BMW (Germany)

Frustrating reanimations, ethical dilemmas, or violence occur from time to time in intensive care and emergency medicine and exceed what is "normal" even in the broader professional sense. Such events often trigger existential questions among healthcare professionals, can lead to afeeling of an unjust world, and can shake their hope or even their sense of meaning. Healthcare professionals need the freedom and safety to address these thoughts and feelings. One approach to supporting those affected in coping is peer support, asystem that has already worked well in the USA and counteracts the rising absenteeism and fluctuation rates among healthcare professionals.

Driving simulators are essential tools to guide automotive research and development. Their motion system requires a motion cueing algorithm (MCA) to keep the simulator motion platform within its physical boundaries, while simultaneously aiming to recreate the sensation of real vehicle motion. While traditional, filter-based approaches are still predominant, optimization-based MCAs have been at the center of MCA research for over a decade due to their ability to systematically improve motion cueing quality through explicit cost function design and constraints handling. However, despite their demonstrated advantages, these optimization-based methods have not yet achieved widespread adoption in driving simulation. This paper therefore provides a comprehensive review of optimization-based MCAs for driving simulation, categorizing and comparing algorithms, describing their key developments and core characteristics. The current limited real-time capability, lack of accurate evaluation methods, challenges in cost function design and its tuning, and the current lack of accurate future reference predictions are identified as key barriers to the practical deployment and widespread use of optimization-based MCAs. These theoretical and practical challenges are further reviewed, providing guidelines to advance the theory and application of optimization-based MCAs. Central in these advancements are a better understanding of which motions constitute a realistic motion experience, a framework allowing to compare the achieved motion fidelity of MCAs across papers, the design of the cost function focusing on human motion perception, and techniques for easing up the tuning process to swiftly reach high quality tunings for different simulators, scenarios, and use cases. We identify the need for improving the real-time capability, and providing high quality motion reference predictions using learning-based approaches on diverse datasets, along with techniques to handle existing uncertainties. Following these guidelines, new foundations for optimization-based algorithms in driving simulation can be achieved, which will significantly impact the research and development of automotive systems.

The production process chain of lithium-ion battery (LiB) cells consists of several interconnected process steps with many parameters that can influence the final properties and quality of the cells. In the development of cylindrical LiB cells, steps such as winding, cell assembly, filling, and formation play an important role in determining the properties of the intermediate product and the final performance of the battery cell. Continuous and low scrap production in the experimental stages of LiB battery cell production is challenging due to the limited understanding of cause-and-effect relationships. For example, the machine parameters that need to be configured depending on the properties of the input material. As a result, individual tuning of production machine settings for different materials is required. This is particularly critical in research pilot production environments, such as the BMW Battery Cell Competence Centre, where different materials are tested and experimental approaches are required to optimize the manufacturing processes. This paper presents an approach for utilizing data analytics and machine learning with Open Platform Communications Unified Architecture (OPC UA) and Internet of Things (IoT) data pipeline to better understand the relationships between process parameters, input material, intermediate product features and final quality in the production of cylindrical LiB battery cells. In this study, different machine learning algorithms were tested to predict the initial setpoints of the routing path and the gripper values from the parameters of the winding process and the characteristics of the input material in order to obtain an intermediate product with the desired quality. The best results were obtained using Random Forest Regression with an R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.976.

