COOPERTRIM: Adaptive Data Selection for Uncertainty-Aware Cooperative Perception
Authors: Shilpa Mukhopadhyay
Amit Roy-Chowdhury
Hang Qiu
Venue: ICLR 2026 Poster (2026)
Keywords: Cooperative Perception
Intermediate Fusion
Uncertainty Estimation
Primary area: applications to robotics, autonomy, planning
TL;DR: This paper introduces CooperTrim, a temporal uncertainty-guided adaptive feature selection mechanism to balance network overhead and task performance in Cooperative Perception..

Abstract: Cooperative perception enables autonomous agents to share encoded representations over wireless communication to enhance each other’s live situational awareness. However, the tension between the limited communication bandwidth and the rich sensor information hinders its practical deployment. Recent studies have explored selection strategies that share only a subset of features per frame while striving to keep the performance on par. Nevertheless, the bandwidth requirement still stresses current wireless technologies. To fundamentally ease the tension, we take a proactive approach, exploiting the temporal continuity to identify features that capture environment dynamics, while avoiding repetitive and redundant transmission of static information. By incorporating temporal awareness, agents are empowered to dynamically adapt the sharing quantity according to environment complexity. We instantiate this intuition into an adaptive selection framework, COOPERTRIM, which introduces a novel conformal temporal uncertainty metric to gauge feature relevance, and a data-driven mechanism to dynamically determine the sharing quantity. To evaluate COOPERTRIM, we take semantic segmentation and 3D detection as example tasks. Across multiple open-source cooperative segmentation and detection models, COOPERTRIM achieves up to 80.28% and 72.52% bandwidth reduction respectively while maintaining a comparable accuracy. Relative to other selection strategies, COOPERTRIM also improves IoU by as much as 45.54% with up to 72% less bandwidth. Combined with compression strategies, COOPERTRIM can further reduce bandwidth usage to as low as 1.46% without compromising IoU performance. Qualitative results show COOPERTRIM gracefully adapts to environmental dynamics, localization error, and communication latency, demonstrating flexibility and paving the way for real-world deployment.

Published as a conference paper ICLR 2026
COOPERTRIM: ADAPTIVE DATA SELECTION FOR UNCERTAINTY-AAA COOPERATIVE PERCEPTION
Shilpa Mukhopadhyay 12; Amit Roy-Chowdhury' Hang Qiu' 'University of California, Riverside, USA. "New Jersey Institute of Technology, New Jersey, USA. { 1sm3934 eenjitenitteee edd, (amitrc, hangq) @ucr.eucruuuu
ABSTRACT
Cooperative perception enables autonomous agents to share encoded representations over wireless communication enhance each other's live situational awareness. However, the tension between the limited communication bandwidth and the rich sensor information hinders its practical deployment. Recent studies have explored selection strategies that share only a subset of features per frame while striving keep the performance on par. Nevertheless, the bandwidth requirement still stresses current wireless technologies. To fundamentally case the tension, take a proactive approach, exploiting the temporal con- tinuity to identify features that capture environment dynamics, while avoiding repetitive and redundant transmission of static information. By incorporating temporal awareness, agents are empowered to dynamically adapt the sharing quantity according to environment complexity. We instantiate this intuition into adaptive selection framework, COOPER- TRIM, which introduces a novel conformal temporal uncertainty metric to gauge feature relevance, and data-driven mechanism to dynamically determine the sharing quantity. To evaluate COOPERTRIM, we take semantic segmentation and 3D detection as exam- ple tasks. Across multiple open-source cooperative segmentation and detection models, COOPERTRIM achieves up to 80.28% and 72.52% bandwidth reduction respectively while maintaining a comparable accuracy. Relative to other selection strategies, COOPERT RIM also improves IoU by much as 45.54% with up 72% less bandwidth. Combined with compression strategies, COOPERTRIM can further reduce bandwidth usage to as low as 1.46% without compromising IoU performance. Qualitative results show COOPERT TRRM gracefully adapts to environmental dynamics, localization error, and communication la- tency, demonstrating flexibility and paving the way for real-world deployment. Project website link for code and pretrained models: https://cis..erree//Cooop
INTRODUCTION
Cooperative perception (Wang al. 2020 Xu al. 2023a) enables autonomous agents exchange infor- mation about invisible or uncertain regions to enhance range of tasks, including detection (Qiu et al. (2022), prediction (Wang al.) 2025), mapping (Ahmad al., 2020), and navigation (Cui et al. [202) 2026). The benefits of additional vantage points come at the cost of communication. Balancing bandwidth overhead and accuracy, modern approaches adopt intermediate (over early and late) fusion (Wang et al., 2020 Xu al. 2023a) to share task-oriented lightweight encoded representations of the environment. To further improve communication efficiency in intermediate fusion, three strategies have been proposed: compression (Wang jet al. 2020; Xu et al., 2022a), which reduces the size of the features but risks information loss if targeted at higher compression ratio (i.e., lossy compression); selection (Hu et al., 2023 Liu et al., 2020). which
"Work done while affiliated with the University of California, Riverside.

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transmits only useful data or selects agents; and hybrid (Yuan et al. 2022 Yang et al., 2023). combining both for optimal bandwidth reduction. For example, Where2comm (Hu et al., 2022) uses threshold-
spatial confidence maps for feature selection, UniSense (Ren et al. (025) focuses on incertainty-drive data
exchange, and SwissCheese (Xie et al. (224)) exploits the disparity in semantic information on features be- tween different spatial regions on different channels to perform fixed threshold-based selection. While these
approaches reduce the exchange volume, the demand still strains wireless bandwidth (Mo et al. 2025).
Fundamentally, the mismatch between limited communication bandwidth and the richness of sensor infor- mation hinders the practical deployment of cooperative perception. To address this mismatch, we take a proactive adaptation approach. Our intuition is twofold: 1) rather than sharing a smaller yet static volume of features per frame, sharing should be made demand variable volume depending on the recipient's s cognitive challenges for its surrounding environment; 2) rather than sharing frame by frame independently, cooperation should leverage the temporal context just the rest of the autonomy stack does to enhance temporal comprehension while reducing repetitive and redundant sharing. Consider a scenario where the ego (recipient) agent is confident most features from its sensor data, but collaborating agents may have varying confidence levels of elements in the scene. In such cases, the ego can learn from its temporal context request only the uncertain features. For instance, complex scenario (e.g..mlltipll road intersections) should demand more features over consecutive frames than a simple scenario (e.g.. intersections).
Building on top of these insights, in this paper, we present COOPERTRIM, RIM, an adaptive cooperative perception framework, which introduces novel conformal temporal uncertainty metric to gauge feature relevance, and a data-driven mechanism to dynamically determine the sharing quantity. Our methodology employs tempo- ral uncertainty estimation using conformal prediction inspired quantile gating mechanism to identify feature deviations across frames, alongside an uncertainty- based attention mechanism to weigh feature importance. It dynamically selects relevant features with adaptive thresholds based on environmental needs and facili- tates efficient feature exchange between agents to minimize bandwidth usage while ensuring robust fusion of multi-source data.
