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In order to realize the accurate prediction of the trajectory of surrounding vehicles, a driving intention recognition and trajectory prediction model based on graph neural network and Gated Recurrent Unit (GRU) was designed by using deep learning method. The driving intention recognition model constructed the interaction relationship between vehicles into a space-time graph, used the graph neural network to learn its interaction rules and used Softmax function to calculate the probability of different driving intentions. The trajectory prediction model adopted an encoded-decoded GRU network and the encoder encoded the vehicle history trajectory information and fused the recognized driving intention information and then realized trajectory prediction through the decoder. Finally, the Next Generation Simulation (NGSIM) dataset was used to train and verify the model and the results show that the proposed model can better identify the driving intention of the vehicle, and the vehicle trajectory prediction model considering the driving intention can effectively improve the prediction accuracy.
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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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