Robust sensor fusion against on-vehicle sensor staleness

TL;DR

This paper introduces a model-agnostic sensor fusion method that effectively handles sensor staleness in autonomous vehicle perception, improving robustness and accuracy despite delays in sensor data arrival.

Contribution

The paper proposes a novel temporal awareness feature and data augmentation strategy to mitigate sensor staleness effects in multi-sensor fusion for autonomous vehicles.

Findings

Enhanced perception robustness under sensor staleness conditions

Consistent performance across synchronized and stale sensor data

Improved trajectory prediction accuracy

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.