MLchartDataset catalogue

Patent · US12430406B2 · B2 · US

Systems and methods of sensor data fusion

(11) Publication number
US12430406B2
(21) Application number
19/024,493
(22) Filing date
2025-01-16
(30) Priority date
2024-12-19
(43) Publication date
2025-09-30
(45) Date of grant
2025-09-30
(51) IPC
B25J 9/16; B60R 19/48; B60T 8/1755; B60T 8/32; B60W 10/18; B60W 10/184; B60W 30/085; B60W 30/09; B60W 50/00; B60W 60/00; B62D 15/02; G01C 21/00; G01C 21/16; G01C 22/00; G01C 3/00; G01S 11/00; G01S 13/08; G01S 13/10; G01S 13/42; G01S 13/931; G01S 15/08; G01S 15/10; G01S 15/42; G01S 17/08; G01S 17/88; G01S 17/894; G01S 5/14; G01S 7/48; G05B 13/02; G05D 101/15; G05D 111/50; G05D 111/67; G06F 16/24; G06F 16/245; G06F 16/2455; G06F 16/33; G06F 16/334; G06F 16/43; G06F 16/53; G06F 16/903; G06F 16/9035; G06F 17/18; G06F 18/21; G06F 18/213; G06F 18/2431; G06F 18/25; G06F 7/14; G06F 7/16; G06N 20/00; G06N 3/02; G06N 3/0464; G06N 5/022; G06N 5/04; G06N 5/045; G06N 5/046; G06N 5/048; G06T 7/521; G06V 10/764; G06V 10/80; G06V 10/82; G06V 20/56; G08B 29/18; G08G 1/01; G08G 1/04; G08G 1/042; H04L 67/12; H04W 4/38
(52) CPC
  • G06F Electric digital data processing: 18/256, 16/24, 16/245, 16/2455, 16/24556, 16/2456, 16/33, 16/334, 16/43, 16/53, 16/903, 16/90335, 16/9035, 17/18, 18/213, 18/217, 18/2431, 18/25, 18/251, 18/253, 7/14, 7/16
  • B25J Manipulators; chambers provided with manipulation devices: 9/163, 9/1664, 9/1694
  • B60R Vehicles, vehicle fittings, or vehicle parts, not otherwise provided for: 19/483
  • B60T Vehicle brake control systems or parts thereof; brake control systems or parts thereof, in general; arrangement of braking elements on vehicles in general; portable devices for preventing unwanted movement of vehicles; vehicle modifications to facilitate cooling of brakes: 2201/00, 2201/03, 8/1755, 8/3275
  • 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: 10/18, 10/184, 2050/0052, 2050/021, 2050/0215, 2050/022, 2420/00, 2420/40, 2420/403, 2420/408, 2420/50, 2510/069, 2510/18, 2520/04, 2540/12, 2554/801, 2554/802, 2556/35, 2710/18, 2754/30, 30/085, 30/09, 60/00, 60/0018, 60/00186
  • B62D Motor vehicles; trailers: 15/0285
  • G01C Measuring distances, levels or bearings; surveying; navigation; gyroscopic instruments; photogrammetry or videogrammetry: 21/165, 21/1652, 21/3804, 21/3811, 21/3848, 22/00, 3/00
  • G01S Radio direction-finding; radio navigation; determining distance or velocity by use of radio waves; locating or presence-detecting by use of the reflection or reradiation of radio waves; analogous arrangements using other waves: 11/00, 13/08, 13/103, 13/42, 13/86, 13/862, 13/865, 13/867, 13/881, 13/931, 15/08, 15/101, 15/42, 17/08, 17/86, 17/87, 17/88, 17/89, 17/894, 17/931, 19/45, 2013/93185, 5/14, 7/417, 7/4808
  • G05B Control or regulating systems in general; functional elements of such systems; monitoring or testing arrangements for such systems or elements: 13/0205
  • G05D Systems for controlling or regulating non-electric variables: 2101/15, 2111/50, 2111/67
  • G06N Computing arrangements based on specific computational models: 20/00, 3/02, 3/0464, 5/022, 5/04, 5/042, 5/045, 5/046, 5/048
  • G06T Image data processing or generation, in general: 2207/10028, 2207/20024, 2207/20084, 2207/30252, 2207/30264, 7/521
  • G06V Image or video recognition or understanding: 10/764, 10/80, 10/803, 10/82, 20/56, 20/58
  • G08B Signalling systems, e.g. personal calling systems; order telegraphs; alarm systems: 29/188
  • G08G Traffic control systems: 1/0133, 1/04, 1/042
  • H04L Transmission of digital information, e.g. telegraphic communication: 67/12
  • H04W Wireless communication networks: 4/38
(73) Assignee
Digital Global Systems Inc
(72) Inventors
Armando Montalvo
(54) Title
Systems and methods of sensor data fusion
(57) Abstract

