ML Object Generator for Simulation Sensor Accuracy
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Solution Overview
Problem
Existing simulation technologies face challenges in accurately replicating real-world environments without direct measurement of object parameters, as collecting specific parameters for each object can be impractical or impossible, leading to inaccuracies in sensor returns within simulations.
Innovation Solution
A system that uses an object-generation interface trained by machine learning to infer object parameters from real-world sensor data, allowing for the creation of accurate object models that produce similar sensor returns in simulations, reducing the need for explicit parameter collection and enhancing simulation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If object parameters are directly measured in the real world for simulation modeling, then simulation accuracy is improved, but measurement and data collection complexity increases significantly
Solution Approach 1:
The patent creates virtual copies of real-world objects by training a machine learning model to generate simulation objects that replicate the sensor return characteristics of actual objects. Instead of directly measuring complex physical parameters, the system learns to copy the observable sensor responses, thereby achieving accurate simulation modeling without the complexity of direct parameter measurement
Solution Approach 2:
The patent replaces the mechanical/physical measurement system with a machine learning-based inference system. Rather than using physical sensors and measurement devices to directly obtain object parameters, the system uses trained ML models to infer parameters from sensor return data, substituting complex physical measurement processes with computational inference
2Manufacturing precision
If direct measurement of object parameters is performed, then manufacturing precision of simulation models is improved, but time and resource consumption increases
Solution Approach 1:
The patent performs preliminary training of the machine learning model using a dataset of objects and their sensor returns before actual simulation modeling is needed. This preliminary action creates a ready-to-use inference system that can quickly generate accurate simulation models without requiring time-consuming direct measurements during the actual simulation setup phase
Solution Approach 2:
The system creates accurate copies of real-world objects in simulation environments by learning from sensor return data during a preliminary training phase, eliminating the need for time-consuming direct parameter measurements when creating new simulation models
3Reliability
If comprehensive object parameters are collected for accurate simulation, then simulation reliability is improved, but ease of operation deteriorates due to impractical data collection requirements
Solution Approach 1:
The patent substitutes the impractical mechanical process of direct parameter collection with a machine learning-based system that infers parameters from easily obtainable sensor return data. This replacement maintains simulation reliability while dramatically improving ease of operation by using data that is already captured during normal sensor operations
Solution Approach 2:
The machine learning model serves as an intermediary between easily collected sensor return data and the comprehensive object parameters needed for reliable simulation. This intermediary translates readily available sensor information into accurate simulation parameters without requiring direct measurement of difficult-to-obtain physical properties
Data Source
AI summary
An object model for a real-world object is constructed using a parameter-based object generator, where the parameters for the object model are determined using sensor data such as photographic images, radar images, or LIDAR images obtained from the real-world object. Conversion of the sensor data to appropriate object parameters is accomplished using a machine learning system. The machine learning system is calibrated by generating a plurality of object parameters, generating a corresponding plurality of learning objects from the plurality of object parameters, generating a plurality of simulated sensor return data for the learning objects, and then providing the simulated sensor return data and object parameters to the machine learning system.