View source for 18082738. METHOD AND APPARATUS FOR DETERMINING A POSE OF A VEHICLE, AND VEHICLE CONTAINING SAME simplified abstract (HUAWEI TECHNOLOGIES CO., LTD.)

=METHOD AND APPARATUS FOR DETERMINING A POSE OF A VEHICLE, AND VEHICLE CONTAINING SAME=


==Organization Name==

[[:Category:HUAWEI TECHNOLOGIES CO., LTD.|HUAWEI TECHNOLOGIES CO., LTD.]]


[[Category:HUAWEI TECHNOLOGIES CO., LTD.]]

==Inventor(s)==

[[:Category:James Gregson of Ottawa (CA)|James Gregson of Ottawa (CA)]][[Category:James Gregson of Ottawa (CA)]]

[[:Category:Shao Hua Chen of Ottawa (CA)|Shao Hua Chen of Ottawa (CA)]][[Category:Shao Hua Chen of Ottawa (CA)]]

==METHOD AND APPARATUS FOR DETERMINING A POSE OF A VEHICLE, AND VEHICLE CONTAINING SAME - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18082738 titled 'METHOD AND APPARATUS FOR DETERMINING A POSE OF A VEHICLE, AND VEHICLE CONTAINING SAME

'''Simplified Explanation''': 
A set of unordered points associated with road markings is processed by an artificial neural network to determine the pose of a vehicle.

'''Key Features and Innovation''':
* Unordered points inputted to a trained artificial neural network
* Non-linear regression applied to generate an output
* Pose of a vehicle determined based on the output

'''Potential Applications''':
This technology can be used in autonomous vehicles, robotics, and computer vision systems for accurate positioning and navigation.

'''Problems Solved''':
This technology addresses the challenge of accurately determining the pose of a vehicle based on road markings.

'''Benefits''':
* Improved accuracy in determining vehicle pose
* Enhanced navigation capabilities for autonomous systems
* Increased safety on the roads

'''Commercial Applications''':
Potential commercial applications include autonomous vehicles, drone navigation systems, and smart city infrastructure development.

'''Prior Art''':
Readers can explore prior research in the fields of computer vision, artificial intelligence, and autonomous systems for related technologies.

'''Frequently Updated Research''':
Stay informed about advancements in artificial neural networks, non-linear regression techniques, and computer vision applications for the latest developments in this technology.

'''Questions about vehicle pose determination''':
1. How does the artificial neural network process the unordered points to determine the vehicle's pose?
2. What are the potential limitations of using non-linear regression in this context?

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==Original Abstract Submitted==

A set of unordered points associated with road markings is received. The unordered points are inputted to a trained artificial neural network. Using the artificial neural network, an output is generated by applying non-linear regression to the unordered points. Based on the output, a pose of a vehicle is determined.



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