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.

翻译：在复杂驾驶场景中实现精确的轨迹预测对自动驾驶车辆至关重要。实践中，不同驾驶场景对轨迹预测模型构成不同程度的挑战。然而，现有研究大多关注预测结果的平均精度，而忽视了输入场景的潜在分布特性。本文提出一种关键样本挖掘方法，采用数据驱动的方式估计轨迹的稀缺性。通过将观测值的稀缺性估计与完整轨迹相结合，所提方法能够在将数据输入特定预测模型之前，有效识别出预测难度相对较高的数据子集。实验结果表明，当对不同下游预测模型应用所挖掘的子集时，其预测误差显著升高——在挖掘5%样本的情况下，误差增幅达+108.1%（相当于数据集平均误差的两倍以上）。进一步分析表明，挖掘得到的关键样本包含突发制动、取消变道等非常规场景，这有助于更深入地理解并提升预测模型的性能。