ASTRA: A Scene-aware TRAnsformer-based model for trajectory prediction
Journal:
arXiv
Published Date:
Jan 16, 2025
Abstract
We present ASTRA (A} Scene-aware TRAnsformer-based model for trajectory
prediction), a light-weight pedestrian trajectory forecasting model that
integrates the scene context, spatial dynamics, social inter-agent interactions
and temporal progressions for precise forecasting. We utilised a U-Net-based
feature extractor, via its latent vector representation, to capture scene
representations and a graph-aware transformer encoder for capturing social
interactions. These components are integrated to learn an agent-scene aware
embedding, enabling the model to learn spatial dynamics and forecast the future
trajectory of pedestrians. The model is designed to produce both deterministic
and stochastic outcomes, with the stochastic predictions being generated by
incorporating a Conditional Variational Auto-Encoder (CVAE). ASTRA also
proposes a simple yet effective weighted penalty loss function, which helps to
yield predictions that outperform a wide array of state-of-the-art
deterministic and generative models. ASTRA demonstrates an average improvement
of 27%/10% in deterministic/stochastic settings on the ETH-UCY dataset, and 26%
improvement on the PIE dataset, respectively, along with seven times fewer
parameters than the existing state-of-the-art model (see Figure 1).
Additionally, the model's versatility allows it to generalize across different
perspectives, such as Bird's Eye View (BEV) and Ego-Vehicle View (EVV).
