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Four field studies were conducted in natural rainfall to develop a model for predicting distances at which drivers are able to see other vehicles in the roadway at various time periods following stoppage of a windshield-wiper stroke. The seeing distance prediction was developed as a function of rain intensity, rain accumulation time, and ambient daylight illumination. Two situations were studied; in the first, drivers seated in a stationary vehicle detected moving vehicles, and in the second, moving drivers detected a stationary vehicle. Useful seeing distance models were developed from the field studies. Seeing distances predicted from the models developed from these earlier studies were compared with seeing distances obtained in a subsequent validation field test. Results indicated that average error in the prediction of seeing distances ranges from 9% to 23%.

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Under the Technical Supervision of ^fo^b* ^n e George Washington University *'*' HUMAN RESOURCES RESEARCH OFFICE Op /) operating under contract with J* THE DEPARTMENT OF THE ARMY ^ U.S. Army Leadership Human Research Unit is established under the command of the Commanding General, United States Continental Army Command. The Human Resources Research Office, the George Washington University, operating under contract with the Department of the Army, employs the Director of Research and other civilian staff members who are assigned to the Unit with the approval of Headquarters, United States Continental Army Command. The Human Resources Research Office provides the Unit with technical supervision in the planning and analysis of the research projects. Conclusions stated herein do not necessarily represent the official opinion or policy of Headquarters, United States Continental Army Command, or the Department of the Army. The Human Resources Research Office is a nongovernmental agency of The George Washington University, operating under contract with the Department of the Army (DA 44-188-ARO-2). HumRRO's mission, stated by AR 70-8, is to conduct studies and research in the fields of training, motivation, leadership, and man-weapons system analysis. Research is reported by HumRRO in publications of several types. 1. Technical Reports are prepared at the completion of a research Task or major portion thereof. They are designed specifically for a military audience and convey recommendations for Army action. 2. Research Reports may be prepared at any time during a Task. They are designed primarily for a research audience but may be of interest to a military audience. They report research findings of interest and value to the scientific community and do not recommend Army action. 3. Research Memoranda may be prepared at any time and need not be directly associated with a particular research Task. They report findings that may be of interest to a research or military audience or to both. They do not recommend Army action. 4. Consulting Reports are prepared following completion of a specifically requested consulting action under HumRRO's Technical Advisory Services. They are designed for a specific military audience and usually convey recommendations for Army action. 5. Research Bulletins are prepared as nontechnical summaries of one or more research Tasks or as reports of other HumRRO activities. They are intended primarily for a military audience and do not present recommendations for Army action. Their distribution usually includes agencies and individuals conducting research, and the general public.

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ABSPRACTThis paper examines the role of human factors in the design of autmbiles. 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 automtive study, the human factors research c o m i t y 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 compnent 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 clrmbersome 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 imensity of the automotive system means that very small driver error rates in control usage quickly accumulate into large nlrmbers of error events nationwide. The best information available suggests that the U. 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 n e r 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 l...