## 3 Method
### Database Structure  
We set up a MS Access Database to capture the relevant information needed for our investigations. The schema consists of the five main tables _Paper_, _Conference_, _Construct_, _CollectionMethod_, and _Parameter_. In the following, we introduce the most relevant properties:  
* **Paper:** For each paper, we collected descriptive information (title, abstract, year, authors, conference), as well as the levels of automation addressed in the study. The following additional information was collected: The type of user (driver, passenger, external), road type (urban, highway, rural, not relevant), study type (lab, test track, real road, survey), the representation of the AV (static text description, sketch, driving simulator, Wizard-of-Oz, real vehicle), study period (single session, short-term, long-term), type of research (basic research, concept evaluation, method development, model development), as well as participant information, such as the number of subjects, their mean age, as well as if they were internally (students, employees, etc.) or externally recruited.
* **Construct:** Represents the topics of investigation. To avoid subjective interpretation by the reviewers, we only collected constructs which were explicitly mentioned by authors in the papers, (such as _Safety_, _Trust_, _Acceptance_, _etc._). All constructs which were only investigated by one single paper are summarized within another construct. Generic investigations on participants' opinion and general perception without directly mentioning specific constructs are summarized with a _General Attitude_ construct.
* **Collection Method:** Relevant data collection methods, such as _driving performance_, _TOR performance_, _secondary task performance_, _ECG_, _EEG_, _standardized questionnaire_, _interview_, _etc._ Secondary task performance in particular refers to a participant's performance in a task not related to driving (e.g., number of missed target stimuli on a tablet). Again, we came up with initial suggestions that were expanded in case a new item emerged during the reviewing process.
* **Parameter:** Parameters that were used in the different data collection methods, for example standard deviation of lateral position (SDLP) [68], response time (could be used to measure driving performance), gaze-off-road time, gaze standard deviation (eye-tracking), technology acceptance model [35], NASA-TLX [69] (examples for standardized questionnaires), etc.
* **Relationships:** To structure our data, we created a relationship table to represent (n:m) relations of papers, collection methods, parameters, and constructs. Thus, each paper can investigate different constructs, where each construct can be assessed by one (or multiple) data collection methods, and each data collection method by one (or multiple) parameters. For each relation, we categorized whether the combination represents behavioral, self-reported, or physiological data. Furthermore, we assessed if the parameter was measured before/during/after a trial in the experiment. This data model allowed us to store all information without duplicates (each combination of paper/construct/collection method/parameter was stored only once usingdatabase key constraints), while the relations allow for performing powerful queries on the data (in comparison to pure list/sheet based representations). For example, we can ask the database which papers investigated a certain construct using physiological sensors, how many papers utilized a certain standardized scale, or how constructs changed over the years.