Two papers accepted at ICCV 2025

Abstract

Predicting future trajectories of dynamic traffic agents is crucial in autonomous systems. While data-driven methods enable large-scale training, they often underperform on rarely observed tail samples, yielding a long-tail problem. Prior works have tackled this by modifying model architectures, such as using a hypernetwork. In contrast, we propose refining the training procedure to unlock each model’s potential without altering its structure. To this end, we introduce the Generative Active Learning for Trajectory prediction (GALTraj), which iteratively identifies tail samples and augments them via a controllable generative diffusion model. By incorporating the augmented samples in each iteration, we directly mitigate dataset imbalance. To ensure effective augmentation, we design a new tail-aware generation method that categorizes agents (tail, head, relevant) and applies tailored guidance of the diffusion model. It enables producing diverse and realistic trajectories that preserve tail characteristics while respecting traffic constraints. Unlike prior traffic simulation methods focused on producing diverse scenarios, ours is the first to show how simulator-driven augmentation can benefit long-tail learning for trajectory prediction. Experiments on multiple trajectory datasets (WOMD, Argoverse2) with popular backbones (QCNet, MTR) confirm that our method significantly boosts performance on tail samples and also enhances accuracy on head samples.

Abstract

Motion planning is a crucial component of autonomous robot driving. While various trajectory datasets exist, effectively utilizing them for a target domain remains challenging due to differences in agent interactions and environmental characteristics. Conventional approaches, such as domain adaptation or ensemble learning, leverage multiple source datasets but suffer from domain imbalance, catastrophic forgetting, and high computational costs. To address these challenges, we propose Interaction-Merged Motion Planning (IMMP), a novel approach that leverages parameter checkpoints trained on different domains during adaptation to the target domain. IMMP follows a two-step process: pre-merging to capture agent behaviors and interactions, sufficiently extracting diverse information from the source domain, followed by merging to construct an adaptable model that efficiently transfers diverse interactions to the target domain. Our method is evaluated on various planning benchmarks and models, demonstrating superior performance compared to conventional approaches.