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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%.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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