18470332. METHOD, NON-TRANSITORY COMPUTER READABLE STORAGE MEDIUM, TRAINING DATA SET, AND DEVICE FOR CONNECTING POINT CLOUD DATA WITH RELATED DATA simplified abstract (TOYOTA JIDOSHA KABUSHIKI KAISHA)

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METHOD, NON-TRANSITORY COMPUTER READABLE STORAGE MEDIUM, TRAINING DATA SET, AND DEVICE FOR CONNECTING POINT CLOUD DATA WITH RELATED DATA

Organization Name

TOYOTA JIDOSHA KABUSHIKI KAISHA

Inventor(s)

Atsushi Kitayama of Nisshin-shi (JP)

Hyacinth Mario Arvind Anand of Lindau (DE)

Ana-Cristina Staudenmaier of Lindau (DE)

METHOD, NON-TRANSITORY COMPUTER READABLE STORAGE MEDIUM, TRAINING DATA SET, AND DEVICE FOR CONNECTING POINT CLOUD DATA WITH RELATED DATA - A simplified explanation of the abstract

This abstract first appeared for US patent application 18470332 titled 'METHOD, NON-TRANSITORY COMPUTER READABLE STORAGE MEDIUM, TRAINING DATA SET, AND DEVICE FOR CONNECTING POINT CLOUD DATA WITH RELATED DATA

Simplified Explanation

A point cloud data is connected with related data, allowing for the prediction of moving routes based on position labels and acquisition times.

  • Point cloud data includes information of a point cloud connected to three-dimensional position information
  • Each point cloud data is connected to acquisition time
  • Groups are generated by classifying the point cloud and assigned position and moving body labels
  • Moving routes of groups with moving bodies are predicted based on position labels
  • Groups are replaced with positions at acquisition times of related data according to predicted moving routes

Potential Applications

This technology could be applied in various fields such as:

  • Autonomous driving systems
  • Traffic flow optimization
  • Surveillance and security systems

Problems Solved

This technology helps in:

  • Predicting moving routes accurately
  • Enhancing data analysis and visualization
  • Improving decision-making processes based on movement patterns

Benefits

The benefits of this technology include:

  • Increased efficiency in route planning
  • Enhanced tracking and monitoring capabilities
  • Improved safety and security measures

Potential Commercial Applications

Potential commercial applications of this technology could include:

  • Transportation and logistics companies
  • Smart city initiatives
  • Real-time tracking and monitoring solutions

Possible Prior Art

One possible prior art for this technology could be:

  • GPS tracking systems used in transportation and logistics industries

What are the limitations of this technology in real-world applications?

The limitations of this technology in real-world applications include:

  • Accuracy of predicting moving routes
  • Integration with existing systems and technologies

How does this technology compare to traditional route planning methods?

This technology offers:

  • More accurate and real-time route predictions
  • Enhanced data visualization and analysis capabilities


Original Abstract Submitted

A point cloud data is connected with a related data. Multiple sets of point cloud data are prepared. Each point cloud data includes information of a point cloud connected to three-dimensional position information, and each point cloud data is connected to acquisition time. At least one group is generated by classifying the point cloud, and the group is assigned to a position label and a moving body label. A moving route of the group with a moving body on-flag is predicted based on the position label of the group. The group is replaced with a position at acquisition time of the related data according to the moving route, and the acquisition time of the related data is connected to the group.

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