# RESBev: Making BEV Perception More Robust

arXiv: https://arxiv.org/abs/2603.09529 · HTML: https://mlsanity.com/paper/2603.09529/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 32 references that are in the map.
- First posted on arXiv 10 Mar 2026.

## Builds on (most cited first, 29 of 29)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D](https://mlsanity.com/paper/2008.05711/) (2020)
- [BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation](https://mlsanity.com/paper/2205.13542/) (2022)
- [GAIA-1: A Generative World Model for Autonomous Driving](https://mlsanity.com/paper/2309.17080/) (2023)
- [Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving](https://mlsanity.com/paper/2311.17918/) (2023)
- [FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Cameras](https://mlsanity.com/paper/2104.10490/) (2021)
- [OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving](https://mlsanity.com/paper/2311.16038/) (2023)
- [Enhancing End-to-End Autonomous Driving with Latent World Model](https://mlsanity.com/paper/2406.08481/) (2024)
- [Simple-BEV: What Really Matters for Multi-Sensor BEV Perception?](https://mlsanity.com/paper/2206.07959/) (2022)
- [DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving](https://mlsanity.com/paper/2405.04390/) (2024)
- [RoboBEV: Towards Robust Bird's Eye View Perception under Corruptions](https://mlsanity.com/paper/2304.06719/) (2023)
- [Benchmarking and Improving Bird's Eye View Perception Robustness in Autonomous Driving](https://mlsanity.com/paper/2405.17426/) (2024)
- [Understanding the Robustness of 3D Object Detection with Bird's-Eye-View Representations in Autonomous Driving](https://mlsanity.com/paper/2303.17297/) (2023)
- [GraphBEV: Towards Robust BEV Feature Alignment for Multi-Modal 3D Object Detection](https://mlsanity.com/paper/2403.11848/) (2024)
- [DiffBEV: Conditional Diffusion Model for Bird's Eye View Perception](https://mlsanity.com/paper/2303.08333/) (2023)
- [BEVCar: Camera-Radar Fusion for BEV Map and Object Segmentation](https://mlsanity.com/paper/2403.11761/) (2024)
- [BEVWorld: A Multimodal World Simulator for Autonomous Driving via Scene-Level BEV Latents](https://mlsanity.com/paper/2407.05679/) (2024)
- [BEVDiffuser: Plug-and-Play Diffusion Model for BEV Denoising with Ground-Truth Guidance](https://mlsanity.com/paper/2502.19694/) (2025)
- [On the Adversarial Robustness of Camera-based 3D Object Detection](https://mlsanity.com/paper/2301.10766/) (2023)
- [Adv3D: Generating 3D Adversarial Examples for 3D Object Detection in Driving Scenarios with NeRF](https://mlsanity.com/paper/2309.01351/) (2023)
- [A Black-Box Evaluation Framework for Semantic Robustness in Bird's Eye View Detection](https://mlsanity.com/paper/2412.13913/) (2024)
- [Minimizing Occlusion Effect on Multi-View Camera Perception in BEV with Multi-Sensor Fusion](https://mlsanity.com/paper/2501.05997/) (2025)
- [Reliability-Driven LiDAR-Camera Fusion for Robust 3D Object Detection](https://mlsanity.com/paper/2502.01856/) (2025)
- [Fool the Hydra: Adversarial Attacks against Multi-view Object Detection Systems](https://mlsanity.com/paper/2312.00173/) (2023)
- [FisheyeGaussianLift: BEV Feature Lifting for Surround-View Fisheye Camera Perception](https://mlsanity.com/paper/2511.17210/) (2025)

## Built on by (most cited first, 1 of 1)

- [NCGR: Noise-Conditional Gated Rectification for Camera Extrinsic Perturbations in BEV 3D Object Detection](https://mlsanity.com/paper/2608.03895/) (2026)

## Abstract (arXiv)

Bird's-eye-view (BEV) perception has emerged as a cornerstone of autonomous driving systems, providing a structured, ego-centric representation critical for downstream planning and control. However, real-world deployment faces challenges from sensor degradation and adversarial attacks, which can cause severe perceptual anomalies and ultimately compromise the safety of autonomous driving systems. To address this, we propose a resilient and plug-and-play BEV perception method, RESBev, which can be easily applied to existing BEV perception methods to enhance their robustness to diverse disturbances. Specifically, we reframe perception robustness as a latent semantic prediction problem. A latent world model is constructed to extract spatiotemporal correlations across sequential BEV observations, thereby learning the underlying BEV state transitions to predict clean BEV features for reconstructing corrupted observations. The proposed framework operates at the semantic feature level of the Lift-Splat-Shoot pipeline, enabling recovery that generalizes across both natural disturbances and adversarial attacks without modifying the underlying backbone. Extensive experiments on the nuScenes dataset demonstrate that, with few-shot fine-tuning, RESBev significantly improves the robustness of existing BEV perception models against various external disturbances and adversarial attacks.