  Motion forecasting plays a crucial role in autonomous driving, with the aim
of predicting the future reasonable motions of traffic agents. Most existing
methods mainly model the historical interactions between agents and the
environment, and predict multi-modal trajectories in a feedforward process,
ignoring potential trajectory changes caused by future interactions between
agents. In this paper, we propose a novel Future Feedback Interaction Network
(FFINet) to aggregate features the current observations and potential future
interactions for trajectory prediction. Firstly, we employ different
spatial-temporal encoders to embed the decomposed position vectors and the
current position of each scene, providing rich features for the subsequent
cross-temporal aggregation. Secondly, the relative interaction and
cross-temporal aggregation strategies are sequentially adopted to integrate
features in the current fusion module, observation interaction module, future
feedback module and global fusion module, in which the future feedback module
can enable the understanding of pre-action by feeding the influence of preview
information to feedforward prediction. Thirdly, the comprehensive interaction
features are further fed into final predictor to generate the joint predicted
trajectories of multiple agents. Extensive experimental results show that our
FFINet achieves the state-of-the-art performance on Argoverse 1 and Argoverse 2
motion forecasting benchmarks.
