标题： 基于风险的Waymo开放运动数据集中有价值驾驶情景过滤 显示英文标题

标题： Risk-Based Filtering of Valuable Driving Situations in the Waymo Open Motion Dataset

摘要： 提高自动驾驶汽车软件需要包含丰富有价值道路使用者交互的驾驶数据。 在本文中，我们提出了一种基于风险的过滤方法，有助于从大型数据集中识别此类有价值的驾驶情况。 具体来说，我们使用概率风险模型来检测高风险情况。 我们的方法通过考虑a) 一阶情况（其中一辆车直接对另一辆车产生影响并引发风险）和b) 二阶情况（其中影响通过中间车辆传播）而突出。 在实验中，我们展示了我们的方法在Waymo Open Motion数据集中有效选择有价值的驾驶情况。 与卡尔曼难度和Tracks-To-Predict（TTP）两种基线交互指标相比，我们的过滤方法能够识别复杂且互补的情况，从而提升自动驾驶汽车测试的质量。 风险数据已开源：https://github.com/HRI-EU/RiskBasedFiltering。 显示英文摘要

摘要： Improving automated vehicle software requires driving data rich in valuable road user interactions. In this paper, we propose a risk-based filtering approach that helps identify such valuable driving situations from large datasets. Specifically, we use a probabilistic risk model to detect high-risk situations. Our method stands out by considering a) first-order situations (where one vehicle directly influences another and induces risk) and b) second-order situations (where influence propagates through an intermediary vehicle). In experiments, we show that our approach effectively selects valuable driving situations in the Waymo Open Motion Dataset. Compared to the two baseline interaction metrics of Kalman difficulty and Tracks-To-Predict (TTP), our filtering approach identifies complex and complementary situations, enriching the quality in automated vehicle testing. The risk data is made open-source: https://github.com/HRI-EU/RiskBasedFiltering.