Awesome-Interaction-aware-Trajectory-Prediction
A curated checklist of state-of-the-art research materials (datasets, papers, code) for interaction-aware trajectory prediction.
What is Awesome-Interaction-aware-Trajectory-Prediction?
Awesome Interaction-Aware Trajectory Prediction is a curated GitHub repository that serves as a checklist and collection hub for state-of-the-art research materials in the field of trajectory and behavior prediction. It aggregates datasets, academic papers, blog posts, and public code related to forecasting the future paths of agents like vehicles, pedestrians, and sports players, with a focus on models that account for interactions between multiple agents. The project aims to be a helpful resource for both academic research and industrial applications in areas like autonomous driving and robotics.
Researchers, graduate students, and engineers working on motion forecasting, autonomous systems, and robotics, particularly those needing a structured overview of available datasets and recent literature.
It provides a uniquely comprehensive and organized entry point into the trajectory prediction field, saving significant time in literature review and dataset discovery compared to scattered searches. Its focus on interaction-aware methods and inclusion of code links makes it particularly valuable for implementing and benchmarking new models.
Overview
A selection of state-of-the-art research materials on trajectory prediction
Use Cases
Best For
- Finding benchmark datasets (Waymo, nuScenes, Argoverse) for training trajectory prediction models
- Discovering recent research papers and survey articles on multi-agent motion forecasting
- Locating public implementations of state-of-the-art prediction models for replication or extension
- Staying updated on the latest methods in interaction-aware prediction for autonomous driving
- Comparing approaches across different domains (vehicles, pedestrians, robots)
- Identifying evaluation metrics and benchmarks for trajectory prediction tasks
Not Ideal For
- Developers needing ready-to-run prediction models or software libraries for immediate deployment
- Researchers seeking detailed tutorials, code walkthroughs, or beginner-friendly introductions to trajectory prediction
- Projects requiring real-time updates or the absolute latest papers from 2024 onward
- Teams that rely on vetted, production-grade code with active maintenance and support forums
Pros & Cons
Pros
The README provides extensive tables of datasets like Waymo and nuScenes with details on agents, scenarios, and sensors, saving researchers significant time in data discovery.
Papers are organized by domain (e.g., vehicles, pedestrians) and research theme (e.g., surveys, physics systems), making it easy to navigate state-of-the-art work efficiently.
It emphasizes interaction-aware and multi-agent trajectory prediction, reflecting the field's cutting edge, as seen in sections like 'Physics Systems with Interaction' and recent CVPR papers.
Maintained by researchers from Stanford and UC Berkeley, with open contributions via pull requests, lending authority and potential for community-driven updates.
Cons
The last update was in September 2023, so it misses papers and datasets from late 2023 onwards, requiring users to supplement with ongoing literature reviews.
As a community list, linked resources like code repositories may be unmaintained, broken, or of variable quality without curation or validation from the maintainers.
It only aggregates resources without providing tutorials, benchmarking tips, or practical advice on how to implement or compare models, leaving users to figure out the next steps independently.
Frequently Asked Questions
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