Resurrecting Driver Workload Metrics: A Multivariate Approach

摘要

This paper presents new, multivariate analyses of data collected during the Driver Workload Metrics (DWM) project. In a cooperative effort with the National Highway Transportation Safety Administration, the DWM project had several goals including the development of performance metrics and test procedures to assess visual, manual, and cognitive aspects of driver workload. Workload was defined as the competition in driver resources (perceptual, cognitive, or physical) between the driving task and a concurrent secondary task, occurring over that task's duration. It was hypothesized that, depending on the type of secondary task performed while driving, measured workload and the correlated quality of driving should either remain the same or decline, but would manifest in degraded measures of lane keeping, longitudinal control, or eye glance behavior. However, the original DWM project had an unrealized goal, i.e. to apply Exploratory Factor Analysis (EFA) methods, in an attempt to uncover the underlying unobserved structure within the project's relatively large set of variables. It is this hidden multi-dimensional structure that must be examined to empirically comprehend the concept of driver workload. DWM kinematic vehicle data, driving performance, and eye glance data were analyzed using Maximum Likelihood Factor Analysis (MLFA). These analyses found that task-induced workload affected driving performance and was multi-dimensional in nature. Visual-manual tasks exhibited fundamentally different performance profiles than auditory-vocal tasks or just driving. Furthermore, when secondary statistical analyses of the normalized factor scores were done using Multivariate Analysis of Variance (MANOVA) the results found highly statistically significant workload differences in age groups, task type, and at times, gender.