To make an autonomous vehicle a part of our daily life, it requires that the driver has complete trust in the vehicle. For that, the vehicle needs to successfully fulfill its tasks and the driver needs to understand its behavior. One way to accomplish this is by explaining the vehicle’s decision-making to the driver. On the one hand, the driver needs to know the information basis that the vehicle uses to form its decisions like the car condition, road condition, or current weather. On the other hand, the system needs to explain why it performed certain actions. We consider that the decision-making related to the driving task is based on Hierarchical Task Network (HTN) planning. The hierarchical structure of HTNs makes it easier for the driver to comprehend the system’s decision-making, which makes it easier to explain the actions that the vehicle performs. We created an HTN planning based domain for autonomous vehicles in the modeling languages HDDL and JSHOP2. For that, we classified all actions that an autonomous vehicle may make into different classes. Depending on the class of the action, we use a different combination of explainability components. The used components are visualization, voice, and text. These components let us provide the information in most cases to the driver even when he is currently distracted. We validated the performance of the explainability components in comparison to the actions related to the driving task of our domain. For that, we took a look at how the explanations are impacted by the time to find a solution and how many extra actions the found solution included. To better understand and show the explainability components, we developed a graphical user interface to demonstrate a possible usage for the explainability components for the driving task of an autonomous Vehicle.