Nuro‘s patent involves a method to assess the readiness of an autonomous system for deployment using scene-based metrics mapped to distance-based metrics. By analyzing scenario categories encountered by a vehicle, the system determines if it meets deployment standards before being deployed. GlobalData’s report on Nuro 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 Nuro, AI assisted drone control was a key innovation area identified from patents. Nuro's grant share as of January 2024 was 30%. Grant share is based on the ratio of number of grants to total number of patents.
Method for determining readiness of autonomous driving system for deployment
A recently granted patent (Publication Number: US11886193B1) outlines a method for evaluating the performance of an autonomous driving system before deployment. The method involves obtaining a test data set with various scenario categories for the vehicle to encounter, determining deployment scene-based metrics, mapping these metrics to the test data set to calculate scene failure rates, and using distance-based metrics to assess if the system meets minimum deployment standards. If the system meets the standard, the autonomous vehicle can be deployed. The method also includes generating scenario categories based on operation design domain (ODD) parameters and object and event detection response (OEDR) parameters.
Furthermore, the patent describes the calculation of an average failure rate for the overall distance in the deployment region, considering scenes where no objects or events are encountered. This average failure rate, measured in miles per critical disengagement (MPCD), is crucial in determining whether the autonomous driving system meets the minimum deployment standard. The method emphasizes the importance of evaluating different scenario categories with varying complexities, lengths, encountered agents, and vehicle speeds to ensure a comprehensive assessment of the system's performance before deployment. Additionally, the method involves generating an average failure rate based on the scene failure rate for each scenario category and the frequency of encountering these categories in the deployment region, providing a detailed evaluation process for autonomous vehicle deployment.
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