# Robust sensor fusion against on-vehicle sensor staleness

arXiv: https://arxiv.org/abs/2506.05780 · HTML: https://mlsanity.com/paper/2506.05780/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 17 references that are in the map.
- First posted on arXiv 6 Jun 2025.

## Builds on (most cited first, 12 of 12)

- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers](https://mlsanity.com/paper/2203.11496/) (2022)
- [BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework](https://mlsanity.com/paper/2205.13790/) (2022)
- [Multi-View Fusion of Sensor Data for Improved Perception and Prediction in Autonomous Driving](https://mlsanity.com/paper/2008.11901/) (2020)
- [CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for Robust 3D Object Detection](https://mlsanity.com/paper/2210.09267/) (2022)
- [The Matter of Time -- A General and Efficient System for Precise Sensor Synchronization in Robotic Computing](https://mlsanity.com/paper/2103.16045/) (2021)
- [Investigating the Effect of Sensor Modalities in Multi-Sensor Detection-Prediction Models](https://mlsanity.com/paper/2101.03279/) (2021)
- [SpotNet: An Image Centric, Lidar Anchored Approach To Long Range Perception](https://mlsanity.com/paper/2405.15843/) (2024)

## Built on by (most cited first, 2 of 2)

- [AsyncBEV: Cross-modal Flow Alignment in Asynchronous 3D Object Detection](https://mlsanity.com/paper/2601.12994/) (2026)
- [Deep Learning for Crack Detection: A Review of Learning Paradigms, Generalizability, and Datasets](https://mlsanity.com/paper/2508.10256/) (2025)

## Abstract (arXiv)

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.