IP Library Granted Patent US 12,499,182
Granted Patent B2
US 12,499,182 · App. 19/081,672 · Granted Dec 16, 2025

Systems and methods of sensor data fusion

Inventor: Armando Montalvo (Winter Garden, FL)
Assignee: Digital Global Systems, Inc.
G06F18/2431B60R19/483B60T8/1755B60T8/3275B60W10/18B60W10/184B60W30/085B60W30/09B60W60/00B60W60/0018B60W60/00186B62D15/0285G01C3/00G01C21/1652G01C21/3804G01C21/3811G01C21/3848G01C22/00G01S5/14G01S7/4808G01S11/00G01S13/08G01S13/103G01S13/42G01S15/08G01S15/101G01S15/42G01S17/08G01S17/88G01S17/894G06F7/14G06F7/16G06F16/24G06F16/245G06F16/2455G06F16/24556G06F16/2456G06F16/33G06F16/334G06F16/43G06F16/53G06F16/903G06F16/90335G06F16/9035G06F18/25G06N3/02G06N3/0464G06N5/04G06N5/042G06N5/045G06N5/046G06N5/048G06N20/00G06T7/521G06V10/764G06V10/80G06V10/803G06V10/82G06V20/56G08B29/188G08G1/0133G08G1/04G08G1/042H04W4/38B60T2201/00B60T2201/03B60W2050/0052B60W2420/00B60W2420/40B60W2420/403B60W2420/408B60W2420/50B60W2510/069B60W2510/18B60W2520/04B60W2540/12B60W2554/801B60W2554/802B60W2556/35B60W2710/18B60W2754/30G01S2013/93185G05D2101/15G05D2111/50G05D2111/67G06T2207/10028G06T2207/20024G06T2207/20084G06T2207/30252G06T2207/30264
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,499,182
App. No.
19/081,672
Granted
Dec 16, 2025
Kind
B2
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 (73)

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, 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 at least one curation engine is operable to adjust an environmental sampling rate;

wherein the at least one link engine is operable to link the first distance measurement and the second distance measurement exceeding a mathematical threshold;

wherein the mathematical threshold is dynamically adjusted based on the at least one query;

wherein the at least one curation engine is operable to curate the first distance measurement by categorizing the first distance measurement into at least one first distance property and/or at least one first distance sub-property;

wherein the at least one first distance property and/or the at least one first distance sub-property includes at least one first data point of the vehicle;

wherein the at least one curation engine is operable to curate the second distance measurement by categorizing the second distance measurement into at least one second distance property and/or at least one second distance sub-property;

wherein the at least one second distance property and/or the at least one second distance sub-property includes the at least one first data point of the vehicle and/or at least one second data point of the vehicle;

wherein the at least one curation engine is operable to filter the first distance measurement and the second distance measurement based in part on the at least one first distance property and/or the at least one first distance sub- property and the at least one second distance property and/or the at least one second distance sub-property;

wherein the at least one fusion engine is operable to fuse the first distance measurement and the second distance measurement;

wherein the at least one fusion engine creates at least one new data set; 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 at least one first distance property and/or the at least one first distance sub-property and the at least one second distance property and/or the at least one second distance sub-property includes a timestamp and/or a measurement over a common period of time.

3 . The system of claim 2 , wherein the at least one curation engine is further operable to curate the first distance measurement and the second distance measurement based in part on the timestamp and/or measurement over the common period of time.

4 . The system of claim 1 , wherein the at least one curation engine is operable to use artificial intelligence to automatically categorize the first distance measurement into the at least one first distance property and/or the at least one first distance sub-property and the second distance measurement into the at least one second distance property and/or the at least one second distance sub-property based in part on historical accuracy of previous categorizations by an artificial intelligence engine.

5 . The system of claim 1 , wherein the at least one curation engine is operable to curate heterogeneous, partially heterogeneous, or homogeneous properties and/or sub-properties.

6 . The system of claim 1 , wherein the at least one computer processor is located on or in a machine, an edge device, at least one server, and/or a cloud.

