Implicit Occupancy Flow Fields for Perception and Prediction in
  Self-Driving
Ben Agro Quinlan Sykora Sergio Casas Raquel Urtasun
A self-driving vehicle (SDV) must be able to perceive its surroundings and
predict the future behavior of other traffic participants. Existing works
either perform object detection followed by trajectory forecasting of the
detected objects, or predict dense occupancy and flow grids for the whole
scene. The former poses a safety concern as the number of detections needs to
be kept low for efficiency reasons, sacrificing object recall. The latter is
computationally expensive due to the high-dimensionality of the output grid,
and suffers from the limited receptive field inherent to fully convolutional
networks. Furthermore, both approaches employ many computational resources
predicting areas or objects that might never be queried by the motion planner.
This motivates our unified approach to perception and future prediction that
implicitly represents occupancy and flow over time with a single neural
network. Our method avoids unnecessary computation, as it can be directly
queried by the motion planner at continuous spatio-temporal locations.
Moreover, we design an architecture that overcomes the limited receptive field
of previous explicit occupancy prediction methods by adding an efficient yet
effective global attention mechanism. Through extensive experiments in both
urban and highway settings, we demonstrate that our implicit model outperforms
the current state-of-the-art. For more information, visit the project website:
https://waabi.ai/research/implicito.