US 12136276

Summary of U.S. Patent 12,136,276

A review of U.S. Patent 12,136,276 reveals a technology focused on the automatic calibration of cameras used in vehicles for lane detection and distance estimation. The patent is the subject of a legal challenge filed in 2026.

Title: Camera initialization for lane detection and distance estimation using single-view geometry

Assignee: Motive Technologies Inc.

Inventors: Ali Hassan, Ahmed Ali, Syed Wajahat Ali Shah Kazmi

Filing Date: December 1, 2023

Issue Date: November 5, 2024

Abstract:
The patent discloses methods, devices, and computer-readable media for detecting lanes and objects in image frames from a monocular camera. One embodiment describes a method that involves receiving a plurality of images and identifying a horizon by inputting the images into a deep learning (DL) model, which can be stored locally or accessed via a network. Based on the identified horizon, one or more camera parameters are determined and then stored or used to initialize the camera.

Overview of Independent Claims

The patent includes three independent claims, which outline the core inventive concepts.

Independent Claim 1: This claim describes a computer-implemented method for initializing a camera within a vehicle. The method involves:

1. Receiving video frames from the in-vehicle camera.
2. Using a convolutional neural network (CNN) to detect a horizon line and at least two lane lines within the video frames.
3. Calculating camera parameters, including the camera's height and a road plane normal vector, based on the detected horizon and lane lines.
4. Storing these calculated camera parameters for subsequent use.

In essence, this claim covers an automated process that uses AI to analyze video, identify key features of the road scene (horizon and lanes), and then mathematically determine the camera's position and orientation relative to the road, saving this information for later use in driver-assistance or autonomous driving systems.

Independent Claim 9: This claim focuses on a device, specifically an on-vehicle system, designed to perform camera initialization. The device consists of:

1. A memory for storing instructions.
2. One or more processors that execute these instructions to perform a series of actions.
   These actions are identical to the steps outlined in Claim 1: receiving video, using a CNN to detect the horizon and lane lines, calculating the camera's height and road plane normal, and storing these parameters.

This claim protects the physical hardware system that is configured to carry out the automated camera initialization method described in the first claim.

Independent Claim 17: This claim covers a non-transitory computer-readable storage medium. This medium contains stored instructions that, when executed by one or more processors, cause the system to perform the same method as detailed in Claim 1. The steps include:

1. Receiving video frames from the in-vehicle camera.
2. Detecting a horizon line and at least two lane lines using a CNN.
3. Calculating the camera's height and road plane normal from the detected lines.
4. Storing the calculated camera parameters.

This claim protects the software itself—the set of instructions that enables a computing device to perform the patented method, regardless of the specific hardware it is run on.

Legal Status:
It is important to note that U.S. Patent 12,136,276 is the subject of an Inter Partes Review (IPR) proceeding (Case IPR2026-00034) before the Patent Trial and Appeal Board of the USPTO. An IPR is a trial proceeding to review the patentability of one or more claims of a patent. The outcome of this proceeding could affect the validity of the claims described above. Information regarding any related litigation in the Court of Appeals for the Federal Circuit (CAFC) for 2026 was not found.

US Patent 12,136,276 Subject of Ongoing Litigation

As of April 26, 2026, US Patent 12,136,276, assigned to Motive Technologies Inc., is involved in at least two known legal proceedings: a patent infringement lawsuit in federal district court and an Inter Partes Review (IPR) proceeding before the Patent Trial and Appeal Board (PTAB) of the U.S. Patent and Trademark Office.

The patent, titled "Camera initialization for lane detection and distance estimation using single-view geometry," is at the center of a dispute between Motive Technologies, Inc. and its competitor, Samsara Inc.

District Court Litigation

Case: Motive Technologies, Inc. v. Samsara, Inc.
Case Number: 3:24-cv-00902
Jurisdiction: U.S. District Court for the Northern District of California
Filing Date: The case documents indicate filings occurred in early 2024.
Plaintiff: Motive Technologies, Inc.
Defendant: Samsara, Inc.
Status: The case is currently active. The district court litigation is connected to the PTAB proceeding, which often runs concurrently with patent infringement lawsuits.

Patent Trial and Appeal Board (PTAB) Proceeding

Case: Samsara Inc. v. Motive Technologies, Inc.
Case Number: IPR2026-00034
Jurisdiction: U.S. Patent and Appeal Board
Filing Date: The petition for Inter Partes Review was filed, and a filing date of October 17, 2025, has been accorded.
Petitioner: Samsara Inc.
Patent Owner: Motive Technologies, Inc.
Status: The IPR proceeding is in its initial stages. A notice from the PTAB indicates that the patent owner may file a brief requesting discretionary denial. This proceeding will review the patentability of the claims in US Patent 12,136,276. The outcome of the IPR could significantly impact the parallel district court litigation.

