Fiber-reinforced composites offer promising lightweight alternatives for automotive applications, yet comprehensive life cycle assessments (LCAs) comparing them with conventional metal components remain limited. This study presents the first cradle-to-grave LCA evaluating a recyclable multi-material glass-fiber-reinforced polymer (GFRP) composite cross car beam (CCB) against an aluminium reference, considering 11 CML midpoint impact categories with explicit composite EoL modelling under different scenarios. The study has been conducted as part of a larger research project aimed at supporting the development of a more sustainable GFRP composite CCB. The composite redesign achieves lower cradle-to-grave impacts across 10 of 11 categories, with reductions of 35-37 % in toxicity-related impact categories and 8-16 % in Global Warming Potential. Ozone Depletion Potential represents the only trade-off (-12-13 % increase), attributable to proxy-based inventory data. Despite replacing-40 % of the aluminium mass, residual aluminium supply for the hybrid composite structure dominates upstream impacts, while end-of-life (EoL) recycling credits prove decisive for resource depletion categories. These results indicate that environmental performance in hybrid multi-material composite architectures is governed by metal substitution, scrap minimization, and EoL recycling quality-conditions increasingly decisive as power-trains electrify and electricity grids decarbonize.
During the development of new vehicles, engineering efforts focus on minimizing injury risks for vulnerable road users and occupants in crash scenarios while maintaining structural integrity. To meet diverse requirements, the nonlinear behavior of passive vehicle safety systems is virtually designed and optimized using numerical Finite-Element (FE) crash simulations. However, due to the complexity of these systems, it is challenging and time-consuming for the engineers involved to understand their behavior. To reduce the time required for assessing crash simulations, we introduce a novel analysis framework that provides flexible data processing and incorporates explainable Artificial Intelligence (AI). The framework allows for examining arbitrary dependencies within parameter-, sensor-, and FE-mesh data by fitting a supervised Machine Learning (ML) model, which is then analyzed using SHapley Additive exPlanations (SHAP). To extend the SHAP methodology to the engineering domain, we introduce System and Difference SHAP values, which facilitate the aggregation of features that describe a system and enable comparisons between two simulations based on input feature contributions in the output space. This allows engineers to intuitively understand contributions to the overall system behavior and generate a data-driven understanding that can be rapidly established. Three industry use-cases from the structural and occupant vehicle safety domain are used to evaluate the framework. The observations achieved demonstrate enhanced and previously unseen insights into the behavior of the crash loaded systems by the effective use of AI within virtual engineering. Comprehensive ablation studies show reproducibility and consistency of the results obtained when using alternative ML models or sensitivity analysis methods.
During the use of advanced driver assistance systems, drivers frequently intervene into the active driving function and adjust the system's behavior to their personal wishes. These active driver-initiated takeovers contain feedback about deviations in the driving function's behavior from the drivers' personal preferences. This feedback should be utilized to optimize and personalize the driving function's behavior. In this work, the adjustment of the speed profile of a Predictive Longitudinal Driving Function (PLDF) on a pre-defined route is highlighted. An algorithm is introduced which iteratively adjusts the PLDF's speed profile by taking into account both the original speed profile of the PLDF and the driver demonstration. This approach allows for personalization in a traded control scenario during active use of the PLDF. The applicability of the proposed algorithm is tested in a driving simulator-based test group study with 43 participants. The study finds a significant increase in driver satisfaction and a significant reduction in the intervention frequency when using the proposed adaptive PLDF. Additionally, feedback by the participants was gathered to identify further optimization potentials of the proposed system.
A phase-resolved multi-reaction model bridges atomistic energetics and electrode behavior, accurately capturing hysteresis and relaxation in Si anodes.
Camera-based perception systems for autonomous driving are typically developed and evaluated using fixed sensor rigs, while real-world vehicle fleets exhibit substantial variation in camera placement, orientation, field of view, and camera count. This mismatch introduces a cross-rig domain gap in which only the geometric observation process changes. To study this effect under controlled conditions, we introduce Plentiful CARLA Camera Rigs, a benchmark that renders identical driving scenes under 14 systematically designed camera rigs. This setup enables direct analysis of cross-rig generalization without confounding changes in scene content or appearance. Using the benchmark, we analyze cross-rig transfer behavior of representative multi-view perception architectures and observe substantial performance shifts induced by geometric rig variation. To facilitate structured analysis, we further introduce two calibration-based descriptors derived from rig metadata: Rig Variance, capturing internal rig diversity, and Rig Contrastive Distance, measuring geometric discrepancy between rigs. Our experiments show that geometric rig differences strongly correlate with relative cross-rig performance shifts and that Rig Contrastive Distance provides a reliable proxy for ranking transfer difficulty between sensor rigs.
