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This study presents post-mortem analyses of polymer electrolyte membrane fuel cell (PEMFC) stacks operated using previously published accelerated durability test (ADT) protocols. Each ADT, lasting 1,200 h, was derived from a 5,500 h automotive-relevant reference test and designed to isolate the effects of load cycling, operating temperature, and humidity cycling. All major membrane electrode assembly components, the membrane, catalyst layers, and gas diffusion layers (GDL), were characterized using SEM, EDX, IR thermography, contact angle measurements and XPS, and the results then correlated with prior in situ diagnostics. Elevated temperature and humidity cycling were identified as the most detrimental stressors under system-relevant operating conditions that included voltage clipping at 850 mV. These conditions led to accelerated carbon corrosion, increased platinum dissolution and cobalt leaching in the cathode catalyst layer, as well as a significant loss of hydrophobicity in the GDL. The membrane remained structurally stable, although localized stress indicators were observed. The results confirm that the applied ADT protocols successfully reproduce realistic degradation patterns and enable a differentiated assessment of stressor-specific aging phenomena. This provides a robust basis for refining accelerated stack testing methodologies and optimizing operating conditions for improved PEMFC stack durability.
Machine Learning (ML)-based LiDAR 3D object detectors in automated driving produce false detections, missed detections, and localisation errors under adverse weather and reduced visibility. Detection errors arising without hardware or software faults constitute performance insufficiencies under ISO 21448, Safety of the Intended Functionality (SOTIF), and the standard requires identification of the triggering conditions responsible. The prescribed analysis methods assume a design specification, but ML-based LiDAR object detectors have no design specification because the mapping from point clouds to bounding boxes is learned from training data. This paper proposes an uncertainty evaluation methodology that uses disagreement among deep ensemble members to separate correct from incorrect detections. Ensemble disagreement and performance insufficiencies arise from insufficient training data coverage of the operating condition. The methodology evaluates whether three uncertainty indicators derived from ensemble disagreement (mean confidence, confidence variance, and geometric disagreement) separate correct from incorrect detections. The evaluation produces outputs mapped to ISO 21448 analysis activities: discrimination metrics, triggering condition rankings by false positive share, frames flagged for investigation, and acceptance gates reporting coverage and false acceptance rate. A case study using simulated ensemble predictions across 22 weather configurations shows that geometric disagreement achieves the strongest separation, with acceptance gates that retain only true detections at reduced coverage. The observed separation arises because false detections produce spatially inconsistent bounding boxes across ensemble members where no physical object constrains the predicted position, while true detections remain spatially consistent.
Uncertainty in LiDAR sensor-based object detection arises from environmental variability and sensor performance limitations. Representing these uncertainties is essential for ensuring the Safety of the Intended Functionality (SOTIF), which focuses on preventing hazards in automated driving scenarios. This paper presents a systematic approach to identifying, classifying, and representing uncertainties in LiDAR-based object detection within a SOTIF-related scenario. Dempster-Shafer Theory (DST) is employed to construct a Frame of Discernment (FoD) to represent detection outcomes. Conditional Basic Probability Assignments (BPAs) are applied based on dependencies among identified uncertainty sources. Yager's Rule of Combination is used to resolve conflicting evidence from multiple sources, providing a structured framework to evaluate uncertainties' effects on detection accuracy. The study applies variance-based sensitivity analysis (VBSA) to quantify and prioritize uncertainties, detailing their specific impact on detection performance.