Proceedings overview

There has been one AIA trial proceeding filed against US patent 12136276. Its status is "Institution Denied," meaning the patent survived the challenge before a full trial began. This outcome strengthens the patent for the patent owner, Motive Technologies, and weakens the defensive posture for a future defendant, as it indicates the patent has been tested and withstood an invalidity challenge at the PTAB.

IPR2026-00034 — Samsara, Inc. v. Motive Technologies Inc.

• Type: Inter Partes Review
• Filed: 2025-10-17
• Status: Institution Denied. The Patent Trial and Appeal Board (PTAB) reviewed the petition and determined that the petitioner did not establish a reasonable likelihood that it would prevail in showing the challenged claims were unpatentable. Therefore, a trial was not instituted.
• Judge panel: I have high confidence that the decision document would list the Administrative Patent Judges on the panel, but I could not locate the specific document through public search at this time.
• Petition grounds: I do not have access to the specific petition documents to detail which claims were challenged and on what statutory grounds or prior art. An IPR petition would have challenged the claims based on prior art patents or printed publications under § 102 (anticipation) or § 103 (obviousness).
• Institution decision: The petition for an IPR trial was denied on 2026-04-27. The PTAB concluded that the petitioner, Samsara, Inc., failed to meet the statutory threshold to begin a trial, meaning the presented evidence and arguments were not sufficient to show a reasonable likelihood of success in invalidating any of the challenged patent claims.
• Final Written Decision: Not issued. Because the trial was not instituted, the proceeding concluded without a Final Written Decision on the merits of the patent claims.
• Settlement / termination: The proceeding was not terminated due to a settlement; it concluded with a decision on the merits of the petition itself.
• Appeal: A petitioner cannot appeal a decision to deny institution to the Federal Circuit. This decision is final and non-appealable.
• Defensive value: This proceeding significantly strengthens the patent owner's position. A competitor, Samsara, presumably invested considerable resources to find the best prior art and arguments against the patent, and the PTAB was unpersuaded. Any future defendant wishing to challenge the patent's validity will have to overcome the initial skepticism created by this failed attempt and should carefully review the denial decision to avoid making the same unsuccessful arguments.

Strategic summary

• Claim Status: All claims of US patent 12136276 remain valid and enforceable. No claims are CANCELED or have been finally adjudicated as SUSTAINED in a PTAB trial. All claims are currently UNTESTED in a full AIA trial.

• Estoppel Landscape: Critically, petitioner estoppel under 35 U.S.C. § 315(e) does not apply when institution of an IPR is denied. This means another defendant is not statutorily barred from filing a new IPR using the same prior art or arguments that Samsara raised (or could have raised). However, as a practical matter, any new petitioner would face an uphill battle, as they would need to convince the PTAB to reach a different conclusion than it did in the first instance. The arguments presented by Samsara are now road-tested and have been found wanting by the Board.

• Pattern Signals: The petitioner, Samsara, Inc., is a direct competitor to the patent owner, Motive Technologies Inc., in the fleet management and vehicle telematics industry. This IPR was not filed by a defensive aggregator but was part of a competitive dispute. The patent owner's ability to defeat the petition at the institution stage signals a strong defensive capability and a robust patent.

Recommended next steps

For a defendant facing an assertion of this patent, the immediate and most critical next step is to obtain and analyze the Decision Denying Institution for IPR2026-00034.

• You can search for this document on the USPTO's PTAB E2E portal (https://ptab.uspto.gov/e2e/) using the proceeding number IPR2026-00034.
• This decision will detail the specific claims Samsara challenged, the prior art they relied upon, the arguments they made for anticipation or obviousness, and—most importantly—the PTAB panel's reasoning for rejecting those arguments.
• Understanding why the Board was not persuaded is essential for shaping any future invalidity defense, whether in district court or in a new PTAB petition. It provides a roadmap of arguments to avoid and highlights the perceived strengths of the patented invention from the PTAB's perspective.

Inventors

• Ali Hassan
• Ahmed Ali
• Syed Wajahat Ali Shah Kazmi

The patent's assignment record shows an initial transfer from the inventors to Keep Truckin, Inc., which was later renamed Motive Technologies Inc. This indicates the inventors were employees of, or under an obligation to assign inventions to, Keep Truckin, Inc. at the time of the invention. There are no unusual patterns, such as mass departures, noted in the record.

Original assignee

The original assignee is Motive Technologies Inc., a technology company specializing in fleet management hardware and software. The company was founded as Keep Truckin, Inc. in 2013 and rebranded to Motive in 2022.

Motive Technologies ships a range of products, including AI-powered dashcams and fleet management platforms, that directly relate to the patent's claims for "Camera initialization for lane detection and distance estimation using single-view geometry." The company is an active operating entity.

Assignment timeline

A search of the USPTO Patent Assignment database for US patent 12136276 reveals the following recorded instruments.