Abstract Purpose This study analyzed the inter-session reliability of the parallel back squat during one-repetition-maximum (1RM) testing in elite youth soccer players and to exploratorily evaluate the consistency with which the prescribed squat depth is achieved. Methods The sample comprised 47 male soccer players (age: 18.1 ± 2.3 years, body mass: 72.4 ± 7.7 kg, height: 1.83 ± 0.06 m) from German youth soccer academy teams competing in top junior leagues (U16-U19). The 1RM was assessed under standardized conditions, with squat depth monitored using a hybrid marker- and silhouette-based motion capture system. The inter-session reliability of 1RM load was quantified using the intraclass correlation coefficient ( ICC ). Systematic bias between sessions was evaluated using a paired-samples t -test (with Cohen’s d for effect size). Within-session deviations from the 90° squat-depth target were assessed using a one-sample t -test (with Cohen’s d for effect size). Results The participants achieved an absolute 1RM of 94.9 ± 13.7 kg (relative: 1.32 ± 0.19). The 1RM test–retest (inter-session) reliability regarding load lifted yielded an ICC of 0.970 (95% CI: 0.948–0.982), with a mean bias of 1.71 kg ( P = 0.018, d = 0.39). An explanatory analysis of the kinetic data from each participant’s best trial (within-session) showed that squat depth deviations (thigh angle 89.7° ± 4.1°) did not differ significantly from the target depth of 90° ( P = 0.630, d = 0.15), where 0° corresponds to full knee extension. Conclusions This study’s parallel back-squat 1RM testing protocol in national-level male youth soccer players demonstrated excellent load reliability. Squat depth may confound reliability, and its full impact on inter-session error warrants further study; nonetheless, familiarization, skilled spotters, and practiced participants can help limit its effect.

This work addresses the limitations of traditional 2D collaboration tools in automotive design, where complex 3D model reviews require immersive spatial interaction. We developed a metaverse-based collaborative platform for automotive design processes using Unreal Engine 5, addressing three research directions: (1) core requirements for automotive design collaboration, (2) effectiveness of immersive technologies in such professional workflows, and (3) an empirical user study of test scenarios comparing the platform as a conventional PC application to an immersive Virtual Reality (VR) experience. Xstudio enables real-time interaction with 3D models and advanced functionalities for design teams. We evaluated the effectiveness of the tool through user studies and technical performance analysis. The expert study of 11 employees, who were part of the engineering department of BMW (a European car manufacturer), was designed to evaluate the usability of the features using the System Usability Score (SUS), which scored a promising value of 76 (VR) and 74 (PC). The NASA Task Load Index (NASA-TLX) resulted in 40.1 for VR and 37.7 in PC mode. A set of quantitative analyses with factors such as task completion time (TCT), error rates (ER), and user interaction metrics (UIM) were applied in the comparison of VR and PC modes. The result shows that the VR mode has higher TCT than the PC mode, but in contrast, VR has a higher interaction rate to complete the task, input precision, and the multi-step manipulation can be optimized. We summarize the findings in guidelines for future research in the collaborative car design process. Key technical contributions include a modular Entity-Component-System architecture for dynamic tool customization and hybrid VR/PC interactions. In the domain-specific automotive engineering space, both application types complement each other, offering efficiency (PC) or immersion (VR) for specific tasks. Future work will focus on UI/UX refinements with rigorous validation in follow-up studies. Specific improvements will include streamlined menus, snap-based selection aids, and pointer-locking tools to address precision challenges.

Ensuring stability and robustness in highly automated vehicle (HAV) control systems is critical for guaranteeing the safety of the intended functionality (SotiF) under real-world uncertainties inside a defined operational design domain (ODD). This paper presents a formal verification framework utilizing sum-of-squares (SOS) optimization to quantify and guarantee nonlinear system stability and robust performance in the presence of parametric uncertainties and environmental disturbances. We analyze a benchmark automated lane-following use case (UC) and verify polynomial system representations of the vehicle architecture against worst-case deviations using region of attraction (RoA) and robust positive invariant (RPI) sets. Simulation-based statistical validation using high-fidelity vehicle models confirms the effectiveness of the proposed method for formal robust control certification.

We investigate the problem of generating common randomness (CR) from a finite compound source aided by unidirectional communication over a rate-limited perfect channel. The two communicating parties observe independent and identically distributed (i.i.d.) samples of a finite compound source and aim to agree on a common random variable with high probability for every possible state. Both parties know the set of source states as well as their statistics. However, they don’t know the actual state. We establish a single-letter formula for the compound CR capacity in the presence of communication over the channel and study key properties of the compound CR capacity: superadditivity, concavity, and continuity. We also consider the case where there is no communication between the terminals, and only the source outputs observed by the terminal at the receiving end of the perfect channel are state-dependent. In this setting, we establish single-letter bounds on the compound CR capacity. The single-letter lower bound is derived under the assumption that the source distributions are pairwise distinct for all states. Finally, within the same setting, we propose a CR generation scheme for a two-state binary source example. Notably, this scheme does not depend on the previously mentioned assumption.

