A Novel Temporal Multi-Gate Mixture-of-Experts Approach for Vehicle
  Trajectory and Driving Intention Prediction
Renteng Yuan Mohamed Abdel-Aty Qiaojun Xiang Zijin Wang Ou Zheng
Accurate Vehicle Trajectory Prediction is critical for automated vehicles and
advanced driver assistance systems. Vehicle trajectory prediction consists of
two essential tasks, i.e., longitudinal position prediction and lateral
position prediction. There is a significant correlation between driving
intentions and vehicle motion. In existing work, the three tasks are often
conducted separately without considering the relationships between the
longitudinal position, lateral position, and driving intention. In this paper,
we propose a novel Temporal Multi-Gate Mixture-of-Experts (TMMOE) model for
simultaneously predicting the vehicle trajectory and driving intention. The
proposed model consists of three layers: a shared layer, an expert layer, and a
fully connected layer. In the model, the shared layer utilizes Temporal
Convolutional Networks (TCN) to extract temporal features. Then the expert
layer is built to identify different information according to the three tasks.
Moreover, the fully connected layer is used to integrate and export prediction
results. To achieve better performance, uncertainty algorithm is used to
construct the multi-task loss function. Finally, the publicly available CitySim
dataset validates the TMMOE model, demonstrating superior performance compared
to the LSTM model, achieving the highest classification and regression results.
Keywords: Vehicle trajectory prediction, driving intentions Classification,
Multi-task