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

TL;DR

This paper analyzes the Stanford Drone Dataset's complex characteristics using information-theoretic measures and demonstrates how these features affect trajectory prediction performance, aiming to improve dataset understanding and reproducibility.

Contribution

It identifies key characteristics of the SDD, visualizes them with custom metrics, and assesses their impact on prediction models, also comparing it with the inD dataset.

Findings

Key dataset features influence prediction accuracy.

Visualization tools clarify dataset complexity.

Comparison highlights differences with inD dataset.

Abstract

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.