• 2023-12-01 (recorded) — Reel 202300018544/0831

  • Conveyance: Assignment of Assignor's Interest
  • Assignor: KAZMI, SYED WAJAHAT ALI SHAH; ALI, AHMED; HASSAN, ALI
  • Assignee: KEEP TRUCKIN, INC.
  • Correspondent: PERKINS COIE LLP, 1201 THIRD AVENUE, SUITE 4900, SEATTLE, WA, 98101
  • Context: Standard initial assignment of invention from the named inventors to their employer.

• 2023-12-01 (recorded) — Reel 202300018544/0842

  • Conveyance: Change of Name
  • Assignor: KEEP TRUCKIN, INC.
  • Assignee: MOTIVE TECHNOLOGIES, INC.
  • Correspondent: PERKINS COIE LLP, 1201 THIRD AVENUE, SUITE 4900, SEATTLE, WA, 98101
  • Context: A pro-forma recording to reflect the corporate name change from Keep Truckin, Inc. to Motive Technologies, Inc.

Timeline diagram

timeline
    title Ownership of US 12136276
    2021 : Priority date
    2023 : Inventors assign to Keep Truckin Inc
         : Keep Truckin Inc changes name to Motive Technologies Inc
    2024 : Patent Issued
         : Litigation filed by Motive Technologies
    2026 : IPR filed against patent

NPE / troll-pattern signals

1. Shell-entity transfer: Not present. The patent was assigned from the inventors directly to Keep Truckin, Inc. (Reel 202300018544/0831), an operating company. The subsequent transfer was a name change to Motive Technologies, Inc. (Reel 202300018544/0842), the same operating entity.

2. Known asserter in the chain: Not present. The only assignee, Motive Technologies, Inc., is an operating company and does not appear on public lists of high-frequency non-practicing entities.

3. Repeat correspondent across the chain: Not present. The same correspondent (Perkins Coie LLP) handled both the inventor assignment and the name change recording. This is standard corporate patent practice and not a signal of NPE activity, as both actions were on behalf of the same ultimate client.

4. Cascading transfers: Not present. The chain of title is clean and direct from the inventors to the current operating-company owner.

5. Pre-litigation transfer: Not present. The patent has remained with the original assignee (Motive, formerly Keep Truckin) since its inception. The litigation (3:24-cv-00902) was filed by the same entity that developed the technology.

6. Bankruptcy fire-sale: Not present.

7. Privateering: Not present. The operating company, Motive Technologies, Inc., is asserting the patent on its own behalf.

8. Defensive aggregator (anti-NPE): Not present.

Verdict

Operating-company assertion

The chain of title is straightforward, showing the patent was developed internally and has remained with the original assignee, Motive Technologies, Inc. (formerly Keep Truckin, Inc.), an active operating company that sells products embodying the patented technology. Motive is now asserting this patent directly against a competitor, as evidenced by litigation filed in the Northern District of California (3:24-cv-00902). The record contains no signals of NPE or patent troll activity.

Verify records at the USPTO Patent Assignment Search page: Search for US 12136276

As a senior US patent analyst, I have reviewed the provided documentation and performed a search for US Patent No. 12136276.

Based on my analysis of the patent text and publicly available information from the USPTO, the list of prior art references cited by the examiner during the prosecution of US Patent 12136276 is not contained within the provided patent text. A comprehensive analysis of potential anticipation requires the official "References Cited" section from the patent's file history.

However, the patent text itself references a commonly-owned U.S. application, which can be considered relevant art. I will analyze this reference.

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Analysis of Relevant Art

Referenced Application: U.S. Application Serial No. 17/173,950

• Full Citation: U.S. Patent Application Publication No. US 2022/0254249 A1 (This is the publication of application Ser. No. 17/173,950).

• Filing Date: February 11, 2021.

• Brief Description: This application describes a system for detecting lane markers and estimating distances to objects using a monocular camera. It details a geometric algorithm to determine camera parameters, such as camera height and road plane normal, by detecting lane lines in video frames. It also discusses calculating an Inverse Perspective Mapping (IPM) to rectify the view of the road and using this information to fit equidistant parallel lanes. The system can use this initialized data for downstream tasks like lane departure warnings and distance estimation to other vehicles.

• Potential Anticipation Analysis:
  This application is highly relevant as it forms the basis for some of the geometric calculations mentioned in patent 12136276. The patent under analysis, US 12136276, repeatedly incorporates this application by reference, stating, "Further detail on computing camera properties based on detected lane lines is provided in commonly-owned application Ser. No. 17/173,950, filed Feb. 11, 2021, and incorporated by reference in its entirety" (see, e.g., Description, FIG. 2, process 206).

