基于融合Dropout与注意力机制的LSTM-GRU车辆轨迹预测  

Vehicle Trajectory Prediction Based on LSTM-GRU Integrating Dropout and Attention Mechanism

作　　者：吴晓建 危一华 王爱春[2] 雷耀 张瑞雪 WU Xiaojian;WEI Yihua;WANG Aichun;LEI Yao;ZHANG Ruixue(School of Advanced Manufacturing,Nanchang University,Nanchang 330031,China;Jiangling Motors Co.,Ltd.,Nanchang 330001,China)

机构地区：[1]南昌大学先进制造学院,江西南昌330031 [2]江铃汽车股份有限公司,江西南昌330001

出　　处：《湖南大学学报（自然科学版）》2023年第4期65-75,共11页Journal of Hunan University:Natural Sciences

基　　金：国家自然科学基金资助项目(52262054,52002163,52062036)。

摘　　要：在智能驾驶环境的车辆轨迹预测环节,为更好地获取环境车辆的轨迹时序特征,在长短期记忆神经网络(LSTM)基础上,嵌入Dropout层以增强网络泛化性,引入注意力机制予以预测效果影响较大的时序数据更大权重从而提高预测结果的可靠性,且将改进的LSTM模型与门控循环单元GRU模型结合,构建LSTM-GRU预测模型以进一步提升环境车辆轨迹预测的准确性.在此基础上,使用NGSIM公开数据集对模型进行训练、验证和测试.研究结果表明,融合了Dropout和注意力机制的LSTM-GRU神经网络轨迹预测模型相较标准的LSTM长短期记忆网络以及GRU门控循环单元,在预测较长时序的车辆轨迹上具有优势,提高了轨迹预测的准确性,降低了实际轨迹和预测轨迹之间的均方根误差和平均绝对误差.In the environmental vehicle trajectory prediction link of intelligent driving,to obtain the trajectory timing characteristics of the surrounding vehicles more accurately,the dropout layer is embedded into the long shortterm memory(LSTM)neural networks model to enhance network generalization,and the attention mechanism is introduced into LSTM to distribute larger weight to the time series data with a marked influence on the prediction effect,so as to improve the reliability of the prediction results.Subsequently,the improved LSTM model is combined with the GRU model(LSTM-GRU)to further improve the prediction accuracy of the surrounding vehicle trajectory.On this basis,the LSTM-GRU model is trained,verified,and tested using the NGSIM public dataset.The results show that,compared with the standard LSTM and GRU model,the LSTM-GRU model integrating dropout and attention mechanism has advantages in predicting the surrounding vehicle trajectory with a longer time sequence,which improves the accuracy of trajectory prediction and reduces the root mean square error and mean absolute error between the actual and the predicted trajectory.

关 键 词：智能汽车 轨迹预测 长短期记忆神经网络 门控循环单元 注意力机制 

分 类 号：U461.91[机械工程—车辆工程]