18125371. METHOD OF PREDICTING A POSITION OF AN OBJECT AT A FUTURE TIME POINT FOR A VEHICLE simplified abstract (Kia Corporation)
METHOD OF PREDICTING A POSITION OF AN OBJECT AT A FUTURE TIME POINT FOR A VEHICLE
Organization Name
Inventor(s)
Jang-Ho Shin of Yongin-si (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 18125371 titled 'METHOD OF PREDICTING A POSITION OF AN OBJECT AT A FUTURE TIME POINT FOR A VEHICLE
Simplified Explanation
The method described in the patent application involves predicting the position of an object at a future time point for a vehicle using video image information, semantic segmentation images, mask images, and ego-motion information of the vehicle. This is done by extracting video image information and mask images, predicting a position distribution of the object through deep learning, and calculating the hypotheses as a Gaussian mixture probability distribution.
- Semantic segmentation images and mask images are extracted from video image information.
- Position distribution of the object is predicted by deriving hypotheses for its future position through deep learning.
- Ego-motion information of the vehicle is used in the prediction process.
- The hypotheses are calculated as a Gaussian mixture probability distribution.
---
- Potential Applications
This technology could be applied in autonomous driving systems, robotics, surveillance systems, and object tracking applications.
- Problems Solved
This technology solves the problem of accurately predicting the position of objects in a vehicle's surroundings, which is crucial for safe and efficient navigation.
- Benefits
The benefits of this technology include improved object detection and tracking, enhanced safety in autonomous vehicles, and better decision-making capabilities for vehicles.
- Potential Commercial Applications
The potential commercial applications of this technology include autonomous vehicles, drone navigation systems, security and surveillance systems, and industrial automation processes.
- Possible Prior Art
One possible prior art for this technology could be existing object detection and tracking systems used in autonomous vehicles and surveillance cameras.
---
- Unanswered Questions
- How does this technology handle occlusions and complex environments in object prediction?
The patent application does not specifically address how the technology deals with occlusions and complex environments that may affect object prediction accuracy.
- What is the computational complexity of the deep learning process for predicting object positions?
The patent application does not provide information on the computational resources required for the deep learning process involved in predicting object positions.
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.
