Abstract
Developing robust models to accurately predict the trajectories of surrounding agents is fundamental to autonomous driving safety. However, most public datasets, such as the Waymo Open Motion Dataset and Argoverse, are collected in Western road environments and do not reflect the unique traffic patterns, infrastructure, and driving behaviors of other regions, including South Korea. This domain discrepancy leads to performance degradation when state-of-the-art models trained on Western data are deployed in different geographic contexts. In this work, we investigate the adaptability of Query-Centric Trajectory Prediction (QCNet) when transferred from U.S.-based data to Korean road environments. Using a Korean autonomous driving dataset, we compare four training strategies: zero-shot transfer, training from scratch, full fine-tuning, and encoder freezing. Experimental results demonstrate that leveraging pretrained knowledge significantly improves prediction performance. Specifically, selectively fine-tuning the decoder while freezing the encoder yields the best trade-off between accuracy and training efficiency, reducing prediction error by over 66% compared to training from scratch. This study provides practical insights into effective transfer learning strategies for deploying trajectory prediction models in new geographic domains.
Abstract (translated)
开发稳健模型以准确预测周围智能体的轨迹，是实现自动驾驶安全的基础。然而，包括Waymo开放运动数据集和Argoverse在内的大多数公开数据集均采集于西方道路环境，未能反映韩国等其他地区独特的交通模式、基础设施和驾驶行为。这种领域差异导致在西方数据上训练的先进模型部署到不同地理环境时性能下降。本研究探讨了以查询为中心的轨迹预测模型（QCNet）从美国数据向韩国道路环境迁移的适应性。利用韩国自动驾驶数据集，我们对比了四种训练策略：零样本迁移、从头训练、全参数微调以及编码器冻结。实验结果表明，利用预训练知识可显著提升预测性能。具体而言，在冻结编码器的情况下选择性微调解码器，实现了精度与训练效率的最佳平衡，相较于从头训练将预测误差降低了超过66%。本研究为在新地理领域部署轨迹预测模型提供了有效的迁移学习策略实践见解。
URL
https://arxiv.org/abs/2604.00402