• Driver fatigue in the context of CAD poses a significant safety concern, necessitating effective countermeasures. • A driving simulator experiment was conducted with 75 participants to investigate a countermeasure against driver fatigue. • While one participant group was allowed to engage in free-choice NDRAs during CAD, the other group was not allowed to engage in any activity. • Driver fatigue (measured with the Karolinska Sleepiness Scale) was significantly lower during CAD in the group with a free-choice NDRA than without; however, a free-choice NDRA did not positively affect driver fatigue and driving performance after CAD. • Findings suggest that free-choice NDRAs during CAD have a positive effect when the activity is performed but not beyond. A critical safety risk during conditional automated driving (CAD, SAE Level 3) is driver fatigue. Researchers have investigated several countermeasures to address this issue. Non-driving related activities (NDRAs) during CAD have shown considerable promise in preventing driver fatigue and should be evaluated in more detail. The aim of this study was to investigate free-choice NDRAs as a potential countermeasure against driver fatigue. For this purpose, 78 participants took part in a driving simulator experiment with a 2x2 mixed design. The participants drove manually before and after CAD to compare the impact of driver fatigue on driving performance. During CAD, the participants were randomly assigned to one of two conditions: with vs. without an NDRA. The participants of the first condition were allowed to engage in NDRAs after reaching a 7 on the Karolinska Sleepiness Scale (KSS) during CAD. Driver fatigue, takeover performance, and driving performance were assessed. The results revealed that driver fatigue was significantly lower during CAD in the group with an NDRA than without. However, the NDRA had no significant effect on driving performance (i.e., distance headway, time-to-collision, standard deviation of lateral position) in the second manual drive. Driver fatigue resulted in significantly worse driving performance in the second drive. The results showed that an NDRA only impacted driver fatigue during CAD, not manual driving performance afterwards.

LCA is increasingly applied to the electromobility sector, in academic and industrial literature, and in policies. While there is awareness on the existence of specific gaps and lack of harmonization in LCA of electromobility, there is however still no common understanding on these and on their relevance. This study intends to develop this common understanding and to further explore potential ways forward. The analysis of gaps and lack of harmonization starts from, and expands the analysis of, the extensive review of state-of-the-art academic and industrial literature, and of existing guidelines and standards, performed by Eltohamy et al. (2024). It further builds on four additional actions: (i) review of position papers; (ii) workshops, gathering LCA experts from industry and research; (iii) in-depth survey on the LCA practice of selected industrials; and (iv) iterative, document-based, approach towards common identification and analysis of gaps. This study accordingly compiles and analyzes the gaps and lack of harmonization in LCA of the electromobility sector per LCA phase. It discusses how these limits alter LCA accuracy, comparability and comprehensiveness and their associated relevance as function of the study context. The identified gaps and lack of harmonization relate to (i) the LCA general method, and more specific rules regarding functional unit, approach to address multi-functionality, system boundaries, electricity modelling, normalization and weighting, sensitivity and uncertainty analysis, etc.; (ii) models used to support LCA, sometimes not accurately representing the real world (e.g. models for data gap filling, prospective EoL modelling, impact characterization); (iii) data, both in terms of (incomplete and imprecise) secondary datasets and of lack of primary data; and (iv) difficulties in implementation. The path forward to overcome these limitations is expected to rely firstly on research and development, particularly crucial to address actual gaps in models and data, and secondly on guidelines setting and consensus building, both key regarding harmonization of method-related issues. This study highlights, classifies and analyzes the existing gaps and lack of harmonization in LCA of the electromobility sector. It flags the key challenges and opportunities to be overcome in the path forward, e.g. regarding trade-offs in accuracy, comprehensiveness and simplicity, or alignment with comparable harmonization initiatives in sectors other than the electromobility one. We now encourage the LCA community to use this study as a blueprint towards improved practices, whether harmonization is key, such as for comparative LCAs of vehicles or parts, or of lower importance, such as in some research contexts.