# Robust Long-Range Perception Against Sensor Misalignment in Autonomous Vehicles

arXiv: https://arxiv.org/abs/2408.11196 · HTML: https://mlsanity.com/paper/2408.11196/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 36 references that are in the map.
- First posted on arXiv 20 Aug 2024.

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [CenterNet: Keypoint Triplets for Object Detection](https://mlsanity.com/paper/1904.08189/) (2019)
- [Multi-Modal Fusion Transformer for End-to-End Autonomous Driving](https://mlsanity.com/paper/2104.09224/) (2021)
- [Deep Continuous Fusion for Multi-Sensor 3D Object Detection](https://mlsanity.com/paper/2012.10992/) (2020)
- [An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection](https://mlsanity.com/paper/1904.09730/) (2019)
- [PointMixup: Augmentation for Point Clouds](https://mlsanity.com/paper/2008.06374/) (2020)
- [Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey](https://mlsanity.com/paper/2307.04370/) (2023)
- [RegNet: Multimodal Sensor Registration Using Deep Neural Networks](https://mlsanity.com/paper/1707.03167/) (2017)
- [Pixel-level Extrinsic Self Calibration of High Resolution LiDAR and Camera in Targetless Environments](https://mlsanity.com/paper/2103.01627/) (2021)
- [Multi-View Fusion of Sensor Data for Improved Perception and Prediction in Autonomous Driving](https://mlsanity.com/paper/2008.11901/) (2020)
- [MVFuseNet: Improving End-to-End Object Detection and Motion Forecasting through Multi-View Fusion of LiDAR Data](https://mlsanity.com/paper/2104.10772/) (2021)
- [LiDAR and Camera Calibration using Motion Estimated by Sensor Fusion Odometry](https://mlsanity.com/paper/1804.05178/) (2018)
- [Deep Fundamental Matrix Estimation without Correspondences](https://mlsanity.com/paper/1810.01575/) (2018)
- [Pattern-Aware Data Augmentation for LiDAR 3D Object Detection](https://mlsanity.com/paper/2112.00050/) (2021)
- [Improvements to Target-Based 3D LiDAR to Camera Calibration](https://mlsanity.com/paper/1910.03126/) (2019)
- [Maximum Likelihood Uncertainty Estimation: Robustness to Outliers](https://mlsanity.com/paper/2202.03870/) (2022)
- [SpotNet: An Image Centric, Lidar Anchored Approach To Long Range Perception](https://mlsanity.com/paper/2405.15843/) (2024)

## Built on by (most cited first, 5 of 5)

- [Refining Ground Truth Poses in Autonomous Driving Datasets via Neural Rendering](https://mlsanity.com/paper/2504.15776/) (2025)
- [Argus: Resilience-Oriented Safety Assurance Framework for End-to-End ADSs](https://mlsanity.com/paper/2511.09032/) (2025)
- [Robust Self-Supervised Cross-Modal Super-Resolution against Real-World Misaligned Observations](https://mlsanity.com/paper/2602.18822/) (2026)
- [RECO: Region-Aware Compensation for Extrinsic Perturbations in Roadside 3D Detection](https://mlsanity.com/paper/2607.20947/) (2026)
- [MM-BEV: Enhancing Timeliness by Computing Where and When it Matters](https://mlsanity.com/paper/2608.15437/) (2026)

## Abstract (arXiv)

Advances in machine learning algorithms for sensor fusion have significantly improved the detection and prediction of other road users, thereby enhancing safety. However, even a small angular displacement in the sensor's placement can cause significant degradation in output, especially at long range. In this paper, we demonstrate a simple yet generic and efficient multi-task learning approach that not only detects misalignment between different sensor modalities but is also robust against them for long-range perception. Along with the amount of misalignment, our method also predicts calibrated uncertainty, which can be useful for filtering and fusing predicted misalignment values over time. In addition, we show that the predicted misalignment parameters can be used for self-correcting input sensor data, further improving the perception performance under sensor misalignment.