Camera-based bird's-eye view (BEV) perception methods such as Fast-BEV have demonstrated promising results with fast inference speed and competitive accuracy. However, their robustness to real-world corruptions remains insufficiently explored, posing risks for autonomous driving deployment where environmental variations are inevitable. In this paper, we propose Sparse Recovery Bird's-Eye View Model Soup (SR-BEVS), a sparse recovery learning approach that simultaneously improves clean accuracy and corruption robustness without requiring additional data or architectural modifications. Our method identifies pruning candidates based on layer-wise weight magnitudes, applies random pruning to create diverse sparse models, and retrains each model with a randomly sampled learning rate. By averaging these diverse recovered models into a single dense model, we achieve enhanced robustness against various corruptions while introducing zero inference overhead. We further provide a mode connectivity analysis demonstrating that our pruning-based diversification preserves the connected low-loss basin necessary for stable weight averaging. Experiments on the nuScenes dataset demonstrate up to 33.8% improvement in corruption robustness and a 1.8% gain in clean accuracy over the Fast-BEV baseline, with consistent improvements across sensor noise, weather distortions, and illumination changes. Our simple yet effective approach may be applicable to other BEV models.
Figures & tables
Fig. 1 : The mAP (a) and NDS (b) performance improvement rates (%) of Model Soup and SR-BEVS compared to the Fast-BEV baseline on the M0 model [ 1 ] . Results are averaged across severity levels for each of the seven corruption types, plus the nuScenes clean data [ 2 ] . The results show that our SR-BEVS consistently outperforms the two baselines.
Fig. 2 : Qualitative comparison under clean and corrupted inputs. Fast-BEV performs well on clean data but produces incorrect predictions under heavy fog, whereas SR-BEVS maintains accurate detection even under severe corruption, yielding more stable and spatially consistent bounding boxes across the scene.
Fig. 3 : Weight diversity comparison between Model Soup and SR-BEVS. (a) L2 distance (higher is better) and (b) cosine similarity (lower is better) between candidate model pairs. Standard Model Soup candidates exhibit high similarity, while SR-BEVS produces substantially more diverse models.
Fig. 4 : Linear interpolation between SR-BEVS candidate model pairs on the M0 configuration. The horizontal axis denotes the interpolation coefficient α in θ(α)=(1−α)θa+αθb . Both mAP (left) and NDS (right) remain stable across the entire path, indicating the absence of a loss barrier between candidates.
Fig. 5 : 2D loss landscape visualization on the M0 configuration. Candidate models are projected onto the two principal directions of the weight space. (a) Standard Model Soup candidates cluster tightly near the pretrained model. (b) SR-BEVS candidates are more spread out while remaining within the same low-loss basin, demonstrating increased diversity without breaking mode connectivity.
Method
Gauss Noise
Gauss Blur
Shot Noise
Fog
Contr.
Bright.
Occl.
5%
10%
15%
σ 1
σ 2
σ 3
5%
10%
M
H
L
H
5%
10%
20%
10%
20%
M0
mAP
Fast-BEV
0.189
0.079
0.017
0.227
0.125
0.051
0.221
0.219
0.234
0.173
0.266
0.197
0.276
0.276
0.275
0.226
0.180
Fast-BEV (FT)
0.190
0.081
0.017
0.227
0.126
0.052
0.222
0.219
0.234
0.174
0.267
0.198
0.276
0.277
0.275
0.227
0.180
Model Soup
0.190
0.080
0.016
0.229
0.127
0.053
0.222
0.220
0.236
0.175
0.266
0.198
0.278
0.278
0.277
0.228
0.182
SR-BEVS
0.198
0.087
0.018
0.231
0.129
0.056
0.230
0.228
0.247
0.192
0.270
0.202
0.282
0.281
0.281
0.233
0.187
NDS
Fast-BEV
0.325
0.181
0.077
0.373
0.290
0.153
0.360
0.359
0.378
0.325
0.403
0.339
0.412
0.411
0.409
0.369
0.324
TABLE I : Robustness evaluation on nuScenes validation set under diverse corruptions and severity levels. Fast-BEV (FT) uses the same additional fine-tuning budget as SR-BEVS recovery.
