# A VR Truck Docking Simulator Platform for Developing Personalized Driver Assistance
These simulators often evolved into VR simulators, providing a higher degree of immersion. Therefore, in recent times, VR simulators have become a helpful tool for researchers to investigate the influence of different factors on driver response behaviours under different environmental and driving conditions [4]. Another important development to study and predict driver behaviour became reasonable through the rapid developments in AI technologies. With the advent of the 3 rd AI wave around 2020, AI started leading to machines capable of learning in a way that is much more similar to how humans learn. These systems will be able to learn from human behaviour, to understand context and meaning, and to use these capacities to adapt to different application fields (in contrast to current systems which only work well in a particular application environment). Modern AI approaches will also require fewer data samples for training, and will learn and function with less supervision as well as communicate with humans in a natural, adaptive and anticipative way, leading finally towards personalized and anticipative AI assistants. The three terms going along with the 3 rd wave of AI applications are: explainability, interpretability and transparency. Explainable AI (XAI) means artificial intelligence in which the system outcome is made transparent so that it can be interpreted and understood by a human (in contrast to current systems where the input parameters and decision behaviour are hidden in a \"black box\") [5; 6]. With these novel competencies, XAI aims to promote a greater understanding and trustiness between humans and machines [7]. Therefore, future AI models must be able to explain their decisions, make their decision behaviour transparent, and accept corrective input from the user (augmented intelligence). The feedback from the user can also be used to further train the AI. Bringing both technologies (XAI and VR/AR simulations) together opens up new perspectives in the development of intelligent vehicles which can learn from multimodal driver data recorded in VR/AR simulations, but can also use the insights to provide novice drivers with individualized and context-sensitive assistance if a deviation from expert behaviour is detected. This allows not only optimized training environments, but also the development of Advanced Driver Assistance Systems (ADAS), which can adapt individually to continuous human driver input and explain their decision behaviour, as well as provide adequate feedback. This will not only lead to a higher acceptance rate for future ADAS, but also increase the drivers' trust in these systems. This is crucial, as drivers cannot be forced to make use of the system while logistics companies seek to limit the cost of damage and unnecessary delays. This paper illustrates the approaches toward such an intelligent VR-simulator platform for the design and evaluation of personalized ADAS for the truck docking process. To explore these novel approaches, a specific driving scenario was chosen. In this scenario, truck drivers perform a rearward docking manoeuver towards a loading bay (dock). The advantage of using this scenario lies in the limited operational design domain, which is less complex and well-structured compared with other well-known driving scenarios in which ADAS systems operate, such as lane change assistance or adaptive cruise control. The rearward docking scenario, by nature, limits the number of vehicles and the speed with which they operate. Additionally, by exploring this driving scenario, it is also possible to profit from the fact that the loading bays are static and their position is well known. This driving scenario has been explored for several years, first within a project INTRALOG targeting the development of autonomous rearward docking [8] and currently through the VISTA project [9] (see Section 1.2). In the context of the VISTA project, a VR Truck-Docking Simulator was developed on which this paper is based and which is described in more detail in Section 2.