## 4 Method
### Method of Classification  
The first step involved determining whether significant differences existed depending on the scenario and the specific indicators under consideration. Building on this, a classification algorithm was developed to distinguish between sober and intoxated drivers. Given the complexity of alcohol's influence--which varies depending on individual factors and blood alcohol concentration--a machine learning approach is well suited for detection. This approach employs classification models based on calculated indicators and key values, which are compared against a more complex machine learning model that leverages the entire time series dataset. Among the most promising models based on the indicators are logistic regression, random forest, and gradient boosting. Logistic regression is advantageous due to its simplicity and ease of interpretation. It models the probability of a binary state by applying a logistic function to a linear combination of the indicators. Random forest, by contrast, is a more complex model that constructs multiple decision trees from randomly selected subsets of indicators, and then classifies based on a majority vote. This method is particularly effective for capturingnonlinear relationships and is more resistant to outliers and overfitting. Gradient boosting also uses decision trees, but it builds them sequentially, with each tree correcting the errors of its predecessor. Although this method can achieve higher accuracy, it is more prone to overfitting and can be sensitive to outliers. For the more complex machine learning model, a Long Short-Term Memory (LSTM) network was utilized. The LSTM model's essential advantage over the others is its ability to process entire time series without the need for feature extraction. In contrast, simpler models rely on summary statistics such as the mean, median, standard deviation, and variance, which can result in a loss of important information. The LSTM's ability to retain more of the original data is expected to yield better classification results. In comparing the models, each driving scenario is analyzed individually, as well as the entire driving sequence. Additionally, the models are trained and evaluated based on four configurations:  
1. Driving data only. 2. Camera data only. 3. Data-level fusion of driving and camera data. 4. Interpretation-level fusion of driving and camera data. The aim was to demonstrate that fusing data from multiple sensors enhances the classification accuracy. Data-level fusion first combines the two data sources--driving data and camera data--before using them to train the models. In contrast, interpretation-level fusion trains and tests models separately on the driving and camera data. During testing, each model provides a prediction about the driver's state, along with a confidence value. Ultimately, the prediction with the higher confidence value is selected as the final result. For training our classification methods, we applied supervised machine learning. The sample set, consisting of only 21 participants (each with two drives: sober and intoxicated), limited the possibilities for machine learning approaches. Using unsupervised machine learning with all observed indicators (see Appendix D) led to overfitting and poor results. Therefore, we opted to select only a subset of indicators for model training. Similarly, for the LSTM model, we selected specific time series rather than key values for training. Data augmentation was not applied, as the number of sober drives matched the number of intoxicated drives, offering no clear advantage from augmentation. Given the relatively small dataset, a leave-one-out cross-validation (LOOCV) approach was applied. In this method, one subject serves as the test set while the remaining subjects were used for training. This process was repeated until each subject was used as a test set once. This technique ensures meaningful insights despite a limited sample size.