论文标题
肉：从代理轨迹提取机动
MEAT: Maneuver Extraction from Agent Trajectories
论文作者
论文摘要
大规模数据集可以实现基于学习的轨迹预测的进步。但是，对此类数据集的深入分析是有限的。此外，对预测模型的评估仅限于数据集中所有样本的指标。我们提出了一种自动化方法，该方法允许从此类数据集中的代理轨迹中提取操作（例如，左转，车道更改）。该方法考虑了有关代理动力学和有关代理商行驶的车道段的信息。尽管可以使用所得的操纵来训练分类网络，但我们将它们用于广泛的轨迹数据集分析和对多个最先进的轨迹预测模型的操纵特定评估。此外，还提供了基于代理动力学的数据集的分析和对预测模型的评估。
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.
