## 2 Literature Review  
The rapid advancement in automotive technology, particularly in ADAS and AV, is changing the landscape of vehicle operation and safety. These sophisticated systems offer promises of enhanced safety and efficiency, but their effectiveness is contingent upon proper user training and understanding [14]. The complexity and diversity of ADAS and AV technologies underscore the importance of effective training methodologies to ensure seamless interaction and user proficiency. One of the primary challenges in this domain is the lack of standardisation across manufacturers regarding ADAS and AV functionalities [24]. This lack of uniformity can lead to inconsistencies in user experience and potentially compromise safety. For example, the differences in the activation and deactivation processes of the same ADAS function across different manufacturers may lead to confusion for users [24]. Furthermore, the absence of specialised training and evaluation mechanisms for users adds to the complexity, highlighting the need for a more unified approach to training [14; 23]. However, the literature lacks comprehensive studies that compare the efficacy of these diverse training methodologies. Central to the user's interaction with ADAS and AV technologies is the concept of mental models. Mental models in the context of ADAS and AV are critical for the safe and competent operation of these systems, due to the increased automation and complexity [25; 26]. For example, the CA function plays a crucial role in reducing the driver's workload. It alerts the driver and can automatically apply brakes when an imminent collision is detected, thereby contributing to accident prevention. Gaining a comprehensive understanding of the CA system's capabilities and limitations is essential. Such knowledge is critical for drivers, helping them to avoid excessive dependence on technology while that they are prepared for timely manual intervention when necessary [9; 10; 11]. A mental model, in the context of automotive technology, refers to a driver's understanding and conceptualisation of a system's capabilities and limitations. It forms the basis for how drivers interact with and utilise the vehicle's advanced features safely and effectively [14]. A well-defined and robust mental model correlates with the precision in activating ADAS functionalities and the promptness of their activations. When considering mental models in the automotive technology domain, two primary dimensions emerge. (i) Understanding ADAS functions: drivers need to understand the various features of ADAS and how they work in different scenarios. This includes knowing what the system can do and recognising when it might not work as expected [27]. (ii) Recognising system status: drivers need to be aware of whether the ADAS is currently active. This means knowing if one or more of its functions are on or if they are turned off or not in use [27]. To evaluate the robustness of these mental models, methodologies such as scoring-based assessments and simulation tests can be employed. For instance, a scoring system based on the accuracy of responses to questions about ADAS functions can serve as a quantifiable measure. Here, accurate responses represent a \"strong\" mental model, while incorrect or incomplete responses indicate a \"weak\" mental model [27; 28]. In practical implications, a precise or'strong' mental model can enhance the safe and effective deployment of ADAS and AV technologies. Conversely, a weak mental model can lead to higher risks of misuse, excessive reliance, and potentially hazardous situations [27; 29]. In the context of evaluating the effectiveness of ADAS and AV training methodologies, it is crucial to select an appropriate simulation platform that aligns with this study's objectives. In the realm of ADAS and AV technology training, various simulation platforms offer distinct advantages each with its unique features.