融合多源轨迹数据，提升自动驾驶规划鲁棒性
Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning
- 用预合并与合并两步法整合不同场景的运动数据
- 在多个基准上表现优于传统方法，提升规划稳定性
- 适合需要跨场景泛化的自动驾驶系统研发者
运动规划是自主机器人驾驶的关键组件。尽管存在多种轨迹数据集，但因代理交互和环境特征差异，有效利用这些数据进行目标域规划仍具挑战。传统方法如领域自适应或集成学习虽能利用多个源数据集，却面临领域不平衡、灾难性遗忘和高计算成本问题。为此，我们提出交互融合运动规划（IMMP），在目标域适应过程中利用不同领域训练的参数检查点。IMMP采用两步流程：预合并阶段捕捉代理行为与交互，充分提取源域多样性信息；合并阶段构建可适配模型，高效将多样化交互迁移至目标域。在多个规划基准与模型上的评估表明，该方法显著优于传统方法。
原文摘要 · Abstract (English)
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.
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