# US 2026/0062033 A1 · Method and a system for run-time operational monitoring of an autonomous vehicle

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- Number: US 2026/0062033 A1
- Document ID: US20260062033A1
- Status: Published application
- Kind: A1
- Application number: 18/823,001
- Filed: 2024-09-03
- Published: 2026-03-05
- Earliest claimed: 2024-09-03
- Assignees: Yinwang Intelligent Technologies Co., Ltd.
- Applicants: Shenzhen Yinwang Intelligent Technologies Co., Ltd.
- Inventors: Ali NASR; Frederic RISACHER
- Web page: https://patentgrep.com/patent/US20260062033A1

## CPC

- B60W 50/0205: {Diagnosing or detecting failures; Failure detection models}
- B60W 60/0015: {specially adapted for safety}
- B60W 50/14: Means for informing the driver, warning the driver or prompting a driver intervention
- B60W 60/0053: {from vehicle to occupant}
- B60W 2050/0215: {Sensor drifts or sensor failures}
- B60W 2050/0292: {Fail-safe or redundant systems, e.g. limp-home or backup systems}
- B60W 2420/403: Image sensing, e.g. optical camera
- B60W 2420/408: Radar; Laser, e.g. lidar
- B60W 2555/20: Ambient conditions, e.g. wind or rain
- B60W 2556/40: High definition maps
- B60W 2556/50: of positioning data, e.g. GPS [Global Positioning System] data
- B60W: CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT

## Abstract

A method and system for controlling a vehicle are provided. The method comprises: in parallel to a first processing pipeline for controlling the vehicle, executing a risk management processing pipeline comprising: acquiring first monitoring data from a first monitoring source used in first processing pipeline; acquiring second monitoring data from a second monitoring source used in the first processing pipeline; generating a cumulative risk assessment value based on a combination of the first monitoring data and the second monitoring data; in response to the cumulative risk assessment value being above a pre-determined main threshold, generating a warning notification; and in response to a frequency of occurrence of the generating the warning notification being above a pre-determined frequency threshold, triggering, independently from the first processing pipeline, a remedial action to be performed by the vehicle. The present technology may allow increasing safety of operation of the vehicle.

## Claims

### Claim 1

A computer-implemented method for controlling a vehicle, the method comprising:
executing a first processing pipeline, including:
triggering control over the vehicle; and
executing, in parallel to the first processing pipeline, a risk management processing pipeline, including:
acquiring first monitoring data from a first monitoring source used in the first processing pipeline;
acquiring second monitoring data from a second monitoring source used in the first processing pipeline;
generating a cumulative risk assessment value based on a combination of the first monitoring data and the second monitoring data;
in response to the cumulative risk assessment value being above a pre-determined main threshold, generating a warning notification; and
in response to a frequency of occurrence of the generating the warning notification being above a pre-determined frequency threshold, triggering, independently from the first processing pipeline, a remedial action to be performed by the vehicle.

### Claim 2 (depends on 1)

The method of claim 1, wherein the method further comprises:
acquiring external factor data indicative of external factors influencing at least one of the first monitoring data and the second monitoring data;
generating, for the at least one of the first and second monitoring data, a respective severity level based on the external factor data; and
using the respective severity level for generating the cumulative risk assessment value.

### Claim 3 (depends on 2)

The method of claim 2, wherein the external factors comprise at least one of a current weather condition, a current traffic situation, a current environment of the vehicle, an ego-speed of the vehicle, and a current time of day.

### Claim 4 (depends on 2)

The method of claim 2, wherein the generating the cumulative risk assessment value comprises generating the cumulative risk assessment value according with a following equation:
R
=
∑
(
F
×
S
)
,
where F is a respective frequency value one of the first and second monitoring data exceeding a respective threshold value; and
S is the respective severity level associated with the one of the first and second monitoring data.

### Claim 5 (depends on 1)

The method of claim 1, wherein a given one of the first and second monitoring sources comprises one of a hardware component of a plurality of hardware components, a learning-enabled component (LEC) of a plurality of LECs, and a non-learning-enabled component (NLEC) of a plurality of NLECs of the vehicle that are used in the first processing pipeline for controlling the vehicle.

### Claim 6 (depends on 5)

The method of claim 5, wherein the plurality of hardware components comprises a plurality of sensors of the vehicle and an electronic device communicatively coupled thereto,
the plurality of sensors comprising:
a camera sensor;
a LIDAR senor;
a RADAR sensor;
a Global Positioning System (GPS) sensor; and
an Inertial Measurement Unit (IMU) sensor; and
the electronic device comprising:
a processor;
a read-access memory (RAM); and
a CAN-bus.

### Claim 7 (depends on 5)

The method of claim 5, wherein the plurality of LECs comprises:
a camera object detection or tracking machine-learning (ML) model;
a LIDAR object detection or tracking ML model;
a RADAR object detection or tracking ML model;
a lane line detecting ML model;
a traffic light recognizing ML model; and
a trajectory predicting ML model.

### Claim 8 (depends on 5)

The method of claim 5, wherein the plurality of NLECs comprises:
a trajectory planning model;
an object tracking model;
a global localization model; and
a high definition (HD) map.

