18532128. TRAVEL CONTROLLER AND METHOD FOR TRAVEL CONTROL simplified abstract (TOYOTA JIDOSHA KABUSHIKI KAISHA)
TRAVEL CONTROLLER AND METHOD FOR TRAVEL CONTROL
Organization Name
TOYOTA JIDOSHA KABUSHIKI KAISHA
Inventor(s)
Wataru Kawashima of Nisshin-shi (JP)
TRAVEL CONTROLLER AND METHOD FOR TRAVEL CONTROL - A simplified explanation of the abstract
This abstract first appeared for US patent application 18532128 titled 'TRAVEL CONTROLLER AND METHOD FOR TRAVEL CONTROL
Simplified Explanation:
A travel controller uses neural networks to predict future surrounding conditions of a vehicle and generate control signals for its travel.
Key Features and Innovation:
- Inputting vicinity images into neural networks to predict future conditions.
- Generating control signals based on current and future images.
- Utilizing two separate neural networks for image processing and control signal generation.
Potential Applications: This technology could be applied in autonomous vehicles, drones, and other self-navigating systems.
Problems Solved: This technology addresses the need for accurate prediction of future surrounding conditions for efficient vehicle control.
Benefits:
- Improved safety and efficiency in vehicle travel.
- Enhanced decision-making capabilities for autonomous systems.
- Potential for reducing accidents and optimizing travel routes.
Commercial Applications: The technology could be utilized in the automotive industry for self-driving cars, in the transportation sector for logistics optimization, and in the aerospace industry for autonomous drones.
Prior Art: Readers interested in prior art related to this technology could explore research on neural network applications in autonomous systems and image processing for vehicle control.
Frequently Updated Research: Stay updated on advancements in neural network technology for autonomous systems and image processing for predictive modeling in vehicle control.
Questions about the Technology: 1. How does this technology improve the efficiency of vehicle travel? 2. What are the potential limitations of using neural networks for predicting future surrounding conditions in vehicles?
Original Abstract Submitted
A travel controller generates a future image by inputting a series of vicinity images representing surrounding conditions of a vehicle up to a current time into a first neural network. The future image represents predicted surrounding conditions of the vehicle at a future time that is a predetermined period after the current time. The travel controller generates a control signal for controlling travel of the vehicle by inputting a vicinity image outputted at the current time of the series of vicinity images, the future image, and the predetermined period into a second neural network different from the first neural network.
