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
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