Several datasets exist which contain annotated information of individuals' trajectories. Such datasets are vital for many real-world applications, including trajectory prediction and autonomous navigation. One prominent dataset currently in use is the Stanford Drone Dataset (SDD). Despite its prominence, discussion surrounding the characteristics of this dataset is insufficient. We demonstrate how this insufficiency reduces the information available to users and can impact performance. Our contributions include the outlining of key characteristics in the SDD, employment of an information-theoretic measure and custom metric to clearly visualize those characteristics, the implementation of the PECNet and Y-Net trajectory prediction models to demonstrate the outlined characteristics' impact on predictive performance, and lastly we provide a comparison between the SDD and Intersection Drone (inD) Dataset. Our analysis of the SDD's key characteristics is important because without adequate information about available datasets a user's ability to select the most suitable dataset for their methods, to reproduce one another's results, and to interpret their own results are hindered. The observations we make through this analysis provide a readily accessible and interpretable source of information for those planning to use the SDD. Our intention is to increase the performance and reproducibility of methods applied to this dataset going forward, while also clearly detailing less obvious features of the dataset for new users.

研究论文通过分析Stanford Drone Dataset的关键特征，使用信息理论方法并实施编码器来展示这些特征对预测性能的影响，并提供SDD和inD数据集的比较，为使用SDD的人提供信息源，以增加方法的性能和可再现性。

斯坦福无人机数据集比我们想象的更复杂：关键特征分析