Fredrik Warg

With the rise of AI-enabled cyber-physical systems, data annotation has become a critical yet often overlooked process in the development of these intelligent information systems. Existing work in requirements engineering (RE) has explored how requirements for AI systems and their data can be represented. However, related interviews with industry professionals show that data annotations and their related requirements introduce distinct challenges, indicating a need for annotation-specific requirement representations. We propose the Data Annotation Requirements Representation and Specification (DARS), including an Annotation Negotiation Card to align stakeholders on objectives and constraints, and a Scenario-Based Annotation Specification to express atomic and verifiable data annotation requirements. We evaluate DARS with an automotive perception case related to an ongoing project, and a mapping against 18 real-world data annotation error types. The results suggest that DARS mitigates root causes of completeness, accuracy, and consistency annotation errors. By integrating DARS into RE, this work improves the reliability of safety-critical systems using data annotations and demonstrates how engineering frameworks must evolve for data-dependent components of today's intelligent information systems.

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.

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.

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 systems 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 environments 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 environments capacity for object-detection sensor assessment.

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.

Automated Driving Systems (ADSs) have the potential to make mobility services both accessible and safe. A multi-pillar Safety Assurance Framework (SAF) has been proposed to support the safety assessment of ADSs, requiring comprehensive test scenario coverage of the system’s Operational Design Domain (ODD). This work addresses two key questions: first, how a Key Performance Indicator (KPI) can be evaluated within the proposed SAF, and second, how heterogeneous test environments contribute to a comprehensive KPI assessment. By focusing on specific KPIs, this paper can demonstrate the complete walk-through of how external requirements for the system under test (SUT) are integrated into the SAF, leading to the generation of concrete test scenarios based on the ODD, dynamic driving task (DDT), and test objectives. The allocation of test cases to relevant test environments, both virtual and physical, follows the proposed methods and the evaluation of safety criteria. The use case is automated truck parking with a semi-trailer in confined logistics environments, such as terminals, providing a controlled setting to validate highly automated vehicles’ low-speed perception and decision-making capabilities (L4). This use case employs simulations and physical tests, using model-scale and full-size trucks, to showcase the SAF’s ability to guide a safety assessment.

Background To gain widespread use, assisted and automated driving (AAD) systems will have to cope with harsh weather conditions, such as rain, fog, and snow. This affects the development and testing of perception and decision-making systems. Since the weather cannot be controlled in field tests, the availability and use of virtual simulation and test facilities that can accurately reproduce harsh weather becomes vital. Test cases subjecting the system under test to harsh conditions, covering all expected weather phenomena in both typical and challenging scenarios, must be defined to evaluate all aspects of the system. Methods State-of-the-art in scenario-based and hash weather testing for AAD systems was analysed; based on the analysis, a team with diverse expertise in AAD development and testing defined a methodology for defining a set of harsh weather test cases. Results This paper proposes, and exemplifies the use of, a methodology to develop a representative set of test cases based on the defined operational design domain and use cases for an AAD system under development, considering the possibility of reproducing tests in different test environments with a focus on harsh weather. Conclusions We believe that our proposed methodology can accelerate the overall testing process and contribute to the difficult safety assurance challenges for automated vehicles.

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.