RRepoGEO

REPOGEO REPORT · LITE

Little-Podi/Collaborative_Perception

Default branch main · commit 0cb2aba0 · scanned 6/5/2026, 6:02:39 PM

GitHub: 611 stars · 63 forks

AI VISIBILITY SCORE
35 /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
3 / 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 Little-Podi/Collaborative_Perception, 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 opening to emphasize 'curated list' nature

    Why:

    CURRENT
    This repository is a paper digest of recent advances in collaborative / cooperative / multi-agent perception for V2I / V2V / V2X autonomous driving scenario.
    COPY-PASTE FIX
    This repository is a curated list and paper digest of recent advances in collaborative / cooperative / multi-agent perception for V2I / V2V / V2X autonomous driving scenario. It serves as an awesome list for researchers and practitioners.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with an appropriate open-source license (e.g., MIT, Apache-2.0, GPL-3.0).
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Add a relevant URL to the repository's homepage field, such as a project page, a related publication, or a GitHub Pages site for the list.

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 Little-Podi/Collaborative_Perception
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
V2VNet
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. V2VNet · recommended 1×
  2. CoBEV (Collaborative Bird's-Eye View) · recommended 1×
  3. DiscoNet (Distributed Collaborative Perception Network) · recommended 1×
  4. V2X-ViT (Vehicle-to-Everything Vision Transformer) · recommended 1×
  5. CoAlign (Collaborative Alignment Network) · recommended 1×
  • CATEGORY QUERY
    What are the latest research advancements in multi-agent collaborative perception for autonomous vehicles?
    you: not recommended
    AI recommended (in order):
    1. V2VNet
    2. CoBEV (Collaborative Bird's-Eye View)
    3. DiscoNet (Distributed Collaborative Perception Network)
    4. V2X-ViT (Vehicle-to-Everything Vision Transformer)
    5. CoAlign (Collaborative Alignment Network)
    6. When2com (When to Communicate)
    7. F-Cooper (Feature-level Cooperative Perception)
    8. OPV2V (Open-source Platform for Vehicle-to-Vehicle Collaborative Perception)
    9. SyncNet (Synchronized Network for Collaborative Perception)
    10. Robust-CoBEV
    11. Decentralized Collaborative Perception (DCP) frameworks
    12. OpenV2V

    AI recommended 12 alternatives but never named Little-Podi/Collaborative_Perception. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find papers on cooperative perception for V2X autonomous driving scenarios?
    you: not recommended
    AI recommended (in order):
    1. Google Scholar
    2. IEEE Xplore Digital Library
    3. IEEE Intelligent Vehicles Symposium (IV)
    4. IEEE International Conference on Intelligent Transportation Systems (ITSC)
    5. IEEE Vehicular Technology Conference (VTC)
    6. IEEE International Conference on Robotics and Automation (ICRA)
    7. IEEE Transactions on Intelligent Transportation Systems
    8. IEEE Transactions on Vehicular Technology
    9. ACM Digital Library
    10. ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS)
    11. ACM MobiCom
    12. arXiv.org
    13. MDPI Journals
    14. Sensors
    15. Vehicles
    16. SpringerLink
    17. ScienceDirect (Elsevier)
    18. Journal of Intelligent & Robotic Systems (Springer)
    19. Robotics and Autonomous Systems (Elsevier)
    20. Transportation Research Part C: Emerging Technologies (Elsevier)

    AI recommended 20 alternatives but never named Little-Podi/Collaborative_Perception. 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 Little-Podi/Collaborative_Perception?
    pass
    AI named Little-Podi/Collaborative_Perception explicitly

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

  • If a team adopts Little-Podi/Collaborative_Perception in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Little-Podi/Collaborative_Perception 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 Little-Podi/Collaborative_Perception solve, and who is the primary audience?
    pass
    AI named Little-Podi/Collaborative_Perception explicitly

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

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Little-Podi/Collaborative_Perception — 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