7 . The system of claim 1 , wherein the at least one first distance sensor and/or the at least one second distance sensor is operable to include a Light Detection and Ranging (LiDAR) sensor, a radar sensor, an ultrasonic sensor, a visible spectrum camera, a Global Positioning System (GPS) sensor, an infrared sensor, and/or a depth camera.

8 . The system of claim 1 , wherein the at least one first distance property and/or the at least one first distance sub-property is operable to include distance between the vehicle and the at least one object and/or a three-dimensional point map of an environment surrounding the vehicle, wherein the at least one second distance property and/or the at least one second distance sub-property is operable to include the distance between the vehicle and the at least one object, a speed of the vehicle, and a speed of the at least one object.

9 . 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, determining by the at least one inference engine at least one inference 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;

adjusting by the at least one curation engine an environmental sampling rate;

linking by the at least one link engine the first distance measurement and the second distance measurement exceeding a mathematical threshold;

dynamically adjusting by the at least one link engine the mathematical threshold based on the at least one query;

curating by the at least one curation engine the first distance measurement by categorizing the first distance measurement into at least one first distance property and/or at least one first distance sub-property;

wherein the at least one first distance property and/or the at least one first distance sub-property includes at least one first data point of the vehicle;

curating by the at least one curation engine the second distance measurement by categorizing the second distance measurement into at least one second distance property and/or at least one second distance sub-property;

wherein the at least one second distance property and/or the at least one second distance sub-property includes the at least one first data point of the vehicle and/or at least one second data point of the vehicle;

filtering by the at least one curation engine the first distance measurement and the second distance measurement based in part on the at least one first distance property and/or the at least one first distance sub-property and the at least one second distance property and/or the at least one second distance sub-property;

fusing by the at least one fusion engine the first distance measurement and the second distance measurement;

creating by the at least one fusion engine at least one new data set; and

instructing by the at least one computer processor the vehicle to brake based on the at least one new data set.

10 . The method of claim 9 , wherein the at least one first distance sensor and/or the at least one second distance sensor is operable to include a Light Detection and Ranging (LiDAR) sensor, a radar sensor, an ultrasonic sensor, a visible spectrum camera, a Global Positioning System (GPS) sensor, an infrared sensor, and/or a depth camera.

11 . The method of claim 9 , further comprising categorizing via the at least one curation engine using artificial intelligence the at least one first distance property and/or the at least one first distance sub-property and the at least one second distance property and/or the at least one second distance sub-property based in part on historical accuracy of previous categorizations by the at least one curation engine.

12 . The method of claim 9 , further comprising curating the first distance measurement and the second distance measurement in real-time.

13 . The method of claim 9 , wherein the at least one first distance property and/or the at least one first distance sub-property and the at least one second distance property and/or the at least one second distance sub-property includes a timestamp and/or a measurement over a common period of time.

14 . The method of claim 13 , further comprising curating the first distance measurement and the second distance measurement based in part on the timestamp and/or measurement over the common period of time.

15 . The method of claim 9 , wherein the at least one first distance property and/or the at least one first distance sub-property and/or the at least one second distance property and/or the at least one second distance sub-property is not associated with time.

16 . The method of claim 9 , further comprising curating heterogeneous, partially heterogeneous, or homogeneous properties and/or sub-properties.

17 . 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, 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 at least one curation engine is operable to adjust an environmental sampling rate;

wherein the at least one link engine is operable to link the first distance measurement and the second distance measurement exceeding a mathematical threshold;

wherein the mathematical threshold is dynamically adjusted based on the at least one query;

wherein the at least one curation engine is operable to curate the first distance by categorizing the first distance into at least one first distance property and/or at least one first distance sub-property based in part on the at least one query;

wherein the at least one first distance property and/or the at least one first distance sub-property includes at least one first data point of the vehicle;

wherein the at least one curation engine is operable to curate the second distance by categorizing the second distance into at least one second distance property and/or at least one second distance sub-property based in part on the at least one query;

wherein the at least one second distance property and/or the at least one second distance sub-property includes the at least one additional data point of the vehicle and/or at least one second data point of the vehicle;

wherein the at least one curation engine is operable to filter the first distance and the second distance based in part on the at least one query;

wherein the at least one fusion engine is operable to fuse the first distance and the second distance;

wherein the at least one fusion engine creates at least one new data set; 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.

