# Robust sensor fusion against on-vehicle sensor staleness Source: [Robust sensor fusion against on-vehicle sensor staleness]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:57:17.533Z Authors: Meng Fan, Yifan Zuo, Patrick Blaes, Harley Montgomery, Subhasis Das Published: 2025-06-06T06:18:54.000Z ## Abstract Sensor fusion is crucial for a performant and robust Perception system in autonomous vehicles, but sensor staleness, where data from different sensors arrives with varying delays, poses significant challenges. Temporal misalignment between sensor modalities leads to inconsistent object state estimates, severely degrading the quality of trajectory predictions that are critical for safety. We present a novel and model-agnostic approach to address this problem via (1) a per-point timestamp offset feature (for LiDAR and radar both relative to camera) that enables fine-grained temporal awareness in sensor fusion, and (2) a data augmentation strategy that simulates realistic sensor staleness patterns observed in deployed vehicles. Our method is integrated into a perspective-view detection model that consumes sensor data from multiple LiDARs, radars and cameras. We demonstrate that while a conventional model shows significant regressions when one sensor modality is stale, our approach reaches consistently good performance across both synchronized and stale conditions. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [3D Object Detection on nuScenes Dynamic Objects (val)](): 3D Object Detection; nuScenes Dynamic Objects (val); NDS: 37.9. - [3D Object Detection on nuScenes (val)](): 3D Object Detection; nuScenes (val); NDS: 70. ## Original sources and implementation [Original paper]() [PDF]()