D1.5 – Mid-term review & progress report

This Mid-Term Review and Progress Report provides an overview of the work carried out during the first 18 months of the iDriving project. The document consolidates progress across all Work Packages, evaluates achievements against planned objectives, and highlights how the consortium has advanced the development of AI-enhanced traffic monitoring, predictive analytics, and intelligent decision-support for urban transport safety. During the reporting period, the consortium successfully established the project’s organisational, ethical, and data governance foundations, including quality assurance procedures, risk management mechanisms, and the Data Management Plan. Core system components were designed and developed, covering the Digital Twin architecture, multimodal data ingestion pipelines, visual analytics workflows, and machine learning modules for safety, mobility, and infrastructure monitoring. Significant progress was also achieved in creating environmental and microclimate monitoring solutions, integrating observational datasets, and supporting climateresilient transport operations. Technical activities have advanced the project’s analytical capabilities. These include the development of AI-based road safety analytics, maintenance monitoring using UAV data, real-time incident detection, trajectory prediction, and decision-support models for traffic management. Pilot preparation activities progressed in parallel, with scenario definitions, operational requirements, and data-sharing protocols agreed with local authorities. The consortium maintained strong coordination through regular technical meetings, structured decisionmaking processes and continuous alignment across WPs Mid-term achievements demonstrate strong alignment with the project’s Scientific, Technical, and User Objectives. The consortium has delivered early prototypes of most key components, validated several analytical models, and prepared the ground for integrated system testing in the upcoming pilot trials. Risks, challenges, and mitigation strategies are monitored continuously, ensuring stable progress toward the overall ambition of enhancing transport system safety, efficiency, and resilience through AI-driven analytics and intelligent decisionmaking. Progress to date also enhances future interoperability and aligns with emerging standards, ensuring sustainable integration into smart mobility ecosystems