解决车载传感器数据延迟问题，提升自动驾驶感知鲁棒性
Robust sensor fusion against on-vehicle sensor staleness
- 为激光雷达与雷达添加相对于摄像头的时序偏移特征，实现细粒度时间对齐
- 在真实车辆数据模式下进行数据增强，模拟传感器延迟现象
- 适用于多传感器融合的感知系统，尤其适合部署于复杂路况的自动驾驶车辆
传感器融合对自动驾驶中的感知系统性能和鲁棒性至关重要，但传感器数据到达时间不一致（即传感器滞留）带来了显著挑战。不同模态间的时间错位会导致目标状态估计不一致，严重影响关键的轨迹预测质量。本文提出一种新型、与模型无关的方法：(1) 为激光雷达和雷达引入相对于摄像头的逐点时间戳偏移特征，实现传感器融合中的细粒度时间感知；(2) 设计数据增强策略，模拟实际部署车辆中观察到的真实传感器滞留模式。该方法被集成至一个透视视角检测模型中，处理来自多个激光雷达、雷达和摄像头的数据。实验表明，当某一传感器模态出现延迟时，传统模型性能明显下降，而本方法在同步与滞留条件下均保持稳定优异的表现。
原文摘要 · Abstract (English)
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.
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