We instantiate the framework using semantic segmentation as an example cooperative task, and evaluate it against existing open-source cooperative segmentation models (Xu et al. [223a 2022b Li et al., (02I) and existing selection strategies. To our knowledge, this is the first work on selective cooperative semantic segmentation. Unlike detection tracking, which can work with sparse, object-level features, segmen- tation demands pixel-level granularity for exact shapes and boundaries, which poses more challenges for bandwidth reduction-a difficult case to address the monndwdth--nffomaatte ERTRIM through extensive benchmarking on the OPV2V (Xu et al., (002b) and V2V4Real (Xu et al. (023b) datasets, assessing the performance and network overhead across multiple open-soure cooperative 3D de- tection and segmentation models, as well as against other existing selection strategies. CooPERTRIM per- forms comparable to baselines with a bandwidth reduction of 80.28% (for segmentation) and 72.52% (for 3D detection) average. Furthermore, we perform a wider network overhead analysis by comparing the bandwidth consumption against 10 existing baselines. COOPERTRIM demonstrates competitive IoU perfor- mance compared to baselines for compression in controlled settings, using only 1.46% bandwidth at 32x compression. To understand the efficacy of the adaptation design, we evaluate data request variations across frames with respect to environment complexity. Our evaluation also shows robustness to localization error and communication latency.
mismatch. We evaluate COOP-
In summary, COOPERTRIM makes the following contributions:
We propose a learning framework to proactively adapt feature selection by dynamically determining fea- ture relevance and sharing quantity- transformative deviation from existing static selection frameworks. We propose a novel relevance assessment strategy by quantifying temporal uncertainty using conformal prediction inspired quantile gating meachanism to compare features across frames. Coupled with the conformal assessment using an adaptive quantile threshold based on conformity score, we use an attention mechanism with an adaptive mask threshold for quantity estimation.
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Our instantiation of COOPERTRIM on cooperative segmentation is the first work that demonstrates feature selection on this task. Segmentation requires transmitting large volumes of data, making selective percep- tion challenging under bandwidth constraints. Our approach addresses this by intelligently selecting and prioritizing critical features, reducing data transmission while preserving segmentation accuracy.
We employ a training method inspired by the e-Greedy exploration strategy from reinforcement learn- ing (Liu et al.) 2022), which balances exploration and exploitation effectively in training, resulting in lower bandwidth and higher task performance.
Our evaluation shows that COOPERTRIN achieves up to 80.28% and 72.52% bandwidth reduction in cooperative segmentation and detection respectively while maintaining a comparable accuracy. Relative to other selection strategies, COOPERTRIM also improves IoU by as much as 45.54% with up 72% less bandwidth. Quantitative frame-by-frane inspection further validates the flexibility, demonstrating graceful adaptation environmental dynamics and paving the way towards real-world deployment.
2 RELATED WORK
Resilient operation of autonomous agents depends on their onboard perception, which often limited by blind spots and uncertainties. A range of cooperative perception mechanisms have been proposed, from early fusion and edge assistance (Zhang al. 2021 Zhu et al. 2024) sharing raw sensor data, to late fusion of regional processing results while missing holistic scene details. The most prominent are intermediate feature fusion methods (Chen et al. 2019), however, there remains gap between bandwidth demand and availability. V2VNet (Wang et al. (020) uses compressed LiDAR BEV features and GNN aggregation, yet often transmitting redundant data. AttFuse (Xu al. (022b) and DiscoNet (Li et al. 2021 offer attentive fusion and collaboration graphs, but lack intelligent data selection. CoBEVT (Xu et al. 2023a integrates multi-vehicle BEV features via SinBEVT and FuseBEVT for segmentation, yet struggles with inefficient feature compression. Unlike these approaches, CooPERTRIM selectively transmits essential perception features, reducing bandwidth usage, improving network efficiency, and maintaining accuracy under constraints.
Cooperative Perception.
Network Efficient Cooperation. The literature on communication-effiee efficieen cooperative perception includes Compression-basee Selection-based and Hybrid approaches. We focus on Selection based methods due to practical wireless broadcasting needs. Where2comm (Hu et al.) 2022) uses threshold based spatial con- fidence maps for selection, ignoring uncertainties and relying on impractical multi-round transmission. UMC (Wang et al. (023) applies entropy-based selection but sending full maps is computationally expen- sive. CenterCOOP (Zhou et al., (023) transmits center-point LiDAR features via mutual information, im- proving bandwidth by 10% on DAIR- V2X while sharing all embeddings. BM2CP (Zhao et al., (023)) shares modality-guide guiided features, overlooking sensor noise. UniSense (Ren et al. 2025 selects critical regions via uncertainty, missing occlusion uncertainties and incurring high bandwidth costs. SwissCheese (Xie et al., 2024) uses fixed thresholds for selection, lacking adaptability dynamic scenarios. Different from meth- ods with fixed thresholds or static selection strategy, COOPERTRIN adapts feature selection to environment dynamics, prioritizes uncertainty-driven critical features using past confident data, balancing bandwidth ef- ficiency and perception performance resilience.
3 METHODOLOGY AND SOLUTION
3.1 PROBLEM STATEMENT
We address the challenge of selective feature sharing for cooperative perception tasks. Rather than designing a new model, aim at selection framework that is applicable to set of similar feature-sharing mecha- nisms (Xu et al. (2023)); Hu et al. 2002)); Li et al. (2021). Given a set of features F = [f1, fn extracted from mpput data, where is the number of feature channels in latent representations, our objective
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is identify subset C F such that the number of selected features |S] is minimized, while the accu- racy of the perception task, denoted as function A(S) of the selected features, is maximized. Formally, the optimization problem can be expressed as minsgr (S] maxser A(S). This bi-objective optimization problem is challenging due to the inherent tension between feature abundance and bandwidth limitations.
3.2 COOPERTRIM DESIGN
To resolve that tension, we present COOPERTRIM, a selective cooperative perception framework that learns to enhance representation learning via temporal uncertainty-drivet feature selection for mandwidt--effiieen accurate perception in multi-agent systems. COOPERT RIM addresses two key research questions:
Relevance. If the bandwidth does not permit all features to be shared, what are the most essential features that are impactful to downstream tasks? The relevance is inevitably recipient-centric, which demands the recipient vehicle to assess its held-of-vier visibility, perception uncertainty, and environment dynamics. Quantity. If most relevant features are prioritized, where is the diminishing return point to stop sharing? Such quantity sweet spot may be dependent on both scene- and task-complexity, which demands dynamic runtime adaptation, sophistication in COOPERT framework design.
COOPERTRIM leverages two key insights in addressing the above research questions. i) Temporal Uncer- tainty as Relevance. Unlike conventional approaches Ren et al. (2025); [ie et al. (2024); Hu et al. (2022). which quantify the feature relevance of each individual frame, we put the features in their temporal contexts, and quantify the temporal uncertainty (e.g., introduced by scene dynamics, changing lighting conditions, occlusions) as relevance determine sharing priority. We use conformal prediction inspired quantile gating method to better assess the temporal uncertainty in continuous frames. ii) Environment Adaptivity. Adapt- ing to environment dynamics, we introduce two learned parameters: data-driven uncertainty threshold used in the uncertainty estimation, and data-driven attention mask threshold, to adjust the runtime sharing quantity for each frame. This flexibility empowers COoPERTRIM scale gracefully while maintaining ef- ficient bandwidth utilization. Figure shows an architectural overview of COOPERTRIN We describe the components in detail.
