## 1 Introduction  
Automated driving is a rapidly evolving technology with the aim to revolutionize transportation by eliminating the need for human drivers. The predicted potentials of automated driving include increased driving comfort and safety, traffic flow optimization, reduced emissions, and enhanced mobility for people with disabilities or limited access to transportation. A common framework to classify different levels of automation is the SAE standard J3016 [1]. The framework categorizes the degree of automation into six levels, starting with Level 0, representing manual driving with no active assistance systems, up to fully automated driving without the need for a driver in Level 5. The introduction of active driver assistance systems up to Level 2 in the past decades has continuously increased the safety of passengers in critical driving situations [2]. Although Level 2 systems are already actively controlling the longitudinal and lateral vehicle guidance simultaneously, the driver is still obligated to monitor the system. The next step towards fully automated driving is, therefore, Level 3 (L3), also described as conditionally automated driving (CAD). L3 is the first level in which the driver is not required to continuously monitor the automated system under specific conditions, enabling users to spend their time with non-driving-related activities. With the shift of responsibility to the automated system, the role of the driver also changes to that of a passenger [3,4], raising the question of how users want to be driven when they are no longer in control of the driving task. Do users prefer their own or a different driving style in automated driving? Only a few studies have started to investigate this question in recent years with the majority using driving simulators. A common approach for investigating user preferences is to let users experience different driving styles in comparison. The driving styles are either predefined or a prerecording of the user's own driving style. Refs. [5,6] found that both defensive and aggressive drivers prefer a defensive style. Ref. [7] comes to the same conclusion for defensive drivers, but found no tendency for aggressive drivers. Refs. [8,9] analyzed driving style preferences in relation to participants' prerecorded own driving style without considering driver types. Whereas participants preferred their own driving style in [8], a more defensive style compared to participants' personal driving style was preferred in [9] for all driving situations except for lane change maneuvers, for which a more aggressive style was preferred. Refs. [3,10,11] analyzed driving style preferences for lane changes on motorways. Without considering the driver type or recordings of participants' driving styles, all studies found that participants preferred a defensive driving style. The presented automated driving styles differ in characteristics of driving dynamics and driving strategies with regard to interaction with other road users and the environment. Under the assumption that drivers will expect their AV to drive like a human, many research papers focus on analyzing [12,13,14] and learning human-like driving behavior for AVs [15,16,17,18,19]. The goal of human-like driving behavior is to include human driving conventions to increase ride comfort and safety as well as predictability for other road users [20]. Human-like features in AVs, including driving behavior, are also studied in many papers under the term anthropomorphism [21,22]. Whether a driving style is preferred for automated driving is often determined by assessments of comfort, safety, and trust. Although definitions for comfort and safety in the field of automated driving can differ depending on the research question, both can be measured by the absence of discomfort and danger or on one dimension from a negative to positive value range representing different degrees of comfort or discomfort.