  Accumulating substantial volumes of real-world driving data proves pivotal in
the realm of trajectory forecasting for autonomous driving. Given the heavy
reliance of current trajectory forecasting models on data-driven methodologies,
we aim to tackle the challenge of learning general trajectory forecasting
representations under limited data availability. We propose to augment both HD
maps and trajectories and apply pre-training strategies on top of them.
Specifically, we take advantage of graph representations of HD-map and apply
vector transformations to reshape the maps, to easily enrich the limited number
of scenes. Additionally, we employ a rule-based model to generate trajectories
based on augmented scenes; thus enlarging the trajectories beyond the collected
real ones. To foster the learning of general representations within this
augmented dataset, we comprehensively explore the different pre-training
strategies, including extending the concept of a Masked AutoEncoder (MAE) for
trajectory forecasting. Extensive experiments demonstrate the effectiveness of
our data expansion and pre-training strategies, which outperform the baseline
prediction model by large margins, e.g. 5.04%, 3.84% and 8.30% in terms of
$MR_6$, $minADE_6$ and $minFDE_6$.
