RRepoGEO

REPOGEO REPORT · LITE

MIV-XJTU/FSDrive

Default branch main · commit 7e95702d · scanned 6/6/2026, 5:13:10 AM

GitHub: 741 stars · 54 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 MIV-XJTU/FSDrive, 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
    Add explicit disambiguation to the README's opening

    Why:

    COPY-PASTE FIX
    Add the following sentence immediately after the initial description paragraph: "Please note: FSDrive is an **autonomous driving system** and is *not* related to file system drivers."
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the chosen open-source license text (e.g., MIT, Apache-2.0, GPL-3.0).
  • mediumreadme#3
    Enhance README with a dedicated 'Key Features' or 'Why FSDrive?' section

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., "## Key Features" or "## Why FSDrive?", detailing the core innovations like "Spatio-Temporal Chain-of-Thought (CoT) for visual reasoning," "End-to-end VLA for trajectory planning," and "Unification of visual generation and understanding with minimal data."

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 MIV-XJTU/FSDrive
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
nuScenes
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. nuScenes · recommended 1×
  2. Waymo Open Dataset · recommended 1×
  3. CenterPoint · recommended 1×
  4. TransFusion · recommended 1×
  5. BEVFormer · recommended 1×
  • CATEGORY QUERY
    How to achieve visual reasoning and spatio-temporal chain-of-thought for autonomous driving systems?
    you: not recommended
    AI recommended (in order):
    1. nuScenes
    2. Waymo Open Dataset
    3. CenterPoint
    4. TransFusion
    5. BEVFormer
    6. BEVDepth
    7. UniAD
    8. OpenLane
    9. nuPlan
    10. PyTorch Geometric (PyG)
    11. DGL (Deep Graph Library)
    12. Hugging Face Transformers
    13. Neural Radiance Fields (NeRF)
    14. 3D Gaussian Splatting
    15. Mip-NeRF 360
    16. Instant-NGP

    AI recommended 16 alternatives but never named MIV-XJTU/FSDrive. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the cutting-edge vision-language models for predictive trajectory planning in self-driving vehicles?
    you: not recommended
    AI recommended (in order):
    1. DriveGPT4
    2. Wayve's LINGO-1
    3. GAIA-1
    4. OpenAI's GPT-4V (Vision)
    5. Google DeepMind's GATO
    6. RT-2 (Robotics Transformer 2)
    7. Tesla's FSD (Full Self-Driving) Stack
    8. PlaNet (Planning Network)
    9. DreamerV3

    AI recommended 9 alternatives but never named MIV-XJTU/FSDrive. 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 MIV-XJTU/FSDrive?
    pass
    AI named MIV-XJTU/FSDrive explicitly

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

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

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

Embed your GEO score

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MARKDOWN (README)
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MIV-XJTU/FSDrive — 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