Multimodal Sensor Data Fusion via Priority-Based Latent Space
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Solution Overview
Problem
Existing environmental monitoring systems for vehicles or robots rely on single measurement modalities, which are unreliable in varying environmental conditions such as direct sunlight, heavy rain, or snow, leading to incomplete or inaccurate data for obstacle detection and navigation.
Innovation Solution
A procedure for processing multimodal measurement data from various sensors, such as cameras, by transferring non-convertible parts of the data into a shared latent space, where different measurement modalities are prioritized based on their relevance to the task at hand, and merged into an overall representation for improved decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple measurement modalities are used for environmental monitoring, then the reliability of information availability is improved, but the complexity of data processing and fusion increases
Solution Approach 1:
The patent segments the measurement data from different modalities into separate processing streams, with dedicated processing units for each modality type. Each processing unit independently handles its specific modality data through encoding and priority assignment, avoiding the complexity of direct multi-modal fusion while maintaining reliable information integration through the shared latent space architecture.
2Loss of information
If multiple measurement modalities are integrated without prioritization, then information redundancy is reduced, but information dilution occurs reducing overall accuracy
Solution Approach 1:
The patent applies local quality by assigning different priorities to different processing units based on their modality-specific relevance to the current task. Each processing unit receives a priority weight that reflects the local importance of its modality for the specific situation, allowing the system to adaptively emphasize the most relevant information sources while reducing the influence of less relevant modalities, thus preventing information dilution.
3Adaptability or versatility
If all measurement modalities are processed equally, then no single modality is disadvantaged, but the contribution of highly relevant modalities is diluted by less relevant ones
Solution Approach 1:
The patent implements dynamics by making the priority assignment adaptive rather than static. The priority weights assigned to different processing units are dynamically adjusted based on task requirements and environmental conditions, allowing the system to flexibly emphasize the most relevant modalities for each specific situation while maintaining the ability to process all modalities when needed, thus resolving the contradiction between adaptability and precision.
Data Source
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AI summary
Method (100) for processing measurement data (3, 3a, 3b) obtained by observing a scene (1) with one or more sensors (2a, 2b) for the purpose of solving a given task, comprising the steps: • different non-overlapping portions (3a, 3b) of the measurement data (3) are transformed into representations (5a, 5b) in the same workspace (5) using different trained processing units (4a, 4b) (110); • priorities (6a, 6b) are assigned (120) to different portions (3a, 3b) of the measurement data (3) and/or representations (5a, 5b) with which they are to be used for the solution of the given task; • it is checked (130) on the basis of at least one given evaluation criterion (7) whether the different components (3a, 3b) of the measurement data (3) and/or representations (5a, 5b) are useful for solving the given task;• Representations (5a, 5b), insofar as they have been identified as usable or relate to usable portions (3a, 3b) of the measurement data (3), are fused into an overall representation (8) according to their priorities (6a, 6b) or the priorities (6a, 6b) of the respective portions (3a, 3b) of the measurement data (3) (140); and • this overall representation (8) is processed with a trained task header (9) to produce an output (10) with respect to the specified task (150).