17984633. REPLACEMENT CONFIRMATION SYSTEM AND REPLACEMENT CONFIRMATION METHOD simplified abstract (TOYOTA JIDOSHA KABUSHIKI KAISHA)

REPLACEMENT CONFIRMATION SYSTEM AND REPLACEMENT CONFIRMATION METHOD

Organization Name

TOYOTA JIDOSHA KABUSHIKI KAISHA

Inventor(s)

Daiki Yokoyama of Shizuoka-ken Gotemba-shi (JP)

Tomohiro Kaneko of Shizuoka-ken Mishima-shi (JP)

REPLACEMENT CONFIRMATION SYSTEM AND REPLACEMENT CONFIRMATION METHOD - A simplified explanation of the abstract

This abstract first appeared for US patent application 17984633 titled 'REPLACEMENT CONFIRMATION SYSTEM AND REPLACEMENT CONFIRMATION METHOD

Simplified Explanation

The abstract describes a replacement confirmation system for vehicle control using machine learning. Here are the key points:

• The system includes a retaining unit that stores a learning model generated by machine learning, which outputs a parameter for vehicle control.
• An acquirer component obtains confirmation from the driver regarding the replacement of a parameter output function with a learning model.
• A determination unit decides to perform the replacement with a learning model when permission is acquired from the driver.

Potential Applications

This technology can have various applications in the automotive industry and beyond. Some potential applications include:

• Autonomous vehicles: The system can be used to replace parameter output functions in autonomous vehicles with more advanced learning models, improving their decision-making capabilities.
• Driver assistance systems: By replacing certain parameter output functions with learning models, driver assistance systems can become more accurate and adaptive to different driving conditions.
• Industrial automation: The technology can be applied to control systems in industrial automation, allowing for the replacement of parameter output functions with learning models to optimize processes.

Problems Solved

The replacement confirmation system addresses several problems in the field of vehicle control and machine learning:

• Limited adaptability: Traditional parameter output functions may not be able to adapt to changing conditions, whereas learning models can continuously improve and adapt based on new data.
• Human confirmation: By involving the driver in the decision-making process, the system ensures that any replacement of parameter output functions is approved by a human, enhancing safety and trust in the technology.
• Optimization potential: The system enables the replacement of less efficient or outdated parameter output functions with more advanced learning models, improving overall system performance.

Benefits

The use of the replacement confirmation system offers several benefits:

• Enhanced vehicle control: By utilizing learning models, the system can improve the accuracy and efficiency of vehicle control, leading to safer and more reliable driving experiences.
• Flexibility and adaptability: Learning models can adapt to changing conditions and learn from new data, allowing for more dynamic and optimized vehicle control.
• Human oversight: Involving the driver in the confirmation process ensures that any replacement of parameter output functions aligns with their preferences and safety concerns.

Original Abstract Submitted

A replacement confirmation system includes: a retaining unit that retains a learning model, which is generated by machine learning and outputs a parameter used for vehicle control; an acquirer that acquires a result of confirmation with a driver of a vehicle regarding permission for replacement of a parameter output function other than a learning model used in vehicle control with a learning model; and a determination unit that determines to perform replacement with a learning model when the acquirer has acquired permission from a driver.