CodeMerge: Codebook-Guided Model Merging for Robust Test-Time Adaptation in Autonomous Driving
Authors: Huitong Yang
Zhuoxiao Chen
Fengyi Zhang
Zi Huang
Yadan Luo
Venue: NeurIPS 2025 poster ()
Keywords: test-time adaptation
autonomous driving
Primary area: applications
TL;DR: an efficient model merging approach for online adapting lidar-based 3D detection and end-to-end autonomous driving systems

Abstract: Maintaining robust 3D perception under dynamic and unpredictable test-time conditions remains a critical challenge for autonomous driving systems. Existing test-time adaptation (TTA) methods often fail in high-variance tasks like 3D object detection due to unstable optimization and sharp minima. While recent model merging strategies based on linear mode connectivity (LMC) offer improved stability by interpolating between fine-tuned checkpoints, they are computationally expensive, requiring repeated checkpoint access and multiple forward passes. In this paper, we introduce CodeMerge, a lightweight and scalable model merging framework that bypasses these limitations by operating in a compact latent space. Instead of loading full models, CodeMerge represents each checkpoint with a low-dimensional fingerprint derived from the source model’s penultimate features and constructs a key-value codebook. We compute merging coefficients using ridge leverage scores on these fingerprints, enabling efficient model composition without compromising adaptation quality. Our method achieves strong performance across challenging benchmarks, improving end-to-end 3D detection 14.9\% NDS on nuScenes-C and LiDAR-based detection by over 7.6\% mAP on nuScenes-to-KITTI, while benefiting downstream tasks such as online mapping, motion prediction and planning even without training. The code is released at \url{https://github.com/UQHTy/CodeMerge}.

CodeMerge: Codebook-Guided Model Merging for Robust Test-Time Adaptation in Autonomous Driving
Huitong Yang
Zhuoxiao Chen Fengyi Zhang UQMM Lab, The University of Queensland
Zi Huang Yadan Luo"
(huitong.yann) yang, zhuoxiao. chen, Sangyizzhang helen huang, y. yuu edu. au
Abstract
Maintaining robust 3D perception under dynamic and unpredictable test-time con- ditions remains critical challenge for autonomous driving systems. Existing test-time adaptation (TTA) methods often fail in high-variance tasks like 3D object detection due to unstable optimization and sharp minima. While recent model merging strategies based on linear mode connectivity (LMC) offer improved sta- bility by interpolating between fine-tuned checkpoints, they are computationally expensive, requiring repeated checkpoint access and multiple forward passes. In this paper, we introduce CodeMerge, lightweight and scalable model merg- ing framework that bypasses these limitations by operating in compact latent space. Instead of loading full models, CodeMerge represents each checkpoint with ciw-dimensional fingerprint derived from the source model's penultimate features and constructs key-value codebook. We compute merging coefficients using ridge leverage scores these fingerprints, enabling efficient model com- position without compromising adaptation quality. Our method achieves strong performance across challenging benchmarks, improving end-to-end 3D detection 14.9% NDS on nuScenes- and LiDAR- based detection by over 7.6% mAP on nuScenes-ter -o-Tness-o-KITTI, KITTI, while benefiting downstream tasks such online mapping, motion prediction and planning even without training. The code released at https://githin github......... www. com/UQHTy/ Coottt/g/odMMegee
Introduction
Real-world autonomous driving scenarios often encounter rapid and unpredictable environmental variations, such as sudden adverse weather conditions (e.g.. fog, snow) or sensor malfunctions (e.g.. dropped frames, missing beams) arising from LiDAR and camera systems, illustrated in Fig. I These abrupt disruptions momentarily render 3D perception modules partially or fully "blind". propagating erroneous decision-making downstream and leading to severe safety hazards in the end-to-end autonomous driving (AD) pipeline. Consequently, a critical yet unresolved question emerges: Can perception models efficiently and robustly adapt onboard to address such unforeseen distributional shifts?
Test-time adaptation (TTA) offers a promising direction by enabling models to adapt online during inference. Prior TTA approaches typically handle shifts by aligning BatchNorm statistics <4 34], enforcing consistency through data augmentations [46), or minimizing sharpness via adversarial perturbations [14 34). Nonetheless, when directly extending them complex tasks such as 3D detection, these approaches often suffer from brittle optimization dynamics and fall into sharp local minima, which can lead to the loss of previously acquired generalization and the ability to cope with future task distributions [9]
Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Camera Corruption
Corruption
Bright
Motion
On-board
Detection
Dark
Snow
Quent
Crash
Beam Missing
Wet Ground
End-to-Ena AD System
LIDAR-based Detector
Figer
Datact/Track
Cross Attortionn
Self- Attention
Cross Attention
Motion
Vasedl Extractor
Sparse Comy
BEV Leyleyer Layer
Reglon Proposal
Clessifier
Transfomer
Head
3D Box
Extractor
Mapping
Plarning Head
LDAR
Regressor
Transforer
Fingerpmnti
Camera
Ego Query
Figure I: Overview of real-world test-time shifts (top) and 3D perception systems considered in this work (hottom). We study test-time adaptation (TTA) in two settings: (1) end-to-end autonomous driving system and (2) a modular LiDAR-based detector, both affected by adverse weather and sensor failures. CodeMerge enables efficient TTA by leveraging compact fingerprints to guide model merging.
Recent studies improve long-term adaptation stability by leveraging model merging techniques 24 grounded in linear mode connectivity (LMC),, which posits that models fine-tuned on different target samples but initialized from the same pretrained source model are "linearly connected" in weight space. Thus, interpolating between such models has been shown to produce reliable pseudo-labels and alleviate model collapse issues in TTA [34. Techniques such as Mean Teacher and its variants exponentially average the weights of past models, but often underutilize valuable diversity across past checkpoints. More recently, Model Synergy (MOS) g extends this idea by maintaining a buffer of Top-K important checkpoints and dynamically merging them using synergy weights, computed via kernel similarities among each model's 's predictions of the current test sample. While effective, MOS incurs significant overhead from repeatedly checkpoint loading and performing K forward passes for calculating synergy weights, limiting its scalability in high-throughput driving applications.
In this work, we introduce a codebook-guides model merging (CodeMerge) approach for adapting 3D perception modules against various shifts at test time. The core idea is to represent each fine- tuned checkpoint Lec by compact "fingerprint" derived from the source model's penultimate activations. These fingerprints serve as keys in a model codebook, mapping to their corresponding checkpoint weights. Crucially, correlations in this low-dimensional fingerprint space reliably mirror those in the high-dimensional weight space (see Fig. B) enabling informed merging decisions without loading full model parameters. CodeMerge employs ridge leverage scores rank the informativeness of fingerprints, a technique theoretically linked to approximations of the inverse Hessian in the parameter space. This procedure needs memory that scales only with the fingerprint dimension and adds negligible latency, yet it lifts end-to-end 3D detection NDS by 14.9%, tracking AMOTA on the nuScenes-C corruption benchmark by 19.3%, and LiDAR- basse detection 3D mAP by 7.6% on the challenging nuScenes-to- Miccenes-to-KITT KITII shift. These improvements seamlessly propagate to downstream motion prediction and planning modules without modification additional training.
2 Preliminaries
We begin by formalizing the problem setting for test-time adaptation (TTA) in 3D object detection and reviewing model merging strategies that exploit linear mode connectivity in such context.
Task Formulation. Let 4 = Φe hgm denote a pretrained 3D object detection model, comprising a feature extractor )e (.) X +
Rd maps an input x E (e.g., a point cloud or multi-view images) to latent feature map z E Z, and the head regresses 3D boxes hgo (.) Z + y IR.. The goal of TTA to sequentially adapt the model a stream of unlabeled torget-domain inputs Drarr or corruptions. The online adaptation must follow in single forward-pass setting, incrementally evolving the model parameters (0) (1) -... (4) improve detection over time.
xx){_1- which may exhibit significant distributional shifts
Linear Mode Connectivity (LMC). LMC 24 12 52
refers to the empirical property that two models (1) and (2) trained from shared initialization (or sufficiently close regions in weight space), can be connected by "linear path" without significant loss degradation. Formally, for any

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Fingerprint
(0)
EMA
Infer
MOS
Mere
Model
CodeBook
Buffer
Fingerpritt
@Fingerprint
Figure 2: Conceptual comparison of model merging strategies for TTA. Unlike EMA (left), which ignores model behavior, or MOS (middle), which requires multiple inferences to compute merging weights, CodeMerge (right) leverages ridge leverage scores in compact fingerprint space efficiently guide model merging.
E [0, 1].
<(11- ((1-))00) + x0(2)) ~ (1 - ())()))) + AC
(I)
This property facilitates efficient model merging through linear interpolation.
Implication for Model Merging in TTA. If LMC holds true between each pair of successive parameters (0(t-1), ((9) fine-tuned from (0), then their interpolated model should yield low loss. This underpins methods like Mean Teacher shown in Fig. 2 in which teacher models recursively
updated with an exponential moving average (EMA) with a decay factor / E (0, 1):
+
( (1 --) ((t)
(1 - B) Σ 3(
(2)
EMA
Under LMC, this leads to approximately linear combinations of multi-task losses:
L(O (1) EMA (1 3) Σ
3 (()(0)
This shows that averaging can reduce variance from balancing multi-task losses. However, EMA's co- efficients are solely based on time steps rather than model behavior, making it potentially suboptimal.
In contrast, MOS S (middle in Fig. 2 adaptively merges model parameters by solving a kerne-- weighted least squares problem over a buffer of candidate checkpoints (0(0) Ji=1 Given a test batch x,, the merged model is computed as:
E
(+)
(t) (+) where wi!t)
(4)
Σr K()
KS Sim (D (x1), Dew (xx)) Sim (dgr)) (xx), deis (x:)),
(5)
where kernel matrix ( R captures pairwise similarity between model outputs under the current batch. To evaluate :6("), MOS requires K forward passes over x,, making it more computationally intensive and thus hard to scale up the horizon K TTA.
3 Our Approach
We introduce CodeMerge, sodebook-guidee model merging scheme for efficient TTA in 3D object detection without triggering repeated inference across past models. To achieve this, we construct model codebook (Sec. 3.1.. where each checkpoint is represented by a compact fingerprint derived from intermediate features of fixed source model. During inference, we compute curvature-aware ridge leverage scores (Sec. 3.2) in the fingerprint space. Finally, perform sign-consistent weighted merge of top-scoring candidate models (Sec. 3.3 promoting both stability and diversity.
3.1
Model CodeBook
At each step J, we maintain model codebook for all past checkpoints along the adaptation trajectory, denoted as:
c(t) {2, e (1
(6)
3

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Painwise Parameter Difference
SECOND: Waymo-KITTI
Painwise Parameter Difference
SECOND: nuScenoss-ITII
Painwise Parameter Difference
SparsaDrive: Corruption
Pairwise Parameter Difference
SparseDrive: Motian Camupptio
2.35
0.75
D 0.862
0.821
0,867
0.676
Pairwise Fingerprint Difference
Pairwise Hnngerrinnt Difference
Pairwise Fin girperinntt Difference
Pairwise Fingerprint Difference
Figure 3: Pairwise fingerprint differences correlate strongly with model weight differences (Pearson r and Kendall Tau т 0.7) across SparseDrive [41] and SECOND [55], showing that the low- dimensional fingerprint space reliably reflects parameter space structure.
Each entry is a key-value pair, where the key 2, E Rd' is low-dimensional fingerprint and the value @ is the corresponding checkpoint fine-tuned at time step i. To compute the key 2, we extract intermediate features from the i-th input batch x; using pretrained feature extractor dyg0) and randomly project them to low-dimensional subspace for efficiency:
á; = RandProj(dggo: (x:)).
(7)
Here, RandProj(-)) Rd - ensures the keys are compact. As the test-time adaptation progresses, we update the codebook incrementally by appending new pairs, i... C(++1) 1) (Zz, (
TRd" is implemented via fixed Gaussian projection matrix where d' <
3.2 Curvature-Aware Merge Scores
To determine which checkpoints the codebook should be merged at time step t, we first compute a merge score for each checkpoint ( E (()) using the ridge leverage score.
Definition 1 (Ridge Leverage Score (RLS)). Let Z, = [2....., 2 | E R((-1)xd be the matrix of all stored keys (fingerprints), where 2; be the fingerprint of the i-th candidate model ( We define the ridge leverage scores of the fingerprint 2;
1 2-
27
2,
where is a regularization parameter. A high leverage score indicates 2, is influential and lessly observed within the current feature space defined by past direction.
Theoretical Analysis. We now connect this leverage score to the inverse of curvature through the lens of LMC. We begin by revisiting the LMC assumption (Eq. (I) through second-order Taylor expansion around (0).
1 00 H5, ((0 = (()())) + VL
Ho, with H :- ViC(0())
(8)
where 6; : (()
(0) refers the model update direction and H is the Hessian at ()), In this view, the curvature along 8, is quantified by the quadratic term 5.7 H Its inverse 6. H-1 suggests 6; explores novel region of the loss landscape, making it indicator for selecting diverse checkpoints.
However, computing the full Hessian in nigh-dimensional parameter space is impractical, especially in TTA tasks. However, considering that 3D object detection models commonly use linear layers as final regression heads, we can effectively analyze curvature through the simpler and analytically tractable ridge regression setting. Specifically, assume a linear regression head parameterized by weights w Idd and fixed feature extractor Φ(-). yielding a ridge regression objective of the form:
1 Z Z + AT),, yell2 + |||w|| H., 2( K
L= N
Σ Iw (Tx)))
where H., is Hessian matrix in parameter space. More precisely, this reveals the inverse of parameter- space curvature is linked to the proposed ridge leverage score under the low-rank surrogate Z,_, Z,-1;

