# OpenTraj: Assessing Prediction Complexity in Human Trajectories Datasets

arXiv: https://arxiv.org/abs/2010.00890 · HTML: https://mlsanity.com/paper/2010.00890/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2020.

## Built on by (most cited first, 21 of 21)

- [UniTraj: A Unified Framework for Scalable Vehicle Trajectory Prediction](https://mlsanity.com/paper/2403.15098/) (2024)
- [Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-based Approaches](https://mlsanity.com/paper/2111.06740/) (2021)
- [trajdata: A Unified Interface to Multiple Human Trajectory Datasets](https://mlsanity.com/paper/2307.13924/) (2023)
- [A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions](https://mlsanity.com/paper/2203.02281/) (2022)
- [Physical Plausibility-aware Trajectory Prediction via Locomotion Embodiment](https://mlsanity.com/paper/2503.17267/) (2025)
- [Under the Hood of Transformer Networks for Trajectory Forecasting](https://mlsanity.com/paper/2203.11878/) (2022)
- [Multi-Agent Inverse Reinforcement Learning in Real World Unstructured Pedestrian Crowds](https://mlsanity.com/paper/2405.16439/) (2024)
- [TrajEvo: Designing Trajectory Prediction Heuristics via LLM-driven Evolution](https://mlsanity.com/paper/2505.04480/) (2025)
- [TrajEvo: Trajectory Prediction Heuristics Design via LLM-driven Evolution](https://mlsanity.com/paper/2508.05616/) (2025)
- [DeepSocNav: Social Navigation by Imitating Human Behaviors](https://mlsanity.com/paper/2107.09170/) (2021)
- [SFMGNet: A Physics-based Neural Network To Predict Pedestrian Trajectories](https://mlsanity.com/paper/2202.02791/) (2022)
- [What we see and What we don't see: Imputing Occluded Crowd Structures from Robot Sensing](https://mlsanity.com/paper/2109.08494/) (2021)
- [GSGFormer: Generative Social Graph Transformer for Multimodal Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2312.04479/) (2023)
- [Characterizing Structured versus Unstructured Environments based on Pedestrians' and Vehicles' Motion Trajectories](https://mlsanity.com/paper/2502.16847/) (2025)
- [DAA*: Deep Angular A Star for Image-based Path Planning](https://mlsanity.com/paper/2507.09305/) (2025)
- [An Information-Theoretic Approach for Estimating Scenario Generalization in Crowd Motion Prediction](https://mlsanity.com/paper/2211.00817/) (2022)
- [Development of a Realistic Crowd Simulation Environment for Fine-grained Validation of People Tracking Methods](https://mlsanity.com/paper/2304.13403/) (2023)
- [PEDRA: Evaluating the Realism of Pedestrian Dynamics in Video Generation](https://mlsanity.com/paper/2510.20182/) (2025)
- [Unveiling Transferability in Trajectory Prediction via Latent Scene Embeddings](https://mlsanity.com/paper/2606.30777/) (2026)
- [STRIDE: Automated Evaluation of Text-to-Trajectory Alignment across Diverse Contexts](https://mlsanity.com/paper/2609.34799/) (2026)
- [Probabilistic Pedestrian Forecasts from a Handheld Phone: World-Frame Heat Maps, Visual-Inertial Height Drift, and Evaluation without Ground Truth](https://mlsanity.com/paper/2610.04736/) (2026)

## Abstract (arXiv)

Human Trajectory Prediction (HTP) has gained much momentum in the last years and many solutions have been proposed to solve it. Proper benchmarking being a key issue for comparing methods, this paper addresses the question of evaluating how complex is a given dataset with respect to the prediction problem. For assessing a dataset complexity, we define a series of indicators around three concepts: Trajectory predictability; Trajectory regularity; Context complexity. We compare the most common datasets used in HTP in the light of these indicators and discuss what this may imply on benchmarking of HTP algorithms. Our source code is released on Github.