NVIDIA patented a method to use real-world sensor data to generate transformed data for testing vehicle functions like AEB, CMW, LDW, ALC, or ACC systems. The system simulates test scenarios, ensuring safety and efficacy testing without real-world risks. GlobalData’s report on NVIDIA 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 NVIDIA, Variational Autoencoders (VAEs) was a key innovation area identified from patents. NVIDIA's grant share as of February 2024 was 63%. Grant share is based on the ratio of number of grants to total number of patents.
Generating simulated sensor data to test vehicle functions
The granted patent (Publication Number: US11927502B2) outlines a method and system for testing vehicle functions using simulated profiles and sensor data. The method involves receiving sensor data and state information from a vehicle, generating a simulated profile with desired state information, determining the closest instance of sensor data for testing, computing a transformation for the closest instance, and executing the function testing based on the updated instance. The system includes processing units to receive sensor data, generate simulated trajectories, select instances of sensor data based on desired state information, generate updated instances, and execute function testing.
The method and system described in the patent focus on testing vehicle functions such as automatic emergency braking, collision mitigation warning, lane departure warning, lane change, and adaptive cruise control systems. By utilizing simulated profiles, desired state information, and sensor data, the technology aims to improve the accuracy and efficiency of function testing in vehicles. The system's ability to generate updated instances based on differences between state information and desired state information enhances the testing process, ensuring that functions operate effectively in various scenarios. Additionally, the patent details the process of determining similarities between state information and desired state information, allowing for the selection of the most appropriate instances for testing based on specific criteria such as velocity, location, and pose differences.
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