## 2 Background and Related Work  
Automated driving technology, also known as autonomous or self-driving vehicle technology, uses a combination of complex sensors and advanced algorithms to navigate and interact with their surroundings without human intervention. As with any new technology, the development and deployment of automated vehicles come with potential risks and challenges that must be addressed. These risks and challengesare related to the safety and reliability of the technology, the ethical and legal implications of its use, and the overall impact on society and the environment [4]. SAE J3016 is widely recognised as a taxonomy and definition reference for Automated Driving Systems (ADSs) [1]. ADS features are categorised under SAE automation levels three to five. These systems are designed to take over the driving task for a portion of a trip, performing operational functions such as vehicle motion control (lateral and longitudinal) and tactical functions like route planning, following, and object and event detection and response (OEDR). Similar to a human driver, ADSs must be able to perceive their location and surroundings, which requires various functionalities. These functional, nonfunctional, and technical requirements are crucial considerations throughout the development, type approval, and consumer testing of ADSs. The assessment of ADS features is significantly influenced by the concept of the operational design domain (ODD) [5, 6]. The ODD refers to the specific operating conditions in which an ADS is designed to function and must be integrated into safety-related functions. The dynamic driving task (DDT) encompasses the real-time operational and tactical functions necessary to operate a vehicle within the ADS's ODD. Several efforts have been made and are ongoing to define and describe an ODD, including standards such as those set by the British Standards Institution (BSI) [7], the International Organization for Standardization (ISO) [8], and the Association for the Standardization of Automation and Measuring Systems (ASAM) OpenODD [9]. Safety-assessment approaches for autonomous systems encompass a range of methodologies and techniques, but many are at least relatable to scenario-based testing and the SAE taxonomy. Another important aspect is the use of scenario-based testing [10, 11, 12]. Scenario-based testing aims to identify and test scenarios that are safety critical for the ADS feature in scope to ensure automated vehicles' safe operation [13]. The approach complements real-world testing and allows for a more comprehensive evaluation of the system's capabilities and limitations. By systematically designing and evaluating scenarios representing realistic and critical situations, developers can gain valuable insights into the system's performance and identify potential failure modes. Other safety-assessment approaches include real-world testing, distance-based evaluation, staged introduction, function-based testing, shadow-mode evaluation, formal verification, and traffic-simulation-based testing [14]. These approaches all enable the assessment of the system's safety and performance in various contexts. However, ensuring that autonomous systems meet the requirements and can operate safely in diverse environments requires a holistic approach. Several efforts are made to develop standardised testing methodologies for ADSs, and some focus on assessments [15]. Examples of standardised testing are the National Highway Traffic Safety Administration (NHTSA) Framework for Automated Driving System Testable Cases and Scenarios (ref. [16]) and the New Assessment/Test Method for Automated Driving (NATM) [17] proposed by the United Nations Economic Commission for Europe (UNECE). We primarily concentrate on NATM due to its significance in the European context. Within the NATM certification process, accelerated testing is combined with validity documentation supplied by the manufacturer in the audit and assessment procedure to cover system-related aspects.