为事件相机流设计无任务依赖的完整性度量，助力自动驾驶安全认证

Event cameras have emerged as a high-bandwidth, low-latency sensing modality for safety-critical perception in automated driving systems (ADS), offering microsecond temporal resolution, 120-140 dB dynamic range, and intrinsic absence of motion blur. However, no task-agnostic quality metric currently operates directly on the asynchronous event stream: state-of-the-art proxies require a downstream task (e.g., detection accuracy, tracking error) to assess stream integrity, which is incompatible with the certification requirements of ISO 21448 (SOTIF) and ISO/PAS 8800:2024. The recent BiasBench benchmark (CVPR 2025) explicitly identifies this gap. This work proposes a unified algebraic framework that lifts the Pearson Correlation Coefficient (PCC), historically used in two prior works for redundancy filtering and ROI selection on frame-based images, to the three standard event representations: Time Surface, Event Frame, and Voxel Grid. The framework yields three metrics: (i) r-TS for stream integrity monitoring against an ego-motion-predicted Time Surface, (ii) r2-EF for adaptive ROI selection requiring only integer comparisons, and (iii) r-VG for temporal redundancy gating. A structural isomorphism is established between the contrast-threshold mechanism of the event camera (|Delta L| >= C) and the PCC-based change criterion, the three lifted metrics are formalized, and pipeline latency and information loss are analyzed symmetrically against the raw stream. Illustrative behavior of each metric is demonstrated on a procedural-synthetic event stream, generated by direct simulation of the emission model rather than drawn from any real or video-derived dataset, including a tunnel-dip integrity-anomaly scenario in which r_C drops from 0.93 (coherent flow) to below 0 (alarm). An explicit epistemic convention ([ESTABLISHED], [SOLID], [HYPOTH.], [OPEN]) delineates the status of every contribution.