44 Informatik, System- und Elektrotechnik
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The validity of the subjective evaluation of steering feedback in driving simulators is crucial for modern vehicle development. Although there are established objective steering characteristics for the assessment of both stationary and dynamic feedback behaviour, factors such as steering wheel vibrations and vehicle body motion, particularly in high-frequency ranges, present challenges in simulator fidelity. This work investigates the influence of steering wheel vibration and vehicle body motion frequency content on the subjective evaluation of steering feedback during closed-loop driving in a dynamic driving simulator. A controlled subject study with 30 participants consisting of a back-to-back comparison of a reference vehicle with an electrical power steering system on a country road and three variants of its virtual representation on a dynamic driving simulator was performed. Subjective evaluation focused on the representation of road feedback in comparison to the reference vehicle. The statistical analysis of subjective results show that there is a significant influence of the frequency content of both steering wheel torque and vehicle motion on the subjective evaluation of steering feedback in a dynamic driving simulator. The results suggest an influence of frequency content on the subjective evaluation quality of steering feedback characteristics that are not associated with the dynamic feedback behaviour in the context of established performance indicators.
Despite the wide body of literature on motion planning for autonomous robots targeting structured agricultural environments, there remains a need for more efficient and reliable approaches for unstructured terrains. This work is motivated by a cooperation with the startup Paltech, which develops a weed-removal robot for grasslands. In this work, we employ a typical layered approach for robot navigation. However, in contrast to standard navigation tasks, such as point-to-point navigation, the global planner must solve the more challenging problem of visiting multiple targets in an optimal way while considering the kinematic constraints of the vehicle.
Explainable Statistical Evaluation of Emergency Braking Functions in Scenario-Based Safety Testing
(2026)
This article presents the integration of the Explainable Statistical Evaluation method (ESE) with an Autonomous Emergency Braking (AEB) using the software library FASim in the context of the European New Car Assessment Programme Car-to-Pedestrian Nearside Child Obstructed 50% scenario (Euro NCAP CPNCO-50 scenario). ESE integrates qualitative and quantitative safety methods for an easy to explain justification of functional safety (FuSa) and safety of the intended functionality (Sotif) for complex systems. The article addresses the European Union’s call for objective, coherent safety metrics for Connected and Automated Vehicles and shows how simulation-based test generation can support a statistically sound argument for safety, for which validation tests do not provide sufficient evidence.
End-user development,where non-programmers create or adapt their own digital tools, can play a key role in driving digital transformation within organizations. Currently, low-code/no-code platforms are widely used to enable end-user development through visual programming, minimizing the need for manual coding.
Recent advancements in generative AI, particularly large language model-based assistants and “copilots”, open new possibilities, as they may enable end users to generate and refine programming code and build apps directly from natural language prompts. This approach, here referred to as AI-assisted end-user coding, promises greater flexibility, broader applicability, faster development, improved reusability, and reduced vendor lock-in compared to the established visual LCNC platforms.
This paper investigates whether AI-assisted end-user coding is a feasible paradigm for end-user development, which may complement or even replace the LCNC model in the future. To explore this, we conducted a case study in which non-programmers were asked to develop a basic web app through interaction with AI assistants.The majority of study participants successfully completed the task in reasonable time and also expressed support for AI-assisted end-user coding as a viable approach for end-user development. The paper presents the study design, analyzes the outcomes, and discusses potential implications for practice, future research, and academic teaching.
Aging workforces in western industrialized countries are leading to a growing loss of experiential knowledge, particularly on the shop floor where expertise is largely tacit and informally acquired. This HICSS presentation reports on a study that examines current challenges in industrial knowledge management and identifies promising application areas for large language models (LLMs) within a broader effort to develop human-centered, GenAI-supported KM solutions. Based on two structured workshops with 23 experts from 14 German manufacturing companies, the study reveals key barriers to knowledge retention and highlights two prioritized LLM use cases. The findings provide concrete design implications for LLM-enabled KM systems and contribute practical insights into addressing demographic change in industrial contexts.
The development and evaluation of an automation concept for high-performance 3D printers in an industrial environment is presented. The paper’s special characteristic is the multi-domain approach, which combines development with parallel simulation studies of the cell. The 3D printing robot cell was designed with an individual gripping system and a magazine for printing plates, and the production performance of the concept was evaluated with simulation studies of the robot performance and cycle time simulation.
The increasing strain of overtourism on popular destinations calls for data-driven strategies that can subtly influence tourist behavior without limiting freedom of choice. This study investigates the use of digital nudges, particularly the decoy effect, within recommender systems to guide visitor flows spatially or temporally. Through a series of focus group-based experiments, this study examines how introducing asymmetrically dominated options can alter destination preferences. The results suggest that spatial steering benefits more consistently from decoy-based interventions than temporal steering. Furthermore, the findings reveal that while nudging has a measurable impact, individual factors and personal preferences play a substantial role in shaping decisions. The study underscores the value of behaviorally informed recommender systems as tools for promoting sustainable tourism by facilitating more balanced visitor flows. It concludes with a call for further development of adaptive, user-aware digital strategies to address the multifaceted nature of tourist decision-making.
Due to their inherent socio-technical nature, the engineering of information systems creates quite distinctive challenges. Addressing them requires not only a deep understanding of technical capabilities, but also of the respective application area, and the behavior of the intended users. Being interdisciplinary by design, the Information Systems field provides a broad tool-set to address these challenges. We are thus delighted to present the proceedings of the 2nd European Symposium on Information Systems Engineering (ESISE), held from September 09 to 11, 2025, in Clervaux, Luxembourg. This symposium brought together researchers and thought leaders to explore the latest advancements and challenges in the field of information systems engineering.
Working with troubles and failures in conversation between humans and robots: workshop report
(2023)
This paper summarizes the structure and findings from the first Workshop on Troubles and Failures in Conversations between Humans and Robots. The workshop was organized to bring together a small, interdisciplinary group of researchers working on miscommunication from two complementary perspectives. One group of technology-oriented researchers was made up of roboticists, Human-Robot Interaction (HRI) researchers and dialogue system experts. The second group involved experts from conversation analysis, cognitive science, and linguistics. Uniting both groups of researchers is the belief that communication failures between humans and machines need to be taken seriously and that a systematic analysis of such failures may open fruitful avenues in research beyond current practices to improve such systems, including both speech-centric and multimodal interfaces. This workshop represents a starting point for this endeavour. The aim of the workshop was threefold: Firstly, to establish an interdisciplinary network of researchers that share a common interest in investigating communicative failures with a particular view towards robotic speech interfaces; secondly, to gain a partial overview of the “failure landscape” as experienced by roboticists and HRI researchers; and thirdly, to determine the potential for creating a robotic benchmark scenario for testing future speech interfaces with respect to the identified failures. The present article summarizes both the “failure landscape” surveyed during the workshop as well as the outcomes of the attempt to define a benchmark scenario.