trajdata: A Unified Interface to Multiple Human Trajectory Datasets (2307.13924v1)

Abstract: The field of trajectory forecasting has grown significantly in recent years, partially owing to the release of numerous large-scale, real-world human trajectory datasets for autonomous vehicles (AVs) and pedestrian motion tracking. While such datasets have been a boon for the community, they each use custom and unique data formats and APIs, making it cumbersome for researchers to train and evaluate methods across multiple datasets. To remedy this, we present trajdata: a unified interface to multiple human trajectory datasets. At its core, trajdata provides a simple, uniform, and efficient representation and API for trajectory and map data. As a demonstration of its capabilities, in this work we conduct a comprehensive empirical evaluation of existing trajectory datasets, providing users with a rich understanding of the data underpinning much of current pedestrian and AV motion forecasting research, and proposing suggestions for future datasets from these insights. trajdata is permissively licensed (Apache 2.0) and can be accessed online at https://github.com/NVlabs/trajdata

• The paper introduces a standardized data format and extensible API that simplifies integrating diverse trajectory datasets.
• It provides an exhaustive empirical evaluation, analyzing agent distributions and motion diversity in prominent AV datasets like Waymo and Lyft.
• The research offers actionable recommendations for improving dataset curation, annotation quality, and future data collection practices.

Insights on "trajdata: A Unified Interface to Multiple Human Trajectory Datasets"

The paper "trajdata: A Unified Interface to Multiple Human Trajectory Datasets" presents a significant contribution to the field of trajectory forecasting, specifically addressing inefficiencies encountered by researchers working with disparate datasets. Its main focus is on the introduction of trajdata, a tool designed to streamline the usage of multiple human trajectory datasets by offering a standardized interface.

Key Contributions

The paper delineates several major contributions. The first is the formulation of a standardized data format for trajectory and map data, accompanied by an extensible API. This standardization is crucial for simplifying the integration of data from a variety of sources. The second major contribution is an exhaustive empirical evaluation of current trajectory datasets. This analysis not only contextualizes the composition and complexity of existing datasets but also serves as a foundation for formulating recommendations for future dataset curations.

Analysis and Results

Central to the paper's offering is the trajdata software package, which consolidates multiple datasets into a unified framework, significantly simplifying cross-dataset analyses. The empirical evaluation shows that modern AV datasets, such as those from Waymo and Lyft, contain extensive data with over dozens of millions of agents and wide-ranging geographical coverage. However, the paper articulates existing challenges related to disparate data formats and unique APIs associated with each dataset.

The evaluation encompassed multiple facets, including agent distributions, motion complexity, and annotation quality. For instance, an analysis of agent distributions revealed that pedestrian datasets possess high-density scenarios conducive to social robotics research settings. Moreover, motion diversity analyses highlighted the challenges associated with unrealistic speeds and long-tailed distributions present in certain datasets. Annotation quality was another focal point, reinforcing the benefits of modern autolabeler performance yet identifying potential artifacts and errors in datasets due to an imperfect data collection process.

Implications and Future Directions

The implications of this research extend across both theoretical and practical domains. By facilitating standardized data access and cross-dataset harmonization, the trajdata interface can support a more streamlined development of trajectory forecasting models that are robust across multiple datasets. Moreover, the comprehensive evaluation of datasets can guide future data collection efforts, emphasizing enhanced annotation methodologies and greater exploration of diverse geographic areas.

Looking towards future research avenues, the trajectory forecast field would benefit from implementations that address semantic data balancing. Expanding the functionality of the trajdata tool to include sensor data processing could provide additional synergies between perception, prediction, and planning modules in autonomous systems. Additionally, enhancing data collection practices to encompass a broader array of agent types and behaviors, especially those representing rare scenarios, would further strengthen the predictive capabilities of models trained using these datasets.

Conclusion

The introduction of trajdata marks a pivotal step toward achieving a cohesive approach to utilizing human trajectory datasets. By meticulously evaluating existing data collections and proposing pragmatic solutions, the authors provide a foundational tool that addresses the inherent complexities in AV and pedestrian trajectory forecasting research. The incorporation of recommendations within future dataset development will be instrumental in advancing this vital research area toward more generalizable and robust trajectory forecasting systems. This paper represents a thoughtful and technical advancement within the trajectory prediction research community, effectively aligning methodological innovations with practical needs.