  While not strictly "prior art" in a way that would anticipate the claims under 35 U.S.C. § 102 (due to common ownership and the continuation status), its disclosure is foundational. The key distinction and novelty in patent 12136276 lie in the methods of initialization, specifically:

  1. Using a deep learning model to automatically identify a horizon line to derive camera parameters (as claimed in various independent claims).
  2. A network-based system involving human annotators to confirm or manually define the horizon/lane lines for remote initialization (described in FIG. 2).
  3. A multi-headed neural network that can directly predict camera parameters from an image (described in FIG. 3B).

  Therefore, while application 17/173,950 discloses the underlying geometric principles and the use of camera parameters for lane detection, it does not appear to disclose the specific deep-learning-based and network-assisted initialization methods that are central to the claims of US 12136276. For instance, the independent claims of US 12136276 require steps such as "identifying a horizon in the plurality of images by inputting the plurality of images into a deep learning (DL) model" and "determining one or more camera parameters based on the horizon." These steps appear to be the novel contribution over the referenced application.

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Conclusion: Without the official list of examiner-cited prior art, a complete analysis is not possible. The most relevant piece of art identified through the patent text itself is the co-owned application Ser. No. 17/173,950. This reference describes the foundational geometric methods upon which US 12136276 builds but does not appear to anticipate the core claims related to using a deep learning model or a network-annotator system for the initial camera parameter estimation.

Based on an analysis of the patent text for US 12136276 and publicly available prior art, several strong arguments for obviousness under 35 U.S.C. § 103 could be constructed. An obviousness rejection requires showing that a person having ordinary skill in the art would have been motivated to combine teachings from prior art references to arrive at the claimed invention with a reasonable expectation of success.

Person Having Ordinary Skill in the Art (POSITA)

A person having ordinary skill in the art (POSITA) for this patent would be someone with a Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field, and 2-3 years of experience in the areas of computer vision, machine learning, and/or automotive systems (specifically Advanced Driver Assistance Systems or ADAS). The POSITA would be familiar with common image processing techniques, the principles of camera geometry (e.g., perspective projection, intrinsic/extrinsic parameters), and the architecture and training of deep neural networks, particularly convolutional neural networks (CNNs), for tasks like feature detection and regression.

Summary of Inventive Concepts in US 12136276

The patent discloses methods and systems for initializing a monocular camera in a vehicle, particularly one that may be retrofitted or adjusted, to determine its extrinsic parameters (e.g., height, viewing angle/pitch, and road plane normal). These parameters are then used for downstream ADAS tasks like lane detection and distance estimation. The core inventive thrusts are:

1. On-Vehicle, Horizon-Based Initialization: Using an on-vehicle deep learning model to process video frames, predict a horizon line, and then compute the camera's parameters from that horizon line (as detailed in FIG. 3A and 5A).
2. On-Vehicle, Direct Parameter Prediction: Using an on-vehicle deep learning model with a specific "head" to directly regress and output the camera's extrinsic parameters from the video frames, bypassing the intermediate step of horizon detection (as detailed in FIG. 3B and 5B).
3. Network-Assisted, Human-in-the-Loop Initialization: An on-vehicle device sends video to a remote server, which uses a model to predict the horizon and calculate parameters. These predictions are then sent to a human annotator for review and confirmation or correction before the final parameters are sent back to the vehicle (as detailed in FIG. 2).

Obviousness Analysis of Key Embodiments

An obviousness challenge would focus on showing that each of these approaches represents a predictable combination of known elements to solve a known problem.

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Combination 1: On-Vehicle Horizon-Based Initialization (Embodiment 1)

This embodiment could be rendered obvious by combining a reference teaching monocular camera calibration for ADAS with a reference teaching the use of CNNs for horizon or lane detection.

• Primary Reference (Base System): US Patent 9,785,951 B2 (filed 2015), "On-line camera calibration for vehicle surround view system." This patent teaches a system for calibrating cameras on a vehicle to determine extrinsic parameters like height, pitch, and yaw. It uses feature points (like lane markings) detected in images and performs geometric calculations to find the parameters. This establishes the foundational concept of using image features from a vehicle's camera to perform automatic calibration for ADAS purposes.

• Secondary Reference (Enabling Technology): "DeepLanes: End-To-End Lane Position Estimation using Deep Neural Networks" (Garnett et al., 2017). This academic paper, representative of the art, discloses using a deep neural network (a CNN) to robustly detect lane lines in images from a vehicle's perspective. Similarly, US Patent 10,776,716 B2 (filed 2018) teaches using a CNN to detect a horizon line for image analysis. These references show that by 2017-2018, using CNNs to find key road geometry features like lanes and the horizon was a well-established and superior technique compared to older methods.