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

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Claims (20)

  1. A system for sensor data fusion for sensor management and utilization in autonomous transportation, comprising: at least one computer processor including a memory; at least one curation engine, at least one link engine, at least one fusion engine, at least one inference engine, and at least one validation engine; at least one first distance sensor operable to capture a first distance measurement from a vehicle to at least one object; and at least one second distance sensor operable to capture a second distance measurement from the vehicle to the at least one object; wherein the at least one computer processor is operable to analyze the first distance measurement and the second distance measurement; wherein the at least one computer processor is operable to receive at least one query; wherein the at least one curation engine is operable to curate the first distance measurement and the second distance measurement, the at least one link engine is operable to link the first distance measurement and the second distance measurement, the at least one fusion engine is operable to fuse the first distance measurement and the second distance measurement, thereby creating fused data, the at least one inference engine is operable to determine at least one inference from the first distance measurement and the second distance measurement, and the at least one validation engine is operable to validate the first distance measurement and the second distance measurement; wherein the fused data includes at least one new data set; wherein the at least one validation engine is operable to actively validate the fused data; wherein the active validation of the fused data includes modification of an orientation of the at least one first distance sensor, and the at least one first distance sensor capturing a third distance measurement from the vehicle to the at least one object at the modified orientation to generate a second data set; wherein the active validation of the fused data further includes the at least one validation engine comparing the fused data to the second data set via a statistical comparison; wherein the at least one new data set is not saved unless the at least one validation engine validates the fused data; wherein the at least one new data set includes an accuracy value for the at least one first distance sensor and the at least one second distance sensor; wherein the at least one inference engine determines the at least one inference by using artificial intelligence based in part on the at least one query and/or the at least one new data set; wherein the at least one inference engine is operable to answer the at least one query based in part on the at least one inference; and wherein the at least one computer processor is operable to instruct the vehicle to brake based on the at least one new data set.
  2. The system of claim 1, wherein the answer to the at least one query includes a distance from the vehicle to the at least one object.
  3. The system of claim 1, wherein the at least one inference engine is operable to determine the at least one inference in real-time.
  4. The system of claim 1, wherein the at least one inference engine is operable to determine which of the at least one first distance sensor and/or the at least one second distance sensor the at least one computer processor responds to based in part on the at least one inference.
  5. The system of claim 1, wherein the at least one inference includes a numerical value.
  6. The system of claim 1, wherein the at least one inference includes a prediction of a future event based in part on the fused data.
  7. A method for sensor data fusion for sensor management and utilization in autonomous transportation, comprising: providing at least one computer processor including a memory; providing at least one curation engine, at least one link engine, at least one fusion engine, at least one inference engine, and at least one validation engine; at least one first distance sensor capturing a first distance measurement from a vehicle to at least one object; at least one second distance sensor capturing a second distance measurement from the vehicle to the at least one object; analyzing by the at least one computer processor the first distance measurement and the second distance measurement; receiving by the at least one computer processor at least one query; curating by the at least one curation engine the first distance measurement and the second distance measurement, linking by the at least one link engine the first distance measurement and the second distance measurement, fusing by the at least one fusion engine the first distance measurement and the second distance measurement, thereby creating fused data, determining at least one inference by the at least one inference engine from the first distance measurement and the second distance measurement, and validating by the at least one validation engine the first distance measurement and the second distance measurement; wherein the fused data includes at least one new data set; actively validating by the at least one validation engine the fused data; wherein actively validating the fused data includes modifying an orientation of the at least one first distance sensor, and the at least one first distance sensor capturing a third distance measurement from the vehicle to the at least one object at the modified orientation, thereby generating a second data set; wherein actively validating the fused data further includes the at least one validation engine comparing the fused data to the second data set via a statistical comparison; wherein the at least one new data set is not saved unless the at least one validation engine validates the fused data; wherein the at least one new data set includes an accuracy value for the at least one first distance sensor and the at least one second distance sensor; determining via the at least one inference engine the at least one inference by using artificial intelligence based in part on the at least one query and/or the at least one new data set; answering via the at least one inference engine the at least one query based in part on the at least one inference; and instructing by the at least one computer processor movement of the vehicle based on the at least one new data set.
  8. The method of claim 7, wherein answering via the at least one inference engine the at least one query includes a distance from the vehicle to the at least one object.
  9. The method of claim 7, wherein the at least one inference includes a numerical value.
  10. The method of claim 7, further comprising predicting via the at least one inference engine a future event based in part on the fused data.
  11. The method of claim 7, wherein determining the at least one inference occurs in real-time.
  12. The method of claim 7, further comprising determining via the at least one inference engine which of the at least one first distance sensor and/or the at least one second distance sensor the at least one computer processor responds to based in part on the at least one inference.
  13. The method of claim 7, further comprising validating the at least one inference by the at least one validation engine by comparing the at least one inference to a second inference.
  14. A system for sensor data fusion for sensor management and utilization in autonomous transportation, comprising: at least one computer processor including a memory; at least one curation engine, at least one link engine, at least one fusion engine, at least one inference engine, and at least one validation engine; and at least two sensors, each of the at least two sensors operable to measure a first distance from a vehicle to at least one object and a second distance from the vehicle to the at least one object; wherein the at least one computer processor is operable to analyze the first distance and the second distance; wherein the at least one computer processor is operable to receive at least one query; wherein the at least one curation engine is operable to curate the first distance and the second distance, the at least one link engine is operable to link the first distance and the second distance, the at least one fusion engine is operable to fuse the first distance and the second distance, thereby creating fused data, the at least one inference engine is operable to determine at least one inference from the first distance and the second distance, and the at least one validation engine is operable to validate the first distance and the second distance; wherein the fused data includes at least one new data set; wherein the at least one validation engine is operable to actively validate the fused data; wherein the active validation of the fused data includes modification of an orientation of at least one of the at least two sensors, the at least one of the at least two sensors capturing a third distance measurement from the vehicle to the at least one object at the modified orientation to generate a second data set; wherein the active validation of the fused data further includes the at least one validation engine comparing the fused data to the second data set via a statistical comparison; wherein the at least one new data set is not saved unless the at least one validation engine validates the fused data; wherein the at least one new data set includes an accuracy value for each of the at least two sensors; wherein the at least one inference engine determines the at least one inference by using artificial intelligence based in part on the at least one query and/or the at least one new data set; wherein the at least one inference engine is operable to answer the at least one query based in part on the at least one inference; wherein the at least one validation engine is operable to validate the at least one inference by comparing the at least one inference to a second inference; and wherein the at least one computer processor is operable to instruct the vehicle to brake based on the at least one new data set.
  15. The system of claim 14, wherein the answer to the at least one query includes a distance from the vehicle to the at least one object.
  16. The system of claim 14, wherein the at least one inference engine is operable to determine which of the at least one first distance sensor and/or the at least one second distance sensor the at least one computer processor responds to based in part on the at least one inference.
  17. The system of claim 14, wherein the at least one inference includes a numerical value.
  18. The system of claim 14, wherein the at least one inference engine is operable to determine the at least one inference in real-time.
  19. The system of claim 14, wherein the at least one inference includes a prediction of a future event based in part on the fused data.
  20. The system of claim 14, wherein the at least one new data set includes an accuracy value for each of the at least two sensors.

