Awesome-Interaction-Aware-Trajectory-Prediction

Why we included this project

Trajectory prediction sits at the messy intersection of computer vision, multi-agent systems, and autonomous driving, and the research is scattered across datasets, papers, and codebases. This list gathers the useful ones and sorts them by domain, so someone scoping a motion forecasting project can jump straight to the vehicles-and-traffic or pedestrian sections without redoing the literature search. The dataset tables are the most practical part: each entry records the agent types, scenarios, and sensors covered, which is exactly what you need when comparing benchmarks for your own experiments. Maintained by researchers at Stanford and Berkeley who work in this area, the list also flags the key methods and evaluation metrics, making it a solid entry point for anyone new to the field.