This study was undertaken to determine which variables and combination of variables could be used for the prediction of on-the-road drowsiness. Numerous driver-vehicle performance measures and secondary task performance measures were collected so that the predictability of several definitional measures of drowsiness could be tested. Twelve volunteer subjects were employed in the algorithm development phase of this study. All subjects were from the driver population in the Blacksburg, Virginia area. The participants were sleep deprived and drove a moving base simulator late at night in order to increase the likelihood that they would experience drowsiness while driving. After completion of data collection, numerous algorithms were developed using multiple regression and discriminant analysis methods. Another twelve volunteer subjects were subsequently employed in the algorithm validation phase of this study. Similar physiological and driving performance measures were collected during both phases of the study. All subjects were from the same driver population. All subjects were run under similar conditions as those in the algorithm development phase. Algorithms that appeared promising which were developed in the first phase of study were validated by applying them to the new data in an attempt to predict drowsiness on a new subject pool. It was found that drowsiness could be detected on a new subject pool and that the rate of correct predictions was quite high. There was no general decrease in predictive power of the drowsiness detection algorithms when applied to new data. Results showed that an accuracy rate of over 90 percent could be accomplished when output from the detection algorithms were classified into categories of "Awake," "Questionable," and "Drowsy." / Ph. D.