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In this study, an attempt was made to apply the steering entropy method, proposed by Boer and Nakayama as a workload measurement technique, to a comparative evaluation of the workload of older and younger drivers. As the first step, driving simulator tests were conducted to examine a method of making comparisons between subjects whose driving performance differed. The same method was then used in making evaluations during driving tests conducted with an actual vehicle. Under the conditions used in this study, the results indicate that it should be possible to compare driving workloads among different subjects through the combined used of Hp and α. Hp is a quantified value of steering perturbation as an information entropy value that is calculated from a time history of steering angle data. It changes between 0 (no steering perturbation) and 1 (absolute randomness) in a theoretical sense.

A human body model has been developed for conducting personal computer simulations to evaluate physical work loads, especially muscle loads, associated with the driving position and arm and leg motions. The validity of the model was confirmed by comparing estimated work loads with electromyographic measurements. Correlation analyses were conducted to examine the relationship between the estimated loads and subjective evaluations. The results indicated the regions of the body where loads had the largest impact on the perceived sensation of physical effort and were used to derive an index for evaluating the overall work load of the entire body. The simulation method was used to evaluate control switch positions, driving position and vehicle entry/exit motions.

This paper describes a newly developed microcomputer-based drowsiness warning system, which detects changes in the driver's alertness through his steering behavior. In developing this system, we first quantified several levels of alertness based on such physiological factors as brain activity and blinking. Tests were then conducted in which drivers fell into different degrees of drowsiness. Using the quantified alertness levels, we defined the “drowsy driver” and found unique steering patterns that could not be seen in normal driving. These patterns were entered into the memory unit of the microcomputer. When the sensor built into the steering wheel detects a drowsy steering pattern, the microcomputer recognizes the driver's drowsiness and activates a buzzer to warn the driver. In this paper, the process of determining the alertness levels is explained, along with the steering characteristics of the drowsy driver.