Conformal Temporal Uncertainty. COOPER'RIIM takes encoded representations from sensor data, con- textualizes them within the ego agent's recent memory, and identifies the uncertain ones as candidates for enhancement through cooperation. Specifically, given current frame feature F, E RH' XW' encoded from sensor input X2 e C it calculates conformity scores by comparing the feature Fr against the fused features from previous frames, denoted as distance function S, = d(Fr, Pf where d(.) is the LI distance defined as 1F+ /ffurdd, effectively capturing deviations between past and current scene understanding. In this context, the use of 'conformal' is inspired by conformal prediction, differing by (a) learning online frame-wise wise instead of using a fixed calibration dataset, and (b) estimating uncertainties in components of regression values rather than direct intervals on entire regression data. Then, inspired by conformal prediction, we use quantile gating to assess the features' relevance, introducing a learnable quan- tile threshold q, and apply gross-attention mechanism between the features and the ranges of those scores above the threshold (i.e. S,( > () to obtain the relevance metric R,
Data-driven Quantity Cutoff. In order to adapt to environment dynamics, we introduce a mechanism to determine the diminishing return point of the sharing volume. Specifically, we introduce second learnable threshold J such that we only share those features whose relevance score is above the threshold (R, >> T). In situations where the perception complexity is high, the range of conformity score will increase due to more temporally diverse feature encodings, which, in turn, yields higher relevance scores. More features would have relevance scores above the learned threshold; the communication of which incurs higher band- width usage. On the contrary, temporal consistency will result in low relevance scores among all features, which yields low sharing volume. Moreover, the temporal consistency not only comes from the low envi-
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Uncertainty Score
Brary Mask
Adeptive Thresnaliing
Learnid
Conformal Temporal
Octput
Threshokd
Sigmeki Acivvation
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Prev: Fused
Uncertainty
Context
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Estimtion
Attentonn
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Softlax
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Ego Feature
Atlention
[Attention
Mocule
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Key
Valuz:
Data- Driven
Quantity Cutoff
Projection
Projecton
Networks
Midule
Relevance
Fused Feature
Response
Request
Uncertsinty
Flattened
Festures
Flattened
Foatures
(a) COOPERTRIM Overview
(b) Cross-Attention Module
Figure 1: (a) COOPERTRIM Overview, COOPERTRIM conducts feature learning, followed by an uncertainty- based selection module using learned features. It estimates adaptive temporal uncertainty (via learned con- fidence) for each feature, performs cross-attention based feature weighting, and selects features using a learned threshold. The ego then broadcasts request vector for selected features, reconstructs received CAV data into full features, fuses them, and sends them to the task head for final results. (b)
Module uses learned projections of temporal uncertainty as queries, and feature projections as keys and val- ues. These matrices pass through an attention module, and a learned threshold at the final output generates a binary mask for selected channels.
ronment dynamics, but also comes from perception stability- COOPERTRIMM leverages this stability to bandwidth as well.
Feature Exchange and Fusion. To communicate recipient-centric relevance assessment, the ego agents send requests for selected high-relevance features. Following prior work assumptions of precise pose es- timation (Xu al.) 2023a), responders use spatial transformation (Jaderberg al.) (015)) match ego perspective and share the requested features. The ego agent blends received features into its own feature map before passing them along to the fusion decoders and task heads.
3.3 TRAINING
Loss Function. To attain the joint goal of bandwidth efficient transmission and task performance, the training of COOPERTRIM can be formulated as constrained optimization problem defined as follows:
0* arg min L(C(0)), P(C(()) C1.6
where C1.6 represents the percentage of channels corresponding to 1.6 Mbps (Mo al. 2025
C(0) repre-
sents the channels selected, P(-) represents the precentage of channels selected, I.(.) represents the percep- tion task loss. To solve this, use the Lagrangian formulation, defining the total loss as
arg min L(C(#)) A (P(C(0)) - Ci.o)
(2)
where À acts as a Lagrange multiplier dynamically adjusted (explained in Appendix A.1 to enforce the constraint time. The strategy starts with unconstrained optimization for initial learning, then introduces and intensifies constraint enforcement over time, using periodic strong adjustments for major deviations and steady increments for fine-tuning.
Training Strategy. For training the loss function COOPERTRIM, we use an c-Greedy method inspired by the reinforcement learning c-greedy exploration strategy (Liu al. 2022). However, make some
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adaptations for our training purpose. With probability we request entire feature set and with (1 (1-6) c) probability we exploit the knowledge. By exploiting the knowledge, we request the data as per learned thresholds (i.e. conformal predictions's quantile threshold q and cross-attention module's 's mask threshold r). We see considerable improvement in bandwidth requirement with this training method. Occasional updates considering all features can stabilize the optimization trajectory by smoothing out erratic updates caused by noise from partial features. This can prevent the model from diverging or oscillating around suboptimal points, leading to smoother convergence to a better solution. The e-Greedy method balances exploration (full data Dtull with probability e) and exploitation (partial data Paaiial with probability (1 e)). This balance stabilizes optimization by reducing gradient noise through periodic full-data updates. As formalized in Proposition[] [] this strategy reduces both the bias and variance of the gradient estimator compared to using only partial data. The expected gradient
[ e [D + (1 - e) E[VL((Dprtii)
(3)
ensures smoother convergence while adhering to bandwidth constraints. We formalize the effectiveness of the e-Greedy training strategy the following proposition.
Theoretical Analysis. We draw theoretical analysis based on the following assumptions. (1) Perfect transmission conditions (no noise introduced during transmission, so any bias or variance arises solely from the data used). (2) A complete dataset Dua and subset partial C Drall representing partial data, (3) The true gradient is VL(Dran), reflecting the loss over the entire dataset.
Proposition 1 (Effectiveness of c-Greedy Training). An e-greedy training strategy that computes the gra- dient of Loss L using full data (Dpa) with probability e and partial data portiat) with probability (1 c) reduces the bias of the gradient estimator compared to using only partial data. Specifically, the bias is scaled down by a factor of (1 c)).
Proof. Detailed proof provided in Appendix A.2
4 EXPERIMENTS
We conduct six experiments to demonstrate COOPERTRIM's efficiency in task performance and bandwidth usage. Referred to as COOPERTRIM, we apply COOPERTRIM to CoBEVT (Xu al.) 2023a) for evaluation, focusing on semantic segmentation
the OPV2V (Xu et al. 2022b) dataset and 3D detection OPV2V and 42V4Real (Xu et al. 20236) datasets. We choose CoBEVT for its robust performance cooperative perception compared to existing works. COOPERTRIM is the first address selective cooperative perception in semantic segmentation.
