Analysis of Lane-Change Behavior and Prediction of Lane-Change Intentions in Interweaving Areas Based on NGSIM Data

Using the interweaving zone road section of US101 in NGSIM as a research case, the effects of various data processing methods were compared, and the characteristics of lane-changing behavior in the interweaving zone were statistically analyzed, and it was found that most of the lane-changing behavior on the detected road section of US101 occurred in the middle section. The results demonstrate that the Multi-head CNN-BiLSTM model has higher accuracy and stronger robustness than the LSTM model in recognizing lane-change intentions in the weaving zone of the highway. The research results can be used in connected autonomous vehicles and vehicle–road collaboration systems for recognition judgments.