# jiachenli94/Awesome-Interaction-Aware-Trajectory-Prediction

A selection of state-of-the-art research materials on trajectory prediction

Repository: https://github.com/jiachenli94/Awesome-Interaction-Aware-Trajectory-Prediction
Canonical: https://ross.abutalabs.com/products/awesome-interaction-aware-trajectory-prediction
Language: TeX
License: MIT
License Family: permissive
Topics: trajectory-prediction, trajectory-generation, social-interactions, behavior-prediction, computer-vision, machine-learning, artificial-intelligence, deep-learning, autonomous-driving, autonomous-vehicles, multiagent-systems, multiagent-learning, motion-prediction, paper, path-predictions, vehicle-trajectory, traffic, pedestrian-trajectories, human-trajectory-prediction, behavior-analysis
Last push: 2026-08-24T20:28:47+00:00

## Health v2 (maintenance only)
Score: 77/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 35, longevity 100
- inputs: {"age_days": 2606, "days_push": 9, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1690, forks 311 (observed 2026-08-28T04:05:22.554560+00:00)

## What it is
A curated awesome-list of state-of-the-art research materials on interaction-aware behavior and trajectory prediction, including datasets, survey papers, code, and benchmarks. It covers vehicles, pedestrians, mobile robots, and sport players, maintained by researchers from Stanford and UC Berkeley.

## Use cases
- find datasets for pedestrian trajectory prediction
- survey state-of-the-art papers on vehicle motion forecasting
- find public code implementations for multi-agent trajectory prediction
- locate benchmarks and evaluation metrics for trajectory forecasting
- research interaction-aware prediction for autonomous driving
- find trajectory prediction datasets for sport player analysis

## When to choose
- starting research on trajectory or motion prediction and needing a literature map
- looking for datasets of vehicle, pedestrian, or sport player trajectories
- comparing methods and benchmarks for interaction-aware forecasting

## When to avoid
- you need a runnable prediction model or library rather than a reading list
- you need production-ready forecasting software
- you need topics outside trajectory prediction such as decision making or motion planning (see the maintainers' companion list)

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, computer-vision, data-science
- domain: autonomous-vehicles, artificial-intelligence, machine-learning, deep-learning, robotics
- platform: cross-platform
- tags: awesome-list, trajectory-prediction, motion-prediction, pedestrian-trajectories, autonomous-driving, multiagent-systems, research-papers, datasets

## Member repositories
- jiachenli94/Awesome-Interaction-Aware-Trajectory-Prediction (main) score 77

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:22.554560+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:38:22.036544+00:00, confidence not recorded.
  - readme: https://github.com/jiachenli94/Awesome-Interaction-Aware-Trajectory-Prediction (fetched 2026-08-28T04:05:22.554560+00:00, sha 88509fee2097)
- Data as of 2026-08-30T08:39:29.467469+00:00.