RRepoGEO

REPOGEO REPORT · LITE

ZhengYinan-AIR/Diffusion-Planner

Default branch main · commit a3a621f0 · scanned 6/29/2026, 6:13:23 AM

GitHub: 1,007 stars · 156 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 ZhengYinan-AIR/Diffusion-Planner, 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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the chosen open-source license (e.g., MIT, Apache-2.0, GPL-3.0).
  • highreadme#2
    Refine README's opening sentence to explicitly state the autonomous driving domain

    Why:

    CURRENT
    The official implementation of **Diffusion Planner**, which **represents a pioneering effort in fully harnessing the power of diffusion models for high-performance motion planning, without overly relying on refinement**.
    COPY-PASTE FIX
    The official implementation of **Diffusion Planner**, a pioneering effort in fully harnessing the power of diffusion models for high-performance motion planning in autonomous driving, without overly relying on refinement.
  • mediumtopics#3
    Add more specific topics to improve categorization

    Why:

    CURRENT
    autonomous-driving, diffusion-models, imitation-learning, motion-planning, pytorch
    COPY-PASTE FIX
    autonomous-driving, diffusion-models, imitation-learning, motion-planning, pytorch, autonomous-vehicles, trajectory-generation

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 ZhengYinan-AIR/Diffusion-Planner
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
google-deepmind/diffuser
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. google-deepmind/diffuser · recommended 1×
  2. Wayve's End-to-End AI Driver · recommended 1×
  3. NVIDIA DRIVE Labs · recommended 1×
  4. OpenAI's DALL-E 2 / GLIDE · recommended 1×
  5. huggingface/diffusers · recommended 1×
  • CATEGORY QUERY
    How to apply diffusion models for robust motion planning in autonomous driving systems?
    you: not recommended
    AI recommended (in order):
    1. Diffuser (google-deepmind/diffuser)
    2. Wayve's End-to-End AI Driver
    3. NVIDIA DRIVE Labs
    4. OpenAI's DALL-E 2 / GLIDE
    5. Hugging Face Diffusers Library (huggingface/diffusers)
    6. PyTorch / TensorFlow

    AI recommended 6 alternatives but never named ZhengYinan-AIR/Diffusion-Planner. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are high-performance learning-based motion planning solutions for autonomous vehicles using PyTorch?
    you: not recommended
    AI recommended (in order):
    1. Open Motion Planning Library (OMPL)
    2. DeepMotionPlanner (DMP)
    3. Stable Baselines3
    4. Ray RLlib
    5. Chomp (Covariant Hamiltonian Optimization for Motion Planning)
    6. MoveIt!

    AI recommended 6 alternatives but never named ZhengYinan-AIR/Diffusion-Planner. 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 ZhengYinan-AIR/Diffusion-Planner?
    pass
    AI named ZhengYinan-AIR/Diffusion-Planner explicitly

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

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

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

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MARKDOWN (README)
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ZhengYinan-AIR/Diffusion-Planner — 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