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TrEP: Transformer-Based Evidential Prediction for Pedestrian Intention with Uncertainty

AAAI 2023 · Video › Video segmentation and tracking · approach: Physics-inspired motion prediction · 68 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use TrEP instead of BiPedPIE intention prediction · PIEAUC: 0.90 -> 0.93Helped
use TrEP instead of MMHAJAAD intention prediction · JAADaccuracy: 0.84 -> 0.87Helped
use TrEP instead of PSI-IntentionPSI intention prediction · PSIaccuracy: 0.76 -> 0.83Helped
add bounding-box center coordinates (ablation)TrEP base model · PIE, JAAD, and PSIaccuracy: PIE: 0.80 -> 0.91; JAAD: 0.79 -> 0.87; PSI: 0.72 -> 0.80Helped
add bounding-box area and length-to-width ratio (ablation)TrEP with center coordinates · PIEaccuracy: 0.91 -> 0.89Hurt
add bounding-box area and length-to-width ratio (ablation)TrEP with center coordinates · JAADaccuracy: 0.87 -> 0.86Hurt
add bounding-box area and length-to-width ratio (ablation)TrEP with center coordinates · PSIaccuracy: 0.80 -> 0.83Helped
add positional encoding (ablation)TrEP base model · PIEaccuracy: 0.90 -> 0.91Helped
add positional encoding (ablation)TrEP base model · JAADaccuracy: 0.85 -> 0.87Helped
add positional encoding (ablation)TrEP base model · PSIaccuracy: 0.81 -> 0.83Helped
reject predictions with uncertainty above 0.6TrEP · PIEaccuracy: 0.91 -> 0.93Helped
reject predictions with uncertainty above 0.6TrEP · JAADaccuracy: 0.87 -> 0.91Helped

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