Social-STAGE: Spatio-Temporal Multi-Modal Future Trajectory Forecast
Srikanth Malla, Chiho Choi, Behzad Dariush

TL;DR
This paper introduces Social-STAGE, a novel multi-modal trajectory forecasting model that incorporates social interactions, spatio-temporal attention, and new evaluation metrics to improve prediction diversity and confidence assessment.
Contribution
It presents a new graph-based multi-attention model for multi-modal trajectory prediction with a novel ranking and evaluation framework.
Findings
Outperforms state-of-the-art on ETH and UCY datasets.
Effectively models social interactions and multi-modality.
Provides new metrics for diversity and confidence in predictions.
Abstract
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.
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Taxonomy
MethodsConvolution
