## 7 Conclusions  
Road safety continues to be a paramount concern worldwide, with driving under the influence (DUI) remaining one of the leading causes of traffic accidents. Investigating DUI is a logical step to enhance safety measures. Our study extends previous research by not only confirming the known effects of alcohol on driving and gaze behavior but also using these indicators to classify a driver's state. In doing so, we took an innovative step further by classifying a driver's impairment state using multiple-sensor data. By combining eye-tracking and driving behavior data, we were able to fuse these sources of information in various ways, leading to valuable insights and high-performing classification models. This study confirms the well-documented impact of alcohol on driving, particularly highlighting risky behaviors such as increased speed, more aggressive maneuvers, and changes in steering and lateral stability. These changes were most pronounced in rural road scenarios, where monotonous driving heightened the visibility of alcohol-induced impairments. Urban environments, while complex and event-heavy, also demonstrated noticeable shifts in driving behavior, though these were less distinct due to the multitude of visual and driving tasks required. Interestingly, our findings reveal that gaze behavior (specifically fixation and saccades) was strongly dependent on the driving scenario, with city settings displaying the most dynamic shifts due to the frequent need for attention shifts when turning or changing lanes. One of the novel contributions of our study was the application of classification models to detect intoxication based on sensor data. The fusion of eye-tracking and driving behavior features allowed for a strong classification performance, particularly in urban and rural settings. Our models achieved test accuracies exceeding 70%, with the more complex LSTM model reaching up to 80% in accuracy on rural roads. These results illustrate that combining data sources enhances the ability to detect a driver's state, offering a more robust method for identifying impairment compared to relying on a single data stream. However, while our models showed strong performance, they were hindered by overfitting, primarily due to the relatively small sample size of participants. This overfitting was evident in the gap between training and testing accuracies, especially on highways, where the more consistent driving environment made it harder for the models to distinguish between sober and intoxated states. Additionally, the moderate BAC level of 0.06% may have limited the detection of more severe impairments that might occur at higher levels of intoxication. Nonetheless, detecting impairment just above the legal limit is particularly important for real-world applications, where even mild impairment can pose significant risks. Looking ahead, future research should focus on addressing the limitations encountered in this study. A larger sample size would help mitigate the overfitting issue and improve the generalizability of the models. Furthermore, investigating higher BAC levels could reveal more pronounced behavioral changes and offer deeper insights into the relationship between alcohol consumption and driving performance. Additionally, exploring correlations between gaze behavior and driving performance could provide valuable insights into how attentional focus is impacted by intoxication. By refining these approaches, future studies could further enhance the accuracy and reliability of classification algorithms, offering powerful tools for improving road safety and potentially informing the development of real-time DUI detection systems in vehicles. In conclusion, this study not only validated previous research on the effects of alcohol on driving but also advanced the field by demonstrating how multiple sensor sources can be leveraged to classify a driver's intoxication level accurately.