@article{KundingerYalavarthiRieneretal.2020, author = {Kundinger, Thomas and Yalavarthi, Phani Krishna and Riener, Andreas and Wintersberger, Philipp and Schartm{\"u}ller, Clemens}, title = {Feasibility of smart wearables for driver drowsiness detection and its potential among different age groups}, volume = {16}, journal = {International Journal of Pervasive Computing and Communications}, number = {1}, publisher = {Emerald}, address = {Bingley}, issn = {1742-7371}, doi = {https://doi.org/10.1108/IJPCC-03-2019-0017}, pages = {1 -- 23}, year = {2020}, language = {en} } @article{GoebelPeuckmannKundingeretal.2025, author = {G{\"o}bel, Jan-Philipp and Peuckmann, Niklas and Kundinger, Thomas and Riener, Andreas}, title = {Fusion of Driving Behavior and Monitoring System in Scenarios of Driving Under the Influence: An Experimental Approach}, volume = {15}, pages = {5302}, journal = {Applied Sciences}, number = {10}, publisher = {MDPI}, address = {Basel}, issn = {2076-3417}, doi = {https://doi.org/10.3390/app15105302}, year = {2025}, abstract = {Driving under the influence of alcohol (DUI) remains a leading cause of accidents globally, with accident risk rising exponentially with blood alcohol concentration (BAC). This study aims to distinguish between sober and intoxicated drivers using driving behavior analysis and driver monitoring system (DMS), technologies that align with emerging EU regulations. In a driving simulator, twenty-three participants (average age: 32) completed five drives (one practice and two each while sober and intoxicated) on separate days across city, rural, and highway settings. Each 30-minute drive was analyzed using eye-tracking and driving behavior data. We applied significance testing and classification models to assess the data. Our study goes beyond the state of the art by a) combining data from various sensors and b) not only examining the effects of alcohol on driving behavior but also using these data to classify driver impairment. Fusing gaze and driving behavior data improved classification accuracy, with models achieving over 70\% accuracy in city and rural conditions and a Long Short-Term Memory (LSTM) network reaching up to 80\% on rural roads. Although the detection rate is, of course, still far too low for a productive system, the results nevertheless provide valuable insights for improving DUI detection technologies and enhancing road safety.}, language = {en} } @article{KundingerSofraRiener2020, author = {Kundinger, Thomas and Sofra, Nikoletta and Riener, Andreas}, title = {Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection}, volume = {20}, pages = {1029}, journal = {Sensors}, number = {4}, publisher = {MDPI}, address = {Basel}, issn = {1424-8220}, doi = {https://doi.org/10.3390/s20041029}, year = {2020}, abstract = {Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially, techniques that include physiological measurements seem to be a promising alternative. However, in a dynamic environment such as driving, only non- or minimal intrusive methods are accepted, and vibrations from the roadbed could lead to degraded sensor technology. This work contributes to driver drowsiness detection with a machine learning approach applied solely to physiological data collected from a non-intrusive retrofittable system in the form of a wrist-worn wearable sensor. To check accuracy and feasibility, results are compared with reference data from a medical-grade ECG device. A user study with 30 participants in a high-fidelity driving simulator was conducted. Several machine learning algorithms for binary classification were applied in user-dependent and independent tests. Results provide evidence that the non-intrusive setting achieves a similar accuracy as compared to the medical-grade device, and high accuracies (>92\%) could be achieved, especially in a user-dependent scenario. The proposed approach offers new possibilities for human-machine interaction in a car and especially for driver state monitoring in the field of automated driving.}, language = {en} } @inproceedings{KundingerRienerSofraetal.2018, author = {Kundinger, Thomas and Riener, Andreas and Sofra, Nikoletta and Weigl, Klemens}, title = {Drowsiness Detection and Warning in Manual and Automated Driving: Results from Subjective Evaluation}, booktitle = {Proceedings: 10th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications}, publisher = {ACM}, address = {New York}, isbn = {978-1-4503-5946-7}, doi = {https://doi.org/10.1145/3239060.3239073}, pages = {229 -- 236}, year = {2018}, language = {en} } @inproceedings{KundingerRienerSofraetal.2020, author = {Kundinger, Thomas and Riener, Andreas and Sofra, Nikoletta and Weigl, Klemens}, title = {Driver drowsiness in automated and manual driving: insights from a test track study}, booktitle = {IUI 2020: Proceedings of the 25th International Conference on Intelligent User Interfaces}, publisher = {ACM}, address = {New York}, isbn = {978-1-4503-7118-6}, doi = {https://doi.org/10.1145/3377325.3377506}, pages = {369 -- 379}, year = {2020}, language = {en} } @inproceedings{KundingerRienerSofra2017, author = {Kundinger, Thomas and Riener, Andreas and Sofra, Nikoletta}, title = {A Robust Drowsiness Detection Method based on Vehicle and Driver Vital Data}, booktitle = {Mensch und Computer 2017 - Workshopband}, editor = {Burghardt, Manuel and Wimmer, Raphael and Wolff, Christian and Womser-Hacker, Christa}, publisher = {Gesellschaft f{\"u}r Informatik}, address = {Regensburg}, doi = {https://doi.org/10.18420/muc2017-ws09-0307}, pages = {433 -- 440}, year = {2017}, language = {en} } @inproceedings{KundingerRiener2020, author = {Kundinger, Thomas and Riener, Andreas}, title = {The Potential of Wrist-Worn Wearables for Driver Drowsiness Detection: a Feasibility Analysis}, booktitle = {UMAP '20: Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization}, publisher = {ACM}, address = {New York}, isbn = {978-1-4503-6861-2}, doi = {https://doi.org/10.1145/3340631.3394852}, pages = {117 -- 125}, year = {2020}, language = {en} } @inproceedings{KundingerWintersbergerRiener2019, author = {Kundinger, Thomas and Wintersberger, Philipp and Riener, Andreas}, title = {(Over)Trust in Automated Driving: The Sleeping Pill of Tomorrow?}, pages = {LBW2418}, booktitle = {CHI'19 Extended Abstracts: Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems}, publisher = {ACM}, address = {New York}, isbn = {978-1-4503-5971-9}, doi = {https://doi.org/10.1145/3290607.3312869}, year = {2019}, language = {en} } @inproceedings{KundingerRienerBhat2021, author = {Kundinger, Thomas and Riener, Andreas and Bhat, Ramyashree}, title = {Performance and acceptance evaluation of a driver drowsiness detection system based on smart wearables}, booktitle = {Proceedings: 13th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications}, publisher = {ACM}, address = {New York}, isbn = {978-1-4503-8063-8}, doi = {https://doi.org/10.1145/3409118.3475141}, pages = {49 -- 58}, year = {2021}, language = {en} } @article{KundingerMayrRiener2020, author = {Kundinger, Thomas and Mayr, Celena and Riener, Andreas}, title = {Towards a Reliable Ground Truth for Drowsiness: A Complexity Analysis on the Example of Driver Fatigue}, volume = {4}, pages = {78}, journal = {Proceedings of the ACM on Human-Computer Interaction}, number = {EICS}, publisher = {ACM}, address = {New York}, issn = {2573-0142}, doi = {https://doi.org/10.1145/3394980}, year = {2020}, language = {en} }