  Nowadays, our mobility systems are evolving into the era of intelligent
vehicles that aim to improve road safety. Due to their vulnerability,
pedestrians are the users who will benefit the most from these developments.
However, predicting their trajectory is one of the most challenging concerns.
Indeed, accurate prediction requires a good understanding of multi-agent
interactions that can be complex. Learning the underlying spatial and temporal
patterns caused by these interactions is even more of a competitive and open
problem that many researchers are tackling. In this paper, we introduce a model
called PRediction Transformer (PReTR) that extracts features from the
multi-agent scenes by employing a factorized spatio-temporal attention module.
It shows less computational needs than previously studied models with
empirically better results. Besides, previous works in motion prediction suffer
from the exposure bias problem caused by generating future sequences
conditioned on model prediction samples rather than ground-truth samples. In
order to go beyond the proposed solutions, we leverage encoder-decoder
Transformer networks for parallel decoding a set of learned object queries.
This non-autoregressive solution avoids the need for iterative conditioning and
arguably decreases training and testing computational time. We evaluate our
model on the ETH/UCY datasets, a publicly available benchmark for pedestrian
trajectory prediction. Finally, we justify our usage of the parallel decoding
technique by showing that the trajectory prediction task can be better solved
as a non-autoregressive task.
