通过生成模型挖掘难预测的驾驶轨迹，提升模型鲁棒性。
Critical Example Mining for Vehicle Trajectory Prediction using Flow-based Generative Models
- 基于流模型评估轨迹罕见度，筛选难预测样本
- 仅5%样本即导致预测误差提升108.1%
- 可定位突发刹车等异常场景，适合模型调试
复杂驾驶场景中的精确轨迹预测对自动驾驶至关重要。现有研究多关注平均预测精度，忽略输入场景分布差异。本文提出一种数据驱动的临界样本挖掘方法，利用流式生成模型估计轨迹的罕见程度，结合观测与完整轨迹，预先识别出较难预测的数据子集。实验表明，该子集在不同下游预测模型上的误差显著上升，当挖掘5%样本时，误差增幅达+108.1%（超过平均值两倍）。进一步分析显示，这些临界样本包含突发刹车、取消变道等非常见场景，有助于深入理解并改进预测模型性能。
原文摘要 · Abstract (English)
Precise trajectory prediction in complex driving scenarios is essential for autonomous vehicles. In practice, different driving scenarios present varying levels of difficulty for trajectory prediction models. However, most existing research focuses on the average precision of prediction results, while ignoring the underlying distribution of the input scenarios. This paper proposes a critical example mining method that utilizes a data-driven approach to estimate the rareness of the trajectories. By combining the rareness estimation of observations with whole trajectories, the proposed method effectively identifies a subset of data that is relatively hard to predict BEFORE feeding them to a specific prediction model. The experimental results show that the mined subset has higher prediction error when applied to different downstream prediction models, which reaches +108.1% error (greater than two times compared to the average on dataset) when mining 5% samples. Further analysis indicates that the mined critical examples include uncommon cases such as sudden brake and cancelled lane-change, which helps to better understand and improve the performance of prediction models.
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