Multi-Attention Sensor Correlation for Cross-Modal Object Detection
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Solution Overview
Problem
Processing different types of sensor data from various modalities, such as audio, vision, and lidar, in a way that improves object detection and classification across modalities is challenging due to the lack of effective methods to combine and correlate features from diverse sensor inputs.
Innovation Solution
The use of a multi-attention machine-learning model that includes a multi-attention component, allowing for the correlation of features between different sensor data modalities. This model synchronizes sensor data from various modalities and uses attention mechanisms to draw attention to relevant features across different data types, enhancing object detection and classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If different types of sensor data from multiple modalities are processed in combination to improve object detection accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent divides the processing of different sensor modalities into separate processing streams, where each modality (audio, vision, lidar, radar) is processed independently through its own neural network architecture. This segmentation allows each modality to be handled with specialized processing while maintaining the ability to combine results, thereby improving detection accuracy without overwhelming system complexity.
Solution Approach 2:
The patent employs a unified attention mechanism that can operate across multiple sensor modalities simultaneously. This multi-functional attention system processes features from different modalities through a common framework, allowing the system to handle diverse sensor inputs with a single versatile processing architecture rather than requiring separate complex systems for each modality.
2Reliability
If sensor data from different modalities is combined to improve detection confidence, then reliability is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The patent introduces an attention mechanism as an intermediary component that mediates between features from different sensor modalities. This attention mechanism selectively weights and correlates features across modalities based on their relevance to object detection, making the combination process more manageable and effective compared to direct integration of all sensor data.
Solution Approach 2:
The attention mechanism applies local quality by selectively emphasizing specific features from different modalities based on their local relevance to detection tasks. Rather than uniformly processing all features, the system dynamically adjusts the importance of individual features across modalities, improving reliability by focusing on the most informative local features while simplifying the overall detection process.
3Measurement precision
If multi-attention mechanisms are used to correlate features across modalities, then measurement precision is improved, but computational overhead increases
Solution Approach 1:
The attention mechanism implements partial action by selectively processing only the most relevant features from each sensor modality rather than exhaustively analyzing all features. This selective attention approach achieves accurate feature correlation by focusing computational resources on critical features, thereby improving measurement precision while reducing overall computational overhead compared to comprehensive feature analysis.
Data Source
AI summary
Techniques for detecting, locating, and/or classifying objects based on multiple sensor data inputs received from different sensor modalities. The techniques may include receiving sensor data generated by different sensor modalities of a vehicle, the sensor data including at least first sensor data generated by a first sensor modality and second sensor data generated by a second sensor modality. In some examples, the sensor data may be input into a machine-learning pipeline. The machine-learning pipeline may be configured to determine locations of objects in an environment surrounding the vehicle based at least in part on a correlation, by the multi-attention component, of the first sensor data and the second sensor data. The techniques may also include receiving, from the machine-learning pipeline, an output indicating a location of an object in the environment.