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A robotic driver has been designed on the basis of an analysis of a human driver's action in following a given driving schedule. The self-learning algorithm enables the robot to learn the vehicle characteristics without human intervention. Based on learned relationships, the robotic driver can determine an appropriate accelerator position and execute other operations through sophisticated calculations using the future scheduled vehicle speed and vehicle characteristics data. Compensation is also provided to minimize vehicle speed error. The robotic driver can reproduce the same types of exhaust emission and fuel economy data obtained with human drivers with good repeatability. It doesn't require long preparation time. Thereby making it possible to reduce experimentation work in the vehicle development process while providing good accuracy and reliability.

A technique has been developed which permits the determination of engine oil consumption on an instantaneous basis. The procedure uses the sulfur in the oil as a tracer. The concentration of sulfur compounds in the exhaust gas is determined using a Flame Photometric Detector (FPD). Special modifications of the FPD reduce the interference of other gases and improve the accuracy of the instrument. Although the unit is operationally simple, its abilities to measure continuously and respond quickly allow it to surpass conventional methods for measurement of oil consumption.