Detecting rare ground-level obstacles (e.g., large boulders) in dense boreal forests from low-altitude UAV RGB imagery is challenging due to limited annotated data, strong background clutter, and expensive field labeling. This paper evaluates two complementary synthetic-data augmentation pipelines for low-data forestry object detection: segmentation-guided diffusion inpainting, where SegFormer-derived forest-floor masks constrain Stable Diffusion inpainting to plausible insertion regions, and simulator-based generation in Unreal Engine 5 with controlled domain randomization and automatic annotations. We conduct a ten-fold cross-validation study on a real UAV dataset of 64 images and report both accuracy and stability across folds. Compared to real-only training (mean mAP50 ≈ 0.579; mAP50-95 ≈ 0.350), inpainting improves mean performance (mAP50 ≈ 0.647; mAP50-95 ≈ 0.435) while substantially reducing cross-fold variance and lifting the worst-case fold from 0.301 to 0.619 in mAP50. Simulator augmentation yields slightly lower mean accuracy (mAP50 ≈ 0.546; mAP50-95 ≈ 0.344) but markedly improves robustness by mitigating collapse on difficult splits (minimum mAP50 0.496 vs. 0.301). These results indicate that carefully curated generative augmentation can reduce failure risk and improve generalization in extremely data-limited forestry detection settings without additional field data collection

Security Chaos Engineering (SCE) is a proactive approach to identify vulnerabilities and enhance security of systems. It embraces continuous security experimentation to build confidence in the capability of systems to withstand malicious conditions. Different SCE techniques are proposed for enhancing the resilience of software systems. The diversity of SCE techniques indicates the need for their collective analysis to uncover valuable practices and potential research opportunities. To fulfill this need, we consolidate and unify the knowledge on SCE practices through a systematic literature review. The results show that there has been limited and unsystematic investigation of SCE by the community, highlighting the importance of creating and promoting guidelines for SCE practices. Therefore, we create SCENE, a comprehensive set of guidelines for systematically reporting SCE. The goal is to support the clarity, consistency, and reproducibility of SCE practices. SCENE guidelines are evaluated by cybersecurity practitioners and active researchers in the field, and is mapped to established methodological guidelines. The results indicates that SCENE is perceived positive in terms of usefulness, understandability, practicality, and completeness. SCENE is also found to complement established experimental reporting guidelines and bridge the gap between academic studies and industrial use

Automated driving systems (ADSs) have the potential to make mobility services both safer and more accessible. The New Assessment/Test Method (NATM) from the UNECE establishes a multi-pillar framework for ADS safety assessment, centred on comprehensive scenario-based testing of the operational design domain (ODD). While NATM sets out the vision, it leaves unresolved how such assessments can be scaled and applied in practice. The SUNRISE safety assurance framework (SAF) addresses this challenge by offering a concrete and scalable pathway for operationalising NATM principles. The core contribution of this paper is the successful execution of the SAF process. Rather than validating the performance of a specific automated driving function, the work demonstrates how the SAF can be applied end-to-end: starting from external requirements for the system under test (SUT), through scenario generation based on ODD, dynamic driving task (DDT), and test objectives to the allocation of scenarios across heterogeneous test environments and the consolidation of outcomes into a structured safety argument. The approach is exemplified through the use case of automated truck docking in confined logistics environments. Simulation (CARLA), a scaled model truck, and a full-size truck are employed not to validate the ADS function itself, but to show that the SAF enables consistent, traceable, and defensible execution of NATM-aligned safety assessment. This walk-through highlights the scalability, practicality, and applicability of the SAF to real-world ADS features.

Color fidelity is often overlooked in simulation-based validation for autonomous vehicles, yet even minor color mismatches can undermine the reliability of AI-driven perception systems. In this paper, we systematically examine how controlled deviations in color reproduction—quantified by \DeltaE{}—affect object detection accuracy across 32 variants of YOLO. Using a Macbeth ColorChecker, we derive calibrations for key color transforms (brightness, contrast, hue, gamma, saturation and color bias) and apply these to the COCO validation set. Our evaluations demonstrate that increasing \DeltaE{} yields significant drops in detection metrics, especially for safety-critical categories such as pedestrians and cyclists. Based on these findings, we propose \DeltaE{} thresholds that define acceptable color fidelity in camera simulations (e.g., \DeltaE{} $\leq 3$ for $\Delta$mAP $\leq 1\%$). Furthermore, we contribute these transformed datasets and scripts as a publicly available benchmark, enabling reproducible comparisons and guiding future research on color-based vulnerabilities in automated driving and other safety-critical domains.

