## 5 Discussion of Findings and Recommendations for Future AD Studies
### Study Setup  
Concerning representation and study type, we found that most studies were conducted using driving simulators. Hence, present research supports high internal validity making interpretations of effects of differing conditions on dependent measures possible. On the downside, the obtained results might lack external validity, and there is no guarantee that they generalize to real world settings, since driving simulators may lack realism due to the insufficient field of view, or motion feedback. Thus, by lacking a feeling of presence [121], the transfer of behavior found in the laboratory to real world settings might be limited [122]. Conditions in realistic driving studies are not as controlled as in a laboratory and might differ in terms of surrounding traffic, weather conditions or vehicle speeds. Despite the restriction of high internal but limited external validity, we can assume that strong and consistent effects such as the dependency of TOR response time on the driver state [123; 124; 13; 15; 21] \begin{table}
\begin{tabular}{l l l} \hline \hline
**Collection Method for Motion Sickness (n)** & **Parameter** & \(\mathbf{n_{p}}\) \\ \hline \multirow{3}{*}{Standardized Questionnaire (4)} & Sickness Questionnaire (SSQ) & 3 \\ \cline{2-3}  & Motion Sickness Assessment & \\  & Questionnaire (MSAQ) & 1 \\ \hline Self-Defined Questionnaire (1) & Other & 1 \\ \hline Heart Rate Variability (1) & HR (BPM) & 1 \\ \hline Driving Performance (1) & Motion Sickness Dose Value (MSDV) & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Motion Sickness collection methods and parameters. will also be reflected in real world driving. Especially when it comes to safety and trust issues in real driving, research should determine validity of the findings. While participants in driving simulator studies might behave in a more liberal way due to the absence of realistic severe consequences, it remains to be seen how these effects pertain to the real road. At this point, the question arises as to whether the scenarios that are tested in driving simulator research and frequency within a study are representative for human-automation interaction. Up to now, there is no valid statement on the frequency of transitions from automated to manual possible. An indication might be available from the California disengagement report [125], where developers of ADS have to report the number of safety driver interventions. As the systems are still under development and have not reached a maturity level for the commercial market, it is questionable whether these numbers resemble future series products. Future research from FOT or NDS data might be valuable for putting the importance of such scenarios into perspective. Relative validity of effects in driving simulator studies can well be assumed [126], but there are still research efforts necessary for validating safety relevant human-automation interaction scenarios. Despite this criticism, we acknowledge that driving simulators are still immersive research tools providing a certain, and, in many cases sufficient, degree of external validity [127; 128]. Nevertheless, the present results point towards the need of conducting studies in real vehicles equipped with driving automation technology [50; 129; 130], or Wizard-of-Oz Settings [48; 49; 131]. \(\Rightarrow\)_REC 1: Existing study results obtained in driving simulators should be validated in realistic on-road settings._  
Review results also revealed that most studies targeted single sessions of interaction, and thus provide only snapshots of first time use. While usability can already make reliable estimates about user behavior within a single session experiment [29], UX, trust, and acceptance might take a longer period of time until behavior and attitudes have reached a stable level [8; 131; 132]. Long-term studies tackling exactly these issues are rather scarce since they require high efforts. One example of such a study comes from [PERSON] et al.