典型文献
The surrounding vehicles behavior prediction for intelligent vehicles based on Att-BiLSTM
文献摘要：
A surrounding vehicles behavior prediction method was presented for intelligent vehicles. The surrounding vehicles' behavior is hard to predict since the significant uncertainty of vehicle driving and environmental changes. This method adopts bidirectional long short-term memory (BiLSTM) model combined with an encoder to ensure the memory of long-time series training. By constructing an attention mechanism based on BiLSTM, we consider the importance of dif-ferent information which could guarantee the encoder's memory under long sequence. The designed attention-bidirection-al LSTM (Att-BiLSTM) model is adopted to ensure the surrounding vehicles' prediction accuracy and effectiveness.
文献关键词：
中图分类号：
作者姓名：
Yunqing Gao;Juping Zhu;Hongbo Gao
作者机构：
Department of Automation,University of Science and Technology of China,Hefei 230022,China;Institute of Advanced Technology,University of Science and Technology of China,Hefei 230088,China
文献出处：
引用格式：
[1]Yunqing Gao;Juping Zhu;Hongbo Gao-.The surrounding vehicles behavior prediction for intelligent vehicles based on Att-BiLSTM)[J].中国科学技术大学学报,2022(09):59-67
A类：
bidirection
B类：
surrounding,vehicles,behavior,prediction,intelligent,Att,BiLSTM,method,was,presented,hard,since,significant,uncertainty,driving,environmental,changes,This,adopts,bidirectional,long,short,term,memory,model,combined,encoder,ensure,series,training,By,constructing,attention,mechanism,we,consider,importance,dif,ferent,information,which,could,guarantee,under,sequence,designed,adopted,accuracy,effectiveness
AB值：
0.503739
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机标中图分类号，由域田数据科技根据网络公开资料自动分析生成，仅供学习研究参考。
