Connected and Autonomous Vehicles (CAVs) still face challenges, especially in dealing with different situations such as unregulated traffic, unexpected obstacles, and complexities in road geometries. Safety, cybersecurity, ethics, liability, and transparency issues also hinder their ability to reach their full potential.
For CAVs to realise their full potential, we need smart roads that support and enhance their operation. Understanding the infrastructure they need is crucial for safe and efficient functioning.
That is why, as part of the framework of the EU-funded project FRONTIER, the International Road Federation (IRF), in collaboration with different partners from the consortium, has developed the Smart Road Infrastructure Classification Index (SRICI).
The Index aims to assess if roads are ready to accommodate CAVs by setting a standard for their infrastructure. It defines the specifications of different components of a Collaborative Automated Driving System, including the physical infrastructure, digital infrastructure, and connectivity.
The Smart Road Infrastructure Classification Index encompasses three key parameter categories: Physical Infrastructure, including road markings, traffic signs, and road geometry; Digital Infrastructure, covering digital maps, cybersecurity, and GNSS localization; and Connectivity, involving signal quality, communication range, and latency.
Through an extensive literature review and expert survey, a total of 56 parameters have been identified within these categories. Each parameter is evaluated on a scale from 1 to 10, assessing its capability for meeting adequate requirements, ensuring seamless operation for CAVs, meeting minimum standards, or being considered inadequate.
The index clearly outlines the definitions and prerequisites for each option, ensuring a transparent and consistent assessment of road infrastructure readiness for CAVs. This method allows for a thorough and standardized evaluation of CAV operational conditions.
Apart from defining parameters for assessing a road’s suitability for CAV operations, the Index proposes a systematic approach that includes data collection and processing for evaluation. During the data collection phase, diverse sources such as georeferenced imagery, on-site measurements, and additional data are employed to gain comprehensive insights. Data processing methods include manual analysis and could be complemented by automated analysis, including machine learning, and real-time analysis.
The role of SRICI in empowering stakeholders to make well-informed decisions and prioritize improvements within this rapidly evolving landscape cannot be emphasised enough. The proposed methodology has already been applied in a real use case for initial validation, specifically for the Attiki Odos freeway in Athens (Greece), showcasing its practical utility.
Nevertheless, continuous research and refinement are imperative. As these fields progress, the index plays a guiding role in efforts to develop safer, more efficient, and interconnected transportation systems.