"Trimming" Existing Cooperative Perception Baselines In Figure 2 assess performance and bandwidth (BW) by applying our temporal uncertainty CоBEVT (Xuet al. al. 2023a), Attfuse (Xu al., 2022b), and DiscoNet (Li et al. 2021). We implement CoOP- ERTRIM on these methods after removing any cxisting compression techniques. This evaluation compares performance accuracy between
method COOPERTRIM to three methods:
COOPPERRRMMM-aae selection and the original methods (without compres- sion/selection), and reports bandwidth usage percentages relative to the originals (40 Mbps). For Dynamic and Static segmentation combined, COOPERTRIM-COBEVT COOPERTRIM-Attfus and COOPERTRIM- DiscoNet use average bandwidths of 27. 77. 21.07%, and 10.18%, respectively, corresponding to 11.16 Mbps, 8.4 Mbps, and 4.07 Mbps based on our 128x32x32 latent representation size. We observe that COOP- ERTRIM achieves comparable performance accuracies the original methods despite significantly lower bandwidth consumption, with an average 80.28% improvement in network overhead over the baselines. Detailed values are in Appendix Table
Additionally, we validate COOPERTRIM'S generalizability on 3D detection using LIDAR data on OPV2V (Xu et al. 2022b) and 42V4Real (Xu al. 2023b) datasets. Figure 2d shows COOPERTRIME
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Baseline
Ours
100
100
100
AP@
0.5 (%)
IOU %
IOU 90
100
APg
0.7 (%)
0
0
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100
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100
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COBEVT
AttFuse DiscoNet
COBEVT
AttFuse
DiscoNet
CoBEVT
DiscoNet
GP42V
(a) Dynamic
(b) Static: Road
(c) Static: Lane
(d) 3D Detection
Figure "Trimming" existing cooperative perception baselines, COOPERTRIM reduces bandwidth signifi-
cantly while preserving accuracy in segmentation (across different semantics, i.e. dynamic, static road, and static lane) and 3D detection tasks.
performance versus baseline CoBEVT (Xu et al. 2023a) at loU 0.5 and 0.7. COOPER TRIM maintains com- parable detection accuracy with 27 27.48% bandwidth usage (72.52% reduction), highlighting its efficiency lowering network overhead.
Selection Strategy Comparison. Table presents the evaluation COoPERTRIM against selection-based network-efficient baselines. Our feature latent representation is sized at 128x32x32, with percentages and bandwidths reported accordingly. We implement two algorithms: Where2Comm (Hu et al. 2022) (feature and agent selection, adapted for segmentation with 0.4 threshold batch confidence map) and Swiss- Cheese (Xie et al., 2024) (feature selection over spatial and channel features, implemented in our camera- based segmentation framework the official code is unavailable). For SwissCheese, at 10 Mbps (comparable to COOPERTRIM, equating to 25% of our latent representation size) to assess segmentation performance. COOPERTRIM achieves lower bandwidth usage and better performance than Where2Comm, and outperforms SwissCheese in dynamic and static accuracy at similar bandwidth levels.
set bandwidth
Table 1: Feature Selection Strategy Comparison: Accuracy and Bandwidth
Accuracy (IoU, %)
Bandwidth (Mbps)
Basclines
Dynamic
Static Lane
Static Road
Where2Comm (Hu al.
2022
8.62
20.40
36.46
39.6
SwissCheese (Xie
2024
35.71
12.81
32.07
COOPERT RIM (ours)
54,03
24.45
44.38
11.16
Post-selection Compression Compatibility. Compression-basee methods like CoBEVT (Xu et al. 2023a and AttFuse (Xu et al., 2022b) reduce data size for network efficiency. Yet, COOPERTRIM'S data selection enhances efficiency by compressing post-selection, optimizing bandwidth. We apply lossy quantization to reduce precision and volume, then lossless methods for further reduction. Figure compares IoU and bandwidth for COOPERTRIM, CoBEVT (Xu et al. 2023a), and AttFuse (Xu al. 2022b) at Ix, 8x, and 32x rates for the segmentation task. COOPERTRIM excels in loU and bandwidth savings, notably at 32x (1.46% vs. 3.76% for CoBEVT and 10.62% for AttFuse in 32x Dyn) [See Appendix A.6 for detailed results).
Environment Adaptation. Figure shows the inference results for COOPERTRIM over multiple frames. We variations in bandwidth implying variable feature request in each frame. The figure shows increased data requests correlate with higher scene complexity. segmenting the dynamic objects, an increase in complexity can be referred to criticality in positioning of increased amount of vehicles and traffic partic-
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Comprexsion
Compression
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1x
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0 4050004050
0 5 10 15 IoU
20
25
0 10 20 IoU
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Dynamic
Lane
Road
Figure 3: Comparison IoU performance and bandwidth usage at compression rates (Ix, 8x, 32x) for COOPERTRIM, CoBEVT, and AttFuse in Dynamic, Road, and Lane scenarios.
BW (Mbps)
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Figure 4: Increased data requests align with higher scene complexity. For dynamic objects, complexity in number and positioning rises in Frames 1200, 200, and 1700. For static elements, complexity grows in Frames 900, 250, and 1600, with more intersections and lane orientations. The right part highlights the frames at vertical lines; Frame 1200, 200, 1700 (dynamic) and 900, 250, 1600 (static). Green dashed lines: baseline CoBEVT IoU. Green solid lines: COOPERTRIM loU.
ipants. Similarly, increase in complexity in the static task can be referred intersections or lane orientations the scene. Figure shows that in both cases, i.e., increased amount of vehicles, and increased topological complexity in the roadways, COoPERTRIN accordingly shares more data to preserve task performance, whereas in other cases, gracefully adapts to request less sharing, saving the precious bandwidth other agents in need. In critical frame ranges such as 1000-1800 (Dynamic), 1000-1500 (Lanc), and 1200-1400 (Road), where baseline CoBEVT (Xu et al.) (203a)) consistently under- performs, COoPERTRIM achieves much higher IoU, illustrating the effectiveness of adaptive threshold masking mechanism in prioritizing key features and managing scene complexity (see Appendix A.5)
increase in the number
Table Training Methods Comparison for Uncertainty Aware Training showing COOPERTRIM (EG+CP+FT) striking a good balance between Accuracy and Bandwidth
Accuracy (IoU, %)
Bandwidth (%)
Training Methods
Dynamic
Static Lane
Static Road
Dynamic
Static
CP+FT
31.37
23.05
36.92
30.75
1.12
Curiculum-CCPFFT
52.47
28.27
45.44
49.25
$1.63
EG+SD+FT
52.55
23.69
38.45
35.64
11.03
EG+CP+FT (Ours)
54.03
24.45
44.38
32.04
23.77
Training Methods Comparison. Table 2 shows the comparative study for training COOPERTRIM using
different training methods, all using the loss function in Section 3.3 (1) COOPERTRIM is trained with
Greedy (EG) based fine-tuning (FT) using Conformal Prediction (CP) for temporal uncertainty estimation
[Section 3.3
termed EG CP FT. (2) The first method uses Standard Deviation (SD) for spatial
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Ground Truth
CoBEVT
COOPERTRING
Figure 5: Qualitative Comparison of Ground Truth, CoBEVT, and COOPERTRIM COOPERTRIN performs better than CoBEVT in estimating dynamic object segments (Column 1 and 4), road segments (Column 2 and 5), and lane segments (Column and 5).
uncertainty estimation latent feature channels (EG+ FT), showing higher network consumption for dynamic objects (35.64% vs. COOPERTRIN at 32.04%) due to lack of temporal consideration SD, and 6% lower performance in static road segmentation. (3) The second method employs basic curriculum for fine-tuning (Curriculum + CP FT)" with four stages: (a) basic fine-tuning, (b) cooss-tttettoon with fixed mask threshold and CP confidence, (c) adaptive mask threshold with fixed CP confidence, and (d) fully adaptive (learned mask and CP confidence) CP- CP-guided selection. This yields good performance but higher bandwidth use (49% dynamic, 51% static). (4) The final method omits EG in FT (CP + FT),, resulting in lower accuracy (static road 8% below COOPERTRIM). EG's occasional full-feature updates stabilize optimization by promoting smoother convergence.
