Tesla. has filed a patent for systems and methods to evaluate machine learning models for autonomous vehicles. The method involves obtaining validation data from vehicle sensors, computing model output, determining metrics, and generating user interface information. GlobalData’s report on Tesla 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 Tesla, Battery management systems was a key innovation area identified from patents. Tesla's grant share as of January 2024 was 51%. Grant share is based on the ratio of number of grants to total number of patents.
Enhanced user interface for analyzing machine learning models in vehicles
The patent application (Publication Number: US20240029482A1) describes a method and system implemented by one or more processors for utilizing machine learning models in autonomous or semi-autonomous vehicle operations. The method involves obtaining information related to the ML model, validation data including video sequences from vehicle image sensors, computing output through the ML model, determining metrics values, and generating user interface information based on the output. The user interface includes graphical representations of detected objects and error information associated with them, adjusting ground truth locations based on errors, and presenting vehicle depictions.
Furthermore, the system includes processors and computer storage media storing instructions to perform similar operations as the method, including obtaining ML model information, validation data with vehicle velocities, computing output, determining metrics, and generating user interface information. The user interface in the system also presents graphical depictions of the vehicle, ground truth object locations, adjusts presentations based on errors, and updates locations based on video sequences. The patent application aims to enhance the user interface experience by providing accurate and visually informative representations of detected objects and errors, ultimately improving the efficiency and reliability of autonomous or semi-autonomous vehicle operations through machine learning technology.
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