# US 2026/0073701 A1 · Consistency-based perturbation detection for perception systems

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 2026/0073701 A1
- Document ID: US20260073701A1
- Status: Published application
- Kind: A1
- Application number: 18/830,392
- Filed: 2024-09-10
- Published: 2026-03-12
- Earliest claimed: 2024-09-10
- Assignees: QUALCOMM Incorporated
- Applicants: QUALCOMM Incorporated
- Inventors: Cong CHEN; Jonathan PETIT; Jean-Philippe MONTEUUIS; Marvin Richard KLINGNER
- Web page: https://patentgrep.com/patent/US20260073701A1

## CPC

- G06V 10/82: using neural networks
- G06V 20/56: exterior to a vehicle by using sensors mounted on the vehicle
- G06V 10/25: Determination of region of interest [ROI] or a volume of interest [VOI]
- G06V 10/764: using classification, e.g. of video objects
- G06V 10/774: Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
- G06V 10/776: Validation; Performance evaluation
- G06V 10/993: {Evaluation of the quality of the acquired pattern}
- G06V 20/64: Three-dimensional [3D] objects
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

## Abstract

Systems and techniques are described herein for perturbation detection. For example, a computing device can produce, using a first model based on input data, first output data. The computing device can produce, using a second model based on the input data, second output data. The computing device can determine, based on the first output data and the second output data, a consistency score indicating a consistency between the first output data and the second output data. The computing device can determine, based on the consistency score being less than a consistency score threshold, the input data comprises a perturbation.

## Claims

### Claim 1

An apparatus for perturbation detection, the apparatus comprising:
at least one memory; and
at least one processor coupled to the at least one memory and configured to:
produce, using a first model based on input data, first output data;
produce, using a second model based on the input data, second output data;
determine, based on the first output data and the second output data, a consistency score indicating a consistency between the first output data and the second output data; and
determine, based on the consistency score being less than a consistency score threshold, the input data comprises a perturbation.

### Claim 2 (depends on 1)

The apparatus of claim 1, wherein the at least one processor is configured to select the input data from sensor data, wherein the input data comprises a region of interest of the sensor data selected based on at least one of an object detected within the region of interest of the sensor data, a use case associated with the sensor data, a time of day the sensor data was obtained, a day the sensor data was obtained, a location associated with the sensor data, or a traffic scenario associated with the sensor data.

### Claim 3 (depends on 1)

The apparatus of claim 1, wherein the input data comprises image sensor data, radar sensor data, or light detection and ranging (Lidar) sensor data.

### Claim 4 (depends on 1)

The apparatus of claim 1, wherein, to determine the consistency score, the at least one processor is configured to:
determine an intersection over union (IoU) of a pairwise comparison between the first output data and the second output data; and
compare the IoU to an IoU threshold value.

### Claim 5 (depends on 1)

The apparatus of claim 1, wherein the at least one processor is configured to determine a region within the input data for the perturbation based on a density of bounding boxes, from at least one of the first output data or the second output data, located at the region.

### Claim 6 (depends on 1)

The apparatus of claim 1, wherein the at least one processor is configured to:
produce, using the first model based on input data without perturbations, third output data;
produce, using a second model based on the input data without perturbations, fourth output data;
determine, based on the first output data and the second output data, an additional consistency score indicating a consistency between the third output data and the fourth output data, wherein the additional consistency score is greater than consistency scores produced by other pairs of models based on the input data without perturbations; and
select the first model and the second model to use as a pair of models based on the additional consistency score being greater than consistency scores produced by the other pairs of models based on the input data without perturbations.

### Claim 7 (depends on 1)

The apparatus of claim 1, wherein the first model is a first type of model and the second model is a second type of model, wherein the second type of model is different from the first type of model, and wherein the first model and the second model.

### Claim 8 (depends on 7)

The apparatus of claim 7, wherein the first model is an object detection model, an instance segmentation model, a depth estimation model, or a traffic sign recognition model, and wherein the second model is a different one of the object detection model, the instance segmentation model, the depth estimation model, or the traffic sign recognition model.

### Claim 9 (depends on 8)

The apparatus of claim 8, wherein the object detection model is a Faster R-CNN model, a you only look once (YOLO) model, a single-stage object detection (SSD) model, or a RetinaNet model.

### Claim 10 (depends on 8)

The apparatus of claim 8, wherein the instance segmentation model is a Mask R-CNN model, a Mask2Former model, a you only look once (YOLO) segmentation (Seg) model, or a successive approximation model (SAM).

### Claim 11

A method for perturbation detection at a device, the method comprising:
producing, by a first model based on input data, first output data;
producing, by a second model based on the input data, second output data;
determining, based on the first output data and the second output data, a consistency score indicating a consistency between the first output data and the second output data; and
determining, based on the consistency score being less than a consistency score threshold, the input data comprises a perturbation.

### Claim 12 (depends on 11)

The method of claim 11, further comprising selecting the input data from sensor data, wherein the input data comprises a region of interest of the sensor data selected based on at least one of an object detected within the region of interest of the sensor data, a use case associated with the sensor data, a time of day the sensor data was obtained, a day the sensor data was obtained, a location associated with the sensor data, or a traffic scenario associated with the sensor data.

### Claim 13 (depends on 11)

The method of claim 11, wherein the input data comprises image sensor data, radar sensor data, or light detection and ranging (Lidar) sensor data.

### Claim 14 (depends on 11)

The method of claim 11, wherein determining the consistency score is based on:
determining an intersection over union (IoU) of a pairwise comparison between the first output data and the second output data; and
comparing the IoU to an IoU threshold value.

### Claim 15 (depends on 11)

The method of claim 11, further comprising determining a region within the input data for the perturbation based on a density of bounding boxes, from at least one of the first output data or the second output data, located at the region.

### Claim 16 (depends on 11)

The method of claim 11, further comprising:
producing, by the first model based on input data without perturbations, third output data;
producing, by a second model based on the input data without perturbations, fourth output data;
determining, based on the first output data and the second output data, an additional consistency score indicating a consistency between the third output data and the fourth output data, wherein the additional consistency score is greater than consistency scores produced by other pairs of models based on the input data without perturbations; and
selecting the first model and the second model to use as a pair of models based on the additional consistency score being greater than consistency scores produced by the other pairs of models based on the input data without perturbations.

### Claim 17 (depends on 11)

The method of claim 11, wherein the first model is a first type of model and the second model is a second type of model, wherein the second type of model is different from the first type of model, and wherein the first model and the second model.

### Claim 18 (depends on 17)

The method of claim 17, wherein the first model is an object detection model, an instance segmentation model, a depth estimation model, or a traffic sign recognition model, and wherein the second model is a different one of the object detection model, the instance segmentation model, the depth estimation model, or the traffic sign recognition model.

### Claim 19 (depends on 18)

The method of claim 18, wherein the object detection model is a Faster R-CNN model, a you only look once (YOLO) model, a single-stage object detection (SSD) model, or a RetinaNet model.

### Claim 20 (depends on 18)

The method of claim 18, wherein the instance segmentation model is a Mask R-CNN model, a Mask2Former model, a you only look once (YOLO) segmentation (Seg) model, or a successive approximation model (SAM).