Integrating Artificial Intelligence (AI) technology in electric vehicles (EV) introduces unique challenges for safety assurance, particularly within the framework of ISO 26262, which governs functional safety in the automotive domain. Traditional assessment methodologies are not geared toward evaluating AI-based functions and require evolving standards and practices. This paper explores how an independent assessment of an AI component in an EV can be achieved when combining ISO 26262 with the recently released ISO/PAS 8800, whose scope is AI safety for road vehicles. The AI-driven State of Charge (SOC) battery estimation exemplifies the process. Key features relevant to the independent assessment of this extended evaluation approach are identified. As part of the evaluation, robustness testing of the AI component is conducted using fault injection experiments, wherein perturbed sensor inputs are systematically introduced to assess the component's resilience to input variance.

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.

The deployment of automated functions that can operate without direct human supervision has changed safety evaluation in domains seeking higher levels of automation. Unlike conventional systems that rely on human operators, these functions require new assessment frameworks to demonstrate that they do not introduce unacceptable risks under real-world conditions. To make a convincing safety claim, the developer must present a thorough justification argument, supported by evidence, that a function is free from unreasonable risk when operated in its intended context. The key concept relevant to the presented work is the intended context, often captured by an Operational Design Domain specification (ODD). ODD formalization is challenging due to the need to maintain flexibility in adopting diverse specification formats while preserving consistency and traceability and integrating seamlessly into the development, validation, and assessment. This paper presents a way to formalize an ODD in the Pkl language, addressing central challenges in specifying ODDs while improving usability through specialized configuration language features. The approach is illustrated with an automotive example but can be broadly applied to ensure rigorous assessments of operational contexts.

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.

The emergence of Connected, Cooperative, and Automated Mobility (CCAM) systems has significantly transformed the safety assessment landscape. Because they integrate automated vehicle functions beyond those managed by a human driver, new methods are required to evaluate their safety. Approaches that compile evidence from multiple test environments have been proposed for type-approval and similar evaluations, emphasizing scenario coverage within the system’s Operational Design Domain (ODD). However, aligning diverse test environment requirements with distinct testing capabilities remains challenging. This paper presents a method for evaluating the suitability of test case allocation to various test environments by drawing on and extending an existing ODD formalization with key testing attributes. The resulting construct integrates ODD parameters and additional test attributes to capture a given test environment’s relevant capabilities. This approach supports automatic suitability evaluation and is demonstrated through a case study on an automated reversing truck function. The system's implementation fidelity is tied to ODD parameters, facilitating automated test case allocation based on each environment’s capacity for object-detection sensor assessment.

Connected automated vehicles (CAVs) are—compared conventional vehicles—expected to provide more efficient, accessible, and safer transport solutions in on-road use cases as well as confined areas such as mines, construction sites or harbours. As development of such vehicles has proved more difficult than anticipated, especially when it comes to ensuring safety, more cautious strategies for introduction are now being pursued. An approach where new automated features are initially released with more basic performance to enable successful safety assurance, followed by gradual expansion of performance and number of use-cases using an iterative development process as the confidence in the solution increases, e.g., due to more available field data, improved machine learning algorithms, or improved verification, is highly interesting. Hence a key research question targeted by the SALIENCE4CAV project was: How to ensure the safety of CAVs while enabling frequent updates for automated driving systems with their comprising elements? Today, many of the used methods and practices for safety analysis and safety assurance are not adequate for continuous deployment. In addition, the project has investigated several open questions raised by the predecessor project ESPLANADE and from needs identified by the industry partners; this includes how to handle safety assurance for machine learning components, use of quantitative risk acceptance criteria as a key part of the safety argument, safety for collaborative CAVs including use in mixed traffic environments, the role of minimal risk manoeuvres, and interaction with human operators.

Some key results are: investigation of safety assurance methods and gaps with regards to frequent updates and other challenges for CAV safety assurance; use of safety contracts as an enabler for continuous integration, continuous deployment and DevOps; a method for human interaction safety analysis; application of the principle of precautionary safety for meeting a quantitative risk norm and using field data for continuous improvements; definition of classes of cooperative and collaborative vehicles and their respective characteristics and definition of minimal risk manoeuvre and minimal risk condition strategies for individual, cooperative and collaborative vehicles; use of out-of-distribution detection for safety of machine learning; a simulation-aided approach for evaluating machine learning components; and methods for variational safety using high-dimensional safety contracts.