Qualitative Results. Figure 5 shows the segmentation results of COOPERTRIM to analyse visually. COOP- ERTRIM is different from CoBEVT (Xu et al. 2023a) only in its feature set before fusion of muti-source data. COoPERTRIM performs better than CoBEVT in estimating road segment, dynamic object segment, and lane segment. This is attributed to the better feature representation achieved by COOPERTRYY selec- tion mechanism, which reduces uncertainty in feature, by focusing on selected data transmission rather than transmitting the entire dataset for fusion.
Sensitivity Analysis. COOPERTRIM'S performance tested across localiza- tion errors (0em, =20cm, =1m) dur- ing inference, as shown in Figure 6 COOPERTRIM remains robust small errors (up to +20em) and maintains comparable IoU at +1m, with stable bandwidth usage, indicating consis- tent communication efficiency. COOP- ERTRIM'S performance under latency (0ms to 200ms) is shown Figure 7 It exhibits strong robustness up 50ms with no loss, and marginal loU drop at 100ms and 200ms. This resilience stems from COOPER- TRIM's 's adaptive mechanisms, two-threshold policy, and ineertainty-driver requests, reducing reliance
Ocm
=20cm
#1m
Oms
100ms
50ms
200ms
50
50
(86)
30
30
IOU
10
Dynamic
Static Hoad
Statfic Lane
Dynamic
Static Road
Static
Figure COOPERTRIM shows comparable performance under
Figure 7: COOPERTRIM shows comparable performance under latency at inference.
localization error at inference.
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misaligned data from CAVs. By requesting less data (23-32%, see details in Appendix Table I. latency impact is minimized, enhancing wallwwrldd robustness. Network usage stayed consistent during testing.
Network Overhead Comparison. We compare the network overhead of COOPERTRIM with used bandwidths of a broader
Bandwieth (Maps) Log Scale
range of existing cooperative perception works. We evaluate
30*
Feature Selection (FS) algorithms UniSense (Ren et al. 2025).
SwissCheese (Xie al.] 2024). Compression-baasee
gorithms STAMP (Gao et al.), CoBEVT (Xu et al. 2023a)
V2X- ViT (Xu et al., 2022a). V2VNet (Wang et al. 2020).
FCooper (Chen al. 2019), AttFuse (Xu et al. (002b)) Dis-
101
coNet (Li al. 2021), ) and Feant+F Selection (AS+FS)
Cooper
STAMP
UniSense
Swisscheend Chesse
Where Where2Comm Comm
CoGEVT
Antuse
VIX-VT
Discolitt
vaviet
Fcooper
method Where2Comm (Hu al. 2022).
Bandwidth comparisons use non-compressed versions (post- selection if applicable) for fairness, reporting original band-
CooperTrim (FS)
FS+AS
Figure 8: Across-the-board Bandwidth
widths from papers or calculating based on our 128x32x32
Comparison. C: Compression. FS: Feature
latent representation size when unspecified. Figure 8 show COOPERTRIM has the lowest network overhead (1 (11.6Mbps) compared to baselines.
Selection. AS: Agent Selection. COOPER-
TRIM consumes the lowest bandwidth.
System Overhead. We compare the system overhead as measured by the processing speed in Frames Per Second (FPS) under static and dynamic settings in the Table 3 The results indicate modest FPS reduction in CooPERTRIM compared to the baseline, with decrease of approximately 2 FPS in both configurations. This reduction is due the additional computational steps our method. The trade-off is reasonable given the significant bandwidth reduction achieved.
Table 3: System Overhead Comparison. COOPERT TRIM has marginal FPS reduction compared to CoBEVT due to added uncertainty and cross-attention modules for data selection.
Method
Configuration
FPS
Latency per Frame
COBEVT
Dynamic
10.59
92.9
COoPERTRIM
Dynamic
8.58
94.7 ms
CoBEVT
Static
10.59
93.1 ms
COOPERTRIM
Static 8.58
119.5 ms
5 CONCLUSION
In this paper, we present COOPERTRIM, an adaptive feature selection framework for cooperative percep- tion that enhances representation learning via temporal uncertainty-drivee feature selection for bandwidth- efficient, accurate perception in multi-agent systems. It tackles both relevance (identifying key features) and quantity (optimal sharing based on complexity) assessment. Using e-greedy training, COoPERTRIM bal- ances bandwidth and performance. Evaluated semantic segmentation and 3D detection, COOPERTRIM achieves up to 80.28% and 72.52% bandwidth reduction, while maintaining accuracy. Compared to base- lines, it boosts IoU by 45.54% with 72% less bandwidth, and shows competitive IoU at 1.46% bandwidth usage under 32x compression. COOPERT RRMM adapts to dynamics, aiding real-world use, and stays robust to localization errors and communication latency.
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ACKNOWLEDGEMENT
This research was sponsored by the OUSD (R&EVRT&L and was accomplished under Cooperative Agree- ment Number W9112N-20-2-0267.. The views and conclusions contained this document are those the authors and should not be interpreted representing the official policies, either expressed implied, of the ONR, ARL and OUSD(R&E)RR&LL or the U.S. Government. The U.S. Government authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.
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A APPENDIX
A.1 LAGRANGE MULTIPLIER ADJUSTMENT
This section explains the dynamic adjustment of Lagrange multiplier for training the loss in Equation 2 The Lagrange multiplier, represented as X, acts penalty term in the loss function to enforce the constraint on the percentage of selected features. It dynamically adjusts based on training progress (epoch count) to balance the primary perception loss (L(C(O))) and the bandwidth constraint (P(C(0)) = C1.s).
Initial Phase (epoch < ITC): For the first few epochs (defined by Initial Tuning Epochs ITC), À 0.0, allowing the model to focus solely minimizing perception loss without bandwidth constraints for strong start.
Periodic Update (every 10th epoch after initial tuning): At every 10th epoch, scales exponentially with scaling factor SF P(C(0)) constraint if far off.
100.0 and À = A 2 aggressively pushing the model towards the target
Incremental Scaling (other epochs after initial tuning): In other epochs, À increases linearly as X X- 0.1-
spech-ITC }), ensuring gradual push towards the constraint without abrupt loss changes.
10
The strategy starts with unconstrained optimization for initial learning, then introduces and intensifies con- straint enforcement over time, using periodic strong adjustments for major deviations and steady increments for fine-tuning.
A.2 MATHEMATICAL PROOF FOR EFFECTIVENESS OF G-GREEDY TRAINING
We provide detailed proof demonstrate the effectiveness of the c-greedy training strategy (Section 3.3 by analyzing the bias of the gradient estimator. This proof shows reductions in bias compared to baseline of using only partial data. We assume perfect transmission, meaning all variability and bias stem from the inherent properties of the data (full or partial) rather than external noise.
