Configuration-Driven Multi-Modal Perception for Sensor Fusion
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Solution Overview
Problem
Current multi-modal AI systems face challenges in integrating heterogeneous sensor data from different sources due to complex synchronization and data format reconciliation, leading to inefficiencies and performance issues, particularly in autonomous systems.
Innovation Solution
A scalable multi-modal perception framework that includes an end-to-end solution for multi-sensor capture, data processing, synchronization, alignment, and 3D rendering, using a configuration data-driven approach to dynamically couple components without manual coding, and supports various sensor fusion methods through a uniform inference interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If extensive hand-coding or programming of complete data pipelines is used to connect AI models, then integration of heterogeneous sensor data is achieved, but performance, latency, and coding efficiency deteriorate
Solution Approach 1:
The framework enables self-service configuration where users can define custom multi-modal perception pipelines by simply providing configuration data without writing code. The system automatically generates the complete data pipeline based on the configuration, eliminating the need for manual coding while maintaining integration reliability.
Solution Approach 2:
The patent replaces the mechanical process of hand-coding data pipelines with an automated configuration-driven system. Instead of manually programming connections between components, users configure pipelines through data structures that are automatically translated into executable pipelines, substituting coding mechanics with configuration-based automation.
2Adaptability or versatility
If multiple disparate frameworks and custom code are stitched together, then sensor data integration is achieved, but system complexity and overhead increase due to switching between and managing different environments and frameworks
Solution Approach 1:
The framework provides a universal pipeline management system that can handle multiple sensor modalities and fusion methods through a single unified interface. The configuration data structure serves as a universal descriptor that works across different sensor types and fusion approaches, eliminating the need to manage multiple disparate frameworks simultaneously.
Solution Approach 2:
The patent segments the complex pipeline into modular components (sensor data sources, fusion methods, post-processing operations) that can be independently configured and combined. This segmentation allows the system to manage complexity by assembling pipelines from standardized modules rather than managing monolithic custom code.
3Measurement precision
If precise synchronization of time-stamped data streams and reconciliation of varied data formats is performed, then perception accuracy is improved, but processing time and computational overhead increase
Solution Approach 1:
The framework performs preliminary actions by pre-defining synchronization and format reconciliation strategies in the configuration data. Time-stamping and coordinate transformation parameters are specified in advance, allowing the system to automatically apply these transformations without real-time computational overhead, thus maintaining accuracy while reducing processing time.
Data Source
AI summary
In various examples, a framework is or provides an end-to-end solution that includes multi-sensor capture, data processing, inferencing, synchronization, alignment, and 3D rendering for multi-modal perception fusion pipelines. A multi-modal perception fusion pipeline may include a mixer, an aligner, an inference environment, and a multi-view renderer. The mixer may merge sensor data from different data sources into a single HashMap frame. The aligner may use calibration data for sensor-to-sensor coordinate transformations. The inference environment may receive multi-modality data and use custom preprocessing and custom postprocessing to generate inference results. The renderer may generate different sensor data renderings. The framework may include an application that uses configuration data to generate or configure a custom multi-modal perception fusion pipeline. The inference environment may access inference models using a uniform inference interface and support remote inference, allowing the pipeline to become an API client of the inference models.