Early detection of Alzheimer's Disease (AD) is critical in creating better outcomes for patients. Performance in complex tasks such as vehicular driving may be a sensitive tool for early detection of AD and serve as a good indicator of functional status. In this study, we investigate the classification of AD patients and controls using driving simulator data. Our results show that machine learning algorithms, especially random forest classifier, can accurately discriminate AD patients and controls (AUC = 0.96, Sensitivity = 87%, and Specificity = 93%). The model-identified most important features include Pothole Avoidance, Road Signs Recalled, Inattention Measurements, Reaction Time, and Detection Times, among others, all of which closely align with previous studies about cognitive functions that are affected by AD.