View source for Hyundai motor company (20240127470). METHOD OF PREDICTING A POSITION OF AN OBJECT AT A FUTURE TIME POINT FOR A VEHICLE simplified abstract

=METHOD OF PREDICTING A POSITION OF AN OBJECT AT A FUTURE TIME POINT FOR A VEHICLE=


==Organization Name==

[[:Category:hyundai motor company|hyundai motor company]]


[[Category:hyundai motor company]]

==Inventor(s)==

[[:Category:Hyung-Wook Park of Seoul (KR)|Hyung-Wook Park of Seoul (KR)]][[Category:Hyung-Wook Park of Seoul (KR)]]

[[:Category:Jang-Ho Shin of Yongin-si (KR)|Jang-Ho Shin of Yongin-si (KR)]][[Category:Jang-Ho Shin of Yongin-si (KR)]]

[[:Category:Seo-Young Jo of Seoul (KR)|Seo-Young Jo of Seoul (KR)]][[Category:Seo-Young Jo of Seoul (KR)]]

[[:Category:Je-Won Kang of Seoul (KR)|Je-Won Kang of Seoul (KR)]][[Category:Je-Won Kang of Seoul (KR)]]

[[:Category:Jung-Kyung Lee of Seoul (KR)|Jung-Kyung Lee of Seoul (KR)]][[Category:Jung-Kyung Lee of Seoul (KR)]]

==METHOD OF PREDICTING A POSITION OF AN OBJECT AT A FUTURE TIME POINT FOR A VEHICLE - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240127470 titled 'METHOD OF PREDICTING A POSITION OF AN OBJECT AT A FUTURE TIME POINT FOR A VEHICLE

==Simplified Explanation==

The patent application describes a method for predicting the position of an object in the future for a vehicle using video image information and deep learning techniques.

* Video image information is extracted from the vehicle's camera at multiple time points.
* Semantic segmentation images and mask images are extracted to identify objects and their attributes.
* Hypotheses for the object's future position are derived through deep learning using the collected data.
* Ego-motion information of the vehicle is used to calculate the hypotheses as a Gaussian mixture probability distribution.

== Potential Applications ==
This technology could be applied in autonomous driving systems, robotics, surveillance systems, and traffic management.

== Problems Solved ==
This technology helps in predicting the future position of objects, which can improve safety, efficiency, and decision-making in various applications.

== Benefits ==
The benefits of this technology include enhanced object tracking, improved navigation systems, and increased situational awareness for vehicles and other systems.

== Potential Commercial Applications ==
Potential commercial applications of this technology include autonomous vehicles, smart city infrastructure, security systems, and industrial automation.

== Possible Prior Art ==
One possible prior art could be related to object tracking and prediction algorithms used in autonomous vehicles and robotics systems.

=== Unanswered Questions ===

=== How does this technology handle occlusions and complex environments? ===
The method described in the patent application may face challenges in predicting object positions accurately in scenarios with occlusions or complex environments. Further research and development may be needed to address these issues.

=== What are the computational requirements for implementing this technology in real-time systems? ===
The patent application does not provide information on the computational resources needed to implement this method in real-time systems. Understanding the computational requirements is crucial for practical applications of this technology.


==Original Abstract Submitted==

in a method of predicting a position of an object at a future time point for a vehicle, video image information at a current time point and at a plurality of time points before the current time point acquired through a camera of the vehicle may be extracted as semantic segmentation image. a mask image imaging an attribute and position information of an object present in each of the video images may be extracted. a position distribution of the object may be predicted by deriving a plurality of hypotheses for a position of the object at a future time point through deep learning by receiving video images at the current time point and the time points before the current time point, a plurality of semantic segmentation images, a plurality of mask images, and ego-motion information of the vehicle, and calculating the plurality of hypotheses as a gaussian mixture probability distribution.



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[[Category:G01S17/89]]
[[Category:G06T7/12]]
[[Category:G06V10/10]]