Generative AI (GenAI) image tools are increasingly integrated into design workflows, prompting HCI research on their interaction methods and interfaces. We reviewed 37 such tools, including 28 HCI research systems and nine commercial systems (2022–July 2025), using three analytical frameworks: interaction methods, creative processes, and tool functionalities. We found that text prompts remain the dominant input method, while visual and attribute-based inputs—particularly in academic tools—are gaining traction and are often combined with text for refinement. Commercial systems emphasize parameter control, whereas academic tools focus on semantic attributes and visual organization. Most tools support ideation and exploration, but provide limited support for refinement and evaluation. Based on these findings, we identify nine design opportunities, including advanced visual interaction, simplified parameter control, precision editing, direct manipulation, workflow integration, default settings that support rapid exploration, and user guidance for later stages. We contribute a framework for analyzing GenAI interfaces and actionable directions for designing more usable, creativity-supportive GenAI image systems.
Driver monitoring systems (DMS) represent a camera-based countermeasure for visual distraction that detect distracted drivers in real-time and subsequently prompt them to look back on the road. However, the effectiveness of DMS in reducing distraction is still being debated, with studies yielding inconsistent results. A correct understanding of a technological system is a key determinant for its effectiveness in terms of enhancing safety. Therefore, addressing a previously unexplored factor in DMS research, this study investigated how drivers' explicit knowledge about DMS influences system effectiveness in reducing visual distraction. Previous studies showed that drivers' understanding of DMS is incomplete if drivers are not instructed. Therefore, in this study, the drivers' explicit knowledge of DMS was systematically manipulated between participants by providing verbal instructions prior to driving. Three experimental conditions were compared: drivers with explicit knowledge of DMS, drivers without knowledge, and a control group with an inactive DMS (between factor). Glance behavior was compared between the first and repeated interaction with the secondary task to assess visual distraction (within factor). Explicit knowledge significantly reduced the number of glances exceeding 2 s, indicating reduced visual distraction. This effect was prevalent, even after repeated interaction with the DMS. Importantly, mere activation of DMS without instruction did not affect glance behavior. Findings highlight the significant role of explicit knowledge in system effectiveness. The present work contributes to the field of DMS research by investigating drivers' mental model of DMS and deriving methodological and practical implications.
The transition toward low-carbon mobility requires material-selection approaches that account for extended use cycles and cross-sector reuse pathways. However, early-stage automotive design decisions are typically informed by single-life cradle-to-grave assessments, which do not explicitly integrate reuse-induced substitution effects or multi-stage emission accounting within development timelines. This study operationalizes an integrated cradle-to-reuse-to-grave (C2R2G) assessment approach by combining established life cycle assessment (LCA), system expansion, and substitution modeling principles into an automotive-specific decision-support structure. The framework formalizes emission accounting across primary use, reuse preparation, secondary application, and end-of-life treatment, including explicit system expansion, substitution, and circular scenario modeling parameters. A bio-based laminated veneer lumber (LVL) seat-shell is evaluated as a demonstrative case within passenger vehicle applications. Scenario modeling compares single-life, recycling, and open-loop reuse pathways under varying lifetime and energy-intensity assumptions. Results indicate that reuse-driven substitution effects can significantly alter cumulative carbon footprints compared to single-life configurations, though outcomes remain highly sensitive to assumed service lifetimes, displaced material emission factors, and energy mixes in second-life sectors. Sensitivity analysis highlights key parameters influencing ranking stability and identifies boundary conditions under which reuse advantages diminish. Rather than proposing a new LCA methodology, this work demonstrates how established analytical components can be systematically integrated to support early-stage automotive material pathway evaluation. The approach enhances transparency in allocation choices and trade-off identification, contributing to more informed low-carbon design decisions in circular mobility systems.
A fail-operational vehicular power system is a prerequisite for autonomous driving functions. Thus, novel vehicular power systems include electronic fuses (eFuses) instead of melting fuses to implement advanced protection mechanisms. In order to perform their functions over the whole lifetime reliably, the semiconductor devices of eFuses must be protected from damage due to a thermal overload. In this paper, a software-based approach is proposed to determine the junction temperature of a transistor using an electrothermal model of the device. Different modeling approaches (Foster, Cauer) are explained and compared regarding technical feasibility. Measurements exhibit that the discretized algorithm for monitoring the temperature of the eFuse is viable when realistic values for the ambient temperature within the power distribution unit are processed.
One of the most significant changes in today's automotive low-voltage power supply is replacing melting fuses with electronic, semiconductor-based ones. This substitute is beneficial to meet the requirements for highly automated driving functions and offers advanced, adjustable protection and diagnosis possibilities. The associated increase in switching dynamics can lead to reinforced parasitic effects such as high overvoltage spikes when turning off. These burdens threaten the reliability of the utilized semiconductor components and, thereby, the overall voltage stability of the power supply. Hence, this paper proposes a way to model different overvoltage protection circuits in state-space, ensuring the correct functionality of electronic fuses over varying fault scenarios and environmental stresses. Moreover, these topologies are analyzed and compared with a focus on the impact of the parametrization of associated components based on the dissipation in the power semiconductors. Finally, a step-wise guideline for the practical implementation and parametrization of such a circuit is given.
