Drowsiness has a globally negative impact on human performance by slowing response time, decreasing situational awareness, and impairing judgment. This paper reports the findings of a Field Operational Test (FOT) of an early prototype Drowsy Driver Warning System (DDWS). Fifty-three research questions were addressed related to performance, capabilities, acceptance, and deployment. The FOT included control and test groups utilizing an experimental design suitable for a field test. The dataset for the analysis consisted of 102 drivers from 3 for-hire trucking fleets using 46 instrumented trucks. Fiftyseven drivers were line-haul and 45 were long-haul operators. The data set contained nearly 12.4 terabytes of video, truck instrumentation, and kinematics data for 2.4 million miles of driving and 48,000 driving-data hours recorded, resulting in the largest data set ever collected by the U.S. Department of Transportation. When considering the operational window of the Driver Fatigue Monitor, results showed that the drivers in the Test Group had lower drowsy measurement values, and that drivers who received feedback from the system had an overall reduction of drowsy driver instances. Whereas, the experimental design was specified to support the statistical reliability of potential findings, the dataset was largely diminished from eyes-off-road time from driver distraction and normal mirror checking tasks, which were incorrectly sensed by this early prototype as drowsy episodes. As a result, no statistically reliable safety benefit was observed. However, novel data reduction procedures were able to extract data during the time periods in which the system was accurately detecting drowsiness, and analysis of these data indicated a slight reduction in critical unsafe driving events related to drowsiness. As a result, while there is some indication that a DDWS may be a promising concept, the particular prototype used in this field test to implement the concept needs significant improvement and further study.