@techreport{SparrerWinkelmannvonHoffmannetal.2023, type = {Working Paper}, author = {Sparrer, Lisa Marie and Winkelmann, Sven and von Hoffmann, Alexander and S{\´a}nchez Morillo, Daniel}, title = {A Novel Framework for the Assessment of the Impact of Affective UI Sounds in a Driving Situation Through Virtual Reality and Physiological Signals}, doi = {10.34646/thn/ohmdok-1202}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:92-opus4-12021}, pages = {1 -- 7}, year = {2023}, abstract = {The objective of this study was to develop an experimental framework that allows research on whether the combined use of a virtual reality (VR) driving simulator, electroencephalography signals collected during driving tests and designed attentional paradigms could be applied to assess the performance of affective warning sounds for re-channelling driver's attention in certain driving situations. Three VR driving scenarios with different stimuli and two driving tasks were designed and implemented. Acquisition of driver's physiological data and behavioural metrics were integrated to be gathered synchronously. In addition, an experimental protocol was designed for the assessment of the impact of affective warning sounds. The technical feasibility of the framework was evaluated with the results showing that the proposed framework enables the data acquisition and analysis for a variety of warning stimuli. After successful technical deployment, a study with experimental subjects will follow to evaluate the effectiveness of affective warning sounds.}, language = {en} } @inproceedings{WinkelmannBuettnerDeivasihamanietal.2023, author = {Winkelmann, Sven and B{\"u}ttner, Max and Deivasihamani,, Dharani and von Hoffmann, Alexander and Flohr, Fabian}, title = {Using Node-RED as a Low-Code Approach to Model Interaction Logic of Machine-Learning-Supported eHMIs for the Virtual Driving Simulator Carla}, series = {AutomotiveUI '23 Adjunct: Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, booktitle = {AutomotiveUI '23 Adjunct: Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, publisher = {ACM}, doi = {10.1145/3581961.3609844}, pages = {323 -- 326}, year = {2023}, abstract = {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.}, language = {en} } @incollection{WinkelmannvonHoffmann2025, author = {Winkelmann, Sven and von Hoffmann, Alexander}, title = {Automotive Human-Machine-Interaction}, series = {Artificial Intelligence in Business and Engineering}, booktitle = {Artificial Intelligence in Business and Engineering}, editor = {Hofbauer, G{\"u}nter}, publisher = {Kohlhammer}, isbn = {978-3-17-046743-9}, doi = {10.17433/978-3-17-046743-9}, pages = {112 -- 128}, year = {2025}, language = {en} }