Multi-Vehicle BEV Feature Fusion for Robust Cooperative Perception
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Solution Overview
Problem
Single-agent multi-sensor BEV frameworks have limitations such as limited resolution, coverage, accuracy, and scalability, especially in adverse weather conditions, and existing techniques fail to handle adaptive fusion of multi-modal sensor combinations and variable BEV grid resolutions in collaborative perception systems.
Innovation Solution
A multi-vehicle BEV system performs early dynamic feature fusion, aligning BEV grids across vehicles with different resolutions and handling dynamic objects and camera setups, using cloud computing for inference and transmitting compact BEV features to enhance perception accuracy and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single-agent BEV frameworks are used, then device complexity is reduced, but measurement precision and coverage are limited
Solution Approach 1:
The patent combines sensor data from multiple vehicles to create a collaborative BEV perception system. By merging data sources across different agents, the system achieves improved measurement precision and extended coverage while distributing the computational complexity across the network rather than concentrating it in a single system.
Solution Approach 2:
The patent transitions from single-vehicle 2D camera views to multi-vehicle 3D BEV representations. By adding the spatial dimension of multiple vehicle positions and utilizing vertical stacking of sensor data, the system achieves comprehensive environmental coverage and precise object detection without requiring each individual vehicle to have complex multi-sensor configurations.
2Area of stationary object
If multi-vehicle cooperative perception is implemented, then coverage and resolution are improved, but communication overhead increases
Solution Approach 1:
The patent extracts only the essential BEV feature representations from each vehicle's sensor data and transmits these compressed features to the cloud server. By taking out only the critical perception information rather than transmitting raw sensor data, the system achieves extended perception coverage while significantly reducing communication overhead and energy consumption.
Solution Approach 2:
The patent transforms raw sensor data into parameterized BEV feature representations with standardized resolutions and formats. By changing the data parameters from high-volume raw sensor readings to compact feature vectors, the system enables multi-vehicle collaboration with reduced communication bandwidth requirements while maintaining improved coverage and resolution.
3Adaptability or versatility
If variable resolution BEV grids are used across different vehicles, then adaptability is improved, but processing complexity increases
Solution Approach 1:
The patent creates a universal BEV feature representation framework that can process data from vehicles with different sensor configurations and resolutions. By establishing a common feature space and standardized processing pipeline, the system achieves heterogeneous vehicle support while maintaining consistent processing complexity through unified transformation operations.
Solution Approach 2:
The patent combines BEV features from multiple vehicles with different resolutions and sensor capabilities into a unified composite representation. By fusing heterogeneous data sources through standardized feature fusion operations, the system achieves adaptability to diverse vehicle configurations while managing processing complexity through consistent fusion algorithms.
4Measurement precision
If early feature fusion is performed in cloud computing environment, then accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary feature extraction and BEV transformation at each vehicle before transmission to the cloud server. By completing the computationally intensive sensor-to-BEV transformation locally as a preliminary action, the system enables accurate early feature fusion in the cloud while reducing the inference time for the collaborative perception task.
Data Source
AI summary
An example system includes one or more memories for storing grid-free vehicle data and one or more processors configured to determine one or more first features from first vehicle data of the vehicle data, the first vehicle data being from a first vehicle of the plurality of vehicles. The one or more processors are configured to determine one or more second features from second vehicle data of the vehicle data, the second vehicle data being from a second vehicle of the plurality of vehicles. The one or more processors are configured to fuse the one or more first features and the one or more second features to generate fused features. The one or more processors are configured to generate a BEV representation based on the fused features.