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Visualization-Assisted Development of Deep Learning Models in Offline Handwriting Recognition
No full textEffect of motion mismatches on ratings of motion incongruence and simulator sickness in urban driving simulations
No full textThis paper investigates the effects of motion mismatches on simulator sickness and subjective ratings of the motion. In an open-loop driving simulator experiment, participants were driven through a recorded urban drive twelve times, in which mismatches were induced by manipulating the following three aspects in motion cueing: (i) mismatches in specific vehicle axes, (ii) mismatch types (scaling, missing, and false cues), and (iii) inconsistent scaling between different motion axes. Subjects (N=52) reported simulator sickness post-hoc (after each drive), as well as continuously during each drive, a first in simulator sickness research. Furthermore, subjective post-hoc motion incongruence ratings on the quality of the motion were extracted. Results show that longitudinal motion mismatches lead to the most simulator sickness and the highest ratings, followed by mismatches in lateral motion, then yaw rate. False cues induce the most sickness, followed by missing and then scaled motion. Inconsistent scaling between the axes has no significant effect. The continuous sickness ratings support that the occurrence and severity of simulator sickness are indeed related to mismatches in simulator motion of specific maneuvers. This paper contributes to an improved understanding of the relationship between simulator motion and sickness, allowing for more targeted motion cueing strategies to prevent and reduce sickness in driving simulators. These strategies may include the appropriate selection of the simulator, the motion cueing, and the sample of participants, following the presented results
Inverted Classroom in der Einführungsveranstaltung Programmierung
No full textDie Lehrveranstaltung Programmierung für Informatik-Studierende wurde an der TH Nürnberg, wie an vielen Hochschulen, bisher als klassische Vorlesungs-Übungs-Kombination realisiert. Nach einer hohen Durchfallquote im Wintersemester 2023/24 wurde im Wintersemester 2024/25 für eine Kohorte ein experimentelles, auf dem Inverted Classroom-Ansatz basierendes Lehrkonzept implementiert. Hierbei bereiten sich die Studierenden durch Literaturarbeit auf die Veranstaltungen vor, in denen vorwiegend aktivierende Lehr-/Lernformen genutzt werden. Die Veranstaltung wurde durch eine Reihe von Datenerhebungen (Teaching Analysis Poll, zwei Umfragen, Lehrtagebuch) begleitet, um das Lernverhalten der Studierenden, beobachtbare Hürden, und Good Practices für zukünftige Veranstaltungen abzuleiten. Das Konzept wurde insgesamt positiv bewertet, wobei sich auch viele Verbesserungsmöglichkeiten im Detail abzeichneten. Im Artikel dokumentieren wir die Ergebnisse der Erhebungen und diskutieren die Implikationen
Cognitive Biases in User Interaction with Automated Vehicles: The Influence of Explainability and Mental Models
No full textTo develop truly human-centered automated systems, it is essential to acknowledge that human reasoning is prone to systematic deviations from rational judgment, known as Cognitive Biases. The present study investigated such flawed reasoning in the context of automated driving. In a multi-step study with N = 34 participants, the occurrence of four Cognitive Biases was examined: Truthiness Effect, Automation Bias, Action Bias, and Illusory Control. Additionally, the study explored how the Explainability of the automation’s behavior and the driver’s Mental Model influenced the manifestation of these biases. The findings indicate a notable susceptibility to the Truthiness Effect and Illusory Control, although all biases appeared highly dependent on the specific driving context. Moreover, Explainability strongly impacted the perceived credibility of information and participants’ agreement with the system’s behavior. Given the exploratory nature of the study, this work aims to initiate a discussion on how Cognitive Biases shape human reasoning and decision-making in interactions with automated vehicles. Based on the results, several directions for future research are proposed: (1) investigation of additional cognitive biases, (2) analysis of biases across different levels of automation, (3) exploration of mitigation strategies versus deliberate use of biases, (4) examination of dynamic and context-dependent manifestations, and (5) validation in high-fidelity simulations or real-world settings
