Almost a million people are killed worldwide each year in motor vehicle crashes, over 42,000 of them in the U.S. Human/driver error (or induced error) is the most commonly identified contributing cause according to crash studies, especially studies conducted in the U.S. Accordingly, if crashes are to be reduced, a human-centered approach is needed.
As part of its Intelligent Transportation Systems program, the U.S. Department of Transportation (U.S. DOT) is funding several major projects (e.g., VII, IVBSS) concerned with active safety, warnings, and communications. As part of these and other projects, several meta-issues have arisen that deserve further attention. These pertain to: (1) what additional information would drivers want to know, or could drivers use about the driving situation, (2) presentation of the driving situation and warnings to drivers, (3) and (4) determining the processing time and detection performance for the entire warning system including the driver, (5) modeling driver responses to warnings, (6) driver distraction detection and warning presentation, and (7) models of driver performance.