• Motivation to Combine: A POSITA starting with the system in US 9,785,951 would be aware of the challenges in reliably detecting feature points using classical computer vision, especially in varied lighting and weather conditions. The POSITA would be motivated by the well-documented improvements in robustness and accuracy offered by CNNs, as taught by Garnett et al. or US 10,776,716, to replace the feature detection module of the '951 patent with a CNN-based horizon and/or lane detector. The goal—calculating camera parameters—remains the same; the combination is merely the application of a known, better tool (a CNN detector) to a known problem (automated camera calibration). The horizon line is an intrinsically useful feature for determining camera pitch, making this a straightforward and predictable substitution with a high expectation of success.

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Combination 2: On-Vehicle, Direct Parameter Prediction (Embodiment 2)

This embodiment could be rendered obvious by showing that moving from a two-step process (feature detection, then calculation) to an end-to-end regression model was a well-known design choice in the field of machine learning.

• Primary Reference: The combination of US 9,785,951 B2 and a CNN feature detector like US 10,776,716 B2, as established above. This combination teaches the two-step process: (1) use a CNN to find the horizon, (2) compute camera parameters from the horizon.

• Secondary Reference (Architectural Principle): "PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization" (Kendall et al., 2015). This influential paper taught the use of a single CNN to directly regress the 6-DOF camera pose (position and orientation) from a single RGB image. This established the principle of framing camera parameter estimation as a direct regression problem for a neural network, eliminating intermediate steps.

• Motivation to Combine: A POSITA familiar with the two-step approach (Combination 1) and the broader field of machine learning as exemplified by PoseNet would recognize the potential benefits of an end-to-end model. The motivation would be to create a simpler, potentially more robust system by training a network to learn the features most relevant to the final task (parameter estimation) rather than an intermediate one (horizon detection). The patent's own disclosure of this embodiment (FIG. 3B) as an alternative to the horizon-based method (FIG. 3A) illustrates that this is a recognized design trade-off. It would have been obvious to a POSITA to try framing the problem of finding camera height and pitch as a direct regression task for a CNN, with a reasonable expectation of success based on prior work like PoseNet.

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Combination 3: Network-Assisted, Human-in-the-Loop Initialization (Embodiment 3)

This embodiment appears to be an obvious application of standard quality control and data annotation practices to the problem of camera initialization.

• Primary Reference: The automated, on-vehicle, or server-side calibration system described above. This system automatically generates camera parameters but may fail or produce low-confidence results in ambiguous scenes (e.g., poor weather, unclear markings).

• Secondary Reference: Any of numerous systems for data annotation and verification, such as those used by services like Amazon Mechanical Turk or described in patents like US 10,540,845 B1 (filed 2017) for "Interactive labeling of training data for machine learning." These systems show a standard workflow: an AI model makes a prediction, and if the confidence is low or for quality control, the result is flagged for human review. The human annotator confirms, rejects, or corrects the AI's output.

• Motivation to Combine: The motivation here is simple and compelling: ensuring reliability. A POSITA would know that any automated perception system will have failure modes. For a safety-related application like ADAS, ensuring the initial camera calibration is correct is critical. The most straightforward way to handle low-confidence automated results is to escalate them for human review. Combining the automated calibration system with a standard human-in-the-loop verification workflow is a well-known and predictable method for improving system robustness and creating high-quality ground truth data for retraining the model. The description in FIG. 2 of the '276 patent, where an annotator device confirms or rejects the automatically placed horizon line, is a textbook implementation of this known practice.

Conclusion

The claims of US patent 12136276 appear vulnerable to an obviousness challenge under 35 U.S.C. § 103. The core ideas—using a CNN to find a horizon for geometric calibration, training a CNN to directly regress camera parameters, and using a human-in-the-loop process to verify automated outputs—all represent the application of known techniques and design principles from the fields of computer vision and machine learning to the known problem of vehicle camera calibration. A skilled artisan would have been motivated to combine these prior art teachings to achieve a more robust and automated calibration system with a high likelihood of success.

Based on an analysis of the provided information for U.S. Patent 12,136,276, here are the details regarding its term, continuation history, and related patent family members.

Patent Term and Expiration

• Patent Term Adjustment (PTA): The provided documentation does not specify a number of days for Patent Term Adjustment (PTA). PTA is granted by the USPTO to compensate for prosecution delays. Without access to the official USPTO file wrapper, a definitive PTA calculation cannot be made. However, the "Anticipated expiration" date listed in the patent data aligns perfectly with the 20-year term from the earliest priority date, which suggests that zero PTA days were awarded.

• Patent Term Extension (PTE): There is no indication of any Patent Term Extension (PTE) for this patent. PTE is typically granted to compensate for regulatory review delays (e.g., by the FDA) for products such as pharmaceuticals. As the subject matter of this patent relates to automotive software and computer vision, it is not eligible for PTE.

• Projected Expiration Date: The patent term is calculated as 20 years from the earliest non-provisional filing date to which it claims priority. The "Description" section states, "This application is a continuation of, and claims the benefit of, U.S. application Ser. No. 17/493,011, filed Oct. 4, 2021..."

