Micromodel for Objective Estimation of Driver Mental Workload from Task Data

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Micromodel for Objective Estimation of Driver Mental Workload from Task Data

Zeitlin, Lawrence R.

Observation of driver performance during a 58 000-km (36,000-mile) field study of van pooling indicates that a good estimate of mental workload can be made from an analysis of objective performance data alone. Drivers traversed a mix of rural secondary roads, limited-access expressways, high-density, limited-access urban drives, and downtown city streets on a daily commute from upstate New York to New York City. Data included road characteristics, time, traffic density, speed, weather, brake applications, subsidiary task performance, and subjective difficulty ratings. Driving workload had two components, a steady-state load dictated by road conditions, speed, and traffic density and a transient load determined by the degree of uncertainty in the driving situation. Brake actuations represent the uncertainty inherent in driving while the log₂ of the speed is a first approximation of the steady-state information processing load imposed by tracking requirements of vehicle control. Unpredictability of traffic appeared to be the major determinant of perceived difficulty. Workload homeostasis occurred as drivers modified their performance to keep workload within a comfortable range. An objective workload index of the general form, workload = f (brake actuation rate + log₂ speed) based on this micromodel of driver behavior predicts subjective driving difficulty. An analysis of variance shows that the workload index distinguished between road types at the p < .0001 level of significance. The workload index correlates at r = .74, 18(df), with the subjective driving degrees of freedom difficulty ratings and at r = .81, 18 df, with the mental workload estimates of the best subsidiary task.