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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 prominentdataset currently in use is the Stanford Drone Dataset (SDD). Despite itsprominence, discussion surrounding the characteristics of this dataset isinsufficient. We demonstrate how this insufficiency reduces the informationavailable to users and can impact performance. Our contributions include theoutlining of key characteristics in the SDD, employment of aninformation-theoretic measure and custom metric to clearly visualize thosecharacteristics, the implementation of the PECNet and Y-Net trajectoryprediction models to demonstrate the outlined characteristics' impact onpredictive performance, and lastly we provide a comparison between the SDD andIntersection Drone (inD) Dataset. Our analysis of the SDD's key characteristicsis important because without adequate information about available datasets auser's ability to select the most suitable dataset for their methods, toreproduce one another's results, and to interpret their own results arehindered. The observations we make through this analysis provide a readilyaccessible and interpretable source of information for those planning to usethe SDD. Our intention is to increase the performance and reproducibility ofmethods applied to this dataset going forward, while also clearly detailingless obvious features of the dataset for new users.

The Stanford Drone Dataset is More Complex than We Think: An Analysis of Key Characteristics