In safety-critical software systems, cybersecurity activities become essential, with risk assessment being one of the most critical. In many software teams, cybersecurity experts are either entirely absent or represented by only a small number of specialists. As a result, the workload for these experts becomes high, and software engineers would need to conduct cybersecurity activities themselves. This creates a need for a tool to support cybersecurity experts and engineers in evaluating vulnerabilities and threats during the risk assessment process. This paper explores the potential of leveraging locally hosted large language models (LLMs) with retrieval-augmented generation to support cybersecurity risk assessment in the forestry domain while complying with data protection and privacy requirements that limit external data sharing. We performed a design science study involving 12 experts in interviews, interactive sessions, and a survey within a large-scale project. The results demonstrate that LLMs can assist cybersecurity experts by generating initial risk assessments, identifying threats, and providing redundancy checks. The results also highlight the necessity for human oversight to ensure accuracy and compliance. Despite trust concerns, experts were willing to utilize LLMs in specific evaluation and assistance roles, rather than solely relying on their generative capabilities. This study provides insights that encourage the use of LLMbased agents to support the risk assessment process of cyber-physical systems in safety-critical domains

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

This paper details the development and onboard evaluation of an Unmanned Aerial System (UAS) specifically designed for maritime firefighting and rescue operations on roll-on/roll-off (ro-ro) ships. Emphasizing the use of open hardware and software, the study focuses on the operational practicality and legal fesibility of a UAS prototype. The assessment of the UASs performance is multifaceted, incorporating expert surveys and a SWOT analysis. Key findings demonstrate the significant potential of UASs in augmenting maritime safety and emergency response capabilities. The paper provides insights into broader opportunities for integrating UAS technology in maritime operations, highlighting its role in enhancing the efficiency and effectiveness of critical maritime functions.

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.

WayWise is an innovative C++ and Qt-based rapid prototyping library designed to advance the development and analysis of connected, autonomous vehicles (CAVs) and Unmanned Arial Systems (UASs). It was deployed on model-sized cars and trucks as well as full-sized mobile machinery, tractors and UASs. It is actively being used in several European research projects. Developed by the RISE Dependable Transport Systems unit, the library facilitates exploration into safety and cybersecurity aspects inherent to various emerging vehicular applications within road traffic and offroad applications. This non-production library emphasizes rapid prototyping, leveraging commercial off-the-shelf hardware and the different protocols for vehicle-control communication, mainly focusing on MAVLINK. The utility of WayWise in rapidly evaluating complex vehicular behaviors is demonstrated through various research projects, thus contributing to the field of autonomous vehicular technology.

This report provides comprehensive information for deciding whether to pursue the deployment of adrone system for increasing safety on ship. The assessments of technical and legal feasibility as wellas usefulness of a drone system for surveying the open decks of a ro-ro ship are presented. The usecases of fire patrol, fire resource management and search & rescue operations are targeted. Aprototype drone system is detailed that is built on open standards and open-source software for highextensibility and reproducibility. Technical feasibility is assessed positively overall using a purpose-designed drone-control software, in-field tests and a demonstration onboard of DFDS PetuniaSeaways. The needs for further development, analysis and long-term tests are described. The legalfeasibility assessment gives an overview of applicable maritime and airspace regulations within theEU. It concludes that the drone system should be seen complementary to existing fire safety systemsand that operational authorization is best applied for in collaboration with a ship owner. Usefulnessis assessed using responses from maritime experts to an online questionnaire on the targeted usecases. Results are positive with two major challenges identified: achieving a reasonable selling priceand obtaining the ship operators’ and crews’ trust in the system. Finally, a SWOT analysis gives aconcise summary of the performed assessments and can be used as input to the strategic businessplanning for a potential drone system provider.