Development of Positioning Methods for ATO in a Scaled Model Environment

The desire for greener transport requires a shift of transportation to rail-based systems.Automation and digitalisation are being used to make rail transport competitive in intermodal transport.Initial automation projects on closed systems such as metros show the potential of automatic train operation (ATO).As more complex systems for automatic train operation are developed and integrated, the need for testing and development opportunities are increasing.Virtual pre-development and subsequent verification on the real field is a well-established process, and the limited availability of field environments for rail vehicles makes the use of scaled models for verification an appropriate solution.In response to the growing demand for efficient, safe, and sustainable rail transportation systems, this research addresses challenges in realworld implementation by utilizing scaled models.By introducing these innovations within scaled models, the research creates a controlled environment for testing and refining ATO systems, components and functions.This ensures robustness and reliability, facilitating the transition to full-scale deployment.In summary, the paper contributes to the evolution of ATO functions, presenting novel features within scaled models that showcase the effectiveness and feasibility of camera-based positioning in enhancing overall automatic train operation capabilities.