Siemens has patented a method for assessing changes in autonomous vehicle hardware or software. The method utilizes a learning-based approach, specifically a trained neural network, to evaluate whether changes affect safety-critical functions before implementation, ensuring safer vehicle control. GlobalData’s report on Siemens gives a 360-degree view of the company including its patenting strategy. Buy the report here.
Smarter leaders trust GlobalData
Access deeper industry intelligence
Experience unmatched clarity with a single platform that combines unique data, AI, and human expertise.
According to GlobalData’s company profile on Siemens, Smart factory applications was a key innovation area identified from patents. Siemens's grant share as of July 2024 was 57%. Grant share is based on the ratio of number of grants to total number of patents.
Method for assessing changes in autonomous vehicle safety functions
The patent US12073316B2 outlines a method for assessing changes to hardware or software units in autonomous vehicles, focusing on safety implications. The process begins with the receipt of input data records that detail the changes and their associated data. A learning-based approach, specifically a trained neural network, is employed to evaluate whether these changes affect safety-critical functions of the vehicle's control system prior to their implementation. The method includes filtering the input data based on specific criteria and determining a result value that indicates the relevance of the change to safety. Approval for the change is contingent upon this result value exceeding a predetermined threshold, which can trigger further actions, such as notifying relevant systems or implementing additional approved changes.
Additionally, the patent describes the structure of a computer system designed to execute this method, comprising a processor, memory, and a storage device containing the necessary program code. The program code facilitates the reception of input data, the application of the neural network for safety assessment, and the implementation of changes based on the approval process. The claims also encompass the possibility of the input data being stored as feature vectors and described in natural language, enhancing the system's ability to process and understand the changes effectively. Overall, the patent emphasizes a systematic approach to ensuring that modifications to autonomous vehicle systems are evaluated for safety before being enacted.
To know more about GlobalData’s detailed insights on Siemens, buy the report here.
Data Insights
From
The gold standard of business intelligence.
Blending expert knowledge with cutting-edge technology, GlobalData’s unrivalled proprietary data will enable you to decode what’s happening in your market. You can make better informed decisions and gain a future-proof advantage over your competitors.
GlobalData, the leading provider of industry intelligence, provided the underlying data, research, and analysis used to produce this article.
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.