The SALIENCE4CAV project ran from January 2021 to December 2023 with the partners Agreat, Comentor, Epiroc Rock Drills, KTH Royal Institute of Technology, Qamcom Research and Technology, RISE Research Institutes of Sweden, Semcon Sweden, Veoneer (during the project acquired by Magna) and Zenseact. Coordination was done by RISE.

This final report is a summary of the project results and contains summaries of content from the project deliverables and publications.

The emergence of Automated Driving Systems (ADSs) has transformed the landscape of safety assessment. ADSs, capable of controlling a vehicle without human intervention, represent a significant shift from traditional driver-centric approaches to vehicle safety. While traditional safety assessments rely on the assumption of a human driver in control, ADSs require a different approach that acknowledges the machine as the primary driver. Before market introduction, it is necessary to confirm the vehicle safety claimed by the manufacturer. The complexity of the systems necessitates a new comprehensive safety assessment that examines and validates the hazard identification and safety-by-design concepts and ensures that the ADS meets the relevant safety requirements throughout the vehicle lifecycle. The presented work aims to enhance the effectiveness of the assessment performed by a homologation service provider by using assessment templates based on refined requirement attributes that link to the operational design domain (ODD) and the use of Key Enabling Technologies (KETs), such as communication, positioning, and cybersecurity, in the implementation of ADSs. The refined requirement attributes can serve as safety-performance indicators to assist the evaluation of the design soundness of the ODD. The contributions of this paper are: (1) outlining a method for deriving assessment templates for use in future ADS assessments; (2) demonstrating the method by analysing three KETs with respect to such assessment templates; and (3) demonstrating the use of assessment templates on a use case, an unmanned (remotely assisted) truck in a limited ODD. By employing assessment templates tailored to the technology reliance of the identified use case, the evaluation process gained clarity through assessable attributes, assessment criteria, and functional scenarios linked to the ODD and KETs.

During the last decade, there has been significant increase in research focused on automated vehicles (AVs) and ensuring safe operation of these vehicles. However, challenges still remain, some involving the cooperation and collaboration of multiple AVs, including when and how to perform a minimal risk manoeuvre (MRM), leading to a minimal risk condition (MRC) when an AV within one of these systems is unable to complete its original goal. As most literature is focused on individual AVs, there is a need to adapt and extend the knowledge and techniques to these new contexts. Based on existing knowledge of individual AVs, this paper explores MRM strategies involving cooperative and collaborative AV systems with different capabilities. Specifically, collaborative systems have the potential to enact local MRCs, allowing continued productivity despite having one (or several) of its constituents encounter a fault. Definitions are provided for local and global MRCs, alongside discussions of their implications for MRMs. Illustrative examples are also presented for each type of system.

The automotive industry is moving towards increased automation, where features such as automated driving systems typically include machine learning (ML), e.g. in the perception system. [Question/Problem] Ensuring safety for systems partly relying on ML is challenging. Different approaches and frameworks have been proposed, typically where the developer must define quantitative and/or qualitative acceptance criteria, and ensure the criteria are fulfilled using different methods to improve e.g., design, robustness and error detection. However, there is still a knowledge gap between quality methods and metrics employed in the ML domain and how such methods can contribute to satisfying the vehicle level safety requirements. In this paper, we argue the need for connecting available ML quality methods and metrics to the safety lifecycle and explicitly show their contribution to safety. In particular, we analyse Out-of-Distribution (OoD) detection, e.g., the frequency of novelty detection, and show its potential for multiple safety-related purposes. I.e., as (a) an acceptance criterion contributing to the decision if the software fulfills the safety requirements and hence is ready-for-release, (b) in operational design domain selection and expansion by including novelty samples into the training/development loop, and (c) as a run-time measure, e.g., if there is a sequence of novel samples, the vehicle should consider reaching a minimal risk condition. [Contribution] This paper describes the possibility to use OoD detection as a safety measure, and the potential contributions in different stages of the safety lifecycle. © 2023, The Author(s)