18 . The system of claim 17 , wherein the at least one curation engine is further operable to curate the first distance and the second distance based in part on a timestamp and/or a measurement over a common period of time.

19 . The system of claim 17 , wherein the at least one curation engine is operable to curate heterogeneous, partially heterogeneous, or homogeneous properties and/or sub-properties.

20 . The system of claim 17 , wherein the at least one curation engine is operable to use artificial intelligence to automatically categorize the first distance into the at least one first distance property and/or the at least one first distance sub-property and the second distance into the at least one second distance property and/or the at least one second distance sub-property based in part on historical accuracy of previous categorizations by an artificial intelligence engine.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 18, 2025
From: MONTALVO, ARMANDO
To: DIGITAL GLOBAL SYSTEMS, INC.
Reel/Frame 070545/0757 →
Continuity (3)
Continuation 19022045 · Jan 15, 2025
Continuation 18988120 · Dec 19, 2024
Related Publication 20250252157A1 · Aug 7, 2025
References Cited (112)
US 6026340A · Corrado et al. · 2000 [cited by applicant]
US 9367683B2 · Kolacinski et al. · 2016 [cited by applicant]
US 10095230B1 · Hardin · 2018 [cited by applicant]
US 10748075B2 · Chowdhary et al. · 2020 [cited by applicant]
US 10766137B1 · Porter et al. · 2020 [cited by applicant]
US 11037320B1 · Ebrahimi Afrouzi et al. · 2021 [cited by applicant]
US 11040444B2 · Cristache · 2021 [cited by applicant]
US 11436429B2 · Jaganathan et al. · 2022 [cited by applicant]
US 11586865B2 · Brahma et al. · 2023 [cited by applicant]
US 11966413B2 · Latapie et al. · 2024 [cited by applicant]
US 11983625B2 · Ahuja et al. · 2024 [cited by applicant]
US 12105211B2 · Carcanague et al. · 2024 [cited by applicant]
US 12112174B2 · Sodani et al. · 2024 [cited by applicant]
US 12217831B2 · Jaganathan et al. · 2025 [cited by applicant]
US 12287851B1 · Montalvo · 2025 [cited by examiner]
US 20040019575A1 · Talbot et al. · 2004 [cited by applicant]
US 20070239314A1 · Kuvich · 2007 [cited by applicant]
US 20080005075A1 · Horvitz et al. · 2008 [cited by applicant]
US 20080312756A1 · Grichnik et al. · 2008 [cited by applicant]
US 20120134280A1 · Rotvold et al. · 2012 [cited by applicant]
US 20150306770A1 · Mittal et al. · 2015 [cited by applicant]
US 20170004411A1 · Hassan et al. · 2017 [cited by applicant]
US 20170048010A1 · Chowdhery et al. · 2017 [cited by applicant]
US 20180284758A1 · Cella et al. · 2018 [cited by applicant]
US 20190206400A1 · Cui et al. · 2019 [cited by applicant]
US 20190258251A1 · Ditty · 2019 [cited by examiner]
US 20190295001A1 · Fusco et al. · 2019 [cited by applicant]
US 20200026289A1 · Alvarez et al. · 2020 [cited by applicant]
US 20200219316A1 · Baik · 2020 [cited by examiner]
US 20200303033A1 · Benz et al. · 2020 [cited by applicant]
US 20200405403A1 · Shelton, IV et al. · 2020 [cited by applicant]
US 20210150230A1 · Smolyanskiy et al. · 2021 [cited by applicant]
US 20210278523A1 · Urtasun et al. · 2021 [cited by applicant]
US 20210290311A1 · Fuerst et al. · 2021 [cited by applicant]
US 20210318121A1 · Laroche · 2021 [cited by examiner]
US 20210331695A1 · Ramakrishnan et al. · 2021 [cited by applicant]
