Advances in learning-based trajectory prediction are enabled by large-scale datasets. However, in-depth analysis of such datasets is limited. Moreover, the evaluation of prediction models is limited to metrics averaged over all samples in the dataset. We propose an automated methodology that allows to extract maneuvers (e.g., left turn, lane change) from agent trajectories in such datasets. The methodology considers information about the agent dynamics and information about the lane segments the agent traveled along. Although it is possible to use the resulting maneuvers for training classification networks, we exemplary use them for extensive trajectory dataset analysis and maneuver-specific evaluation of multiple state-of-the-art trajectory prediction models. Additionally, an analysis of the datasets and an evaluation of the prediction models based on the agent dynamics is provided.

翻译：大型数据集有助于在基于学习的轨迹预测方面取得进展。但是,对此类数据集的深入分析是有限的。此外,预测模型的评价仅限于数据集中所有样本的平均度量。我们提议一种自动方法,从这些数据集中的物剂轨迹中提取动作(例如左转、航道变化),该方法考虑有关物剂动态的信息和关于该物剂沿途所行的车道段的信息。虽然有可能利用由此产生的操作方法来培训分类网络,但我们在广泛的轨迹数据集分析和对多种最先进的轨迹预测模型进行机动性评价方面堪称典范。此外,还根据物剂动态对数据集进行了分析并评价了预测模型。