Das Ziel dieser Arbeit ist die subjektive Bewertung des Einflusses von Schäden an Achsbauteilen auf die Fahrzeugbeherrschbarkeit. Ein besonderer Fokus liegt dabei zum einen auf der Entwicklung von Methoden, um Gesamtfahrzeugmodelle mit Achsbauteilschäden in Echtzeit berechnen zu können. Zum anderen wird ein dynamischer Fahrsimulator verwendet, um die Gesamtfahrzeugmodelle subjektiv zu erleben und deren Funktionalität zu validieren. Für die echtzeitfähige Berechnung von Gesamtfahrzeugmodellen können kennfeldbasierte Zweispurmodelle sowie adaptierte Mehrkörpermodelle verwendet werden. Beide Ansätze werden zunächst an einem dynamischen Fahrsimulator appliziert und das grundlegende fahrdynamische Verhalten mit dem eines Realfahrzeuges verglichen. Anschließend werden als erste Schadenskonfiguration Bauteildeformationen untersucht. Dabei werden verschiedene Ansätze entwickelt, um Bauteildeformationen im Echtzeitmodell zu integrieren. Darauf aufbauend wird mit einer umfangreichen Validierungsstudie untersucht, inwiefern sich die subjektive Wahrnehmung der Schadenskritikalität von deformierten Bauteilen im Realfahrzeug mit der Wahrnehmung im dynamischen Fahrsimulator deckt. Dabei kann für ein breites Spektrum an Bauteildeformationen absolute Verhaltensvalidität nachgewiesen werden. Danach wird untersucht, wie Risse in Bauteilen auf echtzeitfähige Gesamtfahrzeugmodelle übertragen werden können. Ein physikalisches Ersatzmodell, welches die Nichtlinearitäten aus dem Material, der Geometrie und dem Kontakt der Rissufer erfasst, ermöglicht die echtzeitfähige Berechnung angerissener Bauteile. Ebenfalls untersucht wird der vollständige Bauteilabriss. Da infolge eines Lenkerabrisses die Radebene im Allgemeinen nicht mehr statisch definiert ist, sind kennfeldbasierte Modelle für derartige Anwendungsfälle ungeeignet. Durch eine Optimierung des Mehrkörpermodells sowie des zugehörigen numerischen Integrationsverfahrens kann jedoch eine echtzeitfähige Berechnung von spontanen Lenkerabrissen realisiert werden. Bisher ist die Bewertung des Einflusses von Achsbauteilschäden auf die Fahrzeugbeherrschbarkeit auf subjektive Fahreindrücke und somit (virtuelle) Fahrversuche angewiesen. Daher wird abschließend untersucht, ob Korrelationen zwischen objektiven fahrphysikalischen Größen und den Subjektivbewertungen gefunden werden können. Diese können teilweise identifiziert werden und ermöglichen somit eine Prädiktion des Subjektiveindrucks basierend auf Gesamtfahrzeugsimulationen in Kombination mit den Korrelationsmodellen.
For the assessment of axle damages, real vehicle tests have mostly been used so far, but they are dangerous and difficult to reproduce. Therefore, driving simulators are becoming increasingly important for the virtual rating of vehicles. Regardless of whether a real vehicle or a driving simulator is used, the prediction of the subjective perception of axle damages requires time-consuming driving tests. A powerful dynamic driving simulator is used to obtain subjective evaluations of various axle damages. Objective vehicle quantities are logged simultaneously. Subsequently, multilinear regression (MLR) models and artificial neural networks (ANN) are used to identify correlations and predict subjective evaluations based on objective data. Furthermore, real-time capable vehicle models in CarMaker and multibody dynamic (MBD) models in ADAMS/Car are used to virtually carry out driving manoeuvres and generate synthetic data. By combining the simulated vehicle data with an ANN, subjective driver evaluations can be predicted entirely virtual.
Up to now, cracked axle components are subjectively examined in real vehicle tests. In order to save development costs and time, these tests should be carried out in a dynamic driving simulator. A necessary prerequisite for this is a real-time capable full vehicle model that correctly represents all crack-related non-linear effects. This publication develops and validates a purely virtual process chain to describe cracked axle components in real time. Rear axle tie rods with different crack lengths at the same position are considered as an example. First, the stiffness behaviour is simulated at the component level and compared with real tests. The determined stiffness curves serve as a basis for the parameterisation of a physical substitute model. The tie rods are modelled as a non-linear FE component, as a linear flexible body and via the physical substitute model. These modelling approaches are integrated and validated in a FE semi-axle model as well as in the MBS full vehicle model. It is shown that the physical substitute model provides very good results. Finally, the full vehicle model with a cracked tie rod is converted into a real-time model based on elastokinematic maps and validated based on the MBS model.
