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ARXIV:2604.08008 · AUTONOMOUS DRIVING DATASET · SUBMITTED 10 APR · 20:18 UTC · FRESHNESS STALE
ARXIV:2604.08008AUTONOMOUS DRIVING DATASETSUBMITTED 10 APR · 20:18 UTCFRESHNESS STALEFelix Embacher · Jonas Uhrig · Marius Cordts · Markus Enzweiler · arXiv
SearchAD is a large-scale dataset designed for rare image retrieval in autonomous driving, enhancing data curation and perception research.
Opportunity summary
Pain SearchAD is a large-scale dataset designed for rare image retrieval in autonomous driving, enhancing data curation and perception research.
Evidence 0 refs | 4 sources | 67% coverage
Blocker Evidence verified
SearchAD is a large-scale dataset designed for rare image retrieval in autonomous driving, enhancing data curation and perception research. As dataset sizes continue to grow, the key challenge shifts from collecting more data to…
Retrieving rare and safety-critical driving scenarios from large-scale datasets is essential for building robust autonomous driving (AD) systems. As dataset sizes continue to grow, the key challenge shifts from collecting more data to efficiently…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Comprehensive evaluations show that text-based methods outperform image-based ones due to stronger inherent semantic grounding. A public repository is linked, so build verification can…
Autonomous Driving Dataset moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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SearchAD is a large-scale dataset designed for rare image retrieval in autonomous driving, enhancing data curation and perception research.
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Paper Pack
10.48550/arXiv.2604.08008SearchAD is a large-scale dataset designed for rare image retrieval in autonomous driving, enhancing data curation and perception research.
Abstract
Retrieving rare and safety-critical driving scenarios from large-scale datasets is essential for building robust autonomous driving (AD) systems. As dataset sizes continue to grow, the key challenge shifts from collecting more data to efficiently identifying the most relevant samples. We introduce SearchAD, a large-scale rare image retrieval dataset for AD containing over 423k frames drawn from 11 established datasets. SearchAD provides high-quality manual annotations of more than 513k bounding boxes covering 90 rare categories. It specifically targets the needle-in-a-haystack problem of locating extremely rare classes, with some appearing fewer than 50 times across the entire dataset. Unlike existing benchmarks, which focused on instance-level retrieval, SearchAD emphasizes semantic image retrieval with a well-defined data split, enabling text-to-image and image-to-image retrieval, few-shot learning, and fine-tuning of multi-modal retrieval models. Comprehensive evaluations show that text-based methods outperform image-based ones due to stronger inherent semantic grounding. While models directly aligning spatial visual features with language achieve the best zero-shot results, and our fine-tuning baseline significantly improves performance, absolute retrieval capabilities remain unsatisfactory. With a held-out test set on a public benchmark server, SearchAD establishes the first large-scale dataset for retrieval-driven data curation and long-tail perception research in AD: https://iis-esslingen.github.io/searchad/
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Proof status
verified0 refs; 4 sources; 67% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Dimensions overall score 7.0
PROBLEM
SearchAD is a large-scale dataset designed for rare image retrieval in autonomous driving, enhancing data curation and perception research. As dataset sizes continue to grow, the key challenge shifts from collecting more data to efficiently identifying the most relevant samples.
METHOD
Retrieving rare and safety-critical driving scenarios from large-scale datasets is essential for building robust autonomous driving (AD) systems. As dataset sizes continue to grow, the key challenge shifts from collecting more data to efficiently identifying the most relevant sa...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Comprehensive evaluations show that text-based methods outperform image-based ones due to stronger inherent semantic grounding. A public repository is linked, so build verification can inspect implementat...
WHY NOW
Autonomous Driving Dataset moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
SearchAD is a large-scale dataset designed for rare image retrieval in autonomous driving, enhancing data curation and perception research. As dataset sizes continue to grow, the key challenge shifts from collecting more data to efficiently identifying the most relevant samples.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Retrieving rare and safety-critical driving scenarios from large-scale datasets is essential for building robust autonomous driving (AD) systems. As dataset sizes continue to grow, the key challenge shifts from collecting more data to efficiently identifying the most relevant samples.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Comprehensive evaluations show that text-based methods outperform image-based ones due to stronger inherent semantic grounding. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Autonomous Driving Dataset moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
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Materials
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SearchAD is a large-scale dataset designed for rare image retrieval in autonomous driving, enhancing data curation and perception research.
Segment
Autonomous Driving Dataset
Adoption evidence
Public code linked for build inspection
Commercial read
7.0/10 public viability
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CITED BY
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2/3 checks · 67%
Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
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No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
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Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
OpportunityKernel evidence_receipt
0 refs / 4 sources / 67% coverage
stale
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Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
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Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
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Evidence
0 references, 4 sources, 67% evidence coverage.
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Buyer clarity
missing
Current read
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Evidence
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Defensibility
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Defensibility signals are missing.
Evidence
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Gaps
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Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
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Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
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Regulatory load
missing
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Evidence
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Gaps
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
People
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Prototype owner missing.
Build Passport does not name an implementer.
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Operator workflow not sourced.
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People
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People
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Regulatory need unclassified.
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Gaps
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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