Sources of distraction are numerous and varied, and defining and measuring distraction and attention is complicated. The driving task requires constant adjustments and reallocation of attention to cognitive, motor, and visual processes. While it is fairly straightforward to measure distraction in an experimental situation (e.g., simulator, closed course), driver distraction in the real world is highly contextual. While no single metric is capable of capturing the complexities of distraction, several have proved useful in helping researchers gain fuller understanding of it. Few have reached a level of consensus among researchers and user interface designers. ISO and SAE may be considered the ‘gold standard’ for providing mechanisms through which open scientific consensus-based standards can be achieved.

While there are a number of metrics used in predicting distraction, three have been studied closely and are going through the SAE and ISO standards process. They are (1) ‘the occlusion method’; (2) the Lane Change Test (LCT); and (3) the Detection Response Task (DRT). The metrics described here apply generally to the experimental context where driving is tightly controlled. Like any method, there are limitations with each—and they don’t necessarily agree with one another.

Experimental methods and analyses are different than those in naturalistic driving (ND). ND relies more on data mining versus traditional experimental manipulation. ND data are a challenge precisely in that they lack experimental control.

In future, driver metrics will go beyond specific measurement of task load, and will include how drivers self regulate when they choose to be distracted.