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Assessing driver drowsiness and providing timely alerts is the basis for drowsy driver monitoring systems. Though technologies are available that claim to reliably provide this function, they tend to be single-metric systems that may not be sufficiently robust for real-world operation. To address this issue, a prototype system that integrated two drowsiness measures was developed. The prototype combined machine-vision-based drowsy driver monitoring technology and the analysis of driver/vehicle performance parameters with the goal of more reliably assessing driver drowsiness. The prototype concept, called PERCLOS+, used PERCLOS (a slow eye-closure measure) in combination with lane deviation (to assess driver performance). Based on preliminary on-road tests, the prototype was found to be more robust than a single-metric system.

To address the issue of fatigued truck drivers, the U.S. Department of Transportation sponsored research to develop a Drowsy Driver Warning System. This system has been under development for several years and is at a point where it is ready for a Field Operational Test. The experimental plan calls for 102 drivers, each operating one of 34 instrumented heavy trucks for 16 weeks. Each vehicle is instrumented with video cameras and a variety of sensors to capture driver input/performance. This paper describes the method being used to conduct the study, including an overview of the data collection instrumentation.