  Predicting the motion of multiple traffic participants has always been one of
the most challenging tasks in autonomous driving. The recently proposed
occupancy flow field prediction method has shown to be a more effective and
scalable representation compared to general trajectory prediction methods.
However, in complex multi-agent traffic scenarios, it remains difficult to
model the interactions among various factors and the dependencies among
prediction outputs at different time steps. In view of this, we propose a
transformer-based hierarchical feature guided network (HGNET), which can
efficiently extract features of agents and map information from visual and
vectorized inputs, modeling multimodal interaction relationships. Second, we
design the Feature-Guided Attention (FGAT) module to leverage the potential
guiding effects between different prediction targets, thereby improving
prediction accuracy. Additionally, to enhance the temporal consistency and
causal relationships of the predictions, we propose a Time Series Memory
framework to learn the conditional distribution models of the prediction
outputs at future time steps from multivariate time series. The results
demonstrate that our model exhibits competitive performance, which ranks 3rd in
the 2024 Waymo Occupancy and Flow Prediction Challenge.