US 20210342656A1 · Mittal et al. · 2021 [cited by applicant]
US 20220012562A1 · Lazaro-Gredilla et al. · 2022 [cited by applicant]
US 20220035376A1 · Laddah et al. · 2022 [cited by applicant]
US 20220075077A1 · Kato et al. · 2022 [cited by applicant]
US 20220126864A1 · Moustafa et al. · 2022 [cited by applicant]
US 20220146652A1 · Cardei et al. · 2022 [cited by applicant]
US 20220161815A1 · Van Beek et al. · 2022 [cited by applicant]
US 20220179056A1 · Braley et al. · 2022 [cited by applicant]
US 20220289175A1 · Kasarla · 2022 [cited by examiner]
US 20220324464A1 · Collin et al. · 2022 [cited by applicant]
US 20220348223A1 · Zhao et al. · 2022 [cited by applicant]
US 20230082656A1 · Li et al. · 2023 [cited by applicant]
US 20230110027A1 · Bajpayee et al. · 2023 [cited by applicant]
US 20230112441A1 · Tang et al. · 2023 [cited by applicant]
US 20230149101A1 · Mottram et al. · 2023 [cited by applicant]
US 20230166765A1 · Yoon et al. · 2023 [cited by applicant]
US 20230182768A1 · Oh · 2023 [cited by applicant]
US 20230190204A1 · Helm et al. · 2023 [cited by applicant]
US 20230256618A1 · Park et al. · 2023 [cited by applicant]
US 20230270506A1 · Goodchild et al. · 2023 [cited by applicant]
US 20230342944A1 · Sullivan et al. · 2023 [cited by applicant]
US 20230347517A1 · Ma et al. · 2023 [cited by applicant]
US 20230394334A1 · Kang et al. · 2023 [cited by applicant]
US 20240045426A1 · Ditty et al. · 2024 [cited by applicant]
US 20240058948A1 · Zhai et al. · 2024 [cited by applicant]
US 20240112428A1 · Levi et al. · 2024 [cited by applicant]
US 20240123619A1 · Ning · 2024 [cited by applicant]
US 20240138926A1 · Goodchild et al. · 2024 [cited by applicant]
US 20240142994A1 · Ebrahimi Afrouzi · 2024 [cited by applicant]
US 20240144082A1 · Tarapov et al. · 2024 [cited by applicant]
US 20240152734A1 · Ye · 2024 [cited by applicant]
US 20240159891A1 · Patel · 2024 [cited by applicant]
US 20240249165A1 · Roche et al. · 2024 [cited by applicant]
US 20240267779A1 · Wu et al. · 2024 [cited by applicant]
US 20240289930A1 · Yang et al. · 2024 [cited by applicant]
US 20240289981A1 · Kuo et al. · 2024 [cited by applicant]
US 20240296044A1 · Day et al. · 2024 [cited by applicant]
US 20240312219A1 · Choi et al. · 2024 [cited by applicant]
US 20240317254A1 · Tran · 2024 [cited by applicant]
US 20240324838A1 · Ebrahimi Afrouzi · 2024 [cited by applicant]
US 20240331403A1 · Shen et al. · 2024 [cited by applicant]
US 20240378412A1 · Schrader · 2024 [cited by applicant]
US 20240382216A1 · Hage et al. · 2024 [cited by applicant]
US 20240408495A1 · Hibi et al. · 2024 [cited by applicant]
US 20240412494A1 · Balachandran et al. · 2024 [cited by applicant]
US 20250026371A1 · Andert et al. · 2025 [cited by applicant]
US 20250053731A1 · Kantimahanti et al. · 2025 [cited by applicant]
US 20250139807A1 · Zou et al. · 2025 [cited by applicant]
US 20250200431A1 · Marzban et al. · 2025 [cited by applicant]
US 20250217863A1 · Tunkelang · 2025 [cited by examiner]
CN 110598299A · 2019 [cited by applicant]
CN 110909762A · 2020 [cited by examiner]
CN 115561795A · 2023 [cited by applicant]
CN 116522279A · 2023 [cited by examiner]
CN 118427205A · 2024 [cited by applicant]
WO 2024186551A1 · 2024 [cited by applicant]
WO 2024228863A1 · 2024 [cited by applicant]
Eenennaam, Providing over-the-horizon awareness to driver support systems by means of multi-hop ad hoc vehicle-to-vehicle communication (thesis); 2008 [retrieved Jul. 25, 2025], University of Twente, 204 pages. https://… [cited by examiner]
