  Accurately detecting and predicting lane change (LC)processes of human-driven
vehicles can help autonomous vehicles better understand their surrounding
environment, recognize potential safety hazards, and improve traffic safety.
This paper focuses on LC processes, first developing a temporal convolutional
network with an attention mechanism (TCN-ATM) model to recognize LC intention.
Considering the intrinsic relationship among output variables, the Multi-task
Learning (MTL)framework is employed to simultaneously predict multiple LC
vehicle status indicators. Furthermore, a unified modeling framework for LC
intention recognition and driving status prediction (LC-IR-SP) is developed.
The results indicate that the classification accuracy of LC intention was
improved from 96.14% to 98.20% when incorporating the attention mechanism into
the TCN model. For LC vehicle status prediction issues, three multi-tasking
learning models are constructed based on MTL framework. The results indicate
that the MTL-LSTM model outperforms the MTL-TCN and MTL-TCN-ATM models.
Compared to the corresponding single-task model, the MTL-LSTM model
demonstrates an average decrease of 26.04% in MAE and 25.19% in RMSE.
