AI Uncertainty Estimation for Autonomous Object Detection
Summary
USPTO published patent application US20260111771A1 on April 23, 2026, covering systems and methods for uncertainty estimation in object detection for autonomous and semi-autonomous systems. The application, filed March 14, 2025, names six inventors including Nikita Durasov, Jiwoong Choi, Rafid Reza Mahmood, Marc Law, James Robert Lucas, and Jose Manuel Alvarez Lopez. The technology uses data from cameras and/or LiDAR sensors to generate object presence probabilities along with corresponding uncertainty estimates, enabling identification of novel scenes, detection of bounding box errors, and flagging of missed object detections.
“The uncertainty estimates may be used to identify scenes that are significantly different from the training data, detect errors in the bounding shapes for objects, and/or highlight areas where object detections may have been missed.”
About this source
USPTO classification G06N covers computer systems based on specific computational models: neural networks, knowledge representation, fuzzy logic, expert systems, evolutionary algorithms. With the AI patent boom, this is one of the most-filed application classes in the office. Every newly published application in G06N lands in this feed, around 230 a month. Patent applications publish 18 months after filing, so this feed reveals what AI labs and companies were working on in the prior year and a half. Watch this if you compete in machine learning, file freedom-to-operate analyses, scout acquisition targets in AI infrastructure, or track which research groups are converting publications to patents. GovPing pulls each application with the filing number, title, applicant, and abstract.
What changed
USPTO published patent application US20260111771A1 covering AI-based uncertainty estimation for object detection in autonomous systems. The application discloses systems using sensor data from cameras and/or LiDAR to generate representations of surrounding features, then applying models to produce object presence probabilities with corresponding uncertainty estimates. The uncertainty estimates serve multiple functions: identifying scenes that diverge from training data distributions, detecting errors in object bounding shapes, and highlighting areas where object detections may have been missed. The technology may also support auto-labeling of scenes for training purposes.
Manufacturers developing autonomous vehicles, robotics, and related AI systems should note this patent application for potential implications on object detection reliability and safety validation approaches. The uncertainty estimation techniques disclosed could influence how autonomous system developers assess and communicate confidence in perception outputs.
Archived snapshot
Apr 23, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
UNCERTAINTY ESTIMATION FOR OBJECT DETECTION IN AUTONOMOUS AND SEMI-AUTONOMOUS SYSTEMS AND APPLICATIONS
Application US20260111771A1 Kind: A1 Apr 23, 2026
Inventors
Nikita DURASOV, Jiwoong Choi, Rafid Reza Mahmood, Marc Law, James Robert Lucas, Jose Manuel Alvarez Lopez
Abstract
In various examples, systems and methods for uncertainty estimation for object detection in autonomous and semi-autonomous systems and applications are provided. The systems and methods may use data from one or more sensors (e.g., camera(s) and/or LiDAR sensor(s) to generate a representation of features surrounding a machine. A model may be used to generate probabilities of objects being present in the representation of features and uncertainty estimates corresponding to the object presence probabilities. The uncertainty estimates may be used to identify scenes that are significantly different from the training data, detect errors in the bounding shapes for objects, and/or highlight areas where object detections may have been missed. The systems and methods may also be used to auto-label scenes associated with the representation of features, and the auto-labeled scenes may be used for training purposes.
CPC Classifications
G06N 7/01 G06F 18/28 G06N 20/00
Filing Date
2025-03-14
Application No.
19080666
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