## 2 Literature Review
### Driving Style  
In the context of conditional automated driving, the driving style and strategy will become increasingly important for experienced comfort and safety as the driver will have limited predictability of the AV's trajectory and control over the vehicle guidance [10; 36; 40]. This has also been described as a \"loss of controllability\" [4]. So far, no universal definition of driving style exists [41]. However, [PERSON]. [41] has summarized three aspects of a driving style that are commonly shared among various definitions: (1) driving styles are relatively stable and habitual; (2) driving styles differ across groups and individuals; (3) driving styles reflect conscious decisions made by the driver. A common approach to describe a driving style is to use objective parameters such as vehicle kinematics and environment context. In the past, a variety of parameter representations have been introduced, such as time series data [3; 42], statistical characteristics [7; 43; 44], or latent variables, representing a dimensionless combination of different parameter types [9; 45; 46]. In order to differentiate between various driving styles, classification techniques such as manual or data-driven methods are commonly applied. These techniques typically involve analyzing value ranges or data patterns, which are then followed by subjective categorization using terms such as \"defensive\", \"moderate\", or \"aggressive\" [41]. Driving styles can also be differentiated based on their strategies for direct vehicle control, interaction with other traffic participants, or behavior in specific environments, as discussed in various studies [47]. For instance, different deceleration profiles for approaching a slower lead vehicle have been presented as examples of different strategies used for vehicle control [10; 36]. In another study, the behavior of an autonomous vehicle affected whether a cut-in vehicle changed lanes in front of or behind the autonomous vehicle [7] as an example of different strategies for cooperation. In the case of specific environments, Ref. [21] presented different approaches for passing through an intersection. One strategy involves cautious, human-like behavior, whereas the other involves machine-like behavior that conveys a sense of prior knowledge about the environment and other oncoming road users. Many studies suggest that human-like driving behavior will increase user trust, acceptance, predictability, and ride comfort and safety [18; 22; 48]. Human-like driving can be learned from large naturalistic data sets [13; 49], expert demonstration [14; 16], or random subject collectives [50]. A slightly different approach to learning a driving style was taken by [15], specifically asking participants to demonstrate their desired driving style for an automated vehicle through manual driving instead of recording their everyday driving style. The aim of human-like driving styles is to incorporate common driving conventions of human drivers, including in-lane positioning in curves [51], distance regulation for car-following [17], crossing behavior for intersections [52], and others. The use of virtual agents in AVs with human-like features such as voice and appearance [22; 53; 54; 55] is an additional approach to create an even stronger impression of a human-like automated vehicle, which is also referred to as anthropomorphism in the literature. Preferences for automated driving have been analyzed for different maneuvers such as lane changes [8; 10; 11; 36; 43], car-following [56; 57; 58; 59], decelerating to a slower lead vehicle [10; 36], or across different maneuvers and scenarios [6; 7; 25; 26]. A common approach by previous studies in this context is to let users experience a predefined driving style in comparison with other driving styles or their own prerecorded driving style, using driving simulators. A Wizard-of-Oz setup was used by [6; 36]. The amount of studies investigating driving style preferences for automated driving is considered small.