Methods and systems for lidar monitoring

Patent metadata

Field	Value
Publication number	US-12504524-B2
Application number	US-202318329131-A
Country	US
Kind code	B2
Filing date	Jun 5, 2023
Priority date	Jun 5, 2023
Publication date	Dec 23, 2025
Grant date	Dec 23, 2025

Abstract

Systems and methods are provided for controlling a vehicle. In one embodiment, a method includes: receiving, by the controller, return data generated by a lidar device of the vehicle; determining, by the controller, at least one overlapping channel of redundant return data; determining, by the controller, an optimization framework of equations based on modeled errors of the at least one overlapping channel; solving, by the controller, the optimization framework using a least squares method to determine an error value; determining, by the controller, a state of health of the lidar device based on the error value; compensating, by the controller, the return data from the lidar device based on the error value; and controlling, by the controller, the vehicle based on the compensated return data.

First claim

What is claimed is:

1 . A method of controlling a vehicle, comprising: receiving, by a controller, return data generated by a lidar device of the vehicle; determining, by the controller, a region of at least one two overlapping channels of redundant return data by using an upper azimuth, a lower azimuth, and an elevation limit for each of the at least two overlapping channels and then performing a nearest neighbor method on each of a plurality of data points in the region of at least two overlapping channels, using only the redundant return data from adjacent channels; adding at least one of the plurality of data points to a set of channel data in response to a distance to a nearest neighbor being within a threshold distance; filtering the set of channel data to remove at least one of a no return point, a saturation point and an outlier to generate a preprocessed channel data; determining an error equation in response to the preprocessed channel data; determining, by the controller, an optimization framework of equations based on the error equation; solving, by the controller, the optimization framework of equations using at a least squares method to determine an error value; determining, by the controller, a state of health of the lidar device based on the error value; compensating, by the controller, the return data from the lidar device based on the error value; generating a data representative of an environment in a vicinity of the vehicle in response to the return data from the lidar device; generating, by the controller, a control signal in response to the data representative of the environment in the vicinity of the vehicle; and controlling, by the controller, the vehicle including at least one actuator system including a steering system, a brake system, and a propulsion system in response to the control signal.

2 . The method of claim 1 , further comprising:
determining whether enable conditions are met; and in response to determining that the enable conditions have been met, performing the steps of determining the plurality of overlapping channels, determining the optimization framework of equations, determining the state of health, and the compensating the return data.

3 . The method of claim 2 , wherein the enable conditions include weather conditions.

4 . The method of claim 2 , wherein the enable conditions include a relative motion of the vehicle.

5 . The method of claim 2 , wherein the enable conditions include a reflectivity condition.

6 . The method of claim 1 , further comprising maturing, by the controller, the error value over a plurality of frames; and generating a confidence value based on the maturing, and wherein the determining the state of health is based on the confidence value.

7 . The method of claim 1 , wherein the at least one of the plurality of data points in the region of a least two overlapping channels have a same ground truth of an extracted feature.

8 . The method of claim 7 , further comprising extracting at least one feature from the at least one overlapping channel based on at least one of a mean and a median of at least one of a distance and a reflectivity of the return data.

9 . The method of claim 8 , wherein the extracting the at least one feature is based on at least one of all of the pre-processed return data and a subset of the pre-processed return data, wherein the subset is obtained from at least one of automatic clustering and samples after removing the outliers.

10 . The method of claim 9 , further comprising modeling an error associated with at least one of reflectivity and distance of points of the at least one overlapping channel, and wherein the determining the optimization framework of equations is based on the error that is modeled.

11 . A control system for controlling a vehicle, comprising: a non-transitory computer readable medium configured to store return data generated by a lidar device of the vehicle; and
a processor, onboard the vehicle and configured to, receive the return data, determine a region of at least two overlapping channels of redundant return data by using an upper azimuth, a lower azimuth, and an elevation limit for each of the at least two overlapping channels and then performing a nearest neighbor method on each of a plurality of data points in the region of at least two overlapping channels, using only the redundant return data from adjacent channels, adding at least one of the plurality of data points to a set of channel data in response to a distance to a nearest neighbor being within a threshold distance; filter the set of channel data to remove at least one of a return point, a saturation point and an outlier to generate a preprocessed channel data; extract at least one feature from the preprocessed channel data including at least one of a mean of a parameter of interest or a median of the parameter of interest, wherein the parameter of interest includes at least one of a reflectivity of a point within the region of at least two overlapping channels and a distance to the point within the region of at least two overlapping channels; determine an error equation in response the preprocessed channel data including the at least one feature of interest, determine an optimization framework of equations based on the error equation, solve the optimization framework using a least squares method to determine an error value, determine a state of health of the lidar device based on the error value, compensate the return data from the lidar device based on the error value, generate a data representative of an environment in a vicinity of the vehicle in response to the return data from the lidar device, generate, by the controller, in response to the data representative of the environment in the vicinity of the vehicle, and control the vehicle including at least one actuator system including a steering system, a brake system, and a propulsion system in response to the control signal.

12 . The control system of claim 11 , wherein the processor is further configured to determine whether enable conditions are met, and in response to determining that the enable conditions have been met, perform the steps of determining the plurality of overlapping channels, determining the optimization framework, determining the state of health, and the compensating the return data.

13 . The control system of claim 12 , wherein the enable conditions include at least one of a weather condition, a relative motion of the vehicle condition, and a reflectivity condition.

14 . The control system of claim 11 , wherein the processor is further configured to mature the error value over a plurality of frames, and generate a confidence value based on the maturing, and wherein the processor is configured to determine the state of health based on the confidence value.

15 . The control system of claim 11 , wherein the at least one of the plurality of data points in the region of a least two overlapping channels have a same ground truth of an extracted feature.

16 . The control system of claim 15 , wherein the processor is further configured to extract at least one feature from the at least one overlapping channel based on at least one of a mean and a median of at least one of a distance and a reflectivity of the return data.

17 . The control system of claim 16 , wherein the processor is further configured to extract the at least one feature based on at least one of all of the pre-processed return data and a subset of the pre-processed return data, wherein the subset is obtained from at least one of automatic clustering and

Assignees

• Gm Global Tech Operations Llc

Inventors

• Sadjadi Hossein
• Jafarzadeh Ehsan
• Kroeze Zachary T

Classifications

• B60W50/0205

  Diagnosing or detecting failures; Failure detection models · CPC title

• B60W2555/20

  Ambient conditions, e.g. wind or rain · CPC title

• B60W50/029

  Adapting to failures or work around with other constraints, e.g. circumvention by avoiding use of failed parts · CPC title

• B60W2050/0052

  Filtering, filters · CPC title

• G01S17/931

  of land vehicles · CPC title