Description

The present invention relates to physical sensors measuring data and dynamically fusing the physical sensor data to create a new dataset.

It is generally known in the prior art to append data from more than one source.

Prior art patent documents include the following:

U.S. Patent Publication Number 2024/0378412 for methods for topological data analysis ai/ml pipeline (tdaml) with algorithm for multimodal sensor data fusion in autonomy applications, by inventor Paul Thomas Schrader, filed Apr. 25, 2024, published Nov. 14, 2024, directed to a method of topological data analysis feature engineering for data fusion and autonomy is provided. The method comprises providing a Topological Data Analysis AI/ML Pipeline (TDAML) algorithm for multimodal sensor data fusion in autonomy applications system further comprising combining raw heterogeneous multimodal sensor data at the topological level; measuring, recording, and tracking linear representations of an underlying data set; providing a linear representation of the underlying data set which is compatible to existing deep learning (DL) model architectures for training in autonomy tasks; and accessing the entire degree of freedom (DOF) space of raw multimodal sensor data for mitigating sensor modality adversarial threats and environmental attenuation concerns in contested military and civilian (urban) environments.

U.S. Patent Publication Number 2023/0112441 for data fusion and analysis engine for vehicle sensors, by inventors LuAn Tang et al., filed Oct. 6, 2022, published Apr.

