AI-Based Multi-Sensor Fusion and Adaptive Learning System for Enhanced Decision-Making

Abstract

The present invention relates to a AI-Based Multi-Sensor Fusion and Adaptive Learning System for Enhanced Decision-Making. The AI-Based Multi-Sensor Fusion and Adaptive Learning System combines data from multiple sensors through an AI-driven fusion engine and reinforcement learning for improved decision-making in real-time applications. It dynamically adjusts sensor weighting and validates decision confidence, providing adaptability and reliability across diverse environments. Key applications include autonomous vehicles, industrial automation, healthcare monitoring, and smart city management. By leveraging edge computing and federated learning, the system achieves low latency, high accuracy, and privacy-conscious operation in environments that demand robust, context-aware decision-making. Accompanied Drawing [FIG. 1]

Specification

Description:[001] The present invention pertains to advanced data processing systems, particularly those that integrate multiple data sources through multi-sensor fusion and machine learning to facilitate accurate, real-time decision-making. Specifically, this invention is applicable in fields that require adaptive, context-aware data processing, including autonomous vehicles, healthcare monitoring, industrial automation, and smart city management, where robust and immediate decisions are essential.
BACKGROUND OF THE INVENTION
[002] The following description provides the information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[003] Multi-sensor fusion technology is central to systems that require comprehensive environmental awareness. Autonomous systems, healthcare monitors, and industrial automation tools , Claims:1. A system for multi-sensor fusion and adaptive decision-making, comprising:
a multi-sensor fusion engine for integrating diverse sensor data,
an adaptive learning module that applies reinforcement learning to adjust sensor weighting dynamically,
an edge-based decision-making unit for low-latency data processing and immediate response.
2. The system of claim 1, wherein the adaptive learning module refines sensor weights based on real-time feedback from environmental conditions.
3. A multi-sensor fusion system that dynamically re-weights sensors based on contextual factors, using deep neural networks and reinforcement learning for improved accuracy.
4. The system of claim 3, further comprising a Bayesian network for confidence scoring, enhancing decision reliability in real-time applications.
5. A method for adaptive learning in multi-sensor fusion, involving:
feedback acquisition post-decision,
reinforcement learning adjustments to sensor weights,
de-emphasis of low-reliability sensors based on performance h

Documents