Honda Motor has developed a system and method for object-level driver attention reasoning using a graph convolution network. The technology analyzes image data of a vehicle’s surroundings to determine relevant objects that impact vehicle operation, enabling autonomous control based on importance scores. GlobalData’s report on Honda Motor gives a 360-degree view of the company including its patenting strategy. Buy the report here.

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According to GlobalData’s company profile on Honda Motor, Hydrogen fuel cells was a key innovation area identified from patents. Honda Motor's grant share as of January 2024 was 46%. Grant share is based on the ratio of number of grants to total number of patents.

Object-level driver attention reasoning system and method

Source: United States Patent and Trademark Office (USPTO). Credit: Honda Motor Co Ltd

A recently granted patent (Publication Number: US11886506B2) discloses a computer-implemented method for object-level attention reasoning in the context of a vehicle's surrounding environment. The method involves receiving images from a vehicle camera system, analyzing these images to identify relevant objects that impact the vehicle's operation, inputting data related to these objects into a graph convolution network to determine importance scores, and selecting top relevant objects for autonomous vehicle control. The process includes utilizing a regional proposal network to output object-ness scores, indicating the likelihood of relevant objects influencing the vehicle's operation. The method aims to enhance attention reasoning within the vehicle's environment by focusing on key objects identified through image analysis and network processing.

Furthermore, the patent also describes a system and a non-transitory computer-readable storage medium implementing the method for object-level attention reasoning. The system includes components such as memory, a processor, and instructions for receiving images, analyzing relevant objects, inputting data to a graph convolution network, and determining top relevant objects for autonomous vehicle control. The method involves generating object node features and interaction graph edges based on the identified relevant objects, utilizing region of interest pooling, and adjacency matrix computation. The importance scores for each object are determined through a multi-layer perceptron neural network, enhancing the system's ability to autonomously control the vehicle based on comprehensive attention reasoning within its surrounding environment. This patent showcases advancements in leveraging computer vision and neural network technologies to improve object-level attention reasoning for autonomous vehicles, potentially enhancing safety and efficiency in real-world driving scenarios.

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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.