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Record as JSON 
{
  "publication_number": "US12430406B2",
  "country": "US",
  "kind": "B2",
  "title": "Systems and methods of sensor data fusion",
  "abstract": "Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.",
  "claims": [
    "1. A system for sensor data fusion for sensor management and utilization in autonomous transportation, comprising: at least one computer processor including a memory; at least one curation engine, at least one link engine, at least one fusion engine, at least one inference engine, and at least one validation engine; at least one first distance sensor operable to capture a first distance measurement from a vehicle to at least one object; and at least one second distance sensor operable to capture a second distance measurement from the vehicle to the at least one object; wherein the at least one computer processor is operable to analyze the first distance measurement and the second distance measurement; wherein the at least one computer processor is operable to receive at least one query; wherein the at least one curation engine is operable to curate the first distance measurement and the second distance measurement, the at least one link engine is operable to link the first distance measurement and the second distance measurement, the at least one fusion engine is operable to fuse the first distance measurement and the second distance measurement, thereby creating fused data, the at least one inference engine is operable to determine at least one inference from the first distance measurement and the second distance measurement, and the at least one validation engine is operable to validate the first distance measurement and the second distance measurement; wherein the fused data includes at least one new data set; wherein the at least one validation engine is operable to actively validate the fused data; wherein the active validation of the fused data includes modification of an orientation of the at least one first distance sensor, and the at least one first distance sensor capturing a third distance measurement from the vehicle to the at least one object at the modified orientation to generate a second data set; wherein the active validation of the fused data further includes the at least one validation engine comparing the fused data to the second data set via a statistical comparison; wherein the at least one new data set is not saved unless the at least one validation engine validates the fused data; wherein the at least one new data set includes an accuracy value for the at least one first distance sensor and the at least one second distance sensor; wherein the at least one inference engine determines the at least one inference by using artificial intelligence based in part on the at least one query and/or the at least one new data set; wherein the at least one inference engine is operable to answer the at least one query based in part on the at least one inference; and wherein the at least one computer processor is operable to instruct the vehicle to brake based on the at least one new data set.",
    "2. The system of claim 1, wherein the answer to the at least one query includes a distance from the vehicle to the at least one object.",
    "3. The system of claim 1, wherein the at least one inference engine is operable to determine the at least one inference in real-time.",
    "4. The system of claim 1, wherein the at least one inference engine is operable to determine which of the at least one first distance sensor and/or the at least one second distance sensor the at least one computer processor responds to based in part on the at least one inference.",
    "5. The system of claim 1, wherein the at least one inference includes a numerical value.",
    "6. The system of claim 1, wherein the at least one inference includes a prediction of a future event based in part on the fused data.",
    "7. A method for sensor data fusion for sensor management and utilization in autonomous transportation, comprising: providing at least one computer processor including a memory; providing at least one curation engine, at least one link engine, at least one fusion engine, at least one inference engine, and at least one validation engine; at least one first distance sensor capturing a first distance measurement from a vehicle to at least one object; at least one second distance sensor capturing a second distance measurement from the vehicle to the at least one object; analyzing by the at least one computer processor the first distance measurement and the second distance measurement; receiving by the at least one computer processor at least one query; curating by the at least one curation engine the first distance measurement and the second distance measurement, linking by the at least one link engine the first distance measurement and the second distance measurement, fusing by the at least one fusion engine the first distance measurement and the second distance measurement, thereby creating fused data, determining at least one inference by the at least one inference engine from the first distance measurement and the second distance measurement, and validating by the at least one validation engine the first distance measurement and the second distance measurement; wherein the fused data includes at least one new data set; actively validating by the at least one validation engine the fused data; wherein actively validating the fused data includes modifying an orientation of the at least one first distance sensor, and the at least one first distance sensor capturing a third distance measurement from the vehicle to the at least one object at the modified orientation, thereby generating a second data set; wherein actively validating the fused data further includes the at least one validation engine comparing the fused data to the second data set via a statistical comparison; wherein the at least one new data set is not saved unless the at least one validation engine validates the fused data; wherein the at least one new data set includes an accuracy value for the at least one first distance sensor and the at least one second distance sensor; determining via the at least one inference engine the at least one inference by using artificial intelligence based in part on the at least one query and/or the at least one new data set; answering via the at least one inference engine the at least one query based in part on the at least one inference; and instructing by the at least one computer processor movement of the vehicle based on the at least one new data set.",
