Road infrastructure support for highly automated driving

Kulmala, Risto · 2025 · Crossref

DOI: 10.58956/liikenne.148051

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Summary

This document, Deliverable D3.1 of the European Union’s Horizon 2020 Hi-Drive project, addresses the challenge of operational design domain (ODD) fragmentation in connected and highly automated driving (CAD). Building on the predecessor L3Pilot project, Hi-Drive aims to extend the ODD from conditional automation (SAE Level 3) toward higher levels of automation by demonstrating robustness in demanding conditions. The primary objective is to define and categorize use cases (UCs) that demonstrate how integrating specific technology enablers improves automated driving performance within nominal ODDs or extends the ODD into challenging environments. This work facilitates the evaluation of user acceptance, system reliability, and functional safety across a diverse fleet of prototype vehicles. The methodology involved a state-of-the-art review of ODD specification formats and test scenario standards, including ISO 34502. The consortium developed two structured templates: one for specifying Automated Driving Function (ADF) instances and another for describing UCs and associated test scenarios. These templates standardized the collection of data from 20 prototype owners, detailing on-board functionality, embedded enablers, and external network support. The approach distinguished between testing "AD performance" under nominal conditions and "AD availability" under extended, challenging conditions. The resulting UCs were grouped into clusters to enhance readability and facilitate experimental planning across motorway, urban, rural, and parking domains. The study produced a comprehensive catalogue comprising 29 ADF instances and 40 UCs, organized into 18 clusters with 113 associated test scenarios. These scenarios cover a wide range of operational contexts, including cooperative overtaking, lane management in merging areas, hazard awareness, and intersection crossing. Specific challenging conditions targeted include adverse weather (rain, fog, snow), GNSS interruptions, urban canyons, and interactions with vulnerable road users via electronic human-machine interfaces (eHMI). The catalogue explicitly maps each UC to specific technology enabler groups, such as vehicle-to-vehicle (V2V) and infrastructure-to-vehicle (I2V) connectivity, allowing for precise assessment of how these enablers mitigate ODD limitations. The significance of this work lies in its provision of a standardized framework for large-scale, cross-border demonstration of CAD functions. By defining clear UCs and test scenarios, the deliverable guides the preparation of real-life, controlled, and virtual testing campaigns. It ensures interoperability across different vehicle brands and supports parallel work packages focused on methodology, operations, and impact assessment. Ultimately, this structured approach aims to reduce the frequency of take-over requests, thereby promoting a gradual transition from conditional to higher levels of automated driving and supporting the development of viable business models and regulatory standards for CAD deployment.

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Topics

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• driverless ads
• situational awareness
• odd communication
• ehmi external hmi
• automation surprise

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• Methodological Resource: tool software, dataset resource
• Theoretical Contribution: conceptual framework