@inproceedings{,
    author = {Rusch, Tobias; Hübner, Benjamin; Meyer-Nieberg, Silja; Winter, Wolfgang; Leopold, Armin; Hofmann, Marko; Küsel, Cornelia},
    title = {Physiological and psychological performance measurement for the practical driving test},
    editor = {Praetorius, Gesa; Sellberg, Charlott; Patriarca, Riccardo},
    booktitle = {Advances in Human Factors of Transportation : Proceedings of the 15th International Conference on Applied Human Factors and Ergonomics and the Affiliated Conferences},
    series = {Open Access Science in Human Factors Engineering and Human-Centered Computing},
    journal = {},
    address = {New York},
    publisher = {AHFE International },
    edition = {},
    year = {2024},
    isbn = {978-1-964867-24-3},
    volume = {148},
    number =  {},
    pages = {},
    url = {https://openaccess.cms-conferences.org/publications/book/978-1-964867-24-3/article/978-1-964867-24-3_40},
    doi = {10.54941/ahfe1005229},
    keywords = {Stress measurement ; Driving school ; Wearable Technologies},
    abstract = {Even with the advancement of automobile technologies supporting drivers in most situations on the road, the practical driving test required to obtain a driver’s license in Germany is still a major obstacle for students. Although additional tools such as driving simulation systems as well as mobile apps are available for initial driving training, the failure rate stagnated around 37% for the last ten years, according to the ADAC, the largest german automobile club. In Germany, students learn to drive with the help of private companies specialized on teaching and leading the students up to the official practical driving test demanded by government regulations to obtain a driver’s license.Using a multimodal sensory setup obtaining biophysiological and derived mental state data of each driving student, the resulting database is used to identify possible causes about fatal driving errors. Each student is evaluated and accompanied throughout his or her entire learning journey, starting with a questionnaire-based personality trait test. Using synchronization by timestamp, the biophysiological data most related to stress, such as HRV, EDA or eye tracking, are collected both while within the driving simulation system and on the real-world road. Besides collected sensor-based data, the database is also enriched with subjective insights provided by the student before and after each driving session. On the road, the driving instructor assessment, a standardized driving log, containing special events and student behaviour, as well as an event-synchronized video recording is added to each dataset. For analysis of the collected data, particular attention is paid to anomalies correlated to special events during each session to identify situational patterns which may be associated to an increased failure rate in the final, official practical driving test. The gained insights are then used to optimize driving training to actively reduce the practical driving test failure rate.},
    note = {},
    institution = {Universität der Bundeswehr München, Fakultät für Informatik, INF 3 - Institut für Technische Informatik, Professur: },
    
    
}