Recent advances in high-performance graphics and physics engines (e.g., Unreal Engine) have popularized simulators for safety-critical system testing, yet credible validation is essential for reliable outcomes. This paper introduces a novel methodology for validating simulation toolchains, combining principles from SAE and UNECE frameworks with validation cycles to accommodate evolving safety-critical requirements. We demonstrate this approach through a case study evaluating the color fidelity of an Unreal Engine-based perception toolchain for safety-critical applications such as human and obstacle detection. Comparative tests of real and simulated camera outputs show that Unreal Engine’s camera model achieves "Delta E" < 4 under controlled lighting, closely matching the reference colors, but complex real-world lighting and seasonal variations can introduce perceivable color discrepancies. Our iterative methodology enables progressive refinements (reducing "Delta E" variations) and establishes critical traceability links for assessors related to evolving system requirements, toolchain modifications, as well as validation evidence. The resulting framework provides assessors with a verifiable chain of evidence from initial discrepancies to compliance, bridging the gap between adaptive development and certification needs.

Validating connected and automated vehicles (CAVs), specifically Automated Driving Systems (ADS), remains a challenge, particularly in ensuring safety and reliability across diverse operational scenarios. Before an ADS can be considered safe for deployment, it must be evaluated across a wide range of carefully designed test cases that capture both expected and edge case conditions. As recognized in the UNECE's New Assessment/Test Method for Automated Driving (NATM), testing all such scenarios on a real system is often impractical, making virtual testing an essential complement to physical tests. To enable this, we present WayWiseR, an open-source rapid prototyping platform built on ROS2 that supports researchers in developing and evaluating validation methodologies for CAVs. By integrating modular components, simulation environments such as CARLA, and scaled vehicle hardware, WayWiseR enables reproducible experimentation and flexible orchestration of test scenarios across both virtual and physical platforms. We demonstrate the platform through two representative use cases: autonomous reverse docking in a logistics hub, and human detection and emergency braking in forestry environments. The results demonstrate WayWiseR's ability to bridge simulation-based validation with real-world operational testing, thereby supporting the safer deployment of sufficiently validated CAVs

he increased importance of cybersecurity in autonomous machinery is becoming evident in the forestry domain. Forestry worksites are becoming more complex with the involvement of multiple systems and system of systems. Hence, there is a need to investigate how to address cybersecurity challenges for autonomous systems of systems in the forestry domain. Using a literature review and adapting standards from similar domains, as well as collaborative sessions with domain experts, we identify challenges towards CE-certified autonomous forestry machines focusing on cybersecurity and safety. Furthermore, we discuss the relationship between safety and cybersecurity risk assessment and their relation to AI, highlighting the need for a holistic methodology for their assurance.

In recent years, the increasing digitalisation and interconnectedness of railway systems have underscored the critical importance of robust cybersecurity measures. Notable cybersecurity incidents, such as the sabotage of more than 20 trains in Poland via simple "radio-stop" commands using low-cost equipment, highlight the vulnerability of these complex systems to disruptions that can have far-reaching consequences. Moreover, the evolving threat landscape, characterised by increasingly sophisticated ransomware and distributed denial-ofservice (DDoS) attacks, poses ongoing challenges that demand continuous vigilance and adaptation. The regulatory response, including stringent EU directives such as the Cybersecurity Act and the NIS 2 Directive, reflects a concerted effort to elevate the cybersecurity standards that impact the transportation sector. The objective of this work is to provide a cybersecurity risk assessment of the Virtually Coupled Train Set (VCTS) design that is developed within the R2DATO EU Rail project. This work leverages the methodologies developed under the Shift2Rail (S2R) initiative, particularly the X2Rail-5 project. The assessment aims to identify potential vulnerabilities and assess the impact of potential threats. Risk and target security level evaluations for VCTS are presented for identifying applicable security requirements from IEC 62443. By applying a risk assessment tool based on IEC 62443-3-2 and CLC/TS 50701 towards regulatory compliance measures, this work seeks to fortify the cybersecurity of railway systems, ensuring safer and more reliable operations in an increasingly digital landscape.