CONSIDERABLE interest has been evidenced in recent years in traffic flow and car-following theory. Two classes of models are used to describe these phenomena: macroscopic models usually involve structures which relate volume, speed and density for a large number of vehicles; microscopic models concern relation of the driver-vehicle system to intervehicular dynamics. Analogies to kinetic theory of gases, diffusion processes, compressible fluids, etc., are just a few of the approaches to macroscopic modeling that have been studied. This paper concerns itself with microscopic models of the car-following situation and how the driver of the following car responds to the action of the lead car. Microscopic models have importance in (a) better understanding traffic flow concepts, (b) designing automatic systems which closely match human performance and (c) designing systems to aid or assist the driver in the car-following task, i.e. partially automated systems and sensory supplementation. At least three approaches can be taken to the study of the driver-vehicle car-following function. One is to use physical models, e.g. Newtonian-forcemass-acceleration models (Herman, 1961). A second approach, and the one taken in this paper, is curve-fitting from empirical data in order to describe the driver-vehicle system in terms of intervehicular dynamics. Lastly, this paper will conclude with an introduction to a third approach to car-following models, which is presently underway at the Systems Research Group at The Ohio State University. This combines traffic dynamic information with driver psychophysical data to predict control movements from intervehicular dynamics. The purpose of this study was to examine the sensitivity of empirically derived carfollowing models to (a) time delays on predictor variables, (b) lead-car velocity programs, that is, the forcing velocity function (open road versus traffic), (c) subjects and (d) replications within subjects. Emphasis in this work was with regression models of the driver-vehicle system as shown in Fig. 1 using acceleration of the following vehicle as the dependent variable and headway (spacing) lead-car velocity, following-car velocity and lead-car acceleration as possible predictor variables. Time was also used as a predictor variable in the original experiment to measure learning effects. Models predicting the change in following-car acceleration, A& were also investigated. In general, time did not improve data fit which suggests little