Method
M0 (R18)
M2 (R34)
mAP ↑
NDS ↑
mAP ↑
NDS ↑
Fast-BEV
0.277
0.411
0.326
0.455
Fast-BEV (FT)
0.278
0.412
0.327
0.457
Model Soup
0.278
0.412
0.330
0.457
SR-BEVS
0.282
0.417
0.332
0.460
TABLE II : Detection performance on nuScenes validation set. Fast-BEV (FT) denotes additional fine-tuning with the same training budget as SR-BEVS recovery.
Fig. 6 : Qualitative comparison of 3D detection results under severe corruptions on the M0 configuration. Top row: under strong Gaussian noise, Fast-BEV and Model Soup misclassify noise patterns as objects such as pedestrians (green boxes), while SR-BEVS avoids these incorrect predictions. Bottom row: under heavy fog, Fast-BEV and Model Soup miss objects present in the scene, whereas SR-BEVS maintains reliable detection consistent with the ground truth.
Pruning Candidate Ratio ( p )
mAP ↑
NDS ↑
Baseline
0.277
0.411
p=0.1
0.280
0.414
p=0.2
0.282
0.417
p=0.3
0.279
0.413
TABLE III : Effect of pruning candidate ratio p on model performance (M0, clean data)
#Candidates ( N )
mAP ↑
NDS ↑
1
0.278
0.412
2
0.280
0.414
3
0.282
0.417
5
0.284
0.419
TABLE IV : Effect of the number of candidate models N on model performance (M0, clean data)
Model
Method
Weight Diversity
Clean
Corrupted (Avg.)
Cos.Sim. ↓
L2 Dist. ↑
mAP
NDS
mAP
NDS
M0
Soup
0.914
1.706
0.278
0.412
0.191
0.324
SR-BEVS
0.912
5.064
0.282
0.417
0.197
0.333
M2
Soup
0.900
5.795
0.330
0.457
0.233
0.373
SR-BEVS
0.893
24.389
0.332
0.460
0.241
0.381
TABLE V : Weight Diversity Analysis and Performance Comparison
Corruption robustness studies how to keep learning systems reliable when data, inputs, labels, or feedback are noisy, missing, or adversarially altered.
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.
Bird's Eye View (BEV)-based multi-view 3D object detection suffers from challenges of computational complexity, multi-scale feature extraction, and efficiency of dense 2D-to-BEV view transformation. To address these problems, this paper proposes an improved BEV 3D object detection algorithm Sparse-BEVNet. Firstly, a Bi-Level Routing Attention (BRA) mechanism is introduced into the image feature extraction network to reduce the computational burden of the backbone. Second, Cascaded Group Attention (CGA) is employed in the feature fusion module, which enhances deep interaction across features of different hierarchical levels without introducing additional computational overhead. Furthermore, a Sparse Spatial Cross-Attention mechanism is adopted to replace the conventional dense view projection pipeline. Experimental results on the public nuScenes dataset demonstrate that the proposed method achieves a mean Average Precision (mAP) of 45.2% and a nuScenes Detection Score (NDS) of 54.5%, corresponding to 3.6% and 2.8% improvements relative to the baseline model, respectively.
Jing Zhang, Jiaqi Liu, Zibo Wang
Stuart Weitzman School of Design University of Pennsylvania Philadelphia, USA · Courant Institute of Mathematical Science New York University New York, USA
Camera-LiDAR fusion is widely used in autonomous driving to enable accurate 3D object detection. However, bird's-eye view (BEV) fusion detectors can degrade significantly under domain shift and sensor failures, limiting reliability in real-world deployment. Existing robustness approaches often require modifying the fusion architecture or retraining specialized models, making them difficult to integrate into already deployed systems. We propose a Post Fusion Stabilizer (PFS), a lightweight module that operates on intermediate BEV representations of existing detectors and produces a refined feature map for the original detection head. The design stabilizes feature statistics under domain shift, suppresses spatial regions affected by sensor degradation, and adaptively restores weakened cues through residual correction. Designed as a near-identity transformation, PFS preserves performance while improving robustness under diverse camera and LiDAR corruptions. Evaluations on the nuScenes benchmark demonstrate that PFS achieves state-of-the-art results in several failure modes, notably improving camera dropout robustness by +1.2% and low-light performance by +4.4% mAP while maintaining a lightweight footprint of only 3.3 M parameters.
Trung Tien Dong, Dev Thakkar, Arman Sargolzaei +1
College of Engineering, University of South Florida, Tampa, FL 33620, USA