## 1 Introduction
The regulation has already been used in a Proof of Concept (PoC) project, in which it was determined that the currently available ADS solutions still have limitations that complicate their deployment into real-world traffic [7]. At the national level, Germany stands out as an example where the legal regulations allow for the type approval of ADS, but here again, the definition of appropriate test cases to fit the ODD is partly the responsibility of the manufacturer and the TIC companies, with no specific list of test cases that must be followed mandatorily [8]. The testing of ADAS functions and autonomous vehicles is increasingly incorporating various simulations, in addition to on-road testing. The latter is not foreign to more classical vehicle testing methods; however, while a comprehensible amount of so-called 'field test' kilometers were previously required, testing autonomous vehicles would necessitate billions of kilometers [9]. With the advancement of simulations, the time required for actual testing can be significantly reduced. However, examining the authenticity of simulations, the validation processes present particular challenges, and there are also few guidelines regarding their use in homologation [2; 3; 10; 11]. Of course, simulations have many well-known benefits, such as implementing scenarios that would be difficult to reproduce in reality, or even the capability to run faster than real time and cost efficiency, but they also serve well in selecting the right test cases that later need to be evaluated under real-world conditions [12; 13]. In simulation technologies, mixed reality technologies are becoming increasingly widespread. These technologies utilize the interactions of digital twins of both the proving ground and the test scenario participants. Among the most advanced methods are those where this integration can occur in real time, such as in various Vehicle-in-the-Loop (ViL) or Scenario-in-the-Loop (SciL) procedures [14]. The cyber-physical representation of the proving ground has become more widely accepted, and therefore more proving grounds are making Ultra-High Definition (UHD) resolution digital twins available before physical testing is conducted. This trend can be observed in several places, such as M-City (Ann Arbor, MI, USA) or even ZalaZONE (Zalaegerszeg, Hungary) being available within MATLAB/MathWorks(r) software environments [15]. Beyond simulation and public road testing, closed, secure test tracks continue to play a key role in the examination of autonomous vehicles and will continue to do so [16]. During proving ground trials, test scenarios for autonomous vehicles can be defined and categorized in several ways, for example, by considering safety or comfort aspects, as well as adjusting to the planned operational domain of the vehicle. For autonomous vehicles, it is also necessary to properly define the ODD and the vehicle's responses to objects encountered within the ODD, known as Object and Event Detection and Response (OEDR). There are already standardized approaches to adequately describing the ODD, such as those by the Society of Automotive Engineers (SAE) or British Standards Institution (BSI), but even in this area, there is not necessarily a unified terminology for ODD elements and related concepts [17]. The definition of the ODD is an iterative process that should preferably be carried out in the relatively early stages of designing the autonomous function, but it may also be necessary to go back and refine certain attributes during the testing process, or even define new characteristics that were not anticipated earlier [18]. In the field of scenario generation for testing ADS, numerous scientific methods and technologies have been developed to address the growing demand for testing safe, energy-efficient, high-performance vehicle control strategies. The common approach in these methods is to enhance the efficiency of the testing process and accurately simulate real-world traffic situations.