Situation Awareness System Using Static and Dynamic Global Representations
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Solution Overview
Problem
Existing situation awareness systems face challenges in adaptability and accuracy, particularly in environments where real-time detection and tracking methods require prior knowledge of sensor setups and synchronized inputs, making them difficult to apply across various settings.
Innovation Solution
A system that generates static and dynamic global representations of an environment using environmental data from multiple sensors, allowing for the estimation of a target state at a future time point through a target estimation model, which includes feature extraction and updating mechanisms to account for changes and static elements, enabling scalable and adaptive situation awareness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time detection algorithms and tracking-based methods are used to estimate the likelihood of abnormal situations, then measurement precision is improved, but device complexity increases and adaptability to different environments deteriorates
Solution Approach 1:
The system segments environmental data into static global representations and dynamic global representations. Static representations capture stable environmental structures, while dynamic representations capture moving elements and changes. This segmentation allows the system to process complex environments more efficiently and adapt to different settings without requiring complete reconfiguration.
Solution Approach 2:
The system employs a unified framework that can handle both static and dynamic environmental elements using the same core architecture. The global representation approach serves multiple functions: it captures spatial relationships, tracks moving elements, and adapts to different sensor configurations, making the system universally applicable across various environments without requiring environment-specific customization.
2Measurement precision
If synchronized inputs from multiple sensors are used, then measurement precision is improved, but ease of operation deteriorates due to difficulty in application across different environments
Solution Approach 1:
The system dynamically adapts to different sensor configurations and environmental conditions rather than requiring fixed synchronized inputs. The global representation framework can process data from varying numbers and types of sensors, adjusting its processing accordingly. This dynamic approach maintains measurement precision while significantly improving ease of operation across diverse deployment scenarios.
3Measurement precision
If prior knowledge about sensor set-up and placements is required, then measurement precision is improved, but adaptability to different environments deteriorates
Solution Approach 1:
The system performs self-calibration and automatic adaptation to different sensor configurations without requiring pre-programmed knowledge of sensor setups. The global representation framework automatically learns and adapts to the specific sensor placements and characteristics in each environment, maintaining high estimation accuracy while eliminating the need for manual configuration knowledge.
Data Source
AI summary
The present disclosure generally provides systems and methods for situation awareness. When executing a set of instructions stored in at least one non-transitory storage medium, at least one processor may be configured to cause the system to perform operations including obtaining, from at least one of one or more sensors, environmental data associated with an environment corresponding to a first time point, generating a first static global representation of an environment corresponding to the first time point based at least in part on the environmental data, generating a first dynamic global representation of the environment corresponding to the first time point based at least in part on the environmental data, and estimating, based on the first static global representation and the first dynamic global representation, a target state of the environment at a target time point using a target estimation model.