Mersmann, Till Friedrich Konstantin (RWTH Aachen University), Betz, Friedrich (FH Aachen - University of Applied Sciences), Eichenbaum, Julian Daniel (FH Aachen - University of Applied Sciences), Hampel, Fabian (RWTH Aachen University), Klamt, Simon (RWTH Aachen University), Otten, Yannick (FH Aachen - University of Applied Sciences), Scholl, Ingrid (FH Aachen - University of Applied Sciences), Schindler, Christian (RWTH Aachen University)

Towards the Application of Operational Design Domain Based Scene Generation for Artificial Intelligence Training in Railway Automation

Scheduled for presentation during the Regular Session "Sensing, Vision, and Perception V" (FrAT5), Friday, September 27, 2024, 11:30−11:50, Salon 13

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on June 19, 2025

Abstract

For automated, driverless rail transportation applications in open environments, Artificial Intelligence (AI)- based methods are gaining importance, especially in computer vision and perception tasks. The safe operation of complex automated systems requires validation processes. For this purpose, the concept of Operational Design Domains (ODDs), driven by recent developments in the automotive industry, is gaining momentum, allowing to describe different aspects of operating conditions as scenes and scenarios. With regard to safety and authorization using AI-based vision systems, data coverage is needed, which can be enhanced by employing virtual reality in different forms. The creation of virtual scenes and sensor models allows the generation of synthetic sensor data and metadata that can be used as a database for the training of the vision system.