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24 H 24 = 2/ 27
(
21 + 2X/
X 8/
(10)
Empirical analysis (see Fig. 3 confirms that fingerprint vectors strongly correlate (Pearson correlation and Kendall Tau scores often exceeding 0.7) with parameter deltas, confirming that the geometry of fingerprint space reliably mirrors that of parameter space.
3.3 Model Merging
To perform stable model merging, we select top- high- high-scoring checkpoints based on ridge leverage scores, yet their associated parameter directions may exhibit destructive interference. To resolve such conflicts, we adopt a sign-consistent merging inspired by (54), which aligns model parameters based on majority sign consensus before merging. Let (O(A) J denote the top- selected checkpoints and { LK their corresponding leverage scores. For each parameter dimension j, we compute the majority sign mign (t) = mode( {sign(O)) ) ) merged model is then given by:
and zero out inconsistent components. The
6 =
(0) II (ign( maj + e ((0)
(11)
00)
where @ denotes clement-wiss multiplication, and I[-] is a binary mask that retains only parameters aligned with the majority sign. This sign-consistent merge ensures coherent parameter updates and stabilizes adaptation under distribution shifts.
Optimization. Following the protocol in (8), we the merged model to generate pseudo-labeled bounding boxes for self-training the LiDAR-basee detector online. In realistic end-to-end AD systems (see Fig. D perception, mapping. and planning modules are often integrated into monolithic architecture. For efficiency, we freeze all components except for the 3D box regression head. Experiments show that CodeMerge not only improves detection performance but also yields gains in downstream mapping and planning without requiring additional training or modifications (Table ().
4 Experiments
4.1 Experimental Setup
Datasets and Tasks. We conduct comprehensive experiments across five benchmarks for end-to-end autonomous driving and outdoor object detection: KITTI [3, KITTI-C [25], Waymo 40).
nuScenes 4 and nuScenes-C C (53) For test-time adaptation in end-to-end autonomous driving, we pre-train models on the nuScenes driving benchmark and adapt them to eight real-world corruptions in nuScenes-C: Motion Blur (Motion), Color Quantization (Quant), Low Light (Dark), Brightness (Bright), Snow, Fog, Camera Crash (Crash), and Frame Lost. For LiDAR- 3D object detection, first tackle cross-dataset adaptation (Waymo- KITTI. nuScenes KITTI) following [66] 57 6). addressing both object-level shifts (e.g., scale and point density) and environmental differences (e.g., deployment location, beam configuration). We then evaluate adaptation to sensor failures and weather effects via KITTI - KITTI-C, covering Fog, Wet Conditions (Wet.), Snow, Motion Blur (Moti.), Missing Beams (Beam.), Crosstalk (Cross. T), Incomplete Echoes (Inc.), and Cross-Sensor (Cross.S). The detailed evaluation metric and implementation details can be found in Appendix A.
Baselines. We compare the proposed CodeMerge against a broad range of methods: (i) No Adapt., the pretrained model evaluated directly on the target datasets; (ii) SN [48), a weakly supervised DA technique that rescales source objects using target size statistics; (iii) ST3D (56), the first UDA method for 3D detection, employing multi-epoch self-training with pseudo labels; (iv) Tent [44], an
TIA approach that minimizes prediction entropy; (v) CoTTA [46, which combines mrea-teeacher supervision with stochastic augmentations for 7TA; (vi) SAR 34 enhancing Tent by sharrness-awanr
and reliability- awaare entropy minimization: (vii) MemCLR 43), the first online TTA method that
uses memory-augmented mean-tean mean-teacher for 2D detection; (viii) Reg-TTA3D (58). which regularizes 3D box regression by enforcing noise-consistent pseudo labels during 3D 77A; (ix) MOS (8). dynamically fusing a bank of top-K checkpoints through kernel-based synergy for 3D TTA; (x)
DPO ( flattening the test-time loss landscape via dual perturbations for 3D TTA: (xi) Oracle, fully supervised model trained with annotated target datasets.
5

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Table I: Perception and tracking results of the end-to-end SparseDrive model 41 with and without TTA on the nuScenes-C [33] validation set under different corruptions at the highest severity level. The best results for each metric and corruption are highlighted in bold.
3D OBJECT DETECTION
MULTI-OBIECT TRACKING
CORRUPTION
METHOD
mAPt
NDST
MATE)
mASE!
mAOEL
mAVEl
mAAET
AMOTA
AMOTP!
Recaltt
IM IMAGI DEGRADDTTIO
No Adapt.
0.1468
0.3136
0.7792
0.2908
0.8048
0,4835
0.2398
0.0896
1.7983
0.1837
Tent [4
0.2462
0.4113
0.6802
0.2839
0.6039
0.3243
0.2264
0.1736
1,5122
0.2918
MOTION
MOS g
0.2611
0.4125
0.6848
0.2827
0.6588
0,3455
0.2087
0.1902
1,5239
0.3332
Ours
0,2759
0.4206
0.6699
0.2815
0.6437
0,3618
0.2169
0.2192
1,5485
0,3456
No Adagt.
0.2022
0.3767
0.7095
0.2896
0.6478
0,3814
0.2160
0.1548
1,5398
0.2873
Tent 44
0.1424
0,3043
0.6527
0.4169
0.6032
0,5758
0.4200
0.0981
1.6930
0.1788
QUANT
MOS B
0,2560
0,4172
0.6781
0.2848
0.6115
0,3103
0.223
0.2096
1,5195
0.3287
Ours
0.2742
0.4331
0.6575
0.2764
0.5903
0.3018
0.2137
0.2339
1.4868
0,3330
ILLUMINNTION SHIFT
No Adapt.
0.1386
0.2804
0.7375
0.4180
0.6880
0.6285
0.4164
0.1169
1.7520
0.1995
Tent [
0.1266
0.2795
0.7243
0.4116
0.6396
0,6474
0.4151
0.0776
1.7014
0.1697
DARK
MOS 8
0.1726
0,3500
0.7482
0.2920
0.6570
0,4202
0.2459
0.1399
1.7148
0.2153
Ours
0.2060
0.3727
0.7206
0.2852
0.6782
0.3993
0.2196
0.1762
1.6333
0,2557
No Adapt.
0,3300
0.464]
0.6355
0.2749
0.6084
0,3013
0.1892
0.2829
1.4257
0.3982
Tent [+
0.2557
0.4289
0,6345
0.2896
0.5666
0,3143
0.1848
0.1879
1.4836
0.3002
BRIGHT
MOS 8
0.3595
0.4825
0,6100
0.2757
0.6053
0,2908
0.1909
0.3126
1.3566
0,4387
Ours
0.3692
0.4939
0.6138
0.2779
0.5343
0.2885
0.1928
0.3317
1.3389
0.4632
No Adapt.
0.0970
0.2206
0.7974
0.4586
0.9349
0,6614
0.4264
0,0469
1,8822
0.1070
ADVERSE WEATHER
Tent [4
0.1417
0.2791
0.7312
0.4165
0.6904
0.6714
0.4077
0.0779
1.7440
0.1838
SNOW
MOS
0.1478
0.3207
0.7740
0.2995
0.7092
0.5211
0.2284
0.0887
1.7828
0.1747
Ours
0.1828
0.3581
0.7558
0.2930
0.6009
0,4604
0.2222
0.1136
1.7119
0.2293
No Adapt.
0.3162
0,4612
0.6295
0.2775
0.5727
0,2984
0,1910
0.2756
1.4469
0.3859
Tent [4]
0.2964
0,4515
0.6372
0.2837
0.5190
0.3149
0.2121
0,2312
1,4311
0.3623
FOG
MOS
0,3362
0,4690
0.6339
0.2797
0.5798
0.2961
0,2019
0.2907
1.3833
0.4007
Ours
0.3421
0.4761
0.6184
0.2739
0.5597
0,2995
0.1981
0.2997
1.3749
0.4124
No Adapt.
0.0785
0,2753
0.6467
0,4060
0.6078
0,5953
0.3840
0.0670
1.8241
0.1519
SENSOR FAILURES
Tent 44
0.0722
0,2679
0.7426
0.3469
0.6294
0.6658
0.2976
0.0462
1.9007
0.1155
CRASH
MOS
0.0702
0,2659
0.7614
0.3460
0.6685
0.2990
0.0454
1.8978
0.1155
Ours
0.0973
0.3288
0.6979
0.2889
0.6061
0.4175
0.1876
0.0810
1.8372
0.1550
No Adapt.
0.0886
0.3109
0.7314
0,2792
0.6206
0.4717
0,2310
0.0549
1.7638
0.1644
Tent [4
0.0372
0,2371
0.8386
0.2913
0.7439
0.7068
0.2337
0.0029
1.9856
0.0406
LOST
MOS
0.0479
0,2116
0.8913
0.3464
0.7567
0,8008
0.3281
0.0131
1,9670
0.0624
Qurs
0.1172
0.3292
0.7638
0.2787
0.5810
0.4461
0.2243
0.0700
1.7605
0.1788
No Adapt.
0.1747
0.3378
0.7083
0.3368
0.6856
0.4777
0.2867
0.1361
1.6791
0.2347
0.1648
0.3325
0.7052
0.3426
0.6245
0.5276
0.2997
0.1119
1.6815
0.2053
AVERAGE
MOS g
0.2028
0,3551
0.7269
0.3205
0.6633
0.4829
0.2711
0.1599
1.6461
0.2532
Ours
0.2334
0.4016
0.6872
0.2819
0.5993
0.3719
0.2094
0.1907
1.5865
0.2966
4.2
Main Results and Analysis
TTA on End-to-End Autonomous Driving. We comprchensively evaluate our CodeMerge method on nuScenes-C (33 with the end-to-end SparseDrive model [41], covering five downstream tasks: 3D detection, multi-object tracking, online mapping. motion prediction, and trajectory planning under diverse corruptions. Table shows CodeMerge consistently outperforms all baselines, including No Adapt, Tent, and the state-of-the-art MOS [8] in averaged results. In 3D detection, we boost mAP by 33.6% % over no adaptation (0. - 0.2334) and by 13.3% over MOS. CodeMerge also reduces MASE by 4.4% relative to MOS, and lowers mAVE by 19%. Under the Bright corruption, CodeMerge improves mAP by 11.9% over no adaptation, with consistent gains in other metrics. In multi-object tracking, CodeMerge improves AMOTA by 19.3%, reduces AMOTP by 13.8%, and raises recall by 16.5% when compared with the SOTA bascline, MOS. Notably, under the most safety-critical Lost scenario, the proposed method achieves the highest recall (0. 1788) and lowest tracking error among all methods. Although only perception weights are adapted, downstream tasks benefit markedly. As reported in Table 2 CodeMerge increases online mapping mAP by 42.3% (0.2009 - 0.2859) over no adaptation, with +45.7 % on lane boundaries and +39.5% % on obstacles, especially +94.2% % under Dark. For motion prediction, mADE and mFDE fall by 9.3% and 9.7% compared to no adaptation, respectively, while EPA (higher is better) rises by 13.8%. For planning, average lateral deviation falls 8.3% (0.7923 m - 0.7266 m) and collision risk drops 1% compared to no adaptation. These consistent gains achieved without touching non ton-percnntion modules, confirm

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Table 2: Impact of TTA on downstream modules of end-to-end SparseDrive [11. We evaluate online mapping, motion prediction, and trajectory planning on the nuScenes-C [3) under the highest severity of various corruptions. These modules are not fine-tuned; all performance gains stem from TTA applied to the detection module. Best results per metric and corruption are shown in bold.
ONLINE MAPPING
MOTION PREDICTION
PLANNING
CORRUPTION METHOD
AP
APst
APst
mAPt
MADE
mFDEL
MR!
EPA
L2-Avgi
CR-Avgl
IMAGE DEGRADATION
No Adapt.
0.1988
0.2343
0.1999
0.2110
0,8630
1,3483
0.1750
0.2616
0.7877
0.2150
Tent +4
0.3425
0.3794
0.3876
0,3698
0.7786
1.1825
0.1520
0.3712
0.6474
0.0900
MOTION
MOS g
0.3452
0.3943
0.4012
0,3802
0.7348
1.1278
0.1560
0.3742
0.6694
0.1340
Ours
0.3660
0,4212
0.4283
0.4052
0.7264
1.1200
0.1570
0.3945
0.6580
0.1100
No Adapt.
0.1742
0.2317
0.2069
0.2043
0.7620
1.1734
0.1526
0.3204
0.7301
0.1590
Tent 44
0.1526
0.2153
0,2088
0,1922
0,8489
1.3551
0,1602
0.2987
0.6966
0,1200
QUANT
MOS 8
0.2346
0.3208
0.29118
0.2824
0.7040
1.0822
0.1445
0.3668
0.6848
0,1180
Ours
0.2600
0.3445
0.3267
0.3104
0.7002
1,0859
0.1454
0.3840
0.6762
0.1250
ILLUMINATION SHIFT
No Adapt.
0.1173
0.2038
0.1812
0.1675
0.8428
1.3255
0.1714
0.2757
0.7535
0,2760
Tent 44
0,2116
0,2560
0,2481
0.2386
0,8603
1.3314
0.1786
0.2722
0.7049
0.1230
DARK
MOS g
0.2261
0.3090
0,2892
0,2748
0.7956
1.2443
0.1730
0.3066
0.6824
0.1360
Ours
0.2825
0.3637
0.3291
0.3251
0.7493
1.1639
0.1644
0,3397
0.6602
0.1170
Adapt.
0.3777
0.4847
0.4833
0,4486
0.6646
1.0246
0.1369
0.4468
0.6306
0.1260
Tent 44
0.3550
0.4342
0.4591
0.4161
0.6882
1.0739
0.1369
0.3978
0.6487
0,0950
BRIGHT
MOS g
0.4053
0.4960
0,5127
0.4713
0.6468
1.0031
0.1357
0.4593
0.6243
0,1230
Ours
0.4305
0.5224
0,5398
0.4976
0.6504
1.0122
0.1392
0.4680
0.6209
0,0940
No Adapt.
0.0061
0.0322
0.0369
0,0250
1.0643
1.7042
0.1930
0,2113
0,8897
0,4310
ADVERSE WEATHER
Tent 14
0.1083
0.1320
0.1359
0.1254
0.9147
1.4192
0.1753
0,2804
0.7552
0.1320
SNOW
MOS g
0.1237
0.1564
0.1545
0,1448
0.8736
1,3476
0.1737
0.2994
0.7684
0.1920
Ours
0.1134
0.1812
0.1740
0,1562
0,8074
1,2589
0.1717
0.3135
0.7634
0.1900
No Adapt.
0,3600
0.4649
0,4076
0.4109
0.6482
0.9904
0.1347
0.4380
0.6257
0,1050
Tent 44
0.3786
0.4492
0.4438
0.4239
0.6861
1.0631
0,1405
0,4182
0.6533
0.0870
FOG
MOS g
0.4161
0.4950
0.4785
0,4632
0.6549
1 1,0087
0.1401
0.4539
0.6225
0,1060
Ours
0.4276
0,5022
0.4843
0.4714
0.6501
1 1,0088
0.1394
0.4557
0.6200
0,1100
No Adapt
0.1029
0.1019
0.0618
0.0889
0,8662
1.3375
0.1652
0.1920
0.9276
0,3740
SENSOR FAILURES
Tent 44
0.0431
0.0764
0.0141
0.0445
0,8691
1.3548
0.1710
0.1771
0,8852
0.7040
CRASH
MOS g
0.0394
0.0706
0.0100
0,0400
0.8878
1.3895
0.1766
0.1721
0.8977
0.7300
Ours
0.0727
0.1154
0.0279
0.0720
0,8302
1.3022
0.1637
0.1974
0,8539
0.6300
No Adapt.
0.0892
0.0388
0.0250
0.0510
1.0327
1.4772
0.1740
0.1826
0.9932
0.4830
Tent 44
0.0431
0.0547
0.0163
0.0380
1.4194
2.1114
0.2383
0.0737
0.9985
0.6220
LOST
MOS X
0.0180
0.0153
0.0038
0.0124
1,5468
2.3163
0.2155
0,0873
1.0628
0.7340
Ours
0.0723
0.0503
0.0250
0.0492
1.0004
1.4304
0.1739
0.0952
0.9600
0.6610
No Adapt.
0.1783
0.2240
0.2003
0.2009
0.8430
1.2976
0.1629
0.2911
0.7923
0.2711
Tent 44
0.2044
0.2497
0.2392
0.2311
0.8832
1,3614
0.1691
0.2862
0.7487
0.2466
AVERAGE
MOS
0,2260
0.2822
0,2677
0.2586
0.8555
1.3149
0,1644
0.3150
0.7515
0.2841
Ours
0.2531
0.3126
0.2919
0,2859
0.7643
1.1718
0.1568
0.3312
0.7266
0.2546
that the proposed lightweight, fingerprint-guidee merging framework stabilizes the detector and unlocks robust performance across all autonomous driving tasks.
TTA on LiDAR based Detection. We examine CodeMerge's performance in 3D object detection
across two distinct types of domain shifts: Cross-dataset (Waymo KITTI, nuScenes + KITTI) and caruutiioniiduuee shifts (KITTI- KITTI-C). (1) Cross-dataset (Ta- ble 33. Compared with the non- adapted model, CodeMerge lifts APBEY by 25.1% and APnd by 141% on Waymo + KITTI, closing 108.5% of the domain gap and even surpassing the multi-epoch ST3D and fully supervised Oracle in AP APBEV On nuScenes + KITTI, it narrows the gap by 81.3% 15%, again outperforming the strongest TTA baselines (MOS, DPO) and exceeding
Table 4: TTA results on KITTI-C.
We evaluate the LiDAR- based SECOND detector [53 under the highest severity level of various corruptions, reporting AP (hard).
No.dd.
Teut 214
CoTTA
SAR [4
MenCLR 48
DPO 27
MOS 3
Ours
Tog
59.07
Inc.
25.68
26.44
27.85
27.47
27.16
32.18
ClusiT.
75.49
74.67
72.22
74.25
75.52
75:47
Men.
38.21
38.15
38.62
37.57
35.71
Cross..
41.17
42.09
Wes.
76.25
76.36
76.43
76.25
76.89
77.79
79.82
Beam.
$3.93
53.85
53.98
53.75
53.49
54.05
55.91
Mean
$6.74
54.56
$4.73
54.57
$6.61
55. $5.49
57.46
$8.97
7

---

Table TTA results for RiDAR- 3D detection across different datasets. We report A ABEV AD (moderate). "Oracle" = fully-supervised on target, Bold = best; underline = second best.
WAYMO KITTI
NUSCENES KITTI
МETHOD
VENUR
TTA
APsay / AP
Closed Gap
APaxv / AP
Closed Cap
No Adapt.
67.64 27.48
51.84 / 17.92
SN [8]
CVPR 20 CVPR'21
78.96 59.20
+72.33% / +69.00%
40.03 21.23
+37,55% /5.996
ST3D 56
82.19 /19.66..3
+92.97% / 474.72%
75.94 54.13
+76.63% / 465.21
Oracle
83.29 / 73.45
83.29 / /7.45
Tent 44
ICLR'21
65.09 / 30.12
-16.29% +5.74%
46.90 / 18.83
-15.71% +1.64%
CoTTA F6
CVPR'22 22
67.46 / 35,34
-1.15% / +17. 10%
68.81 /47661
+53.96%
+53,47%%
SAR 34
ICLR'23
65. 81 / 30.39
-11.69% +6.33%
61.34 35.74
+30.21% / +32.09%
McmCLR 83
WACV'23
V
65.61 / 29.83
-12.97% +5.11%%
61.47 35.76
+30.62% / +32.13%
DPO 9
MM'24
75.8 81.8111 55.774
+52.20% / +61 .47%
73.27 54.38
+68, 13%
/655 666.666
Reg-TTA3D 68
ECCV'24
81.60 31.60/ / 56.03
+89.20% / +62.11
68.73 /4.55
+53.70%
/47.
MOS 8
ICLR'25
81.90 64.16
+91.12% +79.79%
/1.11
+61.33%
+59.78%
Ours
84.62 66.31
+108.50% +44447
77.41/58.54
+81.30% +73.15%%
Table 5: Ablation study on different checkpoint selection strategies, number of checkpoints to merge (K), and random projection dimension (d') on nuScenes-C (33 (motion blur at the heaviest level).
DETECTION
TRACKING
MAPPING
MOTION
PLANNING
MERGE
K PROJ.-D
mAPt
NDST
AMOTAT
AMOTP.
mAPt
APydTT
MADEL
MFDEJ
1.2-Agll
CR-Avgl
Random
5
0,2740
0.4185
0,2152
1,5461
0,4011
0.3678
0.7251
1,1192
0.6631
0.1120
Recent
0.2480
0.3985
0.1866
16040
0.3748
0.3410
0.7368
1.1436
0.6795
0.1490
EMA
0,2478
0.3992
0.1869
1.6016
0.3748
0.3413
0.7375
1.1447
0.6778
0.1250
KMcans++
5
1024
0,2746
0.4192
0,2157
1,5490
0,4010
0,3678
0.7246
1.1182
0.6625
0. 0.1050
Leverage
5
1024
0.2851
0.4264
0.2241
1,5206
0.4103
0,3713
0.7228
1.1146
0.6504
0. 0.1090
Leverage
3
1024
0,2655
0.4122
0,2077
1,5630
0,3623
0.3928
0.7407
1.1461
0.6651
0. 0.1200
Leverage
9
1024
0,2818
0.4231
0,2195
1,5240
0.4167
0.3814
0.7180
1.1066
0.6534
0.1030
Leverage
256
0,2749
0.4176
0,2168
1,5488
0.4010
0.3678
0.7228
1.1142
0.6615
0,0960
Leverage
512
0.2708
0.4142
0,2117
1.5525
0.3991
0.3695
0.7378
1.1428
0.6588
0.1170
Leverage
5
2048
0.2799
0.4207
0.2140
1.5224
0.4033
0.3630
0.7324
1.1204
0.6488
0.0950
ST3D by +1.9% APBEV and +8.1% 3P (2) Corruption-induced (Table 4). Against KITTI KITTI-C corruptions, CodeMerge raises mean AP3D by +7.7% over no adaptation and +2.6% over the best prior TTA baseline. Under Fog and Wer corruption, gains are pronounced: +9.9% (75.96 vs. 69.11) and +2.6% (79.82 vs. 77.79), respectively, indicating enhanced resilience visibility and environment degradations. These results demonstrate that our latent-space, fingerprint-guien merging not only closes cross-domain gaps more effectively than existing TTA methods but also surpasses dedicated domain adaptation approaches, providing robust performance across diverse and challenging environments.
4.3
Ablation and Sensitivity Study
Impact of Checkpoint Selection Strategy. In Table 5 compare five strategies for choosing K K=5 5 checkpoints under heavy Motion Blur: Random sampling, Recent (the latest five), KMeans++ clustering in feature space, Exponential Moving Average (EMA) model, and our Leverage-score ranking. Random yields a reduced detection mAP of 0.2740, weaker tracking (AMOTA = 0.2152), and planning (CR-Avg = 120). Recent performs worst across all tasks (mAP 0.2480, AMOTA 0.1866, CR-Avg 0. 1499), indicating catastrophic forgetting when only the newest checkpoints are merged, KMeaan++ yields marginal O. 17% lift in NDS over Random and reduces collision risk by 6.3%, reflecting its ability to capture diverse feature modes. However, KMaaas++ is still outper- formed by the proposed method (-3.8% mAp for detection). highlighting that pure feature clustering cannot match the important informativeness captured by leverage-scone ranking. Futhermore, Code- Merge (K- 5. projection dimension d' 1024) consistently outperforms EMA across all tasks: Detection mAP/NDS +0.0373/+0.0272. Tracking AMOTA +0.0372 and AMOTP 0.0810 (L bet- ter); Mapping mAP/AP ped +0.0355/+0.0300, Motion mADE/mFDE 8.0147/-0.0300) Planning L2-Avg/CR-Avg -..0274/0.0166 These gains indicate that our fingerprint-guine guided merging, which selects complementary checkpoints, offers stronger generalization than the uniform, time-local smoothing performed by HMA. Overall, the proposed Leverage-score selection consistently achieves the best results by explicitly identifying the most informative, complementary checkpoints carrying torg-term knowledge.
8

---

Striiht
Figure 4: Visualization of outputs of SparseDrive (bottom) and after CodeMerge adaptation (upper) under severe motion blur. TTA greatly improves detection by capturing more true positive instances, which consequently enhances downstream mapping and planning accuracy (right).
Impact on Number of Merged Checkpoints. Table compares selecting =33, or 9 checkpoints (with d' = 1024) for model merging under Motion Blur corruption. With only K = 3. detection mAP drops from 0.2851 to 0.2655, and tracking AMOTA falls from 0.2241 0.2077, indicating insufficient coverage of knowledge diversity. Increasing to K =9 recovers much of this gap (mAP 0.2818, AMOTA 0.2195) but yields only marginal gains, in mapping mAP (0.4167 vs. 0,4103 at K 5). The near- near-parity between K = 5 and suggests redundant information beyond five checkpoints. Balancing performance and memory fingerprint, we thus adopt K in all experiments.
Impact on Dimension of Random Projection. We additionally examine the effect of varying the random projection dimension d' among (256. 512, 1024, 2048). As Table 5) shows, d = 256, the performance is only slightly below that of d' = 1024 (mAP 0.2749 vs. 0.2851; NDS 0.4176 vs. 0.4264). demonstrating that very compact fingerprints still capture most of the critical variability. At d' = 2048, results nearly match the d = 1024 but at twice the memory cost. Therefore, d' = 1024 offers the best trade-off between performance and fingerprint.
Efficiency Analysis.
We further compare the GPU memory and total TTA run- the SECOND and SparseDrive detectors.
time of CodeMerge against MOS both As reported in Table 66 with SECOND, MOS consumes 17.4 GiB and requires 1,813 seconds per adaptation run, whereas Code:erge uses only 16.0 GiB (-8.0%) and completes in 1,054 seconds (-11.%%). The savings are even more pronounced on SparseDrive: MOS demands 39.4 GiB and 37,619 seconds, while CodeMerge needs just 29.8 GiB (-21.0%) and 27,359 seconds (-37.48%), ). These SparseDrive gains arise from our fingerprint-guide guided merging, which projects each checkpoint into compact embedding and computes leverage weights on the fly (requiring only one extra forward pass), rather than loading and forwarding K full models as MOS does. This design drastically reduces memory footprint and latency, making CodeMerge well-suited for real-time autonomous driving applications.
Table 6: GPU memory (MiB) and total TTA runtime (s) for a single TTA run.
Model
Method
GPU Memory
Runtime
MOS
17,411
1,813
SECOND
Ours
16,041
1,054
MOS
39,420
37,619
Ours
29,868
27,359
Quantitative Analysis. We visualize predictions with CodeMerge (top row) against the non-adapted SpivseDrive bascline (bottom row) in Fig. 4oo illustrate how on-the-fly merging enhances every stage of the end-to-end pipeline. In detection and tracking, CodeMerge produces tight, correctly aligned 3D boxes, but the baseline suffers a large number of missed or misplaced detections (highlighted in red dashed circles). In mapping, our method reconstructs dense, straight-lane boundaries and curb lines, validating its ability to preserve semantic consistency. In contrast, the baseline yields sparse, crooked lanes and missing curbs (highlighted in purple circles/arrows), degrading map fidelity. Finally, MorgMerge's 's planned trajectory remains centered in the lane and safely avoids dynamic objects, while the baseline's 's path drifts toward the curb (highlighted in an orange arrow) and even intersects an oncoming track, demonstrating unsafe behavior. In summary, these qualitative results confirm that

---

leveraging compact fingerprints and leverage-scone scorr--uidedd guided merging yields better detections, more robust tracking, and safer trajectories under severe real-world corruptions.
Conclusion
In this work, we address the challenge of online adaptation to domain shifts for both HaDDR--basede and vision-centric end-to-end AD detection under extreme conditions. Our proposed CodeMerge frame- work effectively mitigates cross-dataset and corruption-induced distribution shifts, while reducing GPU memory consumption and inference latency by approximately 27% compared to state-of-the-art TTA methods. Notably, other downstream modules, such as mapping and planning, receive perfor- mance improvements without task-specifi fine-tuning due to enhanced detection outputs. However, this study represents early attempt address robustness in end-to-end AD, and major experiments have been primarily conducted on the SparseDrive architecture. The primary bottleneck remains that popular architectures, such UniAD and VAD, experience over tenfold performance degradation nuScenes-C, hindering effective adaptation training. Future work will investigate strategies to further accelerate adaptation and enhance robustness under dynamic driving conditions.
6 Acknowledgements
This work was partially supported by ARC DE240100105, DP240101814, DP230101196. DP230101753, BA24006, DE250100363, IH230100013.
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A Technical Appendices and Supplementary Material
We include additional technical details in the following appendices:
Section A.I
Describes the full experimental setup, including training schedules and hyperparameter configurations.
(Implementation Details):
Section A.2
(Evaluation Metrics): Provides definitions and explanations for all evaluation
metrics used across detection, tracking, mapping, and planning tasks.
(Additional Visualizations); Presents qualitative results and visual compar- isons illustrating the adaptation performance of the end-to-end AD system under various distribution shifts.
Section A.3
Section A.4
(Related Work): Summarizes the relevant literature in TTA and model merging.
Section A.5
Runtime): Reports the computational cost and per-frame latency of each TTA
step.
Section A.6 (Random Projection): Evaluates the determinism and robustness of our random
projection strategy.
Section
(Fingerprint Generation): Demonstrates that our fingerprint generation strategy significantly outperforms alternative methods.
A.7
Section A.8 3D Applicability Analysis): Assesses CodeMerge across other detectors beyond SECOND, confirming its strong model-agnostic applicability.
Section A.9 (3D Robustness Analysis): Analyses the robustness of CodeMerge heavy
distribution shift occurs (e.g., Fog & LiDAR dropout).
Section A.11 (Backbone Adapted Analysis): Validates robustness performance when adap- tation is applied solely to the backbone.
Section A.11 End-to-end Generalizability Analysis): Verifies CodeMerge's extensibility and transferability across diverse end-to-end architectures.
A.1 Implementation Details
For the end-to-end autonomous driving task, we employ ResNet50 17 the backhone network to uniformly process image data from both nuScenes and nuScenes-C [53] datasets. All input images are resized to 256x704. We use 900x256 instance query as input to the transformer layers. Our optimization strategy utilizes the AdamW optimizer, configured with weight decay of 0.001 and an initial learning rate of x 10- T, To balance computational efficiency and prediction accuracy, apply a random projection module to reduce the dimensionality of query features extracted from the pretrained model, resulting in compact 1024-dimensional feature vector, and manage predictions through a model bank with a limited capacity of five models. Through self-supervised training on detection and tracking heads, the model accurately predicts ten classes as well the associated instance IDs. For the point cloud detection tasks, we adopt the SECOND (55) as our pretrained model. We configure the training with batch size of 8, learning rate of 0.01, and weight decay of 0.01. Additionally, we utilize a 900x256 dimensional 3D feature vector as input to the leverage module, enabling efficient and effective model merging.
A.2 Evaluation Metries in End-to-End AD
We follow standard evaluation protocols to assess each task module for end-to-end AD system. Detection Metrics. We use nuScenes metrics, including mean Average Precision (mAP) and five error-based scores: mean Average Translation Error (mATE), Scale Error (mASE), Orientation Error (mAOE), Velocity Error (mAVE), and Attribute Error (mAAE). Together, they evaluate spatial, geometric, and semantic aspects of 3D box predictions. The nuScenes Detection Score (NDS) aggregates these metrics into a single score for holistic performance evaluation.
Tracking Metrics. Tracking performance is measured using Average Multi-Objee Tracking Ac- curacy (AMOTA), Precision (AMOTP), and Recall. These metrics capture association quality, localization precision, and coverage of tracked instances.
15

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Online Mapping Metrics.
We compute class-wiss wise Average Precision (AP) for static map elements (e.g., lane dividers, crossings, road boundaries) and report mean AP across categories to reflect mapping accuracy and consistency.
Motion Prediction Metrics.
We evaluate prediction with best-of-X K trajectory metries: minimum Average Displacement Error (minADE), minimum Final Displacement Error (minFDE), and Miss Rate (MR). We also report End-to-end Prediction Accuracy (EPA), which reflects cascading errors across detection, tracking, and forecasting stages.
Planning Metrics. We assess planning quality using two key indicators: collision rate, which measures the frequency of collisions during trajectory execution, and L2 distance to goal, which quantifies the Euclidean distance between the final position and the intended goal. Together, these metrics reflect the safety and goal-reaching accuracy of the planned motion.
A.3 More Visualizations
In Fig. 5 we present additional visualized predictions from both the non-adapted SparseDrive and the DrirseDrive model adapted at test-time using the proposed CodeMerge, illustrating performance across a broader range of corruptions.
A.4 Related Work
Test-time adaptation (TTA) dynamically updates models during deployment to mitigate dis- tribution shifts [26] [5]]. Early approaches primarily focus tuning BatchNorm layers via entropy minimization, adaptive moment estimation, global statistic alignment, or loss land- Subsequent methods explore self-training with 60), feature-level consistency or contrastive regulariza- robustness through data augmentation 62 42 and leveraging guidance from language models [23]. Extending TTA to more challenging perception tasks (e.g., image- or
scape smoothing
44 31
59
63
34
14
33
confidence-fillen filtered pseudo-labels
15
30,
tion 5
22 47
38
64
49).
LiDAR-based object detection 28 29 77, MemCLR aligns 2D detector features using memory- augmented teacher-student framework 43): DPO stabilizes MiDAR-based detection via dual per- turbation optimization [9) Reg-TTA3D generates noise-consistent pseudo-labels to supervise low- confidence 3D boxes using high-confidence ones [58]; and MOS enhances adaptation stability by dynamically merging the top-K diverse checkpoints for supervision [8] Despite their effectiveness. existing methods adapt only perception tasks, while adapting unified end-to-end autonomous driving systems test time remains unexplored.
Model Merging studies how weight-space operations can effectively compose, refine, or repair vision models through checkpoint averaging, gradient matching, arithmetic edits to task-specific weight vectors 32
I 54]. Recent literature highlights the effectiveness of these merg- ing techniques in enhancing generalization across tasks such as zero-shot learning [52), open-set learning (35) domain adaptation and generalization [36] [_ and cross-domain tasks involving 3D LiDAR point clouds (20) g. In this work, we build upon the strengths of model merging techniques to enable efficient, on-the-fly adaptation within end-to end-to-end autonomous driving pipelines.
51 10
88
A.5 Computational cost of each TTA step
The runtime reported in Table 6lis the total time aggregated over all adaptation steps across the entire test set. Below, we report GPU memory usage and per-frame latency alongside AP3d with SECOND on the nuScenes KITTI transfer in Table
Table 7: Comparison of different test-time adaptation methods on GPU memory usage, runtime per
frame, and 3D detection performance (APı)).
Method GPU Memory (MiB)
Runtime (s / Frame)
AP3
TENT
10,832
0..6
18.83
СOТTA
15,099
0.15
47.61
MOS
17,411
0.49
51.11
Code
16,041
0.28
58.54
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CodeMerge
No Adaption
Right
CodeMerge
Do Mtrig
Oo Morgll
No Adaption
No Mriggg
Mari
CodeMerge
Stright
No Adaption
Straggh
Figure 5: Visualization of outputs of Drivse (bottom) and after CodeMerge adaptation (upper) under severe ColorQuant, LowLight, and Snow. TTA greatly improves detection by capturing more true positive instances.
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Table 8: Robustness under different random-projection seeds (RP1-RP3). Small variations across perception, tracking, mapping, motion prediction, and planning indicate minimal sensitivity to
projection randomness.
3D OBJECT DETECTION
MULTI-ORJECT TRACKING
Seed
mAPt
NDST
mATE!
mASE!
mAOR
mAVE,
mAAE,
AMOTA1
AMOTPL
Recalff
IDSL
RPI
0.3735
0.4946
0.6137
0,2786
0.5468
0,2935
0.1892
0.3347
1,3359
0,4714
869
RP2
0.3693
0,4935
0.6149
0,2786
0,5353
0.2925
0.1897
0.3319
1,3572
0,4756
1256
RP3
0.3630
0.4920
0.6068
0.2764
0.5315
0,2837
0.1962
0.3252
1.3411
0.4553
892
ONLINE MAPPING
MOTION PREDICTION
PLANNING
Seed
APped 1
APa
AP, 1
mAPt
mADE
MFDE4
MRJ
EPAT
L2-Avgl
CL2
RPI
0.4305
0.5224
0.5398
0.4976
0.6504
1.122
0.1392
0,4680
0.6209
0.094%
RP2
0.4229
0.5172
0.5349
0.4917
0.6415
0,9964
0.1362
0,4682
0.6219
0.110%
0.4106
0.5109
0.5198
0.4804
0.6406
0.9948
0.1356
0.4660
0.6255
0.125%
Speed comparison. Compared with MOS [8] CodeMerge reduces per-frame latency by ~43%
(0.49 -> 0.28 s) while improving 3D by + +14.5% %. Relative to TENT [44], it yields +210.9 %
AP3D gain at essentially the same latency (0.26 vs. 0.28 s per frame). Compared with CoTTA 46 CodeMerge adds only +0.13s per frame yet improves AP3D by +22.9%.
Memory comparison. CodeMerge uses less GPU memory than MOS (16,041 vs. 17,411 MiB) and slightly more than CoTTA (15,099 MiB), while achieving the highest AP among all methods. Compared with TENT, requires +5,209 MiB (-5.1 GiB) but maintains comparable runtime and delivers markedly larger performance gains (+210.9%)
Overall, CodeMerge offers strong accuracy-efficiency trade-off's on SECOND (55) for the nuScenes + KITTI setting, it achieves the highest APıD with competitive latency and memory, making it practical for real-time test-time adaptation (TTA) in dynamic deployment environments.
A.6 Random Projection Determinism and Robustness
We instantiate a fixed Gaussian random projection once before TTA and keep it frozen throughout adaptation. It is applied to the pretrained source feature extractor's intermediate activations @ (x) to produce low-dimensional fingerprints å (refer to Eq. (7). ensuring all checkpoints are embedded into shared, comparable space across time steps. Because both the fingerprinting features (from
the frozen feature extractor) and the projection matrix are fixed, this mapping is training-free and lightweight.
we verify robustness by resampling the projection matrix with three seeds (RP1- RP3) and re-running the full pipeline. The results vary only slightly: For perception, NDS(0.4920 + 0.4946) and mAP(0.3630) - 0.3735); For tracking, AMOTA - 0.3347); For planning, L2- Avg(0.62099 - 0.6255). These small ranges indicate that the inherent randomness of the projection has a minor impact on end-to-end AD performance.
As shown Table S
Discussion This stability is expected because (1) the projection is fixed Gaussian linear map instantiated once and kept frozen during TTA, applied to features from the fixed source encoder to produce low-dimensional fingerprints (Eq. 77), ensuring all checkpoints space; and (ii) the fingerprint space faithfully mirrors parameter-sppe space geometry (Fig. 3 Pearson and Kendall typically > 0.7), different random draws that preserve this linear embedding yield similar merge decisions. Overall, repeated experiments with different seeds cxhibit only minor fluctuations, consistent with our design rationale for stable, peneerty-preserving random projection.
embedded in shared
A.7 Alternative Fingerprint Generation Strategies
Lightweight pooling-hased strategies. We first investigate a suite of computationally efficient feature-aggregation approaches, such as MaxPool (37), AdaptiveAvgPool [16] Lp-Pooling (3) and Fractional-Max Pooling [6]]. While computationally lightweight, these approaches compress features into coarse statistics and underperform on the Waymo - KITTI transfer task. Our fixed
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Table 9: Comparison of fingerprint generation strategies. Random projection (RP) achieves the highest 3D on Waymo > KITTI, showing that simple pooling operations lose geometric fidelity required for reliable merging.
Method
RP
MaxPool
AdaptiveAvgPool
Lp-Pooling
Fractional-Max
AD3D
63.23
58.58
60.34
61.40
61.78
Table 10: TTA with DSVT on Waymo KITTI. CodeMerge yields the best AP across both BEV and 3D metrics, indicating strong transferability beyond SECOND.
TTA Methods (w. DSVT)
BEYT t
AP3D
No Adapt
65.06
27.14
Tent
63.94
31.07
CoTTA
66.63
34.51
SAR
66.12
37.45
DPO MOS
75,46
45.06
77.38
57.41
CodeMerge
79.88
61.06
Gaussian Random Projection (RP) achieves AP3 = 63.23, outperforming the pooling variants by +4.6%, +2.88% +1.82% and +1 1.44%, respectively in Table This result suggests that simple pooling loses the discriminative structure required for behavior-aware merging.
Learning-based dimensionality reduction methods. We also consider learning braningg-asedd based techniques such as PCA (2I). which requires fitting or updating decompositions on streaming features. However, these incur significant computational overhead at every TTA step, making them incompatible with our single-pass, real-time adaptation setting.
Pooling is high-throughput yet discards geometry and discriminative structure, while PCA-like meth- ods impose computational costs incompatible with real-time operation. Our fixed random projection preserves model-space geometry, satisfies real-time efficiency requirements, and empirically delivers the strongest accuracy among all tested strategies.
A.8 Applicability beyond SECOND
To assess generality, we further evaluate Code on DSVT 455 under the Waymo KITTI transfer in Table TO CodeMerge attains 79.88 APBEY / 61.06 APıD. outperforming the strongest baseline MOS S by +3.2% % and +6.4%, respectively, and surpassing No Adapt by +125% in AP3D- Because CodeMerge builds on training-free random projectpoooection fingerprints and codebook-guided merging rather than architecture-specffi modules, these results indicate our TTA model-agnostic and transfers effectively to detectors beyond SECOND.
A.9 Robustness under Fog & LiDAR Dropout
We conduct additional experiments adapting SECOND from KITTI -> KITTI-C under heavy fog with increasing point dropout (0% 25% - 50%). We report AP at the moderate level in Table [T] With stronger dropout, performance degrades gradually rather than catastrophically: AP drops from 75.96 - 73.87 + 71.95 (-4.01% % 25.3% relative), and AP from 88. 16 85.73 85.04 12%, ~3.5% relative). This smooth, monotonic decline suggests that our checkpoint merging remains reliable even under compounded shifts.
A.10 Backbone-only Adaptation
End-to-end AD framework. To verify the effect of merging and keep TTA lightweight, we freeze all modules except the detection head. As shown Table [2] we additionally enable backbone (ResNet) adaptation and observe negligible deltas relative to head-only TTA (e.g. NDS -0.0034, mAP -0.0055, AMOTA -0.0028, map mAP -0.0097, 1.2-Avg -0.0023), indicating that CodeMerge remains effective even when only the backbone is updated.
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Table 11: Fog & LiDAR point-dropout robustness on KITTI KITTI-C (moderate). Perfor- mance decays smoothly as dropout increases, indicating stable ranking/merginn.
Dropout Ratio
0%
25 %
50% %
AP 1 APREV f
75.96
73.87
71,95
88.16
85.73
85.04
Table 12: Head-only vs. backbone-adapted TTA. Enabling ResNet backbone adaptation yields only minor differences, indicating CodeMerge remains effective with backbone updates.
ColorQuant
OujEcT
MULI-OBJRUTT TRACKING
ND8
mATE]
mAVE
AMOTA1
Recall
CudeMerge del head
0.2742
0:4331
0,6575
0,2764
0.5903 0.3906
0,3018 0,3092
021377
0.2339
1.4868 144799
0.3330
CodeMerge ResNet
0,2637
0,4297
0.6574
0,2778
021119
023111
0.3468
MAPPING
MOTION PREDICTION
ColorQuant
APpe
APa
AP,
mAP
MADE!
mFDE4
MRL
EPAT
L2-Avgl
CL.
CodeMerge det head
0,2600
0.3445
0.3267
0.3104
0.7002
1,0899
0.1454
0,3840
0.6729
0.106%
CodeMerge- + ResNet
0.2407
0.3452
0.3161
0,3007
0,7087
1.0930
0.1453
0,3793
0.6752
0.102%
3D detection framework. Following prior works g 9 we also consider adapting the entire backbone (encoder, detection head, and all batch-norm layers). These studies have shown that, for test-time adaptation in 3D detection, full-backbone adaptation typically surpasses BN-only updates; CodeMerge fits seamlessly into this setting as well.
A.11
Generalizability Beyond SparseDrive
Early end-to-end models under corruption. Some previous end-to-end architectures (e.g.. VAD [9 collapse corrupted scenes even before any TTA is applied. Under heavy snow, VAD attains only 0.0168 mAP and 0.0353 NDS for detection in Table 13 indicating that the base model's predictions already unreliable and nearly collapsed. In this regime, online self-supervision becomes untrustworthy, so any TTA method has little signal to learn from. This reflects robustness limitation of the base model, not of TTA or CodeMerge. Even so, attaching CodeMerge to VAD yields small but consistent gains across perception and planning, and does not degrade VAD's performance, underscoring that the bottleneck lies in model brittleness rather than our adaptation mechanism.
Table 13: VAD under Snow (severe). Detection collapses pre-TTA: CodeMerge yields small but consistent improvements without degradation.
DETECTION
MULTI-ORECT TRACKING
Method
mAPt
mATE,
mASE)
mAOEL
mAAEJ
AMOTA!
IDS
0.9091
0,8577
09713
VoDeredeeerge
0.176
0.0366
0.9699
0.876
0.7199
1.1019
0.337
Method
ONLINE MAPPING
MAP
MOTIOS PREDICTION
PLANNING
APp
AP
AP,
mAPt
minADE:
minFDR]
MR.
KPAT
1.2.agg
CL.
VAD
0,0006386
0,00141679
0.0002426
0.0007830
2.3891
3.7662
0.3022
0.0179
16630
0.01320
VAD-CnuieMerge
0.00057100
0.0015413
0L000E734
0.0007949
2.3265
3 66445
0.2974
0.0119
1.6057
0.01307
Recent architectures with non-collapsed baselines. When the backbone remains minimally com- petent under corruption, CodeMerge is highly effective and broadly applicable. On DrivesionDrive [77 and MomAD [39], CodeMerge delivers consistent improvements across detection, tracking, map- ping, motion, and planning, and reduces collision rate under both Brightness and Snow corruptions, indicating scalability beyond SparseDrive.
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Table 14: Brightness corruption (severe). CodeMerge improves DiffusionDrive and MomAD across modules and reduces collision (CL).
Method
3D OBJECt DETECTION
MUNTI-ORJECY TRACKING
mAPT
NDS1
mATE!
mASE)
mADEL
mAVEL
mAAE]
AMOTAT
AMOTFI
Reallt
IDS.
DiffiiionDrive
0.3278
0.4588
0.6602
0.2782
0.673
0.3005
0.1851
0,2818
14334
0.4766
Diffesientrrivs-CooieMeee Covleegge
0.3580
0.4855
0,620
0.2447
0.5767
0.2811
0.1916
0,3150
13520
04638
873
MomAD
0.3300
0,4211
0.6266
0.2334
0.3931
0.2900
0,1747
0.2944
14241
0.101
731
Method
ONLINE MAPPING
MAP
MOTION PREDICTION
PLANNING
APp4
12-Avgl
0,4125
0.5162
0.5333
1,0261
0.1380
0.5919
0.062%
0.3157
8,500%
MomAD=CCodeMen
0,2755
0.1976
0.3365
0.2699
1.1330
2.2389
0.2211
04147
7,3171
8438%
Table 15: Snow corruption (severe). CodeMerge improves DiffusionDrive and reduces collision; MomAD also benefits across modules.
Method
3D OajECT DETECTION
MULTI--ORECT TRACKING
mAPt
NDS?
IATEL
RADEL
IAAVEL
AMOTAT
AMOTPI
Recall
IDS,
DiffinsonDrive
0.2373
0.7811
0.0503
18648
0.1064
0.1797
0.3492
0.730
0.2956
D/6633
0.4221
0.3424
0.1078
13171
011KK
681
Method
ONLINE MAPPING
MOTTON PREDICTION
PLANNING
APp 1
AP3 1
AP;
mAP1
MADE
mFDE
MR.
RPA1
1.2-vggl
CL.
DiffusionDrivve
0.0106
0.0426
0.0512
0.0348
LO603
1,3077
0.1951
0.2063
0.9300
0.447%
DiffusionDrrve
0.1010
0.1713
0.1688
0.1471
0.8086
1,2838
0.1730
0.3070
0.7730
0.215%
21

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Reviews:
[{"review_id": "OGx4G75Puc", "reviewer": "Reviewer_XHzU", "summary": "Robust 3D perception under unpredictable conditions is a key challenge for autonomous driving, but existing test-time adaptation methods struggle with instability in high-variance tasks like 3D object detection. While model merging approaches based on linear mode connectivity improve stability, they are computationally heavy due to repeated checkpoint access and multiple forward passes. This paper introduces CodeMerge, a lightweight framework that merges models in a compact latent space using low-dimensional fingerprints and a key-value codebook, enabling efficient model composition with minimal overhead. CodeMerge achieves strong results across benchmarks—improving 3D detection by 14.9% NDS on nuScenes-C and over 7.6% mAP on LiDAR-based detection.", "questions": "Please refer to the Weaknesses.", "limitations": "The authors provide one sentence in the Conlusion. But I don't think it is sufficient.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. This paper is well written and easy to follow. The motivation is clear and reasonable.\n\n2. The performances are good, improving 3D detection by 14.9% NDS on nuScenes-C and over 7.6% mAP on LiDAR-based detection.\n\n3. The experimental results are extensive. Plenty ablation and sensitivity studies are provided which are convincing.\n\nWeaknesses:\n\n1. The authors claim that \"Code and pretrained models are released in the supplementary material.\" In abstract but I can not find them.", "was_revised": true, "final_justification": "The quality of the paper is quite good and the authors address my concerns and I keep my initial rating.", "rebuttal": "# Response to Reviewer XHzU \n> W1 - Code availability and pretrained models.\n\n\n**Code availability.** Thank you for pointing this out. The **source code was included in the Supplementary Material** as a ZIP archive attached to the submission. \n\n**Pretrained models.** The **pretrained and adapted checkpoints** for the end‑to‑end models and the 3D detectors are **not uploaded** with the submission due to file‑size limits. We will **release all checkpoints publicly** upon acceptance together with a reproducible repository that includes experiment configs, logs, and instructions for all experiments."}, {"review_id": "S6FsEigXXp", "reviewer": "Reviewer_jES4", "summary": "This paper proposes a novel model merge based TTA method for LiDAR 3D object detection and E2E AD models. In the proposed CodeMerge approach, previously extracted features and corresponding checkpoints are stored. At each time step, the features of these checkpoints are projected into low-dimensional features, fingerprints. RLS are then computed based on these fingerprints to guide the model merging process. Compared to prior model merge based TTA methods such as MOS, this approach demonstrates improved efficiency and performance. The effectiveness of the method is validated across multiple settings, including cross-dataset and clean-to-corruption adaptation.", "questions": "1. Have alternative fingerprint generation strategies been considered?\n2. Can the proposed TTA method be applied to 3D object detection models other than SECOND?\n3. Since the fingerprint is generated using features extracted from a fixed encoder given the i-th input, can it reliably serve as a key of the model parameters themselves?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "[ Strengths ]\n1. The proposed fingerprint-based model merging method resolves the scalability limitations of prior model merging-based method, which is computationally expensive.\n2. The method is evaluated not only for LiDAR-based 3D object detection but also for end-to-end autonomous driving tasks, demonstrating its effectiveness as a TTA method across various application domains. This broadens its potential applicability.\n\n[ Weaknesses ] \n1. Regarding the efficiency analysis, does the reported runtime refer to a single adaptation step? If so, it would be helpful to specify the inference time for SECOND and computational burden introduced by each TTA step. Including runtime comparisons with other baselines such as TENT, CoTTA, and DUA would provide further evidence of the practicality of the proposed method.\n2. The paper employs random projection for generating fingerprints. Is there a rationale behind using random projections? Why are these projections redefined at each time step? Does the inherent randomness introduce performance variance? It would be helpful to clarify whether the method is robust across repeated experiments.", "was_revised": true, "final_justification": "The authors have thoroughly addressed all the concerns raised in the rebuttal. My doubts regarding the efficiency and rationale of the proposed methodology have been fully resolved. Therefore, I am updating my final rating.", "rebuttal": "# Response to Reviewer jES4\n\n> W1 - Computational cost of each TTA step and comparison with baselines on SECOND\n\nThank you for your valuable comments! The runtime in **Table 6** of our paper is **the total time aggregated over all adaptation steps across the full test set**. Below we report GPU memory and per-frame latency with **AP 3D** for **SECOND on nuScenes→KITTI**.\n\n| Method        | GPU Memory (MiB) | Runtime (Seconds / Frame) | AP 3D     |\n| ------------- | ---------------- | ------------------------- | --------- |\n| TENT          | 10,832           | 0.26                      | 18.83     |\n| CoTTA         | 15,099           | 0.15                      | 47.61     |\n| MOS           | 17,411           | 0.49                      | 51.11     |\n| **CodeMerge** | 16,041           | 0.28                      | **58.54** |\n\n**Speed comparison.**\nCompared with **MOS** [A], CodeMerge cuts latency by **\\~43%** (0.49→0.28 s) and raises **AP 3D** by **+14.5%**. Relative to **TENT**, **+210.9%** AP 3D at similar latency (0.26 vs. 0.28 s). Compared with **CoTTA**, only **+0.13 s/frame** yet **+22.9%** AP 3D.\n\n**Memory comparison.**\nCodeMerge uses **less memory than MOS** (16,041 vs. 17,411 MiB) and **slightly more than CoTTA** (15,099 MiB), while achieving the **highest AP 3D**. Compared with **TENT**, it needs **+5,209 MiB (\\~5.1 GiB)** but keeps similar runtime and delivers significant AP gain: **+210.9%**.\n\nOverall, CodeMerge offers **the best accuracy–efficiency trade-off** on SECOND for nuScenes→KITTI: **highest AP 3D** with **competitive latency and memory**, suitable for real-time TTA.\n\n> W2.1 - Rationale behind random projections.\n\n**Efficiency.**\nRandom projection primarily improves memory and runtime. We use a fixed Gaussian projection to compress intermediate features into compact “fingerprints” (262,144→1,024), stored in a codebook to compute merge weights. This training-free step keeps memory/compute proportional to fingerprint dimension (not full model size) and avoids loading/forwarding K past models (as MOS), yielding markedly lower latency and memory in practice (e.g., –41.8% runtime and –8% memory vs. MOS on SECOND).\n\n**Fidelity.**\nDespite compression, the fingerprint space preserves model-space geometry [B]; pairwise fingerprint differences correlate strongly with parameter differences across architectures/datasets (Pearson 𝑟, Kendall τ>0.7). Our ridge-leverage scoring in fingerprint space links to inverse curvature in parameter space, justifying that these low-dimensional features carry the information needed for reliable checkpoint selection.\n\n**Robustness.** Ablations (Table 5 of our paper) over projection dimension ($d' \\in {256,512,1024,2048}$) show small variation, with \\$d'=1024\\$ the best accuracy–efficiency trade-off.\n\nIn summary, random projections give a simple, training-free, compute-efficient way to summarize checkpoints while retaining the structure needed for stable, effective test-time merging.\n\n[A] Chen, etc. MOS: Model Synergy for Test-Time Adaptation on LiDAR-Based 3D Object Detection. ICLR 2025\n\n[B] GONG, etc. Random Projections and Pre-trained Models for Continual Learning.  NeurlPS 2023\n\n> W2.2 - Redefining projections at each time step?\n\nNo. We **instantiate a fixed Gaussian random projection once** before TTA and keep it **frozen**. It is applied to the **pretrained source feature extractor’s** intermediate activations \\$\\phi\\_{\\Theta^{(0)}}(x)\\$ to produce low-dimensional fingerprints \\$\\hat z\\$ (Eq. 7 of our paper), embedding all checkpoints into a **shared** space across time. Because both the fingerprinting features (from the frozen extractor) and the projection are fixed, this mapping is **training-free** and **lightweight**.\n\n> W2.3 - Does the inherent randomness introduce performance variance?\n\nWe verify robustness by resampling the projection with three seeds (RP1–RP3) and re-running the full pipeline. The results vary slightly: Perception **NDS = 0.4920–0.4946**, **mAP = 0.3630–0.3735**; Tracking **AMOTA = 0.3252–0.3347**; Planning **L2-Avg = 0.6209–0.6255**. These small ranges indicate **minor impact** on end-to-end performance.\n\n| Method | mAP    | NDS    | mATE   | mASE   | mAOE   | mAVE   | mAAE   | AMOTA  | AMOTP  | RECALL | IDS  | APped  | APdivider | APboundry | mAP    | minADE | minFDE | MR     | EPA    | L2-Avg | CL     |\n| ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ---- | ------ | --------- | --------- | ------ | ------ | ------ | ------ | ------ | ------ | ------ |\n| RP1    | 0.3735 | 0.4946 | 0.6137 | 0.2786 | 0.5468 | 0.2935 | 0.1892 | 0.3347 | 1.3359 | 0.4714 | 869  | 0.4305 | 0.5224    | 0.5398    | 0.4976 | 0.6504 | 1.0122 | 0.1392 | 0.4680 | 0.6209 | 0.094% |\n| RP2    | 0.3693 | 0.4935 | 0.6149 | 0.2786 | 0.5353 | 0.2925 | 0.1897 | 0.3319 | 1.3372 | 0.4756 | 1256 | 0.4229 | 0.5172    | 0.5349    | 0.4917 | 0.6415 | 0.9964 | 0.1362 | 0.4682 | 0.6219 | 0.11%  |\n| RP3    | 0.3630 | 0.4920 | 0.6068 | 0.2764 | 0.5315 | 0.2837 | 0.1962 | 0.3252 | 1.3411 | 0.4553 | 892  | 0.4106 | 0.5109    | 0.5198    | 0.4804 | 0.6406 | 0.9948 | 0.1356 | 0.4660 | 0.6255 | 0.125% |\n\nThis stability is expected because (i) the projection is a **fixed Gaussian linear map** instantiated once and kept **frozen** during TTA, applied to features from the **fixed source encoder** to produce low-dimensional fingerprints (Eq. 7 of our paper), embedding all checkpoints in a shared space; (ii) the fingerprint space **faithfully mirrors** parameter-space geometry (Figure 3 of our paper: Pearson \\$r\\$ and Kendall \\$\\tau\\$ typically \\$>0.7\\$), so random draws that preserve this embedding yield similar merge decisions.\n\nOverall, repeated runs with different seeds show only minor fluctuations, consistent with our design for a stable, geometry-preserving random projection.\n\n> Q1 - Alternative fingerprint generation strategies?\n\n**Start with lightweight strategies.** We thank you for your constructive suggestions. We have incorporated efficient feature aggregations: MaxPool [C], AdaptiveAvgPool [D], Lp-Pooling [E], and Fractional-Max [F]. Although inexpensive, these compress features to coarse statistics and underperform on Waymo→KITTI; our random projection (RP) achieves \\$AP\\_{3D}=63.23\\$ vs. 58.58/60.34/61.40/61.78 for pooling (gains +4.6%, +2.88%, +1.82%, +1.44%). This suggests simple pooling **loses the discriminative structure** needed for behavior-aware merging.\n\n| Method   | RP  | Maxpool | AdaptiveAvgPool | Lp-Pooling | Fractional-Max Pooling |\n| ------------ | ------- | ------- | --------------- | ---------- | ---------------------- |\n| \\$AP\\_{3D}\\$ | 63.2258 | 58.5847 | 60.3375         | 61.4017    | 61.7825                |\n\n[C] Scherer, etc. Evaluation of pooling operations in convolutional architectures for object recognition. ICANN 2010\n\n[D] He, etc. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. ECCV 2014\n\n[E] Boureau, etc. A theoretical analysis of feature pooling in visual recognition. ICML 2010\n\n[F] Zhai, etc. Pooling With Stochastic Spatial Sampling. CVPR 2017\n\n**Heavier dimensionality reduction isn’t practical online.** Methods like PCA require fitting (or updating) decompositions on streaming features, adding substantial compute at **every** TTA step, which is **incompatible** with our single-pass, real-time constraint.\n\nIn summary, pooling is fast but loses geometry/accuracy; PCA-like reductions are too costly per step. A fixed random projection preserves the geometry needed for robust merging while meeting real-time TTA efficiency, and empirically delivers the strongest performance in our comparisons.\n\n> Q2 - Applicability beyond SECOND\n\n| TTA Methods (w. DSVT) | AP BEV    | AP 3D     |\n| --------------------- | --------- | --------- |\n| No Adapt            | 65.06     | 27.14     |\n| Tent                  | 63.94     | 31.07     |\n| CoTTA              | 66.63     | 34.51     |\n| SAR                  | 66.12     | 37.45     |\n| DPO                  | 75.46     | 45.06     |\n| MOS                  | 77.38     | 57.41     |\n| **CodeMerge**    | **79.88** | **61.06** |\n\nTo assess generality, we evaluate **CodeMerge** on the recent 3D detector **DSVT** [G] for **Waymo → KITTI**. CodeMerge reaches **79.88 AP BEV / 61.06 AP 3D**, outperforming **MOS** by **3.2%** and **6.4%**, and  surpassing **No Adapt** by **125%** in AP 3D. Because CodeMerge builds on **training-free random-projection fingerprints** and **codebook-guided merging** rather than architecture-specific modules, these results indicate our TTA is **model-agnostic** and transfers to detectors beyond SECOND.\n\n[G] Wang, etc. DSVT: Dynamic Sparse Voxel Transformer with Rotated Sets. CVPR 2023\n\n> Q3 - Can fingerprints (from a fixed encoder on input \\$x\\_i\\$) reliably key the model parameters?\n\nYes. Empirically, **pairwise distances in fingerprint space track those in weight space** across datasets and models (Figure 3), with **Pearson \\$r\\$** and **Kendall \\$\\tau\\$** typically **>0.7**, indicating that the low-dimensional geometry preserves parameter-space structure and supports reliable merge decisions.\n\nTheoretically, under a ridge-regression surrogate for the linear detection head (with a fixed encoder), the ridge leverage score of a fingerprint is proportional to the **directional inverse curvature** \\$z\\_i^\\top H\\_w^{-1} z\\_i\\$, so computations in fingerprint space provide a curvature-aware proxy for selecting informative parameter directions (Eq. 9–10). This provides a principled basis for using fingerprints as keys to the parameters they represent.\n\nFinally, **resampling the projection** with different seeds (refer to W2.3) yields **minor performance variation**, further confirming that fingerprinting is **stable and robust** to projection randomness.\n\nOnce again, we sincerely appreciate your thoughtful review and constructive feedback! We will carefully incorporate these discussions into the revised manuscript."}, {"review_id": "SxesoG49Py", "reviewer": "Reviewer_G337", "summary": "The authors tackle test-time adaptation for 3D detection by compressing every intermediate fine-tuned checkpoint into a low-dimensional fingerprint. The compressed features are stored in a codebook and ranked by ridge-leverage scores. Then, the top-k weights are linearly merged. The experiments are conducted with both camera-based and LiDAR-based 3D detectors with various types of sensor degradations. Improved perception performance also benefits the downstream task, including mapping, motion prediction, and planning.", "questions": "Please see the above.", "limitations": "The authors have adequately addressed the limitations and potential negative societal impact of their work.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths\n---\n- The paper is generally well-written and easy to follow.\n- The proposed idea of replacing K forward passes with a projection-space distance is clear.\n- Experiments cover both camera-based and LiDAR-based 3D detectors with various types of degradation.\n\nWeaknesses\n---\n- If I understand correctly, CodeMerge still requires a buffer of previously fine-tuned checkpoints, which means it needs gradient-based updates, and the paper assumes the model can back-propagate per every frame (or every N frames) to grow the buffer. This may be impractical in a real scenario. Regarding this, how many gradient steps are executed per frame to produce each checkpoint? Please correct me if I misunderstood.\n- Lack of robustness analysis. Once a heavy distribution shift occurs (e.g., fog + LiDAR dropout), the model may fail to accurately rank.\n- Similar to the above, does CodeMerge work if the backbone itself is adapted?\n- It would be beneficial to include more discussion on domain-adaptation methods (e.g, SN, ST3D). From my understanding, ST3D and SN are offline domain-adaptation methods, and CodeMerge is evaluated after every test frame. What are pros and cons about the two different directions?\n- Could EMA be another baseline for merging in Table 5?\n\nAdditional Comments\n---\n- Line 251: Quantitative Analysis --> Qualitative Analysis", "was_revised": true, "final_justification": "I appreciate the author's enthusiastic rebuttal. All of my concerns have been addressed well. I will maintain my positive score, raising my confidence from 2 to 3, as I believe I'm not an active researcher working on this specific task. I am happy for further discussion if needed.", "rebuttal": "# Response to Reviewer G337\n\nThanks for your constructive and valuable feedback, which will greatly assist in enhancing our work!\n\n\n> W1 - Back-propagate through buffer? How many steps per frame?\n\nNo. **All stored checkpoints are frozen**. We never back‑propagate through past models. At step $t$, we (i) compute merging weights **in fingerprint space** and form a **sign‑consistent merged teacher** $\\bar{\\Theta}^{(t)}$ from the top‑$K$ buffered checkpoints, and (ii) use $\\bar{\\Theta}^{(t)}$ **only to generate pseudo‑labels** for the incoming batch. **Only the current model** $\\Theta^{(t)}$ is updated **once**; other checkpoints are used for merging **without** gradient updates. This avoids the $K$-forward‑pass overhead of MOS [A], which is why our runtime and memory are lower (refer to Table 6 in the paper and response to W1 of reviewer jES4).\n\n> W2 - Robustness under fog + LiDAR dropout\n\nWe conduct additional experiments adapting SECOND from KITTI to KITTI-C under **heavy fog** with increasing **point-dropout** (0%→25%→50%) and report the AP 3D and AP BEV at the moderate level in the table below. \n\n| Dropout Ratio   | 0%   | 25%    | 50%  | \n| ------  | ------ | ------ | ------ | \n| AP 3D    | 75.96   | 73.87   | 71.95   | \n| AP BEV   | 88.16   | 85.73   |  85.04  |\n\nWith increasing **point-dropout**, performance degrades **gradually** rather than catastrophically: **AP 3D** drops from 75.96→73.87→71.95 (–4.01 %, ≈5.3% relative), and **AP BEV** from 88.16→85.73→85.04 (–3.12 %, ≈3.5% relative). This smooth, monotonic decline suggests that our checkpoint **ranking/merging remains reliable** even under challenging compounded shifts.\n\n\n> W3 - Does CodeMerge still work when the backbone is adapted?\n\n**E2E model:** In the paper’s end‑to‑end setting, we **froze all components except the 3D box head** to isolate the effect of merging and keep TTA lightweight. We conduct additional experiments where **feature extraction backbone (ResNet)** is also adapted and observe **negligible deltas** relative to head‑only TTA (e.g., NDS −0.0034, mAP −0.0055, AMOTA −0.0028, map mAP −0.0097, L2‑Avg −0.0023), indicating that CodeMerge remains effective when the backbone itself is updated. Furthermore, we have evaluated the effectiveness of CodeMerge by changing to different backbone models (see Reviewer hVAv, W1 & Q1), demonstrating the method’s broad applicability.\n\n|     ColorQuant               | mAP↑   | NDS↑   | mATE↓  | mASE↓  | mAOE↓  | mAVE↓  | mAAE↓  | AMOTA↑  | AMOTP↓ | RECALL↑ | IDS↓  | APped↑ | APdivider↑ | APboundry↑ | mAP↑   | minADE↓ | minFDE↓ | MR↓    | EPA↑   | L2-Avg↓ | CL↓     |\n| ------------------------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------- | ------ | ------- | ----- | ------ | ---------- | ---------- | ------ | ------- | ------- | ------ | ------ | ------- | ------- |\n| CodeMerge+det head       | 0.2742 | 0.4331 | 0.6575 | 0.2764 | 0.5903 | 0.3018 | 0.2137 | 0.2339  | 1.4868 | 0.333  | 490   | 0.260 | 0.3445     | 0.3267     | 0.3104 | 0.7002  | 1.0859  | 0.1454 | 0.384 | 0.6729  | 0.106%  |\n| CodeMerge+ResNet     | 0.2687 | 0.4297 | 0.6574 | 0.2778 | 0.5906 | 0.3092 | 0.2119 | 0.2311  | 1.4799 | 0.3468  | 510   | 0.2407 | 0.3452     | 0.3161     | 0.3007 | 0.7087  | 1.0930  | 0.1453 | 0.3793 | 0.6752  | 0.102%  |\n\n**3D detection model:** We simply follow prior works [A, B] to adapt the entire backbone (encoder, detection head, and all batch‑norm layers) as these works have already shown that, for test-time adaptation in 3D detection, adapting the full backbone leads to superior performance than adapting batch norm layers only.\n\n[A] Chen, etc. MOS: Model Synergy for Test-Time Adaptation on LiDAR-Based 3D Object Detection. ICLR 2025\n\n[B] Chen, etc. DPO: Dual-Perturbation Optimization for Test-time Adaptation in 3D Object Detection. ACMM 2024\n\n> W4 — Offline UDA (SN, ST3D) vs. online TTA (CodeMerge): pros & cons\n\n**Different Assumptions and Settings**\n\n**UDA (SN, ST3D):** has **offline access to unlabeled target data** and retrains the model for **multiple epochs** with self‑training / distribution alignment (e.g., ST3D), or target‑size statistics (SN). This typically yields strong target‑specific models **when the target distribution is relatively stationary**, but incurs heavy computation and latency before deployment.\n\n**TTA (CodeMerge):** adapts **online at inference** on a stream of target frames. There is **no multi‑epoch retraining**. So each step is low-cost and fits real-time constraints.\n\n**Strengths & trade‑offs**\n\n**UDA (SN, ST3D):**\n- **Pros:** Can fully specialize to the target dataset with extensive training; strong performance when the deployment domain is fixed.\n- **Cons:** Requires **offline target data** and **multi‑epoch** computation; not reactive to **rapidly changing** in‑field dynamic conditions.\n\n**TTA (CodeMerge):**\n - **Pros:** **Immediate, frame‑by‑frame** adaptation suited to dynamic shifts; **efficient** (e.g., vs. MOS, –41.8% runtime and –8% memory on SECOND; even larger savings on SparseDrive). Robust improvements are observed on cross‑dataset and corruption benchmarks, often surpassing UDA baselines (e.g., > ST3D on nuScenes→KITTI). &#x20;\n  - **Cons:** No offline access to target data; improvements depend on **on‑the‑fly pseudo‑labels** and the diversity/quality of the checkpoint buffer, so substantial shifts may require several steps to accumulate useful checkpoints. (We mitigate this with geometry‑preserving fingerprints and curvature‑aware selection.)&#x20;\n\n> W5 - Merging baseline: EMA\n\nWe conducted additional experiments by replacing the merged model with the EMA model. On the same setting in Table 5 of the paper, **CodeMerge** (k=5, proj‑dim=1024) consistently outperforms **EMA** across all tasks: **Detection** mAP/NDS **+0.0373/+0.0272**; **Tracking** AMOTA **+0.0372** and AMOTP **−0.0810** (↓ better); **Mapping** mAP/APped **+0.0355/+0.0300**; **Motion** mADE/mFDE **−0.0147/−0.0301**; **Planning** L2‑Avg/CR‑Avg **−0.0274/−0.0160**. These gains indicate that our **fingerprint‑guided merging**, which selects complementary checkpoints, offers stronger generalization than the **uniform, time‑local smoothing** performed by EMA.\n\n\n| Method | K   | Proj.-D | Detection      |          | Tracking       |          | Mapping        |          | Motion         |          | Planning        |          |\n|--------|-----|---------|----------------|----------|----------------|----------|----------------|----------|----------------|----------|-----------------|----------|\n|        |     |         | **mAP↑**       | **NDS↑** | **AMOTA↑**     | **AMOTP↓** | **mAP↑**       | **APped↑** | **mADE↓**       | **mFDE↓** | **L2-Avg↓**     | **CR-Avg↓** |\n| EMA    | –   | –       | 0.2478         | 0.3992   | 0.1869         | 1.6016   | 0.3748         | 0.3413   | 0.7375         | 1.1447   | 0.6778          | 0.125    |\n| CodeMerge | 5 | 1024     |      0.2851          |     0.4264     |    0.2241            |    1.5206      |       0.4103         |     0.3713     |      0.7228          |    1.1146      |      0.6504           |     0.109     |\n\n\nThanks again for your helpful comments and questions!"}, {"review_id": "qC1VBviLkb", "reviewer": "Reviewer_hVAv", "summary": "This paper introduces CodeMerge, a novel and efficient framework for test-time adaptation (TTA) in the context of 3D perception for autonomous driving. The primary contribution is a method that avoids the high computational cost associated with existing model merging techniques. Instead of loading and performing inference with multiple full model checkpoints, CodeMerge creates a codebook where each checkpoint is represented by a compact, low-dimensional \"fingerprint.\" These fingerprints are derived from the penultimate features of a source model. The framework then uses ridge leverage scores calculated on these fingerprints to determine the importance of each past checkpoint for merging. The authors demonstrate the effectiveness of CodeMerge on both end-to-end autonomous driving systems and modular LiDAR-based object detectors, showing significant performance improvements on challenging corruption and cross-dataset benchmarks, as well as benefits for downstream tasks like mapping and planning.", "questions": "1. The paper's success is demonstrated compellingly on the SparseDrive architecture. However, the conclusion mentions significant challenges in applying TTA to other architectures like UniAD. Could the authors elaborate on the underlying reasons for the performance degradation on these other models? Does this suggest a limitation in the TTA paradigm for certain architectures, or a property of CodeMerge itself? Answering this would greatly clarify the method's scope and potential for broader application. My score would improve if a convincing argument can be made for its generalizability.\n\n2. Section 3.1 states that fingerprints are computed using the initial pretrained feature extractor ϕΘ(0)​. For the end-to-end experiments, it is mentioned that only the 3D box regression head is updated (Line 141). Does this \"frozen feature extractor\" strategy also apply to the LiDAR-only detector experiments on SECOND, or are more parameters adapted and merged in that setting? Clarifying the scope of adapted parameters Θ(t) for all experimental settings would be helpful.\n\nAs I am not very familiar with this research field, if the answers are convincing enough, I would like to increase my scores.", "limitations": "Yes", "rating": 5, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "# Strengths\n\n- **Significance**: The paper addresses a highly significant and practical problem in autonomous driving: robustness to real-world distribution shifts encountered during deployment. The evaluation of TTA within a full end-to-end pipeline (SparseDrive), demonstrating that improvements in the perception module positively impact downstream tasks. \n\n- **Originality**: The core idea of using low-dimensional fingerprints and ridge leverage scores to guide model merging is novel and insightful. The strong empirical correlation shown between fingerprint and parameter space differences provides good evidence for the validity of this latent-space approach.\n\n- **Quality and Clarity**: The paper is generally well-organized, and is supported by a comprehensive and rigorous set of experiments. \n\n# Weaknesses\n\n- As other popular architectures like UniAD and VAD \"experience over tenfold performance degradation on nuScenes-C, hindering effective adaptation training\" (Lines 272-273), this raises a significant question about the generalizability of CodeMerge. While the results on SparseDrive are strong, the paper lacks evidence that the approach would be effective on other end-to-end models, which currently limits the perceived scope of the contribution.", "was_revised": true, "final_justification": "The authors' rebuttal, particularly the inclusion of new experiments on two additional modern architectures, has decisively addressed my primary reservation about the paper's generalizability.\n\n- Generalizability was my most significant concern, and it has been fully resolved. First, they convincingly argued that the failure of models like VAD is due to the brittleness of the base model itself, not a flaw in the TTA paradigm or CodeMerge; their results showed CodeMerge does not degrade performance even in this collapsed regime. More importantly, they provided **new experimental results** on two recent, state-of-the-art architectures (DiffusionDrive and MomAD). These new results demonstrate clear and consistent performance gains across all downstream tasks, providing strong evidence that CodeMerge is not limited to SparseDrive and is indeed a more generally applicable technique.\n- My question about the adaptation strategy for the SECOND detector was answered clearly and concisely. The authors confirmed they adapt the entire network, which is the established protocol in prior work, and cited relevant papers to support their methodology. This resolves the ambiguity.\n- There are no remaining unresolved issues from my perspective.", "rebuttal": "# Response to Reviewer hVAv\n\nWe are grateful for your time and insightful comments!\n\n> W1 & Q1 - Generalizability beyond SparseDrive: explanation of the underlying causes of degradation, clarification of whether the limitation lies in the TTA paradigm or CodeMerge itself, and empirical evidence or arguments demonstrating broader applicability beyond SparseDrive.\n\n**Early end‑to‑end models under corruption.** Our experiments show that some early architectures (e.g., VAD) collapse on corrupted scenes even **before any TTA is applied**. For example, under heavy snow, VAD attains only **0.0168 mAP** for detection and **0.0353 NDS**, indicating that the base model’s predictions are already unreliable and **model almost collapsed**. In this regime, **online self‑supervision becomes untrustworthy**, so any TTA method has little signal to learn from. Importantly, this reflects **a robustness limitation of the base model**, not of TTA or CodeMerge. Even so, attaching CodeMerge to VAD yields **small but consistent gains** across perception and planning metrics, and **does not degrade** VAD’s performance, which underscores that the bottleneck is the model’s severe brittleness rather than our adaptation mechanism.\n\n| Snow  | mAP↑    | NDS↑   | mATE↓  | mASE↓  | mAOE↓  | mAVE↓  | mAAE↓  | AMOTA↑ | AMOTP↓ | RECALL↑ | IDS↓  | APped↑      | APdivider↑    | APboundry↑    | map mAP↑      | minADE↓    | minFDE↓    | MR↓       | EPA↑      | L2-Avg↓     | CL↓       |\n|-------|---------|--------|--------|--------|--------|--------|--------|--------|--------|---------|-------|-------------|---------------|---------------|-----------|------------|------------|-----------|-----------|-------------|-----------|\n| VAD   | 0.0168  | 0.0353 | 0.9691 | 0.8577 | 0.9713 | 1.1064 | 0.9327 | --     | --     | --      | --    | 0.0006386   | 0.0014679     | 0.0002426     | 0.0007830 | 2.3891099  | 3.7661626  | 0.3022    | 0.0479    | 1.622955    | 0.0132    |\n| VAD+CodeMerge | 0.0176 | 0.0356 | 0.9689 | 0.8576 | 0.9719 | 1.1019 | 0.9337 | --    | --    | --     | --   | 0.0005710   | 0.0015413     | 0.0002724     | 0.0007949 | 2.3264942  | 3.6645312  | 0.2974072 | 0.0519021 | 1.605700    | 0.01307   |\n\n**Recent architectures with non‑collapsed baselines.** When the backbone remains minimally competent under corruption, CodeMerge is **highly effective and broadly applicable**. On **DiffusionDrive** [A] and **MomAD** [B] (both in CVPR 2025), CodeMerge delivers **consistent improvements** across detection, tracking, mapping, motion, and planning, and it **reduces collision rate** under both Brightness and Snow corruptions. These results support that CodeMerge is able to scale beyond SparseDrive.\n\n|     Brightness   | mAP↑   | NDS↑   | mATE↓  | mASE↓  | mAOE↓  | mAVE↓  | mAAE↓  | AMOTA↑  | AMOTP↓ | RECALL↑ | IDS↓  | APped↑ | APdivider↑ | APboundry↑ | mAP↑   | minADE↓ | minFDE↓ | MR↓    | EPA↑   | L2-Avg↓ | CL↓     |\n| ------------------------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------- | ------ | ------- | ----- | ------ | ---------- | ---------- | ------ | ------- | ------- | ------ | ------ | ------- | ------- |\n| Diffusiondrive       | 0.3278 | 0.4588 | 0.6402 | 0.2782 | 0.6473 | 0.3005 | 0.1851 | 0.2818  | 1.4334 | 0.4076  | 787   | 0.3651 | 0.4819     | 0.4794     | 0.4421 | 0.6624  | 1.0282  | 0.1380 | 0.4448 | 0.6001  | 0.089%  |\n| Diffusiondrive+CodeMerge     | 0.3580 | 0.4845 | 0.6210 | 0.2747 | 0.5767 | 0.2811 | 0.1916 | 0.3150  | 1.3529 | 0.4638  | 873   | 0.4125 | 0.5162     | 0.5333     | 0.4873 | 0.6582  | 1.0261  | 0.1380 | 0.4648 | 0.5919  | 0.062%  |\n| MomAD     | 0.3340 | 0.4711 | 0.6276 | 0.2734 | 0.5931 | 0.2900 | 0.1747 | 0.2944  | 1.4241 | 0.4101  | 731   | 0.2490 | 0.1808     | 0.3157     | 0.2485 | 1.3402  | 2.2509 | 0.2212 | 0.3965 | 7.3178  | 8.500%  |\n| MomAD +CodeMerge    | 0.3664 | 0.4952 | 0.6092 | 0.2741 | 0.5496 | 0.2670 | 0.1803 | 0.3341  | 1.3447 | 0.4737  | 824   | 0.2755 | 0.1976     | 0.3365     | 0.2699 | 1.13309  | 2.2389 | 0.2211 | 0.4147 | 7.3171  | 8.438%  |\n\n\n|     Snow   | mAP↑   | NDS↑   | mATE↓  | mASE↓  | mAOE↓  | mAVE↓  | mAAE↓  | AMOTA↑  | AMOTP↓ | RECALL↑ | IDS↓  | APped↑ | APdivider↑ | APboundry↑ | mAP↑   | minADE↓ | minFDE↓ | MR↓    | EPA↑   | L2-Avg↓ | CL↓     |\n| ------------------------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------- | ------ | ------- | ----- | ------ | ---------- | ---------- | ------ | ------- | ------- | ------ | ------ | ------- | ------- |\n| Diffusiondrive       | 0.1006 | 0.2373 | 0.7811 | 0.3794 | 0.9105 | 0.7007 | 0.3585 | 0.0503  | 1.8648 | 0.1064  | 305   | 0.0106 | 0.0426     | 0.0512     | 0.0348 | 1.0603 | 1.7077  | 0.1951 | 0.2063 | 0.9300  | 0.447%  |\n| Diffusiondrive+CodeMerge     | 0.1797 | 0.3492 | 0.7389 | 0.2956 | 0.6673 | 0.4621 | 0.2424 | 0.1078  | 1.7171 | 0.1785  | 681   | 0.1010 | 0.1713     | 0.1688     | 0.1471 | 0.8086  | 1.2838  | 0.1730 | 0.3070 | 0.7730  | 0.215%  |\n\n[A] Liao, etc. Truncated Diffusion Model for End-to-End Autonomous Driving. CVPR2025\n\n[B] Song, etc. Don't Shake the Wheel: Momentum-Aware Planning in End-to-End Autonomous Driving. CVPR 2025\n\n> Q2 - Does this \"frozen feature extractor\" strategy also apply to the LiDAR-only detector experiments on SECOND, or are more parameters adapted and merged in that setting?\n\nFor 3D detection, prior works [C, D, E] have shown that adapting all weights of the entire detector during TTA yields the best performance, and we simply adopt that established experimental setup. Therefore, we train entire networks and then adapt and merge all parameters. Please also refer to Reviewer G337 W3. \n\n[C] Chen, etc. MOS: Model Synergy for Test-Time Adaptation on LiDAR-Based 3D Object Detection. ICLR 2025\n\n[D] Chen, etc. DPO: Dual-Perturbation Optimization for Test-time Adaptation in 3D Object Detection. ACMM 2024\n\n[E] Yuan, etc. Reg-TTA3D: Better Regression Makes Better Test-Time Adaptive 3D Object Detection. ECCV 2024"}]

Author rebuttal:

Meta-review: This paper introduces CodeMerge, a lightweight model merging framework for robust test-time adaptation (TTA) in autonomous driving 3D perception. It uses low-dimensional fingerprints and a key-value codebook to enable efficient model composition, addressing the computational overhead of prior merging methods. Reviewers praised its clear motivation, strong performance gains (14.9% NDS on nuScenes-C, 7.6% mAP on cross-dataset), and broad evaluation across LiDAR/camera detectors and downstream tasks. Concerns included initial ambiguity about code availability, efficiency analysis gaps, checkpoint buffer practicality, and generalizability beyond SparseDrive. However, the authors resolved these in their rebuttal with clarifications, additional experiments on new architectures (DiffusionDrive, MomAD), and runtime comparisons. Despite minor limitations, CodeMerge makes valuable contributions to TTA for safety-critical autonomous driving, with rigorous validation and practical efficiency. The AC concurs this is a significant advancement and recommends acceptance.
Decision: Accept (poster)