Research on Intelligent Prediction Technology of Dangerous Driving Behavior in Highway Freight
Based on the historical driving data of trucks in a province,this paper proposed a prediction method of dangerous driving behavior based on Convolutional Neural Network-Long Short-Term Memory(CNN-LSTM)network and self-attention mechanism.For the characteristics of large amount of truck driving data,high dimension,difficult feature extraction and strong time sequence,this method first used XGBoost to filter the features,then used CNN to extract spatial features and LSTM to further capture the temporal information of driving behaviors.Finally,dangerous driving behaviors were predicted by self-attention mechanism.Experimental results show that the method presented in this paper performs better than other long time series prediction methods on highway freight driving data in a province,with recognition accuracy reaching 85.05％,the weighted average recall rate reaches 83％,and the F1-score reaches 84％.
Highway freightData drivenSelf-attention mechanismDangerous driving behaviorPrediction of driving behavior
万方数据
维普
