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BP-SGCN: Behavioral Pseudo-Label Informed Sparse Graph Convolution Network for Pedestrian and Heterogeneous
IEEE Transactions on Neural Networks and Learning Systems
|March 21, 2025
Summary
This study introduces behavioral pseudo-labels to improve trajectory prediction for autonomous vehicles and surveillance. These labels capture agent behaviors from motion data, enhancing prediction accuracy without costly annotations.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Robotics
Background:
- Trajectory prediction is crucial for autonomous vehicles (AVs) and surveillance, but current methods struggle with heterogeneous agents (vehicles, cyclists) or rely on expensive labels.
- Pedestrian-focused models are limited in mixed-traffic scenarios, while methods using class labels are costly and lack intra-class behavioral nuance.
Purpose of the Study:
- To develop a novel approach for trajectory prediction that accurately models diverse agent behaviors using only motion features.
- To introduce 'behavioral pseudo-labels' that capture behavior distributions for both pedestrians and heterogeneous agents.
- To propose and validate a framework, the behavioral pseudo-label informed sparse graph convolution network (BP-SGCN), for improved trajectory prediction.
Main Methods:
- Developed behavioral pseudo-labels derived solely from motion features to represent agent behavior distributions.
- Proposed the behavioral pseudo-label informed sparse graph convolution network (BP-SGCN) to learn these pseudo-labels and integrate them into a trajectory prediction model.
- Implemented a cascaded training scheme: unsupervised pseudo-label learning followed by supervised end-to-end fine-tuning for trajectory prediction accuracy.
Main Results:
- Behavioral pseudo-labels effectively model distinct behavior clusters within agent trajectories.
- The BP-SGCN framework significantly improves trajectory prediction accuracy compared to existing methods.
- Demonstrated superior performance on both pedestrian-only (ETH/UCY, SDD) and heterogeneous agent datasets (SDD, Argoverse1).
Conclusions:
- Behavioral pseudo-labels offer a powerful, annotation-free method for capturing agent behavior in trajectory prediction.
- The BP-SGCN model provides a robust and accurate solution for trajectory prediction in complex, heterogeneous traffic environments.
- This work advances the state-of-the-art in trajectory prediction, enabling more reliable decision-making for autonomous systems.
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