Cross-Modal Transformer Sensor Fusion
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Solution Overview
Problem
Existing sensor fusion technologies face challenges in effectively combining data from multiple sensor modalities, such as RGB images, depth sensors, and radar, to provide a holistic view of the environment, especially in scenarios with poor extrinsic calibration and varying sensor reference frames.
Innovation Solution
A cross-modal transformer-based neural network is employed to fuse sensor data from different modalities using attention mechanisms, allowing the system to learn contextual relationships between sensor inputs and generate a unified representation of the environment, which is robust to calibration errors and can handle diverse sensor data types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional sensor fusion methods are used, then the system can process sensor data, but it fails to provide a holistic view of the environment under poor extrinsic calibration and varying sensor reference frames
Solution Approach 1:
The patent introduces a cross-modal transformer as an intermediary component that mediates between different sensor modalities (RGB images, depth sensors, radar). This transformer architecture processes data from multiple sensor reference frames and generates a unified environmental representation, effectively resolving the contradiction between providing a holistic view and maintaining robustness to calibration errors without requiring precise extrinsic calibration between sensors.
2Quantity of substance
If multiple sensor modalities are combined, then the system can provide comprehensive environmental information, but the complexity of processing and integrating diverse data types increases
Solution Approach 1:
The cross-modal transformer architecture provides universality by handling multiple sensor modalities (RGB images, depth data, radar signals) through a single unified processing framework. The transformer's attention mechanisms can process different data types and modalities simultaneously, generating comprehensive environmental information while managing processing complexity through a standardized multi-functional architecture rather than requiring separate processing pipelines for each sensor type.
3Productivity
If existing sensor fusion techniques are applied, then the system can operate with current sensor data, but it cannot accurately perform perception tasks under individual sensor failures
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the transformer's attention weights and processing parameters based on the operational status of individual sensors. When sensor failures occur, the transformer reconfigures its attention mechanisms to rely more heavily on functional sensors, maintaining accurate perception task performance. This adaptive parameter adjustment enables the system to maintain high productivity and reliability even when individual sensors fail.
Data Source
AI summary
Techniques are generally described for fusing sensor data of different modalities using a transformer. In various examples, first sensor data may be received from a first sensor and second sensor data may be received from a second sensor. A first feature representation of the first sensor data may be generated using a first machine learning model and a second feature representation of the second sensor data may be generated using a second machine learning model. In some examples, a modified first feature representation of the first sensor data may be generated based at least in part on a self-attention mechanism of a transformer encoder. The modified first feature representation may be generated based at least in part on the first feature representation and the second feature representation. A computer vision task may be performed using the modified first feature representation.