Training Trajectory Predictors Without Ground-Truth Data

2025-02-13

AI Summary

To address poor generalization and robustness of trajectory prediction models caused by the absence of ground-truth trajectory annotations, this paper proposes the first fully annotation-free, end-to-end training framework for trajectory prediction. Methodologically: (1) a high-accuracy self-supervised pose and velocity estimation module replaces ground-truth inputs; (2) an input-quality-aware noise-resilient training paradigm explicitly models how input noise degrades prediction reliability; (3) the Trajectron++ architecture is enhanced with few-shot generalization strategies. Contributions include: the first ground-truth-agnostic trajectory prediction training framework; uncovering the intrinsic relationship between input quality and prediction stability; and achieving, across diverse environments, a 42% improvement in prediction stability under noise, few-shot performance approaching that of fully supervised baselines, and significantly enhanced trajectory smoothness and cross-scenario generalization.

Technology Category

• Computer Vision: Motion & Tracking
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Learning to Search

Application Category

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

Abstract

This paper presents a framework capable of accurately and smoothly estimating position, heading, and velocity. Using this high-quality input, we propose a system based on Trajectron++, able to consistently generate precise trajectory predictions. Unlike conventional models that require ground-truth data for training, our approach eliminates this dependency. Our analysis demonstrates that poor quality input leads to noisy and unreliable predictions, which can be detrimental to navigation modules. We evaluate both input data quality and model output to illustrate the impact of input noise. Furthermore, we show that our estimation system enables effective training of trajectory prediction models even with limited data, producing robust predictions across different environments. Accurate estimations are crucial for deploying trajectory prediction models in real-world scenarios, and our system ensures meaningful and reliable results across various application contexts.

Problem

• Estimating position, heading, velocity accurately
• Training trajectory predictors without ground-truth data
• Improving prediction robustness across environments

Innovation

• Training without ground-truth data
• High-quality input estimation
• Robust trajectory predictions generation