Surrounding agents frequently degrade trajectory prediction accuracy in interactive driving scenes, and integrating a Conditional Information Bottleneck improves results by ignoring non-beneficial contextual signals.
EDA: Evolving and Distinct Anchors for Multimodal Motion Prediction
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A differentiable motion forecasting model retrieves and refines interpretable trajectory anchors from a contrastively learned motion bank to improve transparency without sacrificing multi-modal accuracy.
citing papers explorer
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Super Agents and Confounders: Influence of surrounding agents on vehicle trajectory prediction
Surrounding agents frequently degrade trajectory prediction accuracy in interactive driving scenes, and integrating a Conditional Information Bottleneck improves results by ignoring non-beneficial contextual signals.
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Recall to Predict: Grounding Motion Forecasting in Interpretable Motion Bank
A differentiable motion forecasting model retrieves and refines interpretable trajectory anchors from a contrastively learned motion bank to improve transparency without sacrificing multi-modal accuracy.