Automated vehicles (AVs) are tested in diverse scenarios, typically specified by parameters such as velocities, distances, or curve radii. To describe scenarios uniformly independent of such parameters, this paper proposes a vectorized scenario description defined by the road geometry and vehicles' trajectories. Data of this form are generated for three scenarios, merged, and used to train the motion prediction model VectorNet, allowing to predict an AV's trajectory for unseen scenarios. Predicting scenario evaluation metrics, VectorNet partially achieves lower errors than regression models that separately process the three scenarios' data. However, for comprehensive generalization, sufficient variance in the training data must be ensured. Thus, contrary to existing methods, our proposed method can merge diverse scenarios' data and exploit spatial and temporal nuances in the vectorized scenario description. As a result, data from specified test scenarios and real-world scenarios can be compared and combined for (predictive) analyses and scenario selection.

翻译：自动驾驶汽车需在多样化场景中进行测试，这些场景通常由速度、距离或弯道半径等参数定义。为建立独立于此类参数的统一场景描述方法，本文提出一种基于向量的场景描述，该描述由道路几何结构与车辆轨迹共同定义。针对三种典型场景生成此类数据，通过数据融合训练运动预测模型VectorNet，使其能够预测自动驾驶汽车在未知场景中的轨迹。在场景评估指标预测中，VectorNet部分实现了比分场景单独处理的回归模型更低的误差。然而，为确保全面泛化能力，必须保证训练数据具有充分差异性。因此，与现有方法不同，本文提出的方法可融合不同场景数据，并利用向量化场景描述中的时空细节特征。由此，特定测试场景数据与现实场景数据可实现对比与融合，用于（预测性）分析及场景选择。