Estimating the Workload of Driving Using Video Clips as Anchors 2022-01-0805

As new technology is added to vehicles and traffic congestion increases, there is a concern that drivers will be overloaded. As a result, there has been considerable interest in measuring driver workload. This can be achieved using many methods, with subjective assessments such as the NASA Task Loading Index (TLX) being most popular. Unfortunately, the TLX is unanchored, so there is no way to compare TLX values between studies, thus limiting the value of those evaluations. In response, a method was created to anchor overall workload ratings. To develop this method, 24 subjects rated the workload of clips of forward scenes collected while driving on rural, urban, and limited-access roads in relation to 2 looped anchor clips. Those clips corresponded to Level of Service (LOS) A and E (light and heavy traffic) and were assigned values of 2 and 6 respectively. Subjects said if they would perform any of 3 tasks—dialing a phone, manually tuning a radio, or entering a destination—while they were driving in each test scene. Finally, subjects provided other ratings for a wider range of situations (not shown in clips) to further examine the effects of road geometry, traffic, and other factors on workload. For each clip, vehicle dynamics data were available for the driven vehicle and the relationship of that vehicle to those in the scene. Using regression analysis, the rated workload (averaged across subjects) was estimated to be 8.87 - 3.01(LogMeanRange)+ 0.48(MeanTrafficCount)+ 2.05(MeanLongitudinalAcceleration), which accounted for 87% of the ratings, an extremely high value. In that expression, range is the distance from the subject’s front bumper to the lead vehicle rear bumper, mean traffic count is the mean number of vehicles visible in the road scene to radar, and the mean acceleration is that of the subject vehicle.