a connectivity platform that digitalizes transport infrastructure assets and ensures seamless integration of safety assessment services. By leveraging advanced artificial intelligence, EvoRoads analyses infrastructure monitoring data at various geospatial levels, enabling proactive risk warnings and supporting road operators in managing maintenance more effectively, which enhances both safety and operational efficiency. It also defines safety criteria and quantification methods for Key Performance Indicators (KPIs) to monitor safety performance as part of the "Safe System" approach.
The results are developed alongside five axes: (1) an Evolutionary Safety Assessment Framework, which moves beyond static analysis to provide quantitative safety assessment using real-time data and AI, thereby enabling stakeholders to implement the Safe System Approach; (2) a Road Asset & Safety Management Digital Twin collecting and distributing safety data; (3) Advanced Monitoring Safety Systems using that data to enable infrastructure owners to obtain clear insights into their roads’ level of safety; (4) Proactive Safety Warning Systems ensuring that critical safety issues are raised pro-actively and in real-time to prevent safety hazards; and finally, all these results are combined in (5) Solutions Integration, Augmentation and Impact Assessment tools and products, allowing for market uptake. These axes converge into a Safe Mobility Data Space (SMDS) where all data related to EvoRoads’ safety criteria and solutions are combined.
EvoRoads’ methodologies and technologies will be validated in four Living Labs (LLs) addressing diverse road user scenarios across urban and rural environments to ensure broad applicability and effectiveness. EvoRoads LLs are situated in Spain (with 3 pilot locations, focusing on connectivity network quality, road pavement state and hazard-aware assets), Italy (1 pilot location on Dynamic Road infrastructure safety diagnosis), Latvia (1 pilot location onlow-cost infrastructure monitoring for Vulnerable Road Users - VRUs), and Romania (1 pilot location on Remote sensing and warning technologies). The solutions are validated through these six pilots, ensuring knowledge transfer and contributing to improved quality of life and enhanced road safety. The project employs an interdisciplinary approach, including social innovation and behavioural analysis, with a core activity focused on stakeholder collaboration to ensure the solutions are people-centric and meet societal needs. A scale-up event is foreseen towards the end of the project to allow for the wider market to consider EvoRoads’ results and to help provide ways forward.
The project involves developing a connectivity platform that digitalizes transport infrastructure assets and ensures seamless integration of safety assessment services. By leveraging advanced artificial intelligence, EvoRoads analyses infrastructure monitoring data at various geospatial levels, enabling proactive risk warnings and supporting road operators in managing maintenance more effectively, which enhances both safety and operational efficiency. It also defines safety criteria and quantification methods for Key Performance Indicators (KPIs) to monitor safety performance as part of the "Safe System" approach.
The results are developed alongside five axes: (1) an Evolutionary Safety Assessment Framework, which moves beyond static analysis to provide quantitative safety assessment using real-time data and AI, thereby enabling stakeholders to implement the Safe System Approach; (2) a Road Asset & Safety Management Digital Twin collecting and distributing safety data; (3) Advanced Monitoring Safety Systems using that data to enable infrastructure owners to obtain clear insights into their roads’ level of safety; (4) Proactive Safety Warning Systems ensuring that critical safety issues are raised pro-actively and in real-time to prevent safety hazards; and finally, all these results are combined in (5) Solutions Integration, Augmentation and Impact Assessment tools and products, allowing for market uptake. These axes converge into a Safe Mobility Data Space (SMDS) where all data related to EvoRoads’ safety criteria and solutions are combined.
EvoRoads’ methodologies and technologies will be validated in four Living Labs (LLs) addressing diverse road user scenarios across urban and rural environments to ensure broad applicability and effectiveness. EvoRoads LLs are situated in Spain (with 3 pilot locations, focusing on connectivity network quality, road pavement state and hazard-aware assets), Italy (1 pilot location on Dynamic Road infrastructure safety diagnosis), Latvia (1 pilot location onlow-cost infrastructure monitoring for Vulnerable Road Users - VRUs), and Romania (1 pilot location on Remote sensing and warning technologies). The solutions are validated through these six pilots, ensuring knowledge transfer and contributing to improved quality of life and enhanced road safety. The project employs an interdisciplinary approach, including social innovation and behavioural analysis, with a core activity focused on stakeholder collaboration to ensure the solutions are people-centric and meet societal needs. A scale-up event is foreseen towards the end of the project to allow for the wider market to consider EvoRoads’ results and to help provide ways forward.
