| Summary: | 碩士 === 國立臺灣科技大學 === 電機工程系 === 106 === Due to the advancement of the Internet technology, the amount of Internet of Things(IoT) devices is increasing and the tendency of IoT is also gradually forming. In this case, Narrow Band Internet of Things (NB-IoT) has appeared. NB-IoT is a Low-Power, Wide-Area Network(LPWAN) communication technology with low cost introduced by 3rd Generation Partnership Project (3GPP) Release 13. With the applications of NB-IoT, the industries of IoT such as intelligence agriculture, smart industry, smart city and the Internet of Vehicle (IoV), have also flourished. However, the Internet of Every Things represents a huge amount of sensing information. How to effectively use this data, that is, the application of "Big Data", has become an important issue. Therefore, the analysis of artificial intelligence has become a highly popular technology. Among these application issues, driving style analysis is an intelligent service that utilizes IoT devices and big data. The driving information sensed by the On Board Unit (OBU) can be used to calculate the driving safety degree of the driver and provide insurance discount accordingly.
This study developed the NB-IoT telematics system and proposed the random forest driving event classification mechanism. The NB-IoT telematics system in this study is an OBU applied in IoV. The sensors in the OBU include accelerometer, gyroscope and Global Positioning System (GPS). The motorcycle sensor data can be uploaded to cloud through NB-IoT network module for data analyzing. After data is collected completely, it can extract driving events section by section from the motorcycle sensor data. In order to solve the problem of overlapping and class-imbalance, AutoEncoder-Decoder and Synthetic Minority Over-sampling Technique (SMOTE) is used to preprocess the driving events data. Afterwards, this study uses Random Forest to analyze the driving data to classify the driving event as defensive or sporty. Finally, the safety degree can be calculated by the models. The result of the classification can be provided for insurance companies to achieve the application service of driving style analysis.
This study analyzes driving data by two driving classification models include lateral events classification model and longitudinal events classification model. In the analysis of driving event classification model, the analysis accuracy of lateral events classification model is 93.28% and the standard deviation is 0.9%; the analysis accuracy of longitudinal events classification model is 94.03% and the standard deviation is 2%. In addition, this study also compares to other research of driving behavior. In “ANN Driving Style Classification,” which used Artificial Neural Network (ANN) as the classification algorithm. The analysis accuracy of lateral events classification model is 91% and the analysis accuracy of longitudinal events classification model is 92%. The experimental result shows that the problem of overlapping, class-imbalance and too long training time can really be solved in this study thereby achieve the higher accuracy.
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