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 keyword-based retrieval methods often miss these events because the relevant motion may not be explicitly described in text or captured by lexical overlap. Rule-based retrieval can encode such events more directly, but it is brittle: generated or hand-written rules often fail when their assumptions do not match real drivin
The increasing complexity and scale of autonomous driving data necessitates more sophisticated and efficient video retrieval methods to enhance safety and development cycles.
Improved video retrieval for autonomous driving allows for more precise identification of critical events, accelerating validation and potentially enabling more robust AI systems.
The ability to accurately search for complex dynamic events in vast video datasets will shift autonomous vehicle development paradigms from rule-based to more data-driven, event-centric approaches.
- · Autonomous vehicle developers
- · AI model developers
- · Data curation platforms
- · Regulatory bodies in transportation
- · Legacy rule-based system developers
- · Companies with inefficient data tagging strategies
More efficient debugging and refinement of autonomous driving AI models.
Faster deployment of safer autonomous vehicle features due to accelerated validation cycles.
Enhanced public trust and adoption of autonomous driving technologies as safety improves through rigorous testing and identified edge cases.
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Read at arXiv cs.LG