COOPERTRIM: Adaptive Data Selection for Uncertainty-Aware Cooperative Perception
Shilpa Mukhopadhyay, Amit Roy-Chowdhury, Hang Qiu

TL;DR
COOPERTRIM introduces an adaptive, uncertainty-aware feature selection framework for cooperative perception that significantly reduces bandwidth usage while maintaining high accuracy in autonomous systems.
Contribution
It proposes a novel conformal temporal uncertainty metric and a dynamic sharing mechanism to adapt feature transmission based on environment complexity.
Findings
Achieves up to 80.28% bandwidth reduction in segmentation tasks.
Improves IoU by up to 45.54% over other strategies.
Reduces bandwidth to as low as 1.46% with maintained performance.
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…
Peer Reviews
Decision·ICLR 2026 Poster
**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. **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 train
**Major Weaknesses:** 1. 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. 2. Robustness against localization error and latency is not dis
1. The idea of data selection by using temporal uncertainty is interesting. 2. The theoretical proof is sound. 3. The reduction in communication bandwidth consumption in segmentation tasks is obvious.
1. No ablation studies on how to choose the optimal thresholds. 2. Only one simulated dataset is used; No real-world dataset is evaluated. 3. What is the mathematical expression of the distance function? What is the deep reason for using this distance function? 4. 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
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. 2. The $\epsilon$-greedy training schedule provides a practical stabilizer under bandwidth const
1. Lack 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. 2. Single-benchmark evaluation. Experiments are confined to OPV2V, which limits external v
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Taxonomy
TopicsIndoor and Outdoor Localization Technologies · Underwater Vehicles and Communication Systems · Wireless Networks and Protocols
