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：自动驾驶车辆（AV）通常在由速度、距离或弯道半径等参数指定的多样化场景中进行测试。为摆脱此类参数的依赖以实现场景的统一描述，本文提出一种基于道路几何与车辆轨迹定义的矢量化场景描述方法。针对三种场景生成该形式的数据，经融合后用于训练运动预测模型VectorNet，从而实现对未见场景中自动驾驶车辆轨迹的预测。在场景评估指标的预测中，VectorNet部分实现了比分别处理三种场景数据的回归模型更低的误差。然而，为实现全面泛化，必须确保训练数据具有充分的方差。因此，与现有方法不同，本文所提方法能够融合不同场景的数据，并利用矢量化场景描述中的时空细节。由此，指定测试场景数据与真实场景数据可进行对比与融合，从而支持（预测性）分析及场景选择。