Volkswagen Group (United States)

<div class="section abstract"><div class="htmlview paragraph">This paper presents a novel sensitivity analysis framework for differential braking as a backup steering solution in fail-operational Steer-by-Wire systems. The fault-tolerant design approach of Steer-by-Wire and steering systems for highly automated driving relies on the availability of road wheel actuators (RWA). Redundancies are therefore commonly used to ensure fail-operationality. Since its widespread implementation in production vehicles through electronic stability control, the use of differential braking as a cost-effective measure is desirable to increase functional diversity. However, feasible lateral accelerations through this backup solution are limited compared to conventional steering systems and lie close to ordinary driving scenarios. To address this limitation, this work investigates the influence of chassis parameters on differential braking performance. After defining characteristic values and a simulation test plan, a preliminary analysis using a linear single-track model incorporating differential braking is conducted. The most influential vehicle parameters are identified, including the newly derived effect of rear axle cornering stiffness—an aspect not previously quantified in this context. A second, more detailed sensitivity analysis is performed using a characteristic-based dual-track model equipped with representative brake, steering, and chassis subsystems. Parameter variation ranges are derived from distributed data-based sources. The resulting total effects reveal new insights into the sensitivity of chassis design parameters, including scrub radius, steering friction, rear axle cornering stiffness, and coupling effects of suspension and kinematics. The findings provide valuable guidance for future chassis design in fail-operational steering systems and highlight the importance of underexplored parameters in achieving reliable lateral dynamics through differential braking.</div></div>

In diesem Kapitel werden die grundlegenden Aspekte der Fahrzeugsicherheit, der Crash-Sensierung und der Pre-Crash-Sicherheitssysteme erläutert. Ergänzend werden die relevanten Verfahren des maschinellen Lernens vorgestellt, die in Abschnitt 5.3 zur Anwendung kommen. Diese theoretischen Grundlagen bilden, zusammen mit dem in Kapitel 3 ermittelten Stand der Wissenschaft, eine umfassende Basis für das Verständnis der vorliegenden Arbeit.

The usability of the natural environment for settlement can be analysed based on geoenvironmental conditions: groundwater levels, terrain elevations, subsoil construction suitability. They are all important elements of urban development planning. Developing a method for selecting areas with favourable construction conditions and determining future urban development trends based on geoenvironmental conditions is essential for proper formation of space for construction purposes. This article presents a proprietary approach to determining geoenvironmental indicators and the ongoing correlational changes in these indicators, which are the basis for choosing optimal areas for construction. The selected geoenvironmental factors influence the development and spatial growth of cities. The research area is located within a small agglomeration inhabited by up to 20,000 residents. Direct results concerning geoenvironmental data and urban development were obtained from an area of 9 km2. Within this area, data were collected on subsoil conditions, terrain elevations, groundwater levels, and the spatial pattern of development throughout the historical evolution of the agglomeration up to the present day. The analysis also identified the points in the city’s development history when local flooding or other environmental hazards occurred, which may have resulted from improper urban growth tendencies. Interpretation of the resulting graphs, particularly the interactions between geoenvironmental conditions (indicators) and development during the studied periods, can support urban planning processes.

Despite its popularity, several empirical and theoretical studies suggest that the quantum approximate optimization algorithm (QAOA) has persistent issues in providing a substantial practical advantage. Numerical results for few qubits and shallow circuits are, at best, ambiguous, and the well-studied barren plateau phenomenon draws a rather sobering picture for deeper circuits. However, as more and more sophisticated strategies are proposed to circumvent barren plateaus, it stands to reason which issues are actually fundamental and which merely constitute – admittedly difficult – engineering tasks. By shifting the scope from the usually considered parameter landscape to the quantum state space's geometry we can distinguish between problems that are fundamentally difficult to solve, independently of the parameterization, and those for which there could at least exist a favorable parameterization. Here, we find clear evidence for a 'no free lunch'-behavior of QAOA on a general optimization task with no further structure; individual cases have, however, to be analyzed more carefully. Based on our analysis, we propose and justify a performance indicator for the deep-circuit QAOA that can be accessed by solely evaluating statistical properties of the classical objective function. We further discuss the various favorable properties a generic QAOA instance has in the asymptotic regime of infinitely many gates, and elaborate on the immanent drawbacks of finite circuits. We provide several numerical examples of a deep-circuit QAOA method based on local search strategies and find that – in alignment with our performance indicator – some special function classes, like QUBOs, indeed admit a favorable optimization landscape.

Applications of remote sensing images in both defense and civilian sectors have spurred substantial research interest. In the field of remote sensing, object detection confronts challenges such as complex backgrounds, scale diversity, and the presence of dense small objects. To address these issues, we propose an improved deep learning-based model, the Global Multi-scale Fusion Self-calibration Network, which is expected to contribute to alleviating the challenges. It consists of three main components: the hierarchical feature aggregation backbone, which uses improved modules such as the receptive field context-aware feature extraction module, the global information acquisition module, and the simple parameter-free attention module to extract key features and minimize the background interference. To couple multi-scale features, we enhanced the fusing component and designed the multi-scale enhanced pyramid structure integrating the proposed new modules. During the detection phase, especially when focusing on small object detection, we designed a novel convolutional attention feature fusion head. This head is constructed to integrate local and global branches for feature extraction by leveraging channel shuffling and multi-head attention mechanisms for efficient and accurate detection. Experiments on the Detection in Optical Remote Sensing Images (DIOR), Northwestern Polytechnical University Very High-Resolution‐10 (NWPU VHR‐10), remote sensing object detection (RSOD), and DOTAv1.0 data sets show that our method achieves mAP50(mean average precision at 50% intersection over union) of 69.7%, 91.3%, 94.2%, and 70.0%, respectively, outperforming existing comparative methods. The proposed network is expected to provide new perspectives for remote sensing tasks and possible solutions for relevant applications in the image domain.

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.