1. SETUP AND NOTATION
Datasets: Let Diull represent the complete dataset, and D partial be a subset of Diuns representing partial data.
True Gradient: The target gradient to estimate is VL(Dma),, reflecting the loss over the entire dataset.
Transmission Assumption: Under perfect transmission, computed gradients are unaffected by external noise, so bias or variance arises solely from the data used.
e-Greedy Gradient Estimator: Define the gradient estimator under the e-greedy strategy V which
is:
-DiDD with probability
VL(D partaa) with probability 1 -
BIAS ANALYSIS OF THE GRADIENT ESTIMATOR
The bias of an estimator is the difference between its expected value and the true value. Here, the true gradient is (D(ni)) full data provides the most accurate representation of the loss landscape.
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EXPECTED VALUE OF ESTIMATOR
The expected value of the e-greedy gradient estimator is:
E = c. C. + (1 () EEV partaa))
BIAS CALCULATION The bias is defined as:
Bias(VL.) = E[V (D(n))
Substituting the expected value:
Bias( VLe) = (E + (1-6) -
Simplifying bt-sep:
E L.)) = c. L. + (1 - (Dprtaa
partaal - E[L((D))))
= 1) (D + (1 c) V L puntia)
= (1 -) (E[VL(DPariaal E V V (D(
Thus, the bias expression is:
Bias( V..) = -e) e) (E[ - [V(LL(Dnn)))
3. EXPLANATION OF SPECIFIC CASES AND BIAS SCALING
We explore the implications of the bias expression under different scenarios and explain the origin of the bias scaling by 1 c.
CASE 1: UNBTASED PARTIAL DATA GRADIENT
If the partial data gradient unbiased, i.e., E partia)] E. [D(an))))) the difference term is zero:
E L[VL(D L[VL((Dnn))
Thus:
Bias VL.) = e) .
In this ideal scenario, there is bias in the e-greedy estimator regardless of This occurs when Dpartaal perfectly represents Diali, which rare in practice due to sampling variability.
CASE 2: BIASED PARTIAL DATA GRADIENT AND ORIGIN OF BIAS SCALING
If there is a systematic difference, i.c., L(V partia)) * E define the inherent bias of using only partial data as:
Bias LL (amiaaa) E[VL pannaa))) E E
Substituting into the bias expression:
Bias (L.) (1 c) Bias( (L(D (prria))))
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Considering the magnitude of bias:
Bias( (VLLV = (1 c)
[Biss V L pamial))
Since 0 S S 1, the factor - << < for any 6 <> 0, implying:
Bias (VL)) Bias LD
The factor e comes from the probabilistic weighting in the c-greedy strategy. The expected gradient is weighted average where the partial data gradient (carrying inherent bias) is weighted by 1 c, and the unbiased full data gradient weighted by c. Thus, the contribution of the biased gradient is reduced, scaling the bias by 6.
Origin of Scaling:
4. SUMMARY OF BIAS REDUCTION MECHANISM
Scaling Effect: The bias scales by due the probabilistic blending of full and partial data gradients.
Extreme Cases:
When = 0, only partial data is used, and the bias equals the full inherent bias of the partial data gradient.
When O, incorporating full data reduces the weight of the biased partial data gradient scaling down the bias.
Bias Comparison: The inequality (Bias(VL.) < (aass partia))) holds for any > 0, showing that even small probability of using full data mitigates bias.
This mechanism highlights the e-greedy approach's s advantage balancing computational efficiency (using partial data) with accuracy (using full data to correct bias).
As shown, bias of the gradient estimator reduced by a factor of (1 ee) compared to using only partial data, pulling the expected gradient closer to the true gradient EE(LL This improves the accuracy the optimization direction, leading to better convergence toward the true optimum of L. Under perfect transmission, with external noise, the e-greedy strategy effectively leverages the strengths of full data (lower bias and variance) while maintaining efficiency by using partial data most of the time, achieving a favorable trade-off.
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A.3 PRELIMINARY EXPERIMENTS FOR UNCERTAINTY BASED SELECTION SEGMENTATION TASK
COBEVT (Random)
DiscoNet (Random)
AttFuse (SDS)
CoBFVT (Raseline)
DiscoNet (Baseline)
AttFuse (Random)
CoBEVT
0.24
0.45
Stalic Lane Roffommncc
0.22
Statc Boad Purformanne
0.42
Oyngmic Ferfore
0.40
0.40
0.20
0.38
0.35
0.38
0.18
0.30
0.32
0.25
0.30
0.20
30
20
30
50
0.28
Request Percentage
Percentage (96)
Request Percentage (%)
(a) Dynamic
(b) Static Lane
(c) Static Road
Figure 9: Motivation for COOPERTRIM. Comparison of baseline, random channel selection and SD-based selection (SDS) for uncertainty guidance show uncertainty-guided selection often outperforms random se- lection, though baseline performance remains higher consistently. COoPERTRIM addresses this accuracy gap through its proposed adaptive uncertainty driven selection method.
A.4 "TRIMMING" COOPERATIVE SEGMENTATION BASELINES ADDITIONAL
Additional presentation for Section 4 Cooperative Segmentation Baseline experiments in Table COOPER- TRIM maintains comparable performance accuracies while reducing bandwidth significantly, achieving an average 80.28% improvement in network overhead over the baselines.
Table 4: Application of COOPERTRIM Existing Cooperative Segmentation Methods. "Baseline" and
"Ours" (COOPERTRIM) show the respective IOU %, while "BW" shows COOPERTRIN bandwidth usage.
Dynamic Ours
Statie Lane
Static Road
Avg. BW (Mbps)
Methods
Baseline
Baseline
Ours
Baseline
Ours
CoBEVT
50.23
54.03
32.04%
23.79
24.45
23.77%
45.28
44.38
23.77%
11.16
AufFuse
32.20
30.90
24.76%
24.14
23.93
17.39%
34.86
36.22
17.39%
8.4
DiscoNet
30.03
30.80
10.65%
20.72
22.05
9.72%
38.43
40.02
9.72%
4.07
A.5 ROBUSTNESS OF THRESHOLD BASEED METHOD IN COOPERTRIM
COOPERTRIM, the decision to request data based on a combination of two key factors: the feature representation (which encodes the scene description perceived by the ego vehicle in the current frame) and the uncertainty estimation (which compares the past comprehensive scene description with the current ego vehicle's scene understanding to detect discrepancies, e.g., objects that existed previously but are no longer present in ego's perception range, or vice versa). This dual mechanism ensures robustness even in subtle change scenarios.
For instance, in situation where the overall scene appears static (low temporal change), the ego vehicle's s current feature perception changes-due factors like occlusion the sudden appearance of small object such as a pedestrian- our method will detect this deviation. As a result, the system will trigger more data requests to ensure critical information is not missed. Conversely, if both the scene and the ego vehicle's
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current perception remain largely unchanged, the system will minimize data requests, optimizing bandwidth usage.
To validate this behavior, we analyzed specific frame sequences in our experiments.
