RRepoGEO

REPOGEO REPORT · LITE

Psychic-DL/Awesome-Traffic-Agent-Trajectory-Prediction

Default branch main · commit c4c5c778 · scanned 6/5/2026, 9:02:42 PM

GitHub: 514 stars · 61 forks

AI VISIBILITY SCORE
22 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface Psychic-DL/Awesome-Traffic-Agent-Trajectory-Prediction, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README H1 to clarify "Awesome List" nature

    Why:

    CURRENT
    # Awesome-Traffic-Agent-Trajectory-Prediction
    COPY-PASTE FIX
    # Awesome Traffic Agent Trajectory Prediction: A Curated List of Papers, Datasets, and Code
  • mediumhomepage#2
    Add repository URL to homepage metadata

    Why:

    COPY-PASTE FIX
    https://github.com/Psychic-DL/Awesome-Traffic-Agent-Trajectory-Prediction
  • lowabout#3
    Expand repository description to clarify scope

    Why:

    CURRENT
    This is a list of papers related to traffic agent trajectory prediction.
    COPY-PASTE FIX
    A curated list of research materials, including papers, datasets, and code, focused on traffic agent trajectory prediction.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface Psychic-DL/Awesome-Traffic-Agent-Trajectory-Prediction
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Social-GAN
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Social-GAN · recommended 1×
  2. TrajGRU · recommended 1×
  3. STGAT · recommended 1×
  4. PECNet · recommended 1×
  5. TrajTransformer · recommended 1×
  • CATEGORY QUERY
    What are the best deep learning models for predicting vehicle and pedestrian trajectories?
    you: not recommended
    AI recommended (in order):
    1. Social-GAN
    2. TrajGRU
    3. STGAT
    4. PECNet
    5. TrajTransformer
    6. AgentFormer
    7. Social-LSTM
    8. S-LSTM

    AI recommended 8 alternatives but never named Psychic-DL/Awesome-Traffic-Agent-Trajectory-Prediction. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find research papers and datasets on multi-agent trajectory forecasting?
    you: not recommended
    AI recommended (in order):
    1. Google Scholar
    2. arXiv
    3. Papers With Code
    4. CVPR
    5. ICCV
    6. ECCV
    7. NeurIPS
    8. ICML
    9. IROS
    10. RSS
    11. OpenReview
    12. IEEE Xplore
    13. nuScenes
    14. Waymo Open Dataset
    15. Argoverse
    16. ETH/UCY Pedestrian Datasets
    17. Stanford Drone Dataset (SDD)
    18. PANDA (Pedestrian ANnotation Dataset)
    19. GitHub
    20. Medium
    21. Towards Data Science

    AI recommended 21 alternatives but never named Psychic-DL/Awesome-Traffic-Agent-Trajectory-Prediction. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of Psychic-DL/Awesome-Traffic-Agent-Trajectory-Prediction?
    pass
    AI did not name Psychic-DL/Awesome-Traffic-Agent-Trajectory-Prediction — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts Psychic-DL/Awesome-Traffic-Agent-Trajectory-Prediction in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Psychic-DL/Awesome-Traffic-Agent-Trajectory-Prediction explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo Psychic-DL/Awesome-Traffic-Agent-Trajectory-Prediction solve, and who is the primary audience?
    pass
    AI did not name Psychic-DL/Awesome-Traffic-Agent-Trajectory-Prediction — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of Psychic-DL/Awesome-Traffic-Agent-Trajectory-Prediction. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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Psychic-DL/Awesome-Traffic-Agent-Trajectory-Prediction — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite