The adoption of train automatic stop control (TASC) device is expected to increase according to high demand of platform door system. The challenges of TASC are (1) reduction of adjustment man-hour at TASC introduction and (2) maintenance of stopping position accuracy. These challenges are caused by the difficulty of obtaining vehicle characteristics accurately in real-time. To solve the difficulty, a function for automatic parameter tuning for TASC devices has been developed. The function learns vehicle characteristics from real-time driving data and controls vehicle deceleration based on the learned vehicle characteristics. The developed function was evaluated on a test vehicle, and the results showed that it acquired vehicle characteristics accurately and stopped the test vehicle within a stopping accuracy of ±35cm. In addition, it was confirmed that calculation time was within 70s. This was less than the target time of 80s, which is comparable to the average driving time between two stations on a metro line.
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