Frames 1960-1970: These frames exhibit significant changes in the scene, associated with high data requests (Figure 5). The changes were verified using camera images and ground truth dy- namic data segmentation, confirming the system's 's responsiveness to dynamic scenarios, as shown
in Figure I0
Figure 10: High data requests during significant scene changes in Frames 1960-1970
Frames 940-950: These frames show minimal changes, associated with low data requests (Fig- 5. as shown Figure I0] The lack of significant changes was similarly verified using 4 camera images and ground truth dynamic data segmentation, demonstrating the system's 's ability to conserve bandwidth in stable conditions.
Figure 11: Low data requests during minimal scene changes in Frames 940-950
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A.6 COMPARISON OF COOPERTRIM WITH COMPRESSION-BASSE METHODS
We have added comparative results with network-efficient compression-based methods and updated Sec- tion 4 where have compared COOPERT RIM with SwissCheese (Xie et al. 2024] and Where2Comm (Hu
et al. 2022), the closest related works in feature selection and communication efficiency. While other feature selection methods like Unisense (Ren et al. 2025) exist, their source code is unavailable for direct compar- approaches, such as those used in CoBEVT (Xu et al., 2023a) and AttFuse (Xu ct al. 2022b), are compatible with COOPERTRIM. We show further bandwidth reduction using compression after feature selection. COOPERTRIN, significantly outperforms compression-only methods after applying post-selection compression, while maintaining superior perception accuracy. Below the detailed results for IoU performance and bandwidth usage, with COOPERT RIM achieving bandwidth usage as low as 1.46% of total data high-compression settings.
ison. Compression-
IOU PERFORMANCE COMPARISON AFTER COMPRESSION
Scenario
COoPERTRIM IoU
CoBEVT IoU
AttFuse IoU
Ix (Dynamic & Static)
54.03
50.23
32.20
Ix Lane
24.45
23.79
24.14
Ix Road
44.38
45.28
34.86
8x (Dynamic & Static)
53.99
50.19
32.21
8x Lane
24.52
23.78
24.13
8x Road
44.43
45.27
34.86
32x (Dynamic & Static)
50.32
49.63
32.19
32x Lane
24.62
23.77
24.14
32x Road
45.05
45.27
34.86
BANDWIDTH USAGE COMPARISON AFTER COMPRESSION
Scenario
COOPERTRIM BW
CoBEVT Compressed BW
AttFuse BW
Ix (Dynamic & Static)
32.04%
100%
100%
Ix Lane
22.77%
100%
100%
1x Road
22.77%
100%
100%
8x (Dynamic & Static)
2.67%
5.98%
14.3%
8x Lane
9.25%
14.28%
17.42%
8x Road
9.25%
14.28%
17.42%
32x (Dynamic Static)
1.46%
3.76%
10.62%
32x Lane
6.06%
9.55%
13.71%
32x Road
6.06%
9.55%
13.71%
A.7 TRAINING METHODS COMPARISON ADDITIONAL.
Corresponding to Section Training Methods Comparison, Figure 12 presents the training methods com- parative study for training COOPERTRIM, all using the loss function from Section 3.3
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EG+CP-FT (Ours)
EG+SD+FT
Currtulum++PPFT
CP+FT
100
100
[OU
50
IOU
50
IOU
50
100
100
100
BW %
BW
50
BW
50
50
0
(a) Dynamic
(b) Static Road
(c) Static Lane
Figure 12: Training Methods Comparison. COOPERTRIN balances task performance and network overhead
better than other baselines.
A.8 NETWORK PERFORMANCE METRICS UNDER REALISTIC CONDITIONS
We evaluated COOPERTRIM'S performance by measuring key network-level metrics, including end-to-end latency and packet loss/retransmissionn under loss rates of [0-10]%.
A.8.1 LOSS RATE AND IMPACT
During training, we assumed perfect transmission with no loss. However, during inference, loss rates were simulated randomly between [0, 10]% by applying masks to evaluate robustness. The impact on performance metrics such as IoU and bandwidth (BW) under dynamic and static configurations is summarized in Table
Table 5: Impact of Loss Rate on IoU and Bandwidth Metrics. COOPERTRIM maintains stable IoU and BW metrics.
Loss Rate
Dynamic
loU
Dynamic
BW
Static
Lane loU
Static
Road IoU
Static BW (%)
(%)
(%)
(%)
(%)
0%
54.03
32.04
24.45
44.38
23.77
10%
53.95
32.11
24.49
44.34
23.71
Under simulated loss rates of 0-10%, COOPERTRIM maintains stable IoU and bandwidth metrics, with Dynamic loU being comparable at 10% loss. These results highlight COOPERTRIN' robust fallback behav- ior under packet loss conditions up to 10%, demonstrating its potential for reliable operation in real-world scenarios with imperfect network conditions.
A.9 CONFORMAL TEMPORAL UNCERTAINTY
Figure 13 shows the calculation of uncertainty in intermediate representation using conformal prediction inspired quantile gating mechanism explained in Section 3.2
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Ego Features
Learned Confidence
Quantile
Non-
Uncertainty Intervals
Non- Conformity
Selected
Conformity
Selection
Features
Past
Scores
Feature-
Feature
Heatures
wise Mean
Mask
Figure 13: COOPERTRIM estimates conformal temporal uncertainty by comparing the current frame's en- coded features with the ego vehicle's 's fused features from recent frames, highlighting what has changed over time ConfCoonfyyy Scores). then uses learnable quantile-based gating rule to identify high-deviation (more uncertain) features as the most relevant candidates for request.
A.10
LLM USAGE
the preparation of this paper. Large Language Models (LLMs) were utilized as a supportive tool for polishing the writing and implementing minor code modifications at specific points. However, the core research ideas, code design, and overall framework are entirely our own. The LLMs played no role the ideation or conceptualization of the research.
