Infrastructure for the EXtension of ODDs - applied in connected and aUtomated driving and Standardization procedures

The rapid advancement of autonomous vehicle technology promises enhanced efficiency and safety in transportation.

However, operational constraints within Operational Design Domains (ODDs), including issues in sensing, behaviour prediction, and reliability, limit the potential of automated vehicles.

Expanding the ODD framework is critical to enable these vehicles to navigate challenging scenarios like construction zones, unmarked roads, and adverse weather conditions.

This expansion involves robust perception and decision-making algorithms, reducing the need for human intervention and facilitating integration with human-driven vehicles.

While the benefits are substantial, challenges like data collection, sensor technology, and regulatory frameworks must be addressed through interdisciplinary collaboration.

The iEXODDUS project is at the forefront of advancing digital technologies and navigation services, aligning with goals for increased safety, security, and sustainability in the mobility sector, ultimately paving the way for safer and more reliable automated transportation. iEXODDUS shall meticulously assess existing ODDs to unveil limitations and areas for improvement, fostering a deep understanding of ODD challenges and opportunities.

This analysis serves as the foundation for a framework to assess and categorize ODDs across diverse automated driving scenarios.

A key focus area is the enhancement of sensor technologies and perception capabilities through cutting-edge data fusion methods, expanding ODDs beyond current limits while considering environmental factors like weather conditions and road infrastructure. iEXODDUS envisions autonomous vehicles travelling across Europe, resolving harmonization and legal issues, and making policy recommendations.

Collaboration with industry stakeholders and aiming for real-world demonstrations will enable an industry-tailored approach towards automated driving systems with extended ODDs.