iEXODDUS

Infrastructure for the Extension of ODDs – applied in connected and aUtomated driving and Standardization procedures

The project iEXODDUS (Infrastructure for the Extension of ODDs – applied in connected and aUtomated driving and Standardization procedures) envisions autonomous vehicles travelling across Europe without gaps in the Operational Design Domain (ODD) – resolving harmonization and legal issues, and making policy recommendations. For this purpose, the project will demonstrate highly automated driving systems on highways with tailored technologies and solutions for roadworks, tunnels, and accident sites throughout the European road network.

Although automated vehicles demonstrate substantial potential for enhancing efficiency and safety, operational constraints within existing ODDs pose significant challenges, particularly in complex scenarios like construction zones. The iEXODDUS project aims to address these limitations by advancing perception and decision-making algorithms and improving sensor technologies. By assessing current ODDs and identifying gaps, the project provides a foundation for categorizing and extending ODD frameworks, ensuring automated vehicles can handle increasingly complex environments. These advancements are instrumental to achieve broader goals of safety and sustainability within the mobility sector.

The Institute for Automotive Engineering (ika) contributes to the iEXODDUS project through the collection of highly accurate traffic data in real-world conditions, focusing on smart roadside infrastructure sensors. This data is then further transformed into a digital twin, providing a virtual representation of the traffic environment that mirrors real-life scenarios. As the lead for the data acquisition task, the ika contributes to generating a comprehensive dataset for training deep learning perception models in challenging environments. Additionally, the ika engages in traffic and V2X simulation, leveraging the digital twin to enhance the development and validation of connected and automated driving technologies.