Cooperative Perception Feature Fusion Under Bandwidth Constraints

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Solution Overview

Problem

Existing cooperative perception systems rely on idealized assumptions that require all entities to transmit features of the same spatial size, which is impractical due to varying communication capacities and bandwidths, limiting their effectiveness in real-world scenarios.

Innovation Solution

The introduction of a Pillar Attention Encoder (PAE) that extracts feature data using a multi-head point attention mechanism and adaptive feature filtering to adjust the size of feature data based on communication bandwidth, allowing for flexible feature sharing and fusion among entities with different capabilities.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If all entities transmit features of the same spatial size, then feature fusion is simplified, but communication bandwidth requirements increase and adaptability to different communication capacities is reduced

Engineering Contradiction:

Improveadaptability to different communication capacitiesVSAvoidcommunication bandwidth consumption

Core Design Contradiction:

Adaptability or versatilityVSQuantity of substance

Solution Approach 1:

The system dynamically adjusts the spatial size of transmitted feature data based on the communication bandwidth capacity of each entity. The feature encoder adaptively determines the feature map resolution to match available communication resources, allowing entities with different communication capacities to participate efficiently in cooperative perception without wasting bandwidth or losing critical information.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent changes the spatial parameter (size) of feature data according to communication bandwidth constraints. By varying the feature map resolution and selecting different levels of feature abstraction, the system optimizes the trade-off between information quality and communication cost, enabling scalable cooperative perception across heterogeneous entities.

Inventive Principle:

Principle #35Parameter changes

2Productivity

If feature data size is reduced to match communication bandwidth, then communication efficiency improves, but perception accuracy may deteriorate

Engineering Contradiction:

Improvecommunication efficiencyVSAvoidperception accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The feature encoder extracts and transmits only the most salient and informative features rather than complete feature maps. By selectively encoding and transmitting critical feature information at reduced resolution, the system maintains perception accuracy while significantly improving communication efficiency. The attention mechanism identifies and prioritizes important features for transmission.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system transmits a partial representation of feature data that is sufficient for effective perception but not exhaustive. By sending compressed feature maps at adaptive resolutions, the system achieves adequate perception performance with reduced communication overhead, balancing information completeness against bandwidth constraints.

Inventive Principle:

Principle #16Partial or excessive action

3Adaptability or versatility

If feature maps of different spatial sizes are fused, then adaptability to different entities improves, but fusion complexity increases

Engineering Contradiction:

Improvecompatibility across entitiesVSAvoidfeature fusion complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent resolves spatial size mismatches by operating in a transformed feature space rather than directly fusing pixel-aligned feature maps. The system uses coordinate transformations and feature projections to align features from entities with different resolutions, converting the problem from spatial alignment to feature-space integration, thereby reducing fusion complexity while maintaining adaptability.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

Solution Approach 2:

The feature fusion architecture is designed to handle feature maps of varying spatial dimensions through a universal fusion mechanism. The system employs adaptive pooling and feature aggregation operations that can process inputs of different sizes, creating a size-invariant fusion process that works across heterogeneous entities without requiring exact spatial matching.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250340214A1Systems and methods for a cooperative perception system

Publication Date: 2025.11.06 RGT UNIV OF CALIFORNIA

AI summary

Systems and methods for cooperative perception are described. In some examples, the system can comprise a first subsystem comprising a first sensor, a first communication device, and a processor, which can cause the system to: detect, by the first sensor, first point cloud data, apply a data preprocessing process to the first point cloud data to generate first preprocessed sensor data, apply a feature encoding process to the first preprocessed sensor data to generate first feature data; apply an adaptive feature filtering process to the first feature data to select a first subset of features from the first feature data; apply a cooperative feature aggregation process to fuse the first subset of features with other subsets of features, to generate a fused feature map; and apply an object perception model to the fused feature map to generate object perception data.