研究数据集设计如何影响多智能体轨迹预测，发现简单特征已足够。
Beyond Features: How Dataset Design Influences Multi-Agent Trajectory Prediction Performance
- 用自建德国和美国数据集测试，对比地图与智能体特征效果
- 引入新特征未提升性能，说明现有公开数据特征已够用
- 跨国家数据迁移显示驾驶文化差异影响模型泛化能力
准确的轨迹预测对安全自动驾驶至关重要，但数据集设计对模型性能的影响仍缺乏系统研究。本文系统考察特征选择、跨数据集迁移及地理多样性对多智能体轨迹预测准确性的影响。基于在德国和美国采集的自有数据，构建新型L4 Motion Forecasting数据集，包含增强的地图与智能体特征，并与以美国为中心的Argoverse 2基准进行对比。首先，发现在现有数据集中引入本数据集特有的补充地图与智能体特征，未能带来可测量的性能提升，表明现代模型无需复杂特征集即可达到最优表现，公开数据集的有限特征已足以捕捉复杂的交互关系。其次，开展跨数据集实验，评估领域知识迁移的有效性。最后，按国家分组数据集，检验不同驾驶文化间的知识迁移效果。
原文摘要 · Abstract (English)
Accurate trajectory prediction is critical for safe autonomous navigation, yet the impact of dataset design on model performance remains understudied. This work systematically examines how feature selection, cross-dataset transfer, and geographic diversity influence trajectory prediction accuracy in multi-agent settings. We evaluate a state-of-the-art model using our novel L4 Motion Forecasting dataset based on our own data recordings in Germany and the US. This includes enhanced map and agent features. We compare our dataset to the US-centric Argoverse 2 benchmark. First, we find that incorporating supplementary map and agent features unique to our dataset, yields no measurable improvement over baseline features, demonstrating that modern architectures do not need extensive feature sets for optimal performance. The limited features of public datasets are sufficient to capture convoluted interactions without added complexity. Second, we perform cross-dataset experiments to evaluate how effective domain knowledge can be transferred between datasets. Third, we group our dataset by country and check the knowledge transfer between different driving cultures.
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