We describe a wind speed prediction method using wind vector images as input. The prediction model combines convolutional neural network (CNN) and convolutional long shortterm memory (CLSTM), which are effective for image analysis. Several input image data structures expressing wind vector change are considered and the prediction accuracy is compared between them. The performance of the proposed method is evaluated by the root-mean-square error and correlation coefficient between observed and predicted values.
Abstract-This paper describes a simple and low-cost semiself-driving system which is constructed without cameras or image processing. In addition, a position correction method is presented by using a vehicle dynamics. Conventionally, selfdriving vehicle is operated by various expensive environmental recognition sensors. It results in rise in prices of the vehicle, and also the complicated system with various sensors tends to be a high possibility of malfunction. Therefore, we propose the semi-self-driving system with a single type of global navigation satellite system (GNSS) receiver and a digital compass, which is based on a concept of a preceding vehicle controlled by a human manually and following vehicles which track to the preceding vehicle automatically. Each vehicle corrects coordinate using current velocity and heading angle from sensors. Several experimental and simulation results using our developed smallscale vehicles demonstrate the validity of the proposed system and correction method.
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