TY - CHAP A1 - Winkelmann, Sven A1 - Büttner, Max A1 - Deivasihamani,, Dharani A1 - von Hoffmann, Alexander A1 - Flohr, Fabian T1 - Using Node-RED as a Low-Code Approach to Model Interaction Logic of Machine-Learning-Supported eHMIs for the Virtual Driving Simulator Carla T2 - AutomotiveUI '23 Adjunct: Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications N2 - External Human-Machine Interfaces (eHMI) enable interaction between vehicles and Vulnerable Road Users (VRU), for example, to warn VRUs of the car’s presence. Warning systems should warn of the situation’s urgency, which can be achieved using Machine Learning (ML)-based VRU detection models. ML models and eHMI interaction concepts are usually developed by different teams and tested separately, often resulting in integration problems. This work contributes to a low-code approach to model interaction concepts involving ML models to enable end-to-end prototypes for early integration and User eXperience (UX) testing. We use flow-based modeling with Node-RED, the virtual driving simulator CARLA and YOLOv5 as state-of-the-art deep learning techniques for VRU detection. We show two scenarios (cornering lights and context-aware VRU warning) in an interactive demonstrator, meaning a manual live control of pedestrian and car. We consider our approach to model and evaluate interaction concepts without writing code feasible for non-computer scientists. Y1 - 2023 U6 - https://doi.org/10.1145/3581961.3609844 SP - 323 EP - 326 PB - ACM ER -