### Claim 9 (depends on 5)

The method of claim 5, wherein the given one of the first and second monitoring data is indicative of at least one of:
a connection status of each one of the plurality of hardware components;
a confidence level of a respective output of each one of the plurality of LECs;
consistency among respective outputs of the plurality of LECs and the plurality of NLECs;
a smoothness of a current trajectory traversed by the vehicle; and
a spatial and temporal proximity of the vehicle to at least one object in the surrounding area.

### Claim 10 (depends on 1)

The method of claim 1, wherein the remedial action comprises at least one of generating a safety alert, handing over control of the vehicle to a human driver present in the vehicle, stopping the vehicle, and logging the events occurring in the first processing pipeline.

### Claim 11

A system for controlling a vehicle, the system comprising at least one processor, at least one non-transitory computer-readable memory storing instructions, which, when executed by the at least one processor, cause the system to:
execute a first processing pipeline, including:
triggering control over the vehicle; and
execute, in parallel to the first processing pipeline, a risk management processing pipeline, including:
acquiring first monitoring data from a first monitoring source used in the first processing pipeline;
acquiring second monitoring data from a second monitoring source used in the first processing pipeline;
generating a cumulative risk assessment value based on a combination of the first monitoring data and the second monitoring data;
in response to the cumulative risk assessment value being above a pre-determined main threshold, generating a warning notification; and
in response to a frequency of occurrence of the generating the warning notification being above a pre-determined frequency threshold, triggering, independently from the first processing pipeline, a remedial action to be performed by the vehicle.

### Claim 12 (depends on 11)

The system of claim 11, wherein the instructions further cause the system to:
acquire external factor data indicative of external factors influencing at least one of the first monitoring data and the second monitoring data;
generate, for the at least one of the first and second monitoring data, a respective severity level based on the external factor data; and
use the respective severity level for generating the cumulative risk assessment value.

### Claim 13 (depends on 12)

The system of claim 12, wherein the external factors comprise at least one of a current weather condition, a current traffic situation, a current environment of the vehicle, an ego-speed of the vehicle, and a current time of day.

### Claim 14 (depends on 12)

The system of claim 12, wherein to generate the cumulative risk assessment value, the instructions cause the system to generate the cumulative risk assessment value according with a following equation:
R
=
∑
(
F
×
S
)
,
where F is a respective frequency value one of the first and second monitoring data exceeding a respective threshold value; and
S is the respective severity level associated with the one of the first and second monitoring data.

### Claim 15 (depends on 11)

The system of claim 11, wherein a given one of the first and second monitoring sources comprises one of a hardware component of a plurality of hardware components, a learning-enabled component (LEC) of a plurality of LECs, and a non-learning-enabled component (NLEC) of a plurality of NLECs of the vehicle that are used in the first processing pipeline for controlling the vehicle.

### Claim 16 (depends on 15)

The system of claim 15, wherein the plurality of hardware components comprises a plurality of sensors of the vehicle and an electronic device communicatively coupled thereto,
the plurality of sensors comprising:
a camera sensor;
a LIDAR senor;
a RADAR sensor;
a Global Positioning System (GPS) sensor; and
an Inertial Measurement Unit (IMU) sensor; and
the electronic device comprising:
a processor;
a read-access memory (RAM); and
a CAN-bus.

### Claim 17 (depends on 15)

The system of claim 15, wherein the plurality of LECs comprises:
a camera object detection or tracking machine-learning (ML) model;
a LIDAR object detection or tracking ML model;
a RADAR object detection or tracking ML model;
a lane line detecting ML model;
a traffic light recognizing ML model; and
a trajectory predicting ML model.

### Claim 18 (depends on 15)

The system of claim 15, wherein the plurality of NLECs comprises:
a trajectory planning model;
an object tracking model;
a global localization model; and
a high definition (HD) map.

### Claim 19 (depends on 15)

The system of claim 15, wherein the given one of the first and second monitoring data is indicative of at least one of:
a connection status of each one of the plurality of hardware components;
a confidence level of a respective output of each one of the plurality of LECs;
consistency among respective outputs of the plurality of LECs and the plurality of NLECs;
a smoothness of a current trajectory traversed by the vehicle; and
a spatial and temporal proximity of the vehicle to at least one object in the surrounding area.

### Claim 20

A computer-readable medium storing executable instructions for causing one or more computer processors to:
execute a first processing pipeline, including:
triggering control over the vehicle; and
execute, in parallel to the first processing pipeline, a risk management processing pipeline, including:
acquiring first monitoring data from a first monitoring source used in the first processing pipeline;
acquiring second monitoring data from a second monitoring source used in the first processing pipeline;
generating a cumulative risk assessment value based on a combination of the first monitoring data and the second monitoring data;
in response to the cumulative risk assessment value being above a pre-determined main threshold, generating a warning notification; and
in response to a frequency of occurrence of the generating the warning notification being above a pre-determined frequency threshold, triggering, independently from the first processing pipeline, a remedial action to be performed by the vehicle.