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.

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.

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.