# xuehaouwa/Awesome-Trajectory-Prediction

Repository: https://github.com/xuehaouwa/Awesome-Trajectory-Prediction
Canonical: https://ross.abutalabs.com/products/awesome-trajectory-prediction
License Family: other
Last push: 2023-01-31T21:06:47+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2835, "days_push": 1310, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1249, forks 238 (observed 2026-08-28T04:04:07.933672+00:00)

## What it is
A curated awesome-list of papers, datasets, and code repositories for trajectory prediction research. It covers RNN-based and modern deep learning approaches for forecasting human and vehicle trajectories.

## Use cases
- find papers on pedestrian trajectory prediction
- locate datasets for vehicle trajectory forecasting
- find open-source implementations of trajectory prediction models
- survey the state of the art in trajectory forecasting
- find benchmarks for evaluating trajectory prediction models

## When to choose
- starting research on trajectory prediction and needing a literature overview
- looking for datasets or code baselines for trajectory forecasting

## When to avoid
- needing a runnable trajectory prediction library rather than a reference list
- needing actively maintained tooling rather than curated links

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, documentation
- domain: artificial-intelligence, machine-learning, autonomous-vehicles, awesome-lists
- platform: cross-platform
- tags: awesome-list, trajectory-prediction, papers, datasets, research

## Member repositories
- xuehaouwa/Awesome-Trajectory-Prediction (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:07.933672+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 2026-08-30T05:07:49.237175+00:00, confidence not recorded.
  - readme: https://github.com/xuehaouwa/Awesome-Trajectory-Prediction (fetched 2026-08-28T04:04:07.933672+00:00, sha 8888d6d9f2c6)
- Data as of 2026-08-30T08:39:29.467469+00:00.