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An efficient approach to classify time series physical measurement data of shift control of automatic transmission for vehicles is presented. Comfortable acceleration is the essential factor of today’s vehicles. Shift control of automatic transmission of vehicles directly contributes to the comfortable acceleration. Since calibration of automatic transmission of vehicles is time consuming task for expert engineers, the development of autonomous calibration is desired to reduce product development period in today’s competitive automobile market. In the stage of product development, it is difficult to obtain a large amount of physical measurement data. Therefore, we need to develop machine learning method for limited amount of data. For this purpose, we develop the method to classify time series measurement data of shift control of automatic transmission of vehicles. We use support vector machine (SVM) as a machine learning technique.

Mud adhesion to the rear surfaces of trucks, vans and buses causes troublesome results such as aesthetic degradation, hindered rear view and laborious washing. To raise the product value of trucks and buses, it is important to develop effective measures for suppressing such mud adhesion. In this research the authors first clarified the mechanism of mud adhesion through flow visualization tests. Then, wind tunnel tests were performed to predict the effects of various countermeasures, and prospective ones were put under actual driving tests to verify their effects. The following measures were found effective in suppressing mud adhesion. (1) Aerodynamic improvement by attaching corner vanes to the upper and side edges of the rear surface. (2) Blocking road splash with a slanted plate under the truck and close to the base.