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FrameScope: Temporal Data Valuation for Stream Active Learning in Autonomous Vehicle Systems

arXiv · AI, language, vision and robotics · article · Aug 24, 2026 · UTC

Autonomous vehicles operate in dynamic, ever-changing environments where new scenarios and edge cases constantly emerge. As a result, static learning models are inadequate for ensuring safe and reliable operation. Continuous learning is essential for adapting to these evolving conditions and maintaining robust performance across diverse real-world settings. However, autonomous vehicles generate massive streams of visual data during operation, and existing continuous learning approaches typically rely on heuristic sampling methods that fail to capture temporal dynamics, often overlooking critic

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