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Task acceptability and workload of driving city streets, rural roads, and expressways: ratings from video clips

By J. Schweitzer and P. E. Green

Abstract

SAfety VEhicles using adaptive Interface Technology (SAVE-IT Project) Task 2C: Develop and Validate EquationsSAVE-IT project, funded by U.S. Department of TransportationSubjects rated the workload of clips of forward road scenes (from the advanced collision avoidance system (ACAS) field operational test) in relation to 2 anchor clips of Level of Service (LOS) A and E (light and heavy traffic), and indicated if they would perform any of 3 tasks (dial a phone, manually tune a radio, enter a destination) in driving the scenes shown. After rating all of the clips, subjects rated a wider range of described situations (not shown in clips) and the relative contribution of road geometry, traffic, and other factors to workload. Using logistic regression, predictive equations for the refusal to engage in the 3 tasks were developed as a function of workload, driver age, and sex. Several equations were developed relating real-time driving statistics with workload, where workload was rated on a scale of 1 (minimum) to 10 (maximum). Some 87% of the rating variance was accounted for by the following expression: Mean Workload Rating=8.87-3.01(LogMeanRange)+ 0.48(MeanTrafficCount)+ 2.05(MeanLongitudinalAccleration), where range (to the lead vehicle) and traffic count were both determined by the adaptive cruise control radar. Other estimates were also generated from post-test ratings and adjustments, considering factors such as construction zones, lane drops, curves, and hills. From the results of this report alone, the workload estimates needed by a real-time workload manager could be developed using (1) the real time data, (2) look-up tables based on the clip ratings, (3) look-up tables based on the post-test data, or (4) some combination of those 3 sources

Publisher: University of Michigan, Ann Arbor, Transportation Research Institute
Year: 2007
OAI identifier: oai:deepblue.lib.umich.edu:2027.42/64467
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