The surrounding vehicles behavior prediction for intelligent vehicles based on Att-BiLSTM

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 different information which could guarantee the encoder’s memory under long sequence. The designed attention-bidirectional LSTM (Att-BiLSTM) model is adopted to ensure the surrounding vehicles’ prediction accuracy and effectiveness.