18082738. METHOD AND APPARATUS FOR DETERMINING A POSE OF A VEHICLE, AND VEHICLE CONTAINING SAME simplified abstract (HUAWEI TECHNOLOGIES CO., LTD.)
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METHOD AND APPARATUS FOR DETERMINING A POSE OF A VEHICLE, AND VEHICLE CONTAINING SAME
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METHOD AND APPARATUS FOR DETERMINING A POSE OF A VEHICLE, AND VEHICLE CONTAINING SAME - A simplified explanation of the abstract
This abstract first appeared for US patent application 18082738 titled 'METHOD AND APPARATUS FOR DETERMINING A POSE OF A VEHICLE, AND VEHICLE CONTAINING SAME
Simplified Explanation: A set of unordered points associated with road markings is processed by an artificial neural network to determine the pose of a vehicle.
Key Features and Innovation:
- Unordered points inputted to a trained artificial neural network
- Non-linear regression applied to generate an output
- Pose of a vehicle determined based on the output
Potential Applications: This technology can be used in autonomous vehicles, robotics, and computer vision systems for accurate positioning and navigation.
Problems Solved: This technology addresses the challenge of accurately determining the pose of a vehicle based on road markings.
Benefits:
- Improved accuracy in determining vehicle pose
- Enhanced navigation capabilities for autonomous systems
- Increased safety on the roads
Commercial Applications: Potential commercial applications include autonomous vehicles, drone navigation systems, and smart city infrastructure development.
Prior Art: Readers can explore prior research in the fields of computer vision, artificial intelligence, and autonomous systems for related technologies.
Frequently Updated Research: Stay informed about advancements in artificial neural networks, non-linear regression techniques, and computer vision applications for the latest developments in this technology.
Questions about vehicle pose determination: 1. How does the artificial neural network process the unordered points to determine the vehicle's pose? 2. What are the potential limitations of using non-linear regression in this context?
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Original Abstract Submitted
A set of unordered points associated with road markings is received. The unordered points are inputted to a trained artificial neural network. Using the artificial neural network, an output is generated by applying non-linear regression to the unordered points. Based on the output, a pose of a vehicle is determined.
