The invention discloses an abnormal driving behavior
online identification method based on an
Encoder-Decoder
attention network and an LSTM (
Long Short Term Memory). The method is composed of three main modules, namely an
encoder-decoder based on LSTM, an attention mechanism and a classifier based on SVM, and comprises the steps of input encoding, attention learning, feature decoding,
sequence reconstruction, residual calculation and driving behavior classification. According to the method, on the basis of
mobile phone multi-
sensor fusion data, on the basis of driving behavior data characteristics and behavior
pattern analysis, an
Encoder-Decoder
deep learning model, an Attention attention mechanism and an SVM classification model are fused to recognize abnormal driving behaviors. The method has the advantages of being easy in
data acquisition, non-intrusive, low in cost and the like, not only considers the
time correlation of the driving behaviors, but also considers the difference of different moments, can perform
online identification on abnormal driving behaviors in an end-to-end mode, can provide a method basis for driving behavior evaluation and safety early warning, and has a good application prospect. The method has significant meaning for intelligent driving
system design and traffic safety
decision making.