Li, Belief Space-Guided Navigation for Robots and Autonomous Vehicles (thesis), May 2021 [retrieved Jul. 24, 2025], Texas A&M University, 123 pages. Retrieved: https://oaktrust.library.tamu.edu/server/api/core/bitstream… [cited by examiner]
Shen, Security Challenges and Defense Opportunities of Connected and Autonomous Vehicle Systems in the Physical World (thesis), 2022 (retrieved Jul. 24, 2025), University of California, Irvine, 304 pages. Retrieved: htt… [cited by examiner]
Zhang, Compiler Support for Robust and High Performance Autonomous Driving Environments (thesis), 2022 (retrieved Jul. 24, 2025], University of Michigan Library, 125 pages. https://dx.doi.org/10.7302/6919 (Year: 2022). [cited by examiner]
Chung et al., “Entropy-Based Markov Chains for Multisensor Fusion” Journal of Intelligent and Robotic Systems 29: 161a189, 2000 (Year: 2000). [cited by applicant]
Scibelli et al., “Low-cost Stellar Sensor for Attitude Control of Small Satellites” 2019 Photonics & Electromagnetics Research Symposium—Spring (PIERS—Spring), Rome, Italy, Jun. 17-20, 2019. [cited by applicant]
Tan et al., “A New Approach for Small Satellite Gyroscope and Star Tracker Fusion” Indian Journal of Science and Technology, vol. 9(17), May 2016 (Year: 2016). [cited by applicant]
CN110598299—English translation. [cited by applicant]
CN115561795—English translation. [cited by applicant]
Brush, “Measurement of microwave power—A review of techniques used for measurement of high-frequency RF power” IEEE Instrumentation & Measurement Magazine Apr. 2007 (Year: 2007). [cited by applicant]
Kleber et al., “Cooperative Cross-Correlation Algorithm to Optimize Linearity of Fused RF Sensors” IEEE Sensors Journal, vol. 20, No. 7, Apr. 1, 2020 (Year: 2020). [cited by applicant]
Ebel, Patrick, and et al. “SEN12MS-CR-TS: A remote-sensing data set for multi modal multitemporal cloud removal.” IEEE Transactions on Geoscience and Remote Sensing 60 (2022): 1-14 (Year: 2022). [cited by applicant]
Wei Z, and et al. MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review. Sensors (Basel). Mar. 25, 2022;22(7):2542. doi: 10.3390/s22072542. PMID: 35408157; PMCID: PMC9003130 (Year: 2022). [cited by applicant]
Yeong, D. J., Velasco-Hernandez, G., Barry, J., & Walsh, J. (2021). Sensor and sensor fusion technology in autonomous vehicles: A review. Sensors, 21(6), 2140. (Year: 2021). [cited by applicant]
Chen et al., “Track correlation uncertainty evaluation for Multi-sensor Data Fusion System and its application” 2021 IEEE (Year: 2021). [cited by applicant]
Peli et al., “Feature Level Sensor Fusion” Part of the SPIE Conference on Sensor Fusion: Architectures. Algorithms, and Applications III. Orlando, Florida, Apr. 1999 (Year: 1999). [cited by applicant]
Lanegger et al., “To Fuse or Not to Fuse: Measuring Consistency in Multi-Sensor Fusion for Aerial Robots” Springer, Aug. 2024 (Year: 2024). [cited by applicant]
Liu et al., “A Method for Improving the Pose Accuracy of a Robot Manipulator Based on Multi-Sensor Combined Measurement and Data Fusion” Sensors 2015, 15, 7933-7952 (Year: 2015). [cited by applicant]
Rumfelt et al., “Radio Frequency Power Measurements” Proceedings of the IEEE, vol. 55, No. 6, Jun. 1967 (Year: 1967). [cited by applicant]