SYSTEMS AND METHODS FOR OMNIMODAL SENSING, MULTIMODAL DATA FUSION, AND RESILIENT COMMUNICATION FOR DIAGNOSTICS, SAFETY, AND AUTONOMOUS DECISION-MAKING

Smart overview of the Invention

The patent application describes a sophisticated system designed for omnimodal sensing, data fusion, and autonomous decision-making, aimed at enhancing diagnostics, safety, and autonomous operations. The system integrates various biological-analog sensors, including visual, auditory, olfactory, and tactile, with symbolic data sources like text and digital inputs. These inputs are processed by a data-fusion engine to create a cohesive context for machine-learning models to analyze. The system is particularly focused on detecting impaired driving and includes features for resilient communication and secure data management.

System Components

The system includes a vehicle sensor array equipped with cameras, audio sensors, olfactory sensors, and motion sensors, along with tactile and biometric sensors. These components work together to monitor a driver’s behavior and physiological state. The sensor fusion module aggregates and synchronizes data from these sensors, creating a unified state estimate. This data is then analyzed by machine learning models to detect signs of impairment. The system also features a cloud processing component for advanced analysis and storage, along with encryption and blockchain technology for data security.

Sensor and Data Fusion

The vehicle sensor array features advanced technologies such as high-resolution cameras with infrared capabilities, beamforming microphones, and olfactory sensors using metal oxide and electrochemical principles. Motion sensors include a 6-axis inertial measurement unit to detect erratic driving. The sensor fusion module employs a high-performance system-on-chip and neuromorphic computing elements to ensure low-latency processing. Data fusion occurs in stages, from time synchronization to high-level fusion using an Extended Kalman Filter, providing a comprehensive state estimate for analysis.

Machine Learning and Data Management

Machine learning models, including Convolutional and Recurrent Neural Networks, analyze visual and time-series data to assess driver impairment. The system supports secure AI model training within a Trusted Execution Environment, ensuring data integrity. The cloud component includes a data ingestion pipeline and machine learning pipeline, facilitating real-time and batch processing. Blockchain technology ensures a tamper-evident log of critical events, using smart contracts and consensus mechanisms for data validation and provenance.

Communication and Resilience

The system incorporates a resilient communication subsystem to maintain operations under network constraints. This includes alternative channels like cellular text, low-frequency radio, and optical or acoustic signaling. Such features ensure the system can sustain secure command and control even in challenging environments. The inclusion of symbolic or linguistic data sources, such as electronic health records, further enriches the system's capability to provide comprehensive diagnostics and decision-making support.