    "8. The method of claim 7, wherein answering via the at least one inference engine the at least one query includes a distance from the vehicle to the at least one object.",
    "9. The method of claim 7, wherein the at least one inference includes a numerical value.",
    "10. The method of claim 7, further comprising predicting via the at least one inference engine a future event based in part on the fused data.",
    "11. The method of claim 7, wherein determining the at least one inference occurs in real-time.",
    "12. The method of claim 7, further comprising determining via the at least one inference engine which of the at least one first distance sensor and/or the at least one second distance sensor the at least one computer processor responds to based in part on the at least one inference.",
    "13. The method of claim 7, further comprising validating the at least one inference by the at least one validation engine by comparing the at least one inference to a second inference.",
    "14. A system for sensor data fusion for sensor management and utilization in autonomous transportation, comprising: at least one computer processor including a memory; at least one curation engine, at least one link engine, at least one fusion engine, at least one inference engine, and at least one validation engine; and at least two sensors, each of the at least two sensors operable to measure a first distance from a vehicle to at least one object and a second distance from the vehicle to the at least one object; wherein the at least one computer processor is operable to analyze the first distance and the second distance; wherein the at least one computer processor is operable to receive at least one query; wherein the at least one curation engine is operable to curate the first distance and the second distance, the at least one link engine is operable to link the first distance and the second distance, the at least one fusion engine is operable to fuse the first distance and the second distance, thereby creating fused data, the at least one inference engine is operable to determine at least one inference from the first distance and the second distance, and the at least one validation engine is operable to validate the first distance and the second distance; wherein the fused data includes at least one new data set; wherein the at least one validation engine is operable to actively validate the fused data; wherein the active validation of the fused data includes modification of an orientation of at least one of the at least two sensors, the at least one of the at least two sensors capturing a third distance measurement from the vehicle to the at least one object at the modified orientation to generate a second data set; wherein the active validation of the fused data further includes the at least one validation engine comparing the fused data to the second data set via a statistical comparison; wherein the at least one new data set is not saved unless the at least one validation engine validates the fused data; wherein the at least one new data set includes an accuracy value for each of the at least two sensors; wherein the at least one inference engine determines the at least one inference by using artificial intelligence based in part on the at least one query and/or the at least one new data set; wherein the at least one inference engine is operable to answer the at least one query based in part on the at least one inference; wherein the at least one validation engine is operable to validate the at least one inference by comparing the at least one inference to a second inference; and wherein the at least one computer processor is operable to instruct the vehicle to brake based on the at least one new data set.",
    "15. The system of claim 14, wherein the answer to the at least one query includes a distance from the vehicle to the at least one object.",
    "16. The system of claim 14, wherein the at least one inference engine is operable to determine which of the at least one first distance sensor and/or the at least one second distance sensor the at least one computer processor responds to based in part on the at least one inference.",
    "17. The system of claim 14, wherein the at least one inference includes a numerical value.",
    "18. The system of claim 14, wherein the at least one inference engine is operable to determine the at least one inference in real-time.",
    "19. The system of claim 14, wherein the at least one inference includes a prediction of a future event based in part on the fused data.",
    "20. The system of claim 14, wherein the at least one new data set includes an accuracy value for each of the at least two sensors."
  ],
  "description_excerpt": "The present invention relates to physical sensors measuring data and dynamically fusing the physical sensor data to create a new dataset.\n\nIt is generally known in the prior art to append data from more than one source.\n\nPrior art patent documents include the following:\n\nU.S. Patent Publication Number 2024/0378412 for methods for topological data analysis ai/ml pipeline (tdaml) with algorithm for multimodal sensor data fusion in autonomy applications, by inventor Paul Thomas Schrader, filed Apr. 25, 2024, published Nov. 14, 2024, directed to a method of topological data analysis feature engineering for data fusion and autonomy is provided. The method comprises providing a Topological Data Analysis AI/ML Pipeline (TDAML) algorithm for multimodal sensor data fusion in autonomy applications system further comprising combining raw heterogeneous multimodal sensor data at the topological level; measuring, recording, and tracking linear representations of an underlying data set; providing a linear representation of the underlying data set which is compatible to existing deep learning (DL) model architectures for training in autonomy tasks; and accessing the entire degree of freedom (DOF) space of raw multimodal sensor data for mitigating sensor modality adversarial threats and environmental attenuation concerns in contested military and civilian (urban) environments.\n\nU.S. Patent Publication Number 2023/0112441 for data fusion and analysis engine for vehicle sensors, by inventors LuAn Tang et al., filed Oct. 6, 2022, published Apr.",
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  "assignees": [
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  "inventors": [
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  "filing_date": "2025-01-16",
  "publication_date": "2025-09-30",
  "grant_date": "2025-09-30",
  "priority_date": "2024-12-19",
  "application_number": "US-202519024493-A",
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