Research on driving simulation has increasingly been concerned with the user’s experience of immersion and realism in mixed reality environments. One of the key issues is to determine whether people perceive and respond differently in these environments. Physiological signals provide objective indicators of people’s cognitive load, mental stress, and emotional state. Such data can be used to develop effective computational models and improve future systems. This study was designed to investigate the relationship between the verisimilitude of simple driving simulators and people’s physiological signals, specifically GSR (galvanic skin response), BVP (blood volume pulse) and PR (pupillary response). A within-subject design user experiment with 24 participants for five different driving simulation environments was conducted. Our results reveal that there is a significant difference in the mean of GSR among the conditions of different configurations of simple driving simulators, but this is not the case for BVP and PR. The individual differences of gender, whether people wear glasses and previous experiences of driving a car or using a driving simulator are correlated with some physiological signals. The data is classified using a hybrid GA-SVM (genetic algorithm-support vector machine) and GA-ANN (artificial neural network) approach. The evaluation of the classification performance using 10-fold cross-validation shows that the choice of the feature subset has minor impact on the classification performance, while the choice of the classifier can improve the accuracy for some classification tasks. The results further indicate that the SVM is more sensitive to the selection of training and test data than the ANN. Our findings inform about the verisimilitude of simple driving simulators on the driver’s perceived fidelity and physiological responses. Implications for the design of driving simulators in support of training are discussed.