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STRIVE-D framework boosts autonomous driving video retrieval accuracy

By PulseAugur Editorial · [2 sources] ·

Researchers have developed STRIVE-D, a new framework designed to improve video retrieval for complex queries in autonomous driving scenarios. This system addresses limitations of existing methods by incorporating data calibration to adapt rule-based retrieval and fuse it with vision-language and keyword signals. STRIVE-D has demonstrated significant improvements, achieving up to an 84% relative increase in top-1 accuracy on driving benchmarks, including new event data from DrivingDojo. AI

IMPACT Enhances autonomous driving safety validation and data curation by improving the ability to retrieve specific driving events.

RANK_REASON The cluster contains a research paper detailing a new framework and benchmark results.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Manyi Yao, Sparsh Garg, Christian Shelton, Amit Roy-Chowdhury, Abhishek Aich ·

    Driving Video Retrieval for Complex Queries with Structured Grounding

    arXiv:2606.09109v1 Announce Type: cross Abstract: Video retrieval at scale is central to data curation and safety validation in autonomous driving, where users want to find not only scenes but also dynamic events such as cut-ins and hard braking. Existing vision-language and keyw…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Abhishek Aich ·

    Driving Video Retrieval for Complex Queries with Structured Grounding

    Video retrieval at scale is central to data curation and safety validation in autonomous driving, where users want to find not only scenes but also dynamic events such as cut-ins and hard braking. Existing vision-language and keyword-based retrieval methods often miss these event…

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