  The task of motion prediction is pivotal for autonomous driving systems,
providing crucial data to choose a vehicle behavior strategy within its
surroundings. Existing motion prediction techniques primarily focus on
predicting the future trajectory of each agent in the scene individually,
utilizing its past trajectory data. In this paper, we introduce an end-to-end
neural network methodology designed to predict the future behaviors of all
dynamic objects in the environment. This approach leverages the occupancy map
and the scene's motion flow. We are investigatin various alternatives for
constructing a deep encoder-decoder model called OFMPNet. This model uses a
sequence of bird's-eye-view road images, occupancy grid, and prior motion flow
as input data. The encoder of the model can incorporate transformer,
attention-based, or convolutional units. The decoder considers the use of both
convolutional modules and recurrent blocks. Additionally, we propose a novel
time-weighted motion flow loss, whose application has shown a substantial
decrease in end-point error. Our approach has achieved state-of-the-art results
on the Waymo Occupancy and Flow Prediction benchmark, with a Soft IoU of 52.1%
and an AUC of 76.75% on Flow-Grounded Occupancy.
