COOPERTRIM: Adaptive Data Selection for Uncertainty-Aware Cooperative Perception

2026-02-07

AI Summary

This work addresses the tension between limited communication bandwidth and high-dimensional sensor data that hinders practical deployment in collaborative perception. We propose an adaptive data selection framework that leverages temporal continuity to identify dynamic environmental features, thereby avoiding redundant transmission of static information. By employing conformal prediction to construct a temporal uncertainty metric, our method evaluates feature relevance and dynamically adjusts the volume of shared data through a data-driven mechanism. Evaluated on semantic segmentation and 3D object detection tasks across multiple open-source collaborative models, the approach achieves up to 80.28% bandwidth reduction with comparable accuracy; under a 72% bandwidth reduction, it improves IoU by 45.54%; and when combined with compression techniques, it reduces bandwidth usage to merely 1.46% without performance degradation.

Technology Category

• Machine Learning: Dimensionality Reduction/Feature Selection
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Data Mining & Knowledge Management: Data Compression

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• cooperative perception
• communication bandwidth
• data selection
• temporal redundancy
• uncertainty-aware

Innovation

• adaptive data selection
• temporal uncertainty
• cooperative perception
• bandwidth reduction
• conformal prediction