## 2 Literature Review
### Research Gap  
With the changing role of the driver in conditional automated driving, understanding user preferences becomes an important factor for the acceptance of this new technology. The studies introduced in Section 2, therefore, provide first important insights into the question of how users would like to be driven in their role as a passenger. However, due to the small number of studies and their varying focus on different driving environments and situations, further research is necessary to fully understand how users want to be driven in an AV.  
As the majority of existing studies in this context are conducted in driving simulators, it is essential to also conduct studies on public roads to take advantage of the complementary benefits of both methods in analyzing user preferences in automated driving. Particularly, motorways are of special interest as the introduction of further and enhanced automated L3 systems or higher is most likely for this road type. The present study is, therefore, conducted on a German motorway around Stuttgart. Furthermore, a new approach is chosen to analyze user preferences by calculating the difference between the experienced automated driving style and the participants' personal driving style.  
As a result of loss of control through possible delayed interventions by the driver due to non-driving-related tasks and limited predictability of the AV's trajectory, we hypothesize that participants prefer a more defensive driving style than their own for automated driving (H1). For the same reasons, we further hypothesize that participants who experience an ADS that is more defensive than their PDS show a higher increase in trust than participants who experience an ADS that is equal or similar to their PDS (H2).  
In addition to a large field of studies focusing on extracting and learning human-like driving behavior, the present study analyzes how human-like driving behavior is perceived in an automated vehicle. Under the assumption that human-like driving behavior is preferred for automated driving, we hypothesize that participants who prefer the presented automated driving style also perceive the AV as more human-like (H3) compared to participants who do not prefer the presented automated driving style.  
Whereas a variety of studies focus on developing driving strategies for different maneuvers and scenarios, only a few studies focus on the influence of driving strategies on driving style preferences in automated driving. We, therefore, specifically asked participants to rate the overall driving strategy after the experienced automated driving style and analyzed which strategic decisions by the AV were rated during the drive. Under the assumption that the driving strategy influences whether a presented automated driving style is preferred, we hypothesize that participants who prefer the presented automated driving style also perceive the AV's overall driving strategy as more desirable and comprehensible compared to participants who do not prefer the presented automated driving style (H4).  
The methodology for determining preferred driving styles in automated driving also shows potential for new approaches. In previous studies, participants were only able to choose from their own or predefined automated driving styles. Although participants were asked in [15] to demonstrate their desired driving style, the primary objective of the paper was to utilize reinforcement learning to acquire driving styles for acceleration and lane change maneuvers within a specific velocity range. However, the analysis of the learned driving policies and their presentation to the participants is left open for future work. Therefore, we also asked participants in this study to demonstrate their desired driving style for automated driving and analyzed their demonstrated driving style to investigate if there are any differences compared to their personal driving style.