TrajPRed: Trajectory Prediction with Region-based Relation Learning
The core message of this work is to propose a robust trajectory prediction framework that models two major stimuli of human behavior: external social interactions and individual stochastic goals. The framework learns region-based social relations to capture the dynamics of crowd density changes, which is more robust to spatial noise perturbations compared to edge-based relation learning approaches. It also estimates multiple plausible goals to account for the stochasticity in human behavior.
Generating Synthetic Ground Truth Distributions for Multi-step Trajectory Prediction using Probabilistic Composite Bézier Curves
This paper proposes a novel approach to synthetic dataset generation based on composite probabilistic Bézier curves, which is capable of generating ground truth data in terms of probability distributions over full trajectories, enabling the use of more expressive error metrics like the Wasserstein distance for model evaluation.
Enhancing Trajectory Prediction Robustness: FlexiLength Network for Handling Varying Observation Lengths
The FlexiLength Network (FLN) framework effectively addresses the Observation Length Shift issue in trajectory prediction, enabling robust performance across a range of observation lengths without substantial modifications to existing models.
Test-Time Training of Trajectory Prediction via Masked Autoencoder and Actor-specific Token Memory
Enhancing trajectory prediction through test-time training with a masked autoencoder and actor-specific token memory.
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