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Reviews:
[{"review_id": "7WtiIXW9nh", "reviewer": "Reviewer_Yo96", "summary": "This paper presents CooperTrim, an adaptive, uncertainty-aware feature selection framework for cooperative perception. The framework leverages conformal prediction to estimate temporal uncertainty for feature relevance and employs a data-driven adaptive mechanism to select an appropriate quantity of shared features based on environmental complexity. CooperTrim is plugged in and extensively evaluated on semantic segmentation using several co-perception methods on the OPV2V dataset, demonstrating significant reductions in bandwidth usage without sacrificing accuracy.", "questions": "1. Can the method adjust the bandwidth requirement during operation? Or a new model (a newly learned threshold generator) is needed to cope with a new network condition?\n2. There will be a potential delayed response to a new traffic pattern as history information is used to generate the threshold. Are there any results on the influence of the temporal window size?\n3. How well would CooperTrim's adaptivity generalize to other perception tasks (detection, tracking, etc.)? In those tasks, the ROI is much sparser.\n\nSee the weaknesses section for more details.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "**Compelling Motivation and Scope:** The paper focuses on the bandwidth-accuracy trade-offs in cooperative perception, arguing for temporally and contextually adaptive feature selection that is not covered by static or threshold-based approaches. \n\n**Effectiveness of Sub-modules:** CooperTrim's integration of conformal temporal uncertainty estimation with a cross-attention-based selection mechanism is well described, addressing both feature relevance and adaptivity. Detailed elaboration on training strategies provides hints on reproduction.\n\n**Concrete Adaptivity Insights:** The claim of scene adaptivity is validated by the qualitative results given in Fig. 4.", "weaknesses": "**Major Weaknesses:**\n1. Although more components are incorporated, the proposed method remains a threshold masking mechanism. The adaptivity claim needs more quantitative validation. For example, in Fig. 4 (left), a convincing result would be to show that the IoU curve is relatively stable, or at least more stable than the BW curve (\"complexity\" curve). The current result cannot prove that the adaptivity benefits the final results.\n2. Robustness against localization error and latency is not discussed.\n\n**Minor Weaknesses:**\n1. The method is only tested on OPV2V, which is a relatively simple simulated dataset.\n2. More network-efficient baselines are expected to be included. Presenting results for the object detection task can also help enhance comparisons with previous methods.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "vV1BJOGuiq", "reviewer": "Reviewer_ypSd", "summary": "The paper proposes an adaptive data selection framework called COOPERTRIM for cooperative perception in autonomous agents. The main idea is to exploit the temporal continuity of the environment to identify relevant features and avoid transmitting redundant or static information. This reduces the communication bandwidth required while maintaining comparable accuracy to existing selection strategies. The proposed framework uses a conformal temporal uncertainty metric to measure feature relevance and a data-driven mechanism to determine the amount of data shared. The evaluation shows significant bandwidth reduction and improved IoU compared to other selection strategies. However, there are some limitations, such as no ablation study on choosing optimal thresholds, only one simulated dataset was used, and the method has not been evaluated on real-world datasets. Additionally, the threshold-based method may not be robust in scenarios where only minor changes occur in the scene. The method is currently only evaluated on segmentation tasks and further evaluation on detection tasks would demonstrate its generalizability. Finally, the impact of the computation cost after introducing this data selection to collaboration perception models needs to be evaluated. Overall, the idea of using temporal uncertainty for data selection is interesting and the theoretical proof is sound, but further research is needed to address the limitations mentioned above.", "questions": "Please refer to the weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The idea of data selection by using temporal uncertainty is interesting.\n2. The theoretical proof is sound.\n3. The reduction in communication bandwidth consumption in segmentation tasks is obvious.", "weaknesses": "1. No ablation studies on how to choose the optimal thresholds.\n2. Only one simulated dataset is used; No real-world dataset is evaluated.\n3. What is the mathematical expression of the distance function? What is the deep reason for using this distance function?\n4. How robust is the threshold-based method? For example, in a certain scenario, maybe the overall scene doesn't change much, only a small object (e.g., a new pedestrian emerges), probably leading to a small temporal uncertainty, and how will the system take actions to this?\n5. Although the method is advantageous for segmentation tasks, it should be better evaluated on detection tasks as well to demonstrate its generalizability. \n6. Apart from the bandwidth consumption, one important factor is how the computation costs change after introducing this data selection to the collaboration perception models. What is the processing speed (evaluated by FPS)? Can this method be used in a real-time driving system (Typically more than 100 FPS)?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "0yKFfD3MQ8", "reviewer": "Reviewer_Cjxj", "summary": "# What the paper does\nCOOPERTRIM is an adaptive data-selection framework for cooperative perception that uses temporal uncertainty to decide what features to share and how much to share under bandwidth limits.\n# Key idea\n* Compute a conformal, temporal uncertainty signal by comparing current encoded features (F_t) with the previous fused features (F_{t-1}^{\\text{fused}}); uncertainty indicates where collaboration helps most.\n* Use an uncertainty-guided attention to score relevance per channel/region and apply adaptive thresholds to (i) select features and (ii) determine sharing quantity frame-by-frame.\n* Train with an ε-greedy–inspired regimen to balance exploration/exploitation so the model learns robust selection under bandwidth constraints.", "questions": "1.\tBenchmark against asynchrony-robust methods and/or integrate BEV flow.\nCan you compare to CoBEVFlow or prepend a BEV-flow pre-alignment module, reporting IoU–bandwidth trade-offs under controlled time offsets (e.g., ±50/100/200 ms) on OPV2V/OPV2V-Async? Does your two-threshold policy still add gains beyond flow alone?\n2.\tReport system- and network-level metrics under realistic conditions.\nMeasure end-to-end latency (encode→select→transmit→align→fuse→decode), packet loss/retransmissions, and congestion behavior across link budgets (e.g., 3/6/12 Mbps) and loss rates (0–10%). Plot IoU–bandwidth–latency curves and characterize degradation/fallback under losses.\n3.\tQuantify computational overhead and deployment feasibility.\nDetail added FLOPs/memory and per-frame latency from attention/masking on embedded automotive hardware (e.g., Jetson/SoC) and desktop GPUs. Compare compute-communication trade-offs against feature-compression/distillation baselines at equal accuracy.\n4.\tEstablish cross-dataset and cross-task generalization.\nEvaluate beyond OPV2V (e.g., DAIR-V2X, V2X-Sim, OPV2V-Async) and beyond semantic segmentation (detection/occupancy/tracking). Include fine-tuned and zero-shot transfers, reporting full IoU–bandwidth curves to substantiate external validity.\n\nI would support acceptance provided the authors satisfactorily resolve all identified concerns.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The temporally driven, uncertainty-aware communication scheme is conceptually clear and well structured: it measures discrepancies between the previous fused representation and the current features, applies conformal quantile thresholding to select candidates, and then uses attention with an adaptive mask cutoff to decide both what to transmit and how much—focusing bandwidth on high-value regions.\n2. The $\\epsilon$-greedy training schedule provides a practical stabilizer under bandwidth constraints: intermittent full-feature updates interleaved with predominantly masked updates smooth optimization and reduce variance, yielding stronger performance than standard-deviation–only uncertainty baselines and curriculum-style fine-tuning.\n3. The method is readily portable: as a drop-in component for cooperative semantic segmentation backbones (e.g., CoBEVT, AttFuse, DiscoNet), it delivers consistent improvements at equal or lower communication budgets.", "weaknesses": "1.\tLack of comparison with asynchrony-robust methods (e.g., CoBEVFlow). While the task settings may differ, CoBEVFlow demonstrates that estimating BEV flow and propagating prior features can effectively counter temporal variation; this capability should be considered—either as a baseline or as a complementary design—when claiming advantages in time-varying scenes and realistic, asynchronous communications.\n2.\tSingle-benchmark evaluation. Experiments are confined to OPV2V, which limits external validity. Broader evidence across datasets (e.g., DAIR-V2X, V2X-Sim, OPV2V-Async) and tasks beyond semantic segmentation would strengthen generality claims.\n3.\t“Conformal” is used primarily as quantile gating rather than as standard conformal prediction with finite-sample coverage guarantees. The paper lacks formal coverage analyses or mismatch bounds, so the terminology risks overstating the method’s theoretical assurances.\n4.\tLimited system- and communication-layer characterization. Reported metrics focus on bandwidth ratios/Mbps and IoU, with no measurements of end-to-end latency, packet loss/retransmissions, congestion behavior, or the computation/runtime overhead introduced by attention and masking under different hardware budgets. Deployment-level compute-communication trade-offs thus remain underexplored.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)