Real Lab High Definition Testbed

Intelligent infrastructure is used to supplement the limited situational awareness of individual road users through additional roadside sensors and local data processing. It is particularly useful when it detects critical situations early on in complex and confusing traffic scenarios - such as at urban intersections - and provides information and warnings collectively or cooperatively.

As part of the KIVI project (Artificial Intelligence in the Ingolstadt Transportation System), the Ingolstadt High-Definition Test Field (HDT) was established in 2022 to research the potential of smart infrastructure in a connected mobility system. The HDT, which was realized in close cooperation with the City of Ingolstadt, comprises three high-traffic intersections connected to form a triangle. At these intersections, local sensor technology is used to capture real-time data on all road users. The sensors used include laser scanners with edge computing devices and the latest generation of thermal cameras, ensuring that data protection requirements are met from the outset. Data-driven artificial intelligence methods are used to improve traffic control and road safety. To this end, the      C-IAD and AImotion institutes are working in close collaboration.

As an initial application to improve the safety of vulnerable road users, a warning system was implemented to defuse critical situations at intersections at an early stage. This system is based on high-performance communication networks and on-site computers that run real-time situation-recognition algorithms.

Since 2024, the SiRaMiS (Cyclist Safety in Mixed Traffic with Intelligent Road Users and Intelligent Road Infrastructure) project has been investigating how AI-based methods and intelligent transportation infrastructure can enhance the safety of vulnerable road users - particularly cyclists - at critical locations in urban environments. At the same time, the project aims to record and analyze conflict situations involving vulnerable road users (VRUs) in a way that allows these data to be used to optimize traffic flow. The research focuses on AI-based methods for conditional perception in infrastructure sensors, high-precision state estimation of road users, the assessment of dilemma zones, and the optimization of Green Light Optimal Speed Advisory by taking conflict situations into account. The developed concepts are tested in realistic scenarios. Demonstration implementations of the researched measures for interaction between the intelligent infrastructure and an e-bike or research vehicles take place both in the HDT Real lab and in the physical twin at the CARISSMA Outdoor Test Facility.

In the future, the HDT is intended to serve as the foundation for research into methods for a safe and fault-tolerant transportation system that incorporates autonomous vehicles.