| Summary: | 碩士 === 元智大學 === 資訊工程學系 === 106 === In advanced driver assistance system (ADAS), non-timely braking action is one of the important issues because it makes drivers exposed to a terrible and dangerous driving environment. For this reason, predicting driver braking action early and accurately must appear up, which can lower the potential of unsafe driving behavior and provide drivers more time to react. In this paper, we have sensory fusion data source from inside and outside of the car and our proposed multi-layer deep learning architecture (CBL) to predict braking action, which consists of convolutional neural network (CNN) and bidirectional long short-term memory units (BL) while CNN is good at extracting driving characteristic and BL is useful for keeping time-series data. The result points that the CBL performs much better than the other two architectures: bidirectional LSTM (BL) and uni-LSTM (UL) based on the high accuracy and f-score, and it also shows leave-one-out cross validation for drivers and many interesting differences in the speed, turning and intersection from time -5s to 0s.
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