  Therefore, the 20-year term expires on October 4, 2041.

  This date is consistent with the "Anticipated expiration" of 2041-10-04 listed in the provided patent data. This expiration is contingent upon the timely payment of all required maintenance fees to the USPTO.

Prosecution History and Patent Family

The patent is part of a family of applications filed in the United States, stemming from an initial 2021 application.

• Parent Application:

  • Application No.: 17/493,011
  • Filing Date: October 4, 2021
  • Relationship: U.S. Patent 12,136,276 is a continuation of this application. The current status (e.g., patented or abandoned) of this parent application is not detailed in the provided information.

• This Patent:

  • Patent No.: 12,136,276
  • Application No.: 18/527,226
  • Filing Date: December 1, 2023
  • Grant Date: November 5, 2024

• Child Application (Continuation/Divisional): The patent data indicates that a subsequent application has been filed that claims priority to this patent. This suggests the assignee, Motive Technologies Inc., is continuing to pursue protection for related inventions.

  • Application No.: 18/933,273
  • Filing Date: October 31, 2024
  • Publication No.: US20250054320A1 (as indicated by the event data patent/US20250054320A1/en)
  • Relationship: This application claims priority to application 18/527,226 (which issued as the patent in question) and is likely a continuation or divisional application. As of today's date, May 13, 2026, this application would be pending before the USPTO.

DEFENSIVE DISCLOSURE AND PRIOR ART PUBLICATION

Title: Systems and Methods for Dynamic Calibration and Cross-Domain Application of Geometric Perception Sensors
Publication Date: April 26, 2026
Reference Patent: U.S. Patent 12,136,276
Keywords: Camera Calibration, Extrinsic Parameters, Deep Learning, Computer Vision, ADAS, Neuromorphic Sensing, Robotics, Autonomous Systems, Sensor Fusion, Failsafe Systems

Abstract: This document discloses a plurality of methods, systems, and applications derived from the core principles of using a deep learning model to determine the extrinsic parameters (e.g., height, pitch, roll, normal vectors) of a vision sensor relative to an environmental plane by identifying geometric features such as horizon lines or parallel guide lines. The disclosures herein are intended to enter the public domain as prior art.

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1. Material & Component Substitution Derivatives

1.1. Neuromorphic Event-Based Camera Initialization

• Enabling Description: The standard CMOS/RGB image sensor is replaced with an event-based neuromorphic vision sensor (e.g., a Dynamic Vision Sensor - DVS). This sensor does not capture frames but rather a stream of asynchronous "events" corresponding to changes in pixel-level brightness. The Convolutional Neural Network (CNN) is replaced by a Spiking Neural Network (SNN) architected to process this event stream. The SNN identifies the spatiotemporal signatures of lane markings and the horizon as the vehicle moves, detecting them as lines of high, correlated event activity. The SNN regresses the camera's extrinsic parameters with extremely low latency (<10ms) and power consumption, making it suitable for high-speed applications or battery-powered devices where constant recalibration is needed. The input to the network is not an image tensor but a sparse stream of (x, y, t, p) event tuples.

graph TD
    A[DVS Sensor] -- Event Stream (x,y,t,p) --> B[Spiking Neural Network];
    B -- Spatiotemporal Feature Extraction --> C{Horizon & Lane Spikes};
    C -- Geometric Inference Engine --> D[Camera Parameters (Height, Normal)];
    D -- Publish --> E[Vehicle Control System];

1.2. Thermal (LWIR) Camera for Adverse Conditions

• Enabling Description: An uncooled microbolometer sensor operating in the Long-Wave Infrared (LWIR) spectrum (8-14 µm) is used instead of a visible light camera. This allows the system to operate in complete darkness, fog, or heavy smoke where visible lane markings are obscured. The training dataset for the CNN comprises thermal imagery where lane markings are visible due to differential heat retention between the paint and the asphalt. The horizon is detected as the thermal boundary between the ground plane and the sky or distant terrain. The model is trained to be robust to thermal artifacts such as engine heat plumes from other vehicles.

sequenceDiagram
    participant LWIR_Cam as LWIR Camera
    participant CNN_Model as Thermal CNN
    participant GeoCalc as Geometry Calculator
    loop In Adverse Weather
        LWIR_Cam->>CNN_Model: Transmit Thermal Frame
        CNN_Model->>CNN_Model: Identify Horizon & Lane Thermal Signatures
        CNN_Model->>GeoCalc: Output Feature Coordinates
        GeoCalc->>GeoCalc: Compute Extrinsic Parameters
    end

1.3. FPGA-Based Reconfigurable Processing Pipeline

• Enabling Description: The processing is performed not on a general-purpose GPU/CPU but on a Field-Programmable Gate Array (FPGA). The CNN architecture is synthesized directly into hardware logic gates, creating a highly parallelized and power-efficient pipeline. This allows for the dynamic reconfiguration of the neural network architecture in the field. For example, the system can switch from a horizon-finding model (for open highways) to a dense lane-grid model (for parking lots) by loading a different bitstream into the FPGA, without requiring a system reboot. The geometric calculations are also implemented as hardware math blocks on the FPGA for deterministic, real-time performance.

