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

2026-08-24

AI Summary

为解决自动驾驶系统中连续学习的问题，提出FrameScope框架，通过评估流式视觉数据的价值来选择关键帧进行云端标注，从而提高样本效率并减少灾难性遗忘。

Abstract

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 critical learning opportunities or selecting redundant frames. In this paper, we introduce FrameScope, a temporal data valuation framework for continuous learning in autonomous vehicles. FrameScope extends neural tangent kernel theory to temporal domains, enabling principled valuation of streaming visual data. Unlike cloud-centric methods that transmit all video data for processing, our approach performs principled, local frame selection on the vehicle and queries a cloud-based oracle model only for labels of those high-value frames. Extensive experiments across multiple domain shifts show that FrameScope consistently outperforms existing methods, achieving higher sample efficiency and significantly reducing catastrophic forgetting in autonomous vehicle perception. By valuing data on the vehicle and querying only labels for selected frames, FrameScope reduces bandwidth requirements, enabling scalable operation with a lightweight cloud labeling service.

Problem

• autonomous vehicles
• continuous learning
• temporal dynamics
• visual data
• heuristic sampling

Innovation

• temporal data valuation
• neural tangent kernel theory
• continuous learning
• autonomous vehicles
• local frame selection