Learning Velocity and Acceleration: Self-Supervised Motion Consistency for Pedestrian Trajectory Prediction

2025-03-31

AI Summary

In pedestrian trajectory prediction, supervised learning suffers from long-tailed data distributions and struggles to model anomalous behaviors such as abrupt stops or sharp turns. To address this, we propose the first self-supervised framework that explicitly and jointly models position, velocity, and acceleration. Our method introduces a hierarchical velocity/acceleration feature injection architecture, enforces physical kinematic consistency via a novel self-supervised mechanism, and integrates a pseudo-label generation strategy to enable cooperative prediction and dynamic coupling constraints among the three motion variables. Crucially, the framework requires no ground-truth velocity or acceleration annotations—only raw trajectory coordinates are needed for training. Evaluated on ETH-UCY and Stanford Drone datasets, our approach achieves state-of-the-art performance, demonstrating significant improvements in both prediction accuracy and robustness for anomalous motions.

Technology Category

Machine Learning: Unsupervised & Self-Supervised LearningComputer Vision: Motion & TrackingHumans and AI: Human-Aware Planning and Behavior Prediction

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingResponsible Web: Machine-in-the-loop, human agency and autonomySemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Understanding human motion is crucial for accurate pedestrian trajectory prediction. Conventional methods typically rely on supervised learning, where ground-truth labels are directly optimized against predicted trajectories. This amplifies the limitations caused by long-tailed data distributions, making it difficult for the model to capture abnormal behaviors. In this work, we propose a self-supervised pedestrian trajectory prediction framework that explicitly models position, velocity, and acceleration. We leverage velocity and acceleration information to enhance position prediction through feature injection and a self-supervised motion consistency mechanism. Our model hierarchically injects velocity features into the position stream. Acceleration features are injected into the velocity stream. This enables the model to predict position, velocity, and acceleration jointly. From the predicted position, we compute corresponding pseudo velocity and acceleration, allowing the model to learn from data-generated pseudo labels and thus achieve self-supervised learning. We further design a motion consistency evaluation strategy grounded in physical principles; it selects the most reasonable predicted motion trend by comparing it with historical dynamics and uses this trend to guide and constrain trajectory generation. We conduct experiments on the ETH-UCY and Stanford Drone datasets, demonstrating that our method achieves state-of-the-art performance on both datasets.

Problem

• Improving pedestrian trajectory prediction accuracy
• Addressing limitations of supervised learning methods
• Enhancing motion modeling via self-supervised consistency

Innovation

• Self-supervised motion consistency for trajectory prediction
• Hierarchical injection of velocity and acceleration features
• Motion consistency evaluation based on physical principles