Social-STAGE: Spatio-Temporal Multi-Modal Future Trajectory Forecast
This addresses trajectory forecasting for autonomous systems, though it appears incremental with improvements on existing datasets.
The paper tackles the problem of predicting multiple plausible future trajectories with confidence rankings, proposing Social-STAGE which outperforms state-of-the-art methods on ETH and UCY datasets.
This paper considers the problem of multi-modal future trajectory forecast with ranking. Here, multi-modality and ranking refer to the multiple plausible path predictions and the confidence in those predictions, respectively. We propose Social-STAGE, Social interaction-aware Spatio-Temporal multi-Attention Graph convolution network with novel Evaluation for multi-modality. Our main contributions include analysis and formulation of multi-modality with ranking using interaction and multi-attention, and introduction of new metrics to evaluate the diversity and associated confidence of multi-modal predictions. We evaluate our approach on existing public datasets ETH and UCY and show that the proposed algorithm outperforms the state of the arts on these datasets.