Technical work during the period focused on establishing foundational architectures, frameworks, and initial prototypes across all WPs:
• WP1: Established the Evolutionary Safety Assessment Framework by defining a comprehensive, multi-layered safety-criteria catalogue compliant with the Safe System approach. It defined requirements through stakeholder engagement in Living Labs and initiated the deployment of the Safe Mobility Data Space (SMDS) architecture to serve as the core data foundation.
• WP2: Focused on designing and prototyping tools for dynamic road infrastructure monitoring. Key achievements include specifying the architecture for the Data Acquisition and Processing Platform (DAP), developing core software modules for Predictive Maintenance, and achieving integration level for on-the-edge sensing platforms which utilize AI for real-time hazard detection.
• WP3: Established the Digital Twin framework, setting up the shared cloud platform and APIs for heterogeneous data integration. It developed hardware innovations, including prototypes of low-cost, plastronics-based smart beacons for dynamic warnings, and developed both the conceptual nudging engine and an AI-based system for monitoring the infrastructure's readiness for Connected and Automated Vehicles (CAVs).
• WP4: Successfully completed the definition and comprehensive planning of all testing activities, defining six pilots across four European demonstration sites, and delivering the official Demonstrations Planning Guidelines.
• WP1: Established the Evolutionary Safety Assessment Framework by defining a comprehensive, multi-layered safety-criteria catalogue compliant with the Safe System approach. It defined requirements through stakeholder engagement in Living Labs and initiated the deployment of the Safe Mobility Data Space (SMDS) architecture to serve as the core data foundation.
• WP2: Focused on designing and prototyping tools for dynamic road infrastructure monitoring. Key achievements include specifying the architecture for the Data Acquisition and Processing Platform (DAP), developing core software modules for Predictive Maintenance, and achieving integration level for on-the-edge sensing platforms which utilize AI for real-time hazard detection.
• WP3: Established the Digital Twin framework, setting up the shared cloud platform and APIs for heterogeneous data integration. It developed hardware innovations, including prototypes of low-cost, plastronics-based smart beacons for dynamic warnings, and developed both the conceptual nudging engine and an AI-based system for monitoring the infrastructure's readiness for Connected and Automated Vehicles (CAVs).
• WP4: Successfully completed the definition and comprehensive planning of all testing activities, defining six pilots across four European demonstration sites, and delivering the official Demonstrations Planning Guidelines.
EvoRoads introduces several results beyond the State of the Art by transitioning safety assessment from a static, reactive approach to a dynamic, data-driven one. Key innovations include the Evolutionary Safety Assessment Framework, which uses AI to deliver quantitative safety assessment; the SMDS, a federated platform for harmonizing heterogeneous mobility data; the Dynamic Infrastructure Readiness Evaluation system for continuously assessing road readiness for CAVs; and Plastronics-based Smart Beacons, a low-cost, scalable solution for digitizing physical infrastructure.
However, the effective uptake of these innovations across Europe requires addressing three critical needs: Standardisation to ensure the interoperability of the SMDS and the Evolutionary Safety Assessment Framework; a supportive Regulatory Framework to incentivise or mandate the adoption of dynamic safety tools; and sustained efforts in Demonstration and Validation through large-scale testing to optimize the complex AI models.
However, the effective uptake of these innovations across Europe requires addressing three critical needs: Standardisation to ensure the interoperability of the SMDS and the Evolutionary Safety Assessment Framework; a supportive Regulatory Framework to incentivise or mandate the adoption of dynamic safety tools; and sustained efforts in Demonstration and Validation through large-scale testing to optimize the complex AI models.