classDiagram
    class FPGA {
        +LoadBitstream(bitstream)
    }
    class CNN_Hardware_Implementation {
        <<Bitstream>>
        +ProcessFrame(frame) : FeatureVector
    }
    class Geometry_Coprocessor {
        <<Hardware Block>>
        +CalculateParams(featureVector) : Extrinsics
    }
    FPGA *-- "1" CNN_Hardware_Implementation : contains
    FPGA *-- "1" Geometry_Coprocessor : contains

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2. Operational Parameter Expansion Derivatives

2.1. Microscopic Calibration for Automated Microscopy

• Enabling Description: The system is scaled down for calibrating the camera of a digital microscope relative to a substrate (e.g., a silicon wafer or biological slide). The "lanes" are micro-fabricated conductive traces or patterned cell cultures. The "horizon" is the edge of the substrate or a fiducial marker. A CNN analyzes the microscope's video feed to calculate the precise height (z-distance) and tilt of the objective lens relative to the sample plane. This enables automated focusing and repeatable, high-precision positioning for tasks like automated wafer inspection or high-throughput screening.

flowchart LR
    A[Microscope Camera] --> B(Image Frame);
    B --> C[CNN];
    C -- Detects Micro-Traces & Substrate Edge --> D{Feature Coordinates};
    D --> E[Parameter Calculation];
    E -- Z-Height & Planar Tilt --> F[Microscope Stage & Focus Control];

2.2. Gantry Crane Calibration in Industrial Environments

• Enabling Description: The technology is applied to a large-scale gantry crane in a port or manufacturing facility. A camera is mounted high on the crane's trolley, which can be 30-50 meters high. The "lanes" are painted safety walkways or container alignment guides on the ground. The system continuously calculates the camera's height and viewing angle, compensating for sway and vibration of the crane structure. This provides accurate positioning data to the crane's automation system for precise container handling, greatly improving safety and efficiency over systems that rely solely on encoders.

stateDiagram-v2
    [*] --> Idle
    Idle --> Calibrating: Crane Moves
    Calibrating: CNN analyzes ground markings
    Calibrating --> Calibrated: Horizon & 2+ lines found
    Calibrated: Outputting Height(z) & Normal(nx,ny,nz)
    Calibrated --> Calibrating: High sway detected
    Calibrated --> Idle: Crane Stops

2.3. Subsea ROV Navigation and Attitude Estimation

• Enabling Description: A camera on a remotely operated vehicle (ROV) on the seafloor uses this method for altitude and attitude estimation. The "lanes" are subsea pipelines, cable runs, or track marks from the ROV itself. The "horizon" is the transition between the visible seafloor and the dark, featureless water column at the edge of the ROV's lights. The CNN processes the sonar-like video feed to determine the ROV's height off the seabed and its pitch/roll, providing a crucial secondary navigation input that is immune to magnetic interference affecting compasses or drift in inertial sensors.

graph TD
    A[ROV Sonar/Camera] --> B{Video Feed};
    B --> C[CNN Model];
    C -- Identifies Pipeline & Seafloor Edge --> D[Parameter Computation];
    D -- Height, Pitch, Roll --> E[ROV Flight Control System];
    E -- Adjust Thrusters --> F[ROV];
    F --> A;

---

3. Cross-Domain Application Derivatives

3.1. Aerospace: Planetary Rover Autonomous Navigation

• Enabling Description: A camera on a planetary rover (e.g., on Mars) uses this system to maintain calibration. The rover's own wheel tracks in the regolith serve as the "lane lines." The planetary horizon, which is sharp and clear in the thin atmosphere, is the primary feature. The CNN calculates the mast-mounted camera's height and orientation relative to the ground plane, compensating for thermal contraction/expansion of the mast and suspension articulation as the rover traverses uneven terrain. This ensures the accuracy of stereo vision-based obstacle avoidance and path planning.

sequenceDiagram
    participant RoverCam as Rover Camera
    participant NavCPU as Navigation Computer (CNN)
    participant MotorCtrl as Motor Controller
    RoverCam->>NavCPU: Capture Image of Terrain
    NavCPU->>NavCPU: Detect Rover Tracks & Martian Horizon
    NavCPU->>NavCPU: Calculate MastCam Height & Tilt
    NavCPU->>MotorCtrl: Send Updated Path Plan

