  Lane-changing is an important driving behavior and unreasonable lane changes
can result in potentially dangerous traffic collisions. Advanced Driver
Assistance System (ADAS) can assist drivers to change lanes safely and
efficiently. To capture the stochastic time series of lane-changing behavior,
this study proposes a temporal convolutional network (TCN) to predict the
long-term lane-changing trajectory and behavior. In addition, the convolutional
neural network (CNN) and recurrent neural network (RNN) methods are considered
as the benchmark models to demonstrate the learning ability of the TCN. The
lane-changing dataset was collected by the driving simulator. The prediction
performance of TCN is demonstrated from three aspects: different input
variables, different input dimensions and different driving scenarios.
Prediction results show that the TCN can accurately predict the long-term
lane-changing trajectory and driving behavior with shorter computational time
compared with two benchmark models. The TCN can provide accurate lane-changing
prediction, which is one key information for the development of accurate ADAS.
