## 4 Method
### Method of Significance Analysis  
Data preparation followed the principles of exploratory data analysis (EDA). Initially, the data were validated and checked for completeness and plausibility. Subsequently, the data were correctly labeled and cleansed of impurities. This process included a graphical representation of the signals, computational checks, and comparison with video recordings of the experiment. Larger outliers were filtered, and the overall data distribution was  
Figure 4: Overview of the driving routes.  
Figure 3: City Map with overview over the events (E). The orange, green, and red events are traffic light events with the respective colors of a traffic light. The blue events are stop events, where the driver has to stop due to an event.  
analyzed. In cases of a non-normal data distribution, the Wilcoxon signed-rank test was applied as an alternative.  
The literature describes behavioral changes under the influence of alcohol, particularly affecting driving and gaze behavior, which serve as indicators of alcohol influence. Specific changes in driving behavior, such as acceleration, speed, braking, distance, lane keeping, steering behavior, and reaction times, were examined. Additionally, camera-based tracking showed changes in gaze patterns (fixations and saccades), facial expressions, and eye-steering coordination. In the next step, the recorded signals were compared with the indicators described in the literature. Paired \(t\)-tests were employed to identify significant differences between the data collected under the influence of alcohol and in a sober state.  
In the study design, each driver completed five drives: one test drive, two sober drives, and two drives under the influence of alcohol. To compare the sober drives with the alcohol-affected drives, whether significant differences existed within the groups (sober and alcoholized) was first examined. If no significant differences were found within the groups, the drives were pooled and then compared.  
Each drive included three scenarios: city driving, rural roads, and highways. Therefore, the analysis was conducted both across all scenarios and individually for each scenario. This approach was necessary because calculating averages, variances, and other statistical values for the entire route might obscure effects that become evident when analyzing individual scenarios. Furthermore, it was crucial to determine in which scenarios the most significant differences occurred to identify the situations where a drunk driver could be detected most reliably.  
After identifying significant differences between the individual indicators, each was analyzed in detail to assess the nature of the observed changes. For instance, speed behavior was examined to determine how it evolved--whether it tended to increase or decrease, how the variance shifted, and in which scenarios the changes were most pronounced. While a paired \(t\)-test can confirm significant changes between two paired groups, it does not reveal the direction or characteristics of those changes. Therefore, a closer speed analysis was conducted, focusing on trends and variance.  
Additionally, reaction behavior was specifically evaluated using events from the driving scenarios. These events allowed for the straightforward measurement of reaction times and the corresponding changes. Reaction time was assessed in two ways: first, during events requiring an emergency brake or stop, and second, in response to a traffic light turning from red to green, where acceleration was measured.