MetaDAT: Generalizable Trajectory Prediction via Meta Pre-training and Data-Adaptive Test-Time Updating

TL;DR

MetaDAT introduces a meta-learning framework with data-adaptive test-time updating for trajectory prediction, significantly improving adaptation accuracy under distribution shifts and demonstrating robustness across various challenging datasets.

Contribution

The paper proposes a novel meta-learning approach combined with data-adaptive online updating for trajectory prediction, enabling fast, accurate adaptation during test time.

Findings

Outperforms state-of-the-art test-time training methods in cross-dataset scenarios

Achieves superior adaptation accuracy under distribution shifts

Demonstrates robustness with suboptimal learning rates and high FPS

Abstract

Existing trajectory prediction methods exhibit significant performance degradation under distribution shifts during test time. Although test-time training techniques have been explored to enable adaptation, current approaches rely on an offline pre-trained predictor that lacks online learning flexibility. Moreover, they depend on fixed online model updating rules that do not accommodate the specific characteristics of test data. To address these limitations, we first propose a meta-learning framework to directly optimize the predictor for fast and accurate online adaptation, which performs bi-level optimization on the performance of simulated test-time adaptation tasks during pre-training. Furthermore, at test time, we introduce a data-adaptive model updating mechanism that dynamically adjusts the predefined learning rates and updating frequencies based on online partial derivatives and hard sample selection. This mechanism enables the online learning rate to suit the test data, and focuses on informative hard samples to enhance efficiency. Experiments are conducted on various challenging cross-dataset distribution shift scenarios, including nuScenes, Lyft, and Waymo. Results demonstrate that our method achieves superior adaptation accuracy, surpassing state-of-the-art test-time training methods for trajectory prediction. Additionally, our method excels under suboptimal learning rates and high FPS demands, showcasing its robustness and practicality.