The availability of high-quality datasets is crucial for the development of behavior prediction algorithms in autonomous vehicles. This paper highlights the need for standardizing the use of certain datasets for motion forecasting research to simplify comparative analysis and proposes a set of tools and practices to achieve this. Drawing on extensive experience and a comprehensive review of current literature, we summarize our proposals for preprocessing, visualizing, and evaluation in the form of an open-sourced toolbox designed for researchers working on trajectory prediction problems. The clear specification of necessary preprocessing steps and evaluation metrics is intended to alleviate development efforts and facilitate the comparison of results across different studies. The toolbox is available at: https://github.com/westny/dronalize.

翻译：高质量数据集对于自动驾驶车辆行为预测算法的发展至关重要。本文强调了在运动预测研究中标准化使用特定数据集以简化比较分析的必要性，并提出了一套实现这一目标的工具与实践方案。基于丰富的经验及对当前文献的全面回顾，我们以开源工具箱的形式总结了预处理、可视化及评估方面的建议，该工具箱专为从事轨迹预测问题的研究人员设计。明确必要的预处理步骤与评估指标，旨在减轻开发工作并促进不同研究之间的结果对比。工具箱可通过以下链接获取：https://github.com/westny/dronalize。