RRepoGEO

REPOGEO REPORT · LITE

JohnsonJiang1996/Awesome-VLA4AD

Default branch main · commit aa7875bf · scanned 6/15/2026, 11:33:06 PM

GitHub: 605 stars · 53 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 JohnsonJiang1996/Awesome-VLA4AD, 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
  • hightopics#1
    Add specific topics for VLA4AD and autonomous driving

    Why:

    COPY-PASTE FIX
    vision-language-action, autonomous-driving, vla4ad, multimodal-ai, survey, research-papers, awesome-list
  • highreadme#2
    Strengthen README's opening to clarify VLA4AD and repo type

    Why:

    CURRENT
    Welcome to **Awesome VLA4AD**—a curated, continuously updated collection of research papers and resources on Vision–Language–Action models for Autonomous Driving (VLA4AD). This repository tracks the latest advances in VLA4AD, from explanatory perception modules to end-to-end reasoning and control architectures.
    COPY-PASTE FIX
    **Awesome VLA4AD** is the definitive curated collection of research papers and resources on **Vision–Language–Action models for Autonomous Driving (VLA4AD)**. This repository serves as the official companion to our survey paper, tracking the latest advances in VLA4AD from explanatory perception to end-to-end reasoning and control architectures.
  • mediumabout#3
    Refine About description to emphasize "awesome list" type

    Why:

    CURRENT
    Vision–Language–Action models for Autonomous Driving (VLA4AD) resources, serving as the companion repository to the survey paper “A Survey on Vision–Language–Action Models for Autonomous Driving”.
    COPY-PASTE FIX
    A curated awesome list of Vision–Language–Action models for Autonomous Driving (VLA4AD) resources, serving as the companion repository to the survey paper “A Survey on Vision–Language–Action Models for Autonomous Driving”.

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 JohnsonJiang1996/Awesome-VLA4AD
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
CLIP
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. CLIP · recommended 1×
  2. DALL-E 2/3 · recommended 1×
  3. GATO · recommended 1×
  4. RT-1 · recommended 1×
  5. RT-2 · recommended 1×
  • CATEGORY QUERY
    What resources exist for combining vision and language with action in self-driving systems?
    you: not recommended
    AI recommended (in order):
    1. CLIP
    2. DALL-E 2/3
    3. GATO
    4. RT-1
    5. RT-2
    6. VALL-E
    7. VALL-E X
    8. DETR
    9. OWL-ViT
    10. Hugging Face Transformers Library (huggingface/transformers)
    11. ROS
    12. TensorFlow (tensorflow/tensorflow)
    13. PyTorch (pytorch/pytorch)
    14. CARLA Simulator (carla-simulator/carla)

    AI recommended 14 alternatives but never named JohnsonJiang1996/Awesome-VLA4AD. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find recent research on multimodal AI for autonomous vehicle control?
    you: not recommended
    AI recommended (in order):
    1. arXiv.org
    2. Google Scholar
    3. IEEE Xplore
    4. ACM Digital Library
    5. OpenReview
    6. CVPR
    7. ICCV
    8. ECCV
    9. NeurIPS
    10. ICLR
    11. RSS
    12. ICRA
    13. IROS
    14. IEEE Transactions on Robotics
    15. International Journal of Robotics Research (IJRR)
    16. Journal of Field Robotics
    17. Autonomous Robots
    18. IEEE Transactions on Intelligent Transportation Systems
    19. Hugging Face
    20. Kaggle

    AI recommended 20 alternatives but never named JohnsonJiang1996/Awesome-VLA4AD. 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 JohnsonJiang1996/Awesome-VLA4AD?
    pass
    AI did not name JohnsonJiang1996/Awesome-VLA4AD — 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 JohnsonJiang1996/Awesome-VLA4AD in production, what risks or prerequisites should they evaluate first?
    pass
    AI named JohnsonJiang1996/Awesome-VLA4AD 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 JohnsonJiang1996/Awesome-VLA4AD solve, and who is the primary audience?
    pass
    AI did not name JohnsonJiang1996/Awesome-VLA4AD — 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?

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JohnsonJiang1996/Awesome-VLA4AD — 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