In the development process of passenger cars, various scopes are defined by subjective criteria, which have to be determined in road tests. Therefore driving simulators are increasingly used in order to improve the efficiency of the development process. In the context of vehicle dynamics, map-based models are used predominantly, as they are unconditionally real-time capable. Multi-body simulation (MBS) models have a higher complexity and are therefore more accurate. However, adherence to the real-time condition depends on the available computing power and model complexity. A main advantage of this approach is that no conversion into map-based models is required. As a result, spontaneous changes can be made to the full vehicle model, which significantly enhances the tuning process at the driving simulator. First, a theoretical comparison of the two simulation approaches is made. This shows that real-time MBS models deliver significantly better results than map models, especially at higher frequencies. Subsequently, expert drivers assess both, the full vehicle models (map-based model and real-time MBS model) on a dynamic driving simulator in direct comparison with the real vehicle on the proving ground. The vehicle's controllability and steering behaviour through lane change manoeuvres and sinusoidal steering are assessed. It turns out that the subjective assessments between the driving simulator and the real vehicle agree very well. There are hardly any differences between the two simulation approaches for the driving manoeuvres examined. As a result, the process for integrating vehicle dynamics models on a driving simulator can be significantly downsized by the use of realtime multi-body models without experiencing any loss of evaluation quality. At the same time, this opens up potential for assessing comfort issues on the driving simulator.
Adverse weather conditions, particularly rainfall, present substantial challenges to camera-based perception systems in Advanced Driver Assistance Systems (ADAS). Unlike human drivers, camera sensors are more vulnerable to visibility degradation caused by raindrops, which can impair essential functions such as object and lane detection. In this paper, we introduce Rainy-nuScenes, a novel extension of the widely-used nuScenes dataset, specifically designed for benchmarking water droplet detection and segmentation in automotive camera images. The dataset comprises 762 annotated images containing over 1,700 labeled water droplets, enabling a detailed analysis of their spatial distribution and geometric characteristics. We conduct a comparative study between Rainy-nuScenes and related datasets, including WoodScape, emphasizing key differences in droplet coverage, distribution patterns, and annotation strategies. Furthermore, we evaluate various camera defisheye techniques—such as linear and orthographic projections—in conjunction with U-Net [1] based convolutional neural networks (CNNs) trained on both Rainy-nuScenes and fisheye-derived WoodScape images [2]. Our experiments show that the orthographic defisheye approach significantly improves the robustness and generalization capabilities of segmentation models. Rainy-nuScenes serves as a comprehensive benchmark for advancing ADAS algorithms in adverse weather, contributing to the development of safer and more reliable autonomous systems.
The data and code are available at: https://github.com/timdietereberhardt/rainynuscenes
This article traces the evolution of Driver Assistance Systems (DAS) from mechanical aids and electronic control to sensor fusion, perception, and AI-viewed through the dual lens of a university professor and a museum curator. Building on the history of advanced driver assistance systems at the Deutsches Museum, Verkehrszentrum [1], it uses 23 exhibits to show how people have interacted with, trusted, and gradually delegated control to machines. Early milestones (Benz Patent Car, Mercedes Simplex, signaling systems) are set against modern functions such as automated parking, blind-spot detection, and higher automation levels. An international teaching perspective from the Shibaura Institute of Technology (Tokyo) highlights how cultural and regulatory contexts shape design and acceptance. The article emphasizes human-centered design, regulatory frameworks (SAE Levels), and user acceptance, and outlines didactic approaches that connect historical insight with engineering education.