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.

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 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.