@article{FrisonForsterWintersbergeretal.2020, author = {Frison, Anna-Katharina and Forster, Yannick and Wintersberger, Philipp and Geisel, Viktoria and Riener, Andreas}, title = {Where We Come from and Where We Are Going: A Systematic Review of Human Factors Research in Driving Automation}, volume = {10}, pages = {8914}, journal = {Applied Sciences}, number = {24}, publisher = {MDPI}, address = {Basel}, issn = {2076-3417}, doi = {https://doi.org/10.3390/app10248914}, year = {2020}, abstract = {During the last decade, research has brought forth a large amount of studies that investigated driving automation from a human factor perspective. Due to the multitude of possibilities for the study design with regard to the investigated constructs, data collection methods, and evaluated parameters, at present, the pool of findings is heterogeneous and nontransparent. This literature review applied a structured approach, where five reviewers investigated n = 161 scientific papers of relevant journals and conferences focusing on driving automation between 2010 and 2018. The aim was to present an overview of the status quo of existing methodological approaches and investigated constructs to help scientists in conducting research with established methods and advanced study setups. Results show that most studies focused on safety aspects, followed by trust and acceptance, which were mainly collected through self-report measures. Driving/Take-Over performance also marked a significant portion of the published papers; however, a wide range of different parameters were investigated by researchers. Based on our insights, we propose a set of recommendations for future studies. Amongst others, this includes validation of existing results on real roads, studying long-term effects on trust and acceptance (and of course other constructs), or triangulation of self-reported and behavioral data. We furthermore emphasize the need to establish a standardized set of parameters for recurring use cases to increase comparability. To assure a holistic contemplation of automated driving, we moreover encourage researchers to investigate other constructs that go beyond safety.}, language = {en} } @article{ShirurBirknerBleecketal.2023, author = {Shirur, Naveen and Birkner, Christian and Bleeck, Matthias and Forster, Andreas and Deserno, Thomas Martin and Henze, Roman}, title = {3D simulations and laboratory experiments to evaluate a dynamic airbag valve}, volume = {29}, journal = {International Journal of Crashworthiness}, number = {2}, publisher = {Taylor \& Francis}, address = {London}, issn = {1754-2111}, doi = {https://doi.org/10.1080/13588265.2023.2252214}, pages = {378 -- 388}, year = {2023}, abstract = {Airbag pressure determines the restraint effect during a vehicle crash. The pressure required to restrain the occupant depends on pre-crash detection, collision parameters and the occupant's mass and position. This work modulated airbag pressure for optimum safety using a novel airbag control valve for cold-gas inflators. This paper evaluates the valve's stationary and dynamic performances for Helium by 3D flow simulations using a pressure-based solver in ANSYS Fluent® and SAE J2238 laboratory tank tests. The predicted and measured tank pressures for the fully open (stationary) valve were agreed by an average 93.73\% with an excellent correlation (correlation coefficient, R = 0.9995). For the first dynamic operation with 10 ms switching time, the results agreed by 92.78\% with R = 0.9975. In the second test with 30 ms switching, 83.67\% agreement was observed with R = 0.9893. The research concluded that the valve modulates the bag pressure and is implementable in vehicles.}, language = {en} }