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In order to improve the recognition accuracy and pre-judgment ability of autonomous vehicles in high-speed dynamic complex traffic scenarios, The lane-changing intention recognition model based on convolutional residual Bidirectional Long Short-Term Memory (BiLSTM) with fusion attention mechanism is proposed. It uses the one-dimensional Convolutional Neural Network (CNN) to extract the vehicle’s motion state features. The constructed feature vector is used as the input information of BiLSTM network. The residual connection is used to solve the problems of optimization bottlenecks and gradient disappearance in multi-layer BiLSTM network. It’s achieved to a adjust the weight of the output of the residual BiLSTM network at different moments with the attention mechanism. And the driving intent probability can be calculated by the Softmax function. The validity of the model is verified by using the expressway data set in NGSIM, the performance and effect of the other 4 models are compared with the model. The results show that the recognition accuracy of the lane-changing intention is the highest, which reaches 97.44%, and prediction accuracy of the vehicle’s lane-changing intention is 90% and higher within 2.5 s before the changing lanes, it shows that the model has better intent recognition accuracy and prediction ability.
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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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