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Assuring an AI-based perception component for highly automated driving (HAD) requires evidence from ISO 26262 (functional safety), ISO 21448 (SOTIF, safety of the intended functionality), ISO/SAE 21434 (cybersecurity), and ISO/PAS 8800 (safety of AI in road vehicles). The standards cross-reference each other normatively at the level of requirements and evidence. No standard prescribes how the claims combine under a single top-level goal in Goal Structuring Notation (GSN), and the ISO/PAS 8800 Annex B pattern does not integrate cybersecurity claims. This paper extends the Annex B pattern with those cybersecurity claims, alongside SOTIF claims from ISO 21448 and functional safety claims from ISO 26262. The pattern is positioned at the AI component level inside the encompassing system safety case and instantiated for a LiDAR-based 3D object detection component. The contributions are (i) an integrated GSN argument pattern with clause-level traceability to the cited standards, (ii) a catalogue of decision points the engineer must resolve where the standards defer to application context, and (iii) a classification of integration-induced findings that emerge only between standard scopes, distinguished from open methodological problems. Combining the cited standards reveals an evidence asymmetry at the verification and validation goal: the evidence types are disjoint by design, the cybersecurity evidence type (attack success rate) adds a fourth measurement scale to the three safety evidence types, and no standard prescribes their combination into a single sufficiency claim.
The development of fuel cell electric vehicles (FCEVs) remains challenged by complex system integration, powertrain design, and the limited availability of experimental data under realistic operating conditions, which constrains the validation of energy management systems (EMSs) and system-level performance assessment. To address this gap, this study presents a validated test bench platform for fuel cell powertrains that combines a hardware-based powertrain test bench with a simulation environment for EMS analysis.
The platform enables the integration and testing of a fuel cell powertrain in an electric van under realistic operating conditions. Validation under the US06 driving cycle shows an equivalent hydrogen consumption deviation of only 5.1 g (2.4%) between the hardware and simulation environments, demonstrating high platform reliability. A comparative analysis of load-following and average load power strategies is conducted. Results indicate that the average load power strategy achieves higher energy efficiency, reducing equivalent hydrogen consumption by 1.8%, 3.8%, and 6.4% under city, rural, and highway conditions, respectively. The efficiency advantage becomes increasingly pronounced as power demand rises. The proposed platform provides a validated framework for system-level development, validation, and evaluation of fuel cell powertrain systems.
Accurate road condition assessment requires the fusion of geometric and visual information from LiDAR and camera systems. In this work, we investigate the feasibility of multimodal registration between LiDAR-derived height and intensity maps and RGB image data for road surface analysis under real-world conditions.
Our experiments show that classical feature- and intensity-based registration methods are not suitable for this application. The homogeneous nature of road surfaces, the lack of stable cross-modal correspondences, and the limited structural quality of LiDAR intensity data prevent robust and reproducible alignment.
As a result, we rely on a time- and velocity-based alignment strategy that leverages sensor synchronization and vehicle motion. This approach provides a stable coarse registration but does not allow precise multimodal fusion and is sensitive to temporal offsets and dynamic measurement conditions.
These findings indicate that reliable multimodal fusion cannot currently be achieved using classical methods in real-world road scenarios. We therefore outline learning-based approaches, such as cross-modal feature learning and self-supervised methods, as promising directions for future work.
Safety is a fundamental requirement for the acceptance of modern vehicles. When considering autonomous driving functions, this factor is crucial for the spread of this technology in the market. Human behavior plays a key role in the analysis, as these systems must be adapted and optimized to meet human needs in order to achieve a high level of market penetration. Accordingly, effect chains must be known in detail to identify influencing factors and to be able to optimize them. This paper presents a theoretical model for perceived safety - analyzing the sub-criteria of humans, vehicles, and the environment also demonstrating and linking their influences on the basis of existing investigations. These findings are intended to contribute to the understanding of human behavior in order to better adapt systems to the needs of the users.
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.
