The Dimensions of Driver Performance during Secondary Manual Tasks

Abstract

Summary: This analysis identified the underlying dimensions of driver performance using data obtained from drivers engaged in secondary manual tasks. Randomly chosen subjects balanced for age and gender used one of five advanced navigation and communication systems while driving on a closed roadway. Fifteen driver performance variables were averaged and standardized across subjects for 79 tasks. There were high correlations between all variables. Principal Component Analysis (PCA) found that the vector of loadings defining the first principal component (PC1) was positive for all 15 variables, accounting for 61 percent of the total variation across all tasks. It is interpreted as overall driver demand. PC2 loaded with one sign on event detection and response variables, but opposing sign on visual-manual workload variables. It identified tasks making drivers more inattentive to outside events than expected, given a task's visual-manual workload, and accounted for 17 percent of total variation. It is interpreted as low-workloadbut-high-inattentiveness. PC3 had loadings of opposing sign for peripheral vs. central event variables (5 percent of total variation). It is interpreted as peripheral insensitivity. The first three components together accounted for 83 percent of total variation, which is deemed substantial. Thus most of the information available through the 15 original variables can be summarized by only three PC variables. Because the vectors of loadings defining the components are orthogonal to each other as defined by PCA, no single variable by itself can capture all the important variations in driver performance during secondary manual tasks. A multivariate design and analysis is required.

Road-to-Lab: Validation of the Static Load Test for Predicting On-Road Driving Performance While Using Advanced In-Vehicle Information and Communication Devices

“…It accomplished this result despite the near collinearity of several of the variables (see Young & Angell, 2003), contrary to conventional statistical wisdom. We believe this result is because the variables selected for the MLR model were based on extensive analysis, and expertise from experienced applied statistical analysts.…”

“…Ten driver performance variables were measured for the static and dynamic tests (Table 1, see also Angell, et al 2002;Young & Angell, 2003). There were two trials for each task for each participant for tasks in sets 1 and 2.…”

“…The multiple variables on the dynamic side as well as the static side can be reduced into a few components that are more robust than any single variable (Young & Angell, 2003). Advanced statistical methods can then be used to optimize the prediction from the lab to road components, rather than the individual variables.…”

“…Advanced statistical methods can then be used to optimize the prediction from the lab to road components, rather than the individual variables. At least three components must be used both statically and dynamically, because on-road driver demand is known to require at least three separate dimensions to specify it fully, at least under these test conditions (Young & Angell, 2003).…”

Validation of the Static Load Test for Event Detection During Hands-Free Conversation

“…Yet conventional driver workload variables had high lab-to-road correlations for the identical tasks (r > 0.9), suggesting something unusual about event detection variables that warranted further investigation. Young and Angell (2003) did a principal component analysis of a large set of driver performance variable means, for a wide range of visual-manual tasks, using event lights on the hood and side mirror while driving on a closed road. They showed that across tasks, event detection on the road is positively correlated with driver workload variables on the first dimension, but negatively on the second.…”