  Human trajectory forecasting is a key component of autonomous vehicles,
social-aware robots and advanced video-surveillance applications. This
challenging task typically requires knowledge about past motion, the
environment and likely destination areas. In this context, multi-modality is a
fundamental aspect and its effective modeling can be beneficial to any
architecture. Inferring accurate trajectories is nevertheless challenging, due
to the inherently uncertain nature of the future. To overcome these
difficulties, recent models use different inputs and propose to model human
intentions using complex fusion mechanisms. In this respect, we propose a
lightweight attention-based recurrent backbone that acts solely on past
observed positions. Although this backbone already provides promising results,
we demonstrate that its prediction accuracy can be improved considerably when
combined with a scene-aware goal-estimation module. To this end, we employ a
common goal module, based on a U-Net architecture, which additionally extracts
semantic information to predict scene-compliant destinations. We conduct
extensive experiments on publicly-available datasets (i.e. SDD, inD, ETH/UCY)
and show that our approach performs on par with state-of-the-art techniques
while reducing model complexity.
