Heavy vehicle driver workload assessment. Task 3, task analysis data collection

736067437269471Final Report; July 1991-September 1995Goodman, Michael J.9418-9, 9422-7PDFResearch Paperhttp://ntl.bts.gov/lib/9000/9400/9425/lm01.pdfhttp://ntl.bts.gov/lib/16000/16500/16502/PB2000104129.pdfDOT HS-808 467DTNH22-91-C-07003Driver support systemsAdvanced driver information systemsWorkloadHuman factors engineeringCognitionVisual perceptionTask analysisData collectionUnited States. Joint Program Office for Intelligent Transportation SystemsKiger, StevenRockwell, ThomasNiswonger, SteveTijerna, LouisMyers, LouisNygren, ThomasBattelle Memorial InstituteUnited States. National Highway Traffic Safety AdministrationNTL-FREIGHT-Trucking IndustryNTL-INTELLIGENT TRANSPORTATION SYSTEMS-INTELLIGENT TRANSPORTATION SYSTEMSNTL-PLANNING AND POLICY-Technology TransferNTL-SAFETY AND SECURITY-Human FactorsNTL-SAFETY AND SECURITY-Vehicle DesignBTS Data DirectoryIntelligent Transportation Systems Joint Program OfficeThis technical report consists of a collection of task analytic data to support heavy vehicle driver workload assessment and protocol development. Data were collected from professional drivers to provide insights into the following issues: the meaning of the term "workload" to heavy vehicle drivers (N = 41 drivers interviewed); the demand placed on drivers (N = 55) by various driving conditions using a psychological scaling approach; the safety criticality and difficulty of selected standard driving tasks (N = 30 drivers assessed); the perceptual, motor, and cognitive loads imposed by various tasks while on the road under various conditions (N = 9 drivers observed over-the-road during revenue runs); and a preliminary analysis of the key features of generic high-technology in-cab devices. When professional truck drivers speak of workload, they tend to talk in terms of time stress or stress caused by delays to their schedules. Various driving conditions were unidimensionally scaled in terms of order of impact on drivers, and those results are presented and discussed. Safety criticality and difficulty assessment results provided some validation for the scaling outcomes. Visual allocation measures in this study suggested that the use of common in-cab tasks would serve as baseline measures, based on the revenue runs. Finally, key features considered in evaluating voice communication systems, vehicle navigation and route guidance systems, single/integrated displays, and text communication devices were determined