View source for 17933554. UNIVERSAL MACHINE LEARNING BASED SYSTEM FOR ESTIMATING A VEHICLE STATE simplified abstract (GM GLOBAL TECHNOLOGY OPERATIONS LLC)

=UNIVERSAL MACHINE LEARNING BASED SYSTEM FOR ESTIMATING A VEHICLE STATE=


==Organization Name==

[[:Category:GM GLOBAL TECHNOLOGY OPERATIONS LLC|GM GLOBAL TECHNOLOGY OPERATIONS LLC]]


[[Category:GM GLOBAL TECHNOLOGY OPERATIONS LLC]]

==Inventor(s)==

[[:Category:Amir Khajepour of Waterloo (CA)|Amir Khajepour of Waterloo (CA)]][[Category:Amir Khajepour of Waterloo (CA)]]

[[:Category:Amin Habibnejad Korayem of Markham (CA)|Amin Habibnejad Korayem of Markham (CA)]][[Category:Amin Habibnejad Korayem of Markham (CA)]]

[[:Category:Ehsan Hashemi of Waterloo (CA)|Ehsan Hashemi of Waterloo (CA)]][[Category:Ehsan Hashemi of Waterloo (CA)]]

[[:Category:Qingrong Zhao of Warren MI (US)|Qingrong Zhao of Warren MI (US)]][[Category:Qingrong Zhao of Warren MI (US)]]

[[:Category:SeyedAlireza Kasaiezadeh Mahabadi of Novi MI (US)|SeyedAlireza Kasaiezadeh Mahabadi of Novi MI (US)]][[Category:SeyedAlireza Kasaiezadeh Mahabadi of Novi MI (US)]]

[[:Category:Yechen Qin of Beijing (CN)|Yechen Qin of Beijing (CN)]][[Category:Yechen Qin of Beijing (CN)]]

==UNIVERSAL MACHINE LEARNING BASED SYSTEM FOR ESTIMATING A VEHICLE STATE - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17933554 titled 'UNIVERSAL MACHINE LEARNING BASED SYSTEM FOR ESTIMATING A VEHICLE STATE

==Simplified Explanation==

The patent application describes a universal machine learning-based system for estimating the state of a vehicle using dynamic variables and historical data. Here is a simplified explanation of the abstract:

* The system receives dynamic variables and historical data related to a vehicle.
* It performs a sensitivity analysis to determine the importance of each dynamic variable.
* Based on the sensitivity levels, it selects relevant dynamic variables.
* The selected variables are standardized into a generic format applicable to any vehicle configuration.
* The vehicle state is estimated using machine learning algorithms based on the standardized dynamic variables.

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### Potential Applications of this Technology
1. Autonomous driving systems
2. Vehicle health monitoring and diagnostics
3. Predictive maintenance for vehicles

### Problems Solved by this Technology
1. Efficient and accurate estimation of a vehicle's state
2. Standardization of dynamic variables for different vehicle configurations
3. Improved decision-making for vehicle control systems

### Benefits of this Technology
1. Enhanced safety and performance of vehicles
2. Cost-effective maintenance and repair processes
3. Real-time monitoring and analysis of vehicle conditions

### Potential Commercial Applications of this Technology
#### Optimizing Vehicle Performance and Safety through Machine Learning

### Possible Prior Art
There are existing systems for vehicle state estimation using machine learning algorithms, but the innovation lies in the standardization of dynamic variables for any vehicle configuration.

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### Unanswered Questions
#### How does the system handle real-time data updates for dynamic variables?
The patent application does not specify how the system deals with continuous updates to dynamic variables during operation.

#### What is the computational complexity of the sensitivity analysis algorithm?
The abstract does not provide information on the computational resources required for the sensitivity analysis algorithm.


==Original Abstract Submitted==

A universal machine learning based system for estimating a vehicle state of a vehicle includes one or more controllers executing instructions to receive a plurality of dynamic variables and corresponding historical data. The controllers execute a sensitivity analysis algorithm to determine a sensitivity level for each dynamic variable and corresponding historical data and select two or more pertinent dynamic variables based on the sensitivity level of each dynamic variable and the corresponding historical data. The controllers standardize the two or more pertinent dynamic variables into a plurality of generic dynamic variables, wherein the plurality of generic dynamic variables are in a standardized format that is applicable to any configuration of vehicle, and estimate the vehicle state based on the plurality of generic dynamic variables by one or more machine learning algorithms.



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[[Category:B60W40/13]]
[[Category:G07C5/08]]