The Stanford Drone Dataset Is More Complex Than We Think: An Analysis of Key Characteristics
نویسندگان
چکیده
Several datasets exist which contain annotated information of individuals’ trajectories. Such 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) (Robicquet et al. , 2016). Despite its prominence, discussion surrounding characteristics this insufficient. We demonstrate how insufficiency reduces available to users can impact performance. Our contributions include outlining key SDD, employment an information-theoretic measure custom metric clearly visualize those characteristics, implementation PECNet (Mangalam 2020) Y-Net 2021) models outlined characteristics’ on predictive performance, lastly we provide a comparison between SDD Intersection (inD) Dataset. analysis SDD’s important because without adequate about user’s ability select most suitable their methods, reproduce one another’s results, interpret own results hindered. The observations make through readily accessible interpretable source planning SDD. intention increase performance reproducibility methods applied going forward, while also detailing less obvious features new users.
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ژورنال
عنوان ژورنال: IEEE transactions on intelligent vehicles
سال: 2023
ISSN: ['2379-8904', '2379-8858']
DOI: https://doi.org/10.1109/tiv.2022.3166642