Resonance: Learning to Predict Social-Aware Pedestrian Trajectories as Co-Vibrations
This work addresses the problem of social-aware trajectory prediction for autonomous systems, but it appears incremental as it builds on existing vibration-inspired methods.
The paper tackles the challenge of forecasting pedestrian trajectories by accounting for intentions and social behaviors in an explainable way, proposing the Resonance model that decomposes trajectories into 'co-vibrations' and achieves verified improvements on multiple datasets.
Learning to forecast trajectories of intelligent agents has caught much more attention recently. However, it remains a challenge to accurately account for agents' intentions and social behaviors when forecasting, and in particular, to simulate the unique randomness within each of those components in an explainable and decoupled way. Inspired by vibration systems and their resonance properties, we propose the Resonance (short for Re) model to encode and forecast pedestrian trajectories in the form of ``co-vibrations''. It decomposes trajectory modifications and randomnesses into multiple vibration portions to simulate agents' reactions to each single cause, and forecasts trajectories as the superposition of these independent vibrations separately. Also, benefiting from such vibrations and their spectral properties, representations of social interactions can be learned by emulating the resonance phenomena, further enhancing its explainability. Experiments on multiple datasets have verified its usefulness both quantitatively and qualitatively.