This paper examines the role of human factors in the design of automobiles. A prime objective of our human factors profession is to improve the design of machines, thereby benefiting users in terms of comfort, convenience, operating speeds, accuracy and safety. Although the purpose of an automotive human factors program may be to achieve all of these objectives by improving vehicle design, the mechanisms for doing so probably cannot be discovered by focusing research attention on the vehicle element of the driver/vehicle/road system. In fact, the nonvehicle parts of this system are probably by far the most productive topics for future human factors research. The abilities of drivers, their limitations, and the tasks imposed upon them by the traffic environment should indicate how vehicles can be designed to best serve the drivers' needs. After twenty years of automotive study, the human factors research community is surprisingly unprepared to participate in vehicle design projects. The vehicle has too often ended up the subject of human factors research and researchers have been faced with the job of finding ways to improve the vehicle or a vehicle component without knowing enough about the intended user or the job the user must perform. The research community has only rudimentary and often incomplete background information about drivers and their traffic environments. The meager data base which is available suggests that traditional empirical approaches for evaluating machine design may be too cumbersome and time consuming to keep pace with other aspects of automotive technological evolution. The tradition of developing alternative versions of hardware and subjecting the alternatives to human performance tests may not be a viable methodology in the future. A look at the total automotive system shows why. Drivers in the United States accumulate about 1.6 trillion miles of travel each year. During the year, a typical driver makes over 60,000 discrete control operations not counting steering wheel movements. The immensity of the automotive system means that very small driver error rates in control usage quickly accumulate into large numbers of error events nationwide. The best information available suggests that the D. S. driving public uses their turn signals 854 billion times a year. This amounts to a nationwide rate of 27,000 times per second. If the generic human error rate in using the turn signal can assumed to be one error per 1000 operations, then turn signal errors are being made at the rate of 27 per second nationwide. Human factors research has tended to avoid error rate as a principal measure of performance in research programs. The reason becomes apparent when the number of tests which must be conducted to detect changes in rare events such as turn signal errors is computed. If two turn signal designs are to be compared and the researcher wants to be able to detect with 95 percent certainty (at the 5% level of significance) that the error rate has been cut in half by one of the two designs, then a large experiment is required. At a base human error rate of 1 per thousand, about 130,000 observations must be conducted to reliably detect the desired reduction in errors. If the base human error rate is only 1 in 100, then only 13,000 observations will be needed. Unfortunately, information on the frequency of driver control usage is sketchy, and data on driver error rates when using controls under the natural loading of the driving task is all but nonexistent. Other measures of human performance, such as speed of operation and accident involvement rates, have limited application in automotive design for reasons that are discussed in this paper. Some of the data bases which have been accumulated for human factors evaluations by Ford Motor Company are described in this paper. It is concluded that, if the human factors profession is to keep pace with automotive technological evolution, more research effort is going to have to be devoted to the study of drivers and driving-environment factors. For the sake of research efficiency, human factors principles and systems models which can be reliably generalized across vehicle designs must be developed. Several systems models that are under development at Ford are briefly described.
Human factors challenges for the safe use of artificial intelligence in patient care
