解决轨迹预测数据分布不均问题，提升高密度场景下的模型鲁棒性。
Den-TP: A Density-Balanced Data Curation and Evaluation Framework for Trajectory Prediction
- 按车辆密度划分数据区域，用梯度子模选择法均衡采样。
- 数据量减半仍保持性能，高密度场景误差下降显著。
- 新评估协议可暴露传统方法忽略的长尾失效问题。
自动驾驶中的轨迹预测长期以模型为中心。然而现有数据集在场景密度上呈现强长尾分布，常见低密度场景占主导，而高密度的安全关键场景严重缺失。这种失衡限制了模型鲁棒性，并掩盖了其在常规评估中被平均掉的失败模式。本文从数据中心视角出发，提出Den-TP框架，实现密度感知的数据筛选与评估。该框架首先使用车辆数量作为无依赖于数据集的交互复杂度代理，将数据划分为密度条件区域；随后采用基于梯度的子模选择目标，在各区域内选取代表性样本并显式平衡不同密度间的分布。所得子集压缩50%数据量，同时维持整体性能，并显著提升高密度场景下的鲁棒性。我们进一步引入密度条件评估协议，揭示传统指标忽视的长尾失败模式。在Argoverse 1和2上的实验表明，先进模型的鲁棒性不仅取决于数据规模，更依赖于场景密度的均衡分布。
原文摘要 · Abstract (English)
Trajectory prediction in autonomous driving has traditionally been studied from a model-centric perspective. However, existing datasets exhibit a strong long-tail distribution in scenario density, where common low-density cases dominate and safety-critical high-density cases are severely underrepresented. This imbalance limits model robustness and hides failure modes when standard evaluations average errors across all scenarios. We revisit trajectory prediction from a data-centric perspective and present Den-TP, a framework for density-aware dataset curation and evaluation. Den-TP first partitions data into density-conditioned regions using agent count as a dataset-agnostic proxy for interaction complexity. It then applies a gradient-based submodular selection objective to choose representative samples within each region while explicitly rebalancing across densities. The resulting subset reduces the dataset size by 50\% yet preserves overall performance and significantly improves robustness in high-density scenarios. We further introduce density-conditioned evaluation protocols that reveal long-tail failure modes overlooked by conventional metrics. Experiments on Argoverse 1 and 2 with state-of-the-art models show that robust trajectory prediction depends not only on data scale, but also on balancing scenario density.
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