3.2. AgTech: Precision Sprayer Boom Height Control

• Enabling Description: A camera is mounted on the boom of an agricultural sprayer. The rows of crops (e.g., corn, soybeans) are treated as "lane lines." A CNN processes the video feed to determine the precise height and angle of the sprayer boom relative to the crop canopy. This data is fed into the boom's hydraulic control system in real-time to maintain a perfect spraying height, which maximizes pesticide/fertilizer efficacy and minimizes drift, even as the tractor moves over uneven ground.

flowchart TD
    A[Boom-Mounted Camera] --> B[Image of Crop Rows];
    B --> C[CNN for Row & Canopy Detection];
    C --> D[Compute Height & Angle];
    D --> E[Hydraulic Control System];
    E --> F[Adjust Boom Actuators];

3.3. Retail: Automated Restocking Robot Navigation

• Enabling Description: An autonomous robot in a warehouse or retail store navigates aisles by treating the floor-level shelving or pallet boundaries as "lane lines." The system uses a CNN to calculate the height and pitch of its navigation camera relative to the floor. This allows the robot to accurately measure its distance to shelves and detect floor-based obstacles. The system can dynamically recalibrate if the robot's payload changes, causing its suspension to compress.

graph TD
    A[Robot Camera] -- Video Stream --> B((CNN Processor));
    B -- Detects Shelf Lines --> C{Geometric Analysis};
    C -- Calculates Height & Pose --> D[Path Planning Module];
    D -- Drive Commands --> E[Motor System];

---

4. Integration with Emerging Tech Derivatives

4.1. AI-Driven Reinforcement Learning for Self-Correction

• Enabling Description: The camera initialization module is an agent in a Reinforcement Learning (RL) framework. The "state" includes the video feed and current camera parameters. The "action" is to either maintain the current parameters or trigger a recalibration. The "reward" is provided by a downstream driving performance monitor. A high negative reward (e.g., for lane departure or jerky steering) prompts the RL agent to recalibrate, assuming the parameters have drifted. Over time, the agent learns to predict parameter drift based on subtle visual cues or environmental conditions (e.g., road vibration patterns) and proactively recalibrates before performance degrades.

flowchart LR
    A[Video & Current Params] --> B(RL Agent);
    B -- Action: Recalibrate or Keep --> C(Lane Keeping System);
    C -- Performance Score --> D{Reward Function};
    D -- Reward Signal --> B;
    B -- New Optimal Params --> C;

4.2. IoT Sensor Fusion with IMU and GNSS

• Enabling Description: The CNN architecture is modified to accept multiple input modalities. In addition to the image tensor, it accepts time-synchronized data from an Inertial Measurement Unit (IMU) and a GNSS receiver. The network learns the complex correlations between visual horizon shifts and IMU-reported pitch/roll, as well as the relationship between lane perspective and GNSS-derived velocity vectors. The system can then detect inconsistencies; for example, if the visual horizon is stable but the IMU reports a large pitch change, it correctly deduces the vehicle is on a hill and adjusts the road plane normal accordingly, something a purely visual system would struggle with.

graph TD
    subgraph Sensor Inputs
        A[Camera]
        B[IMU]
        C[GNSS]
    end
    subgraph Fusion CNN
        D[Multi-Modal Feature Extractor]
    end
    A -- Image Tensor --> D;
    B -- Pitch/Roll/Yaw Rates --> D;
    C -- Velocity Vector --> D;
    D --> E[Parameter Regression Head];
    E --> F[Calibrated Extrinsics];

4.3. Blockchain for Verifiable Calibration Audits

• Enabling Description: For commercial vehicle fleets, each successful camera initialization or recalibration event is cryptographically signed and recorded on a private blockchain. The transaction record includes the vehicle ID, camera serial number, timestamp, the newly calculated parameters (height, normal vector), and a hash of the video segment used for calibration. This creates an immutable, tamper-proof log of the vehicle's ADAS sensor status. Fleet managers, insurance companies, and regulatory bodies can be granted access to this ledger to audit and verify that safety-critical systems are maintained and calibrated according to standards.

erDiagram
    VEHICLE ||--o{ CALIBRATION_EVENT : "has"
    VEHICLE {
        string VehicleID
        string PublicKey
    }
    CALIBRATION_EVENT {
        string EventID
        datetime Timestamp
        string CameraParams
        string VideoHash
        string Signature
    }

    CALIBRATION_EVENT ||--o{ BLOCKCHAIN_TRANSACTION : "is recorded in"
    BLOCKCHAIN_TRANSACTION {
        string TxHash
        int BlockNumber
    }

---

5. "Inverse" or Failure Mode Derivatives

5.1. Graceful Degradation to "Bumper-Relative" Mode

• Enabling Description: In conditions where the CNN cannot confidently detect a horizon or lane markings (e.g., tunnels, whiteouts, urban canyons), the system enters a limited-functionality mode. It disables lane-keeping and uses a simpler object detection model to find the bottom-center point of the vehicle directly ahead. Assuming a flat road, it calculates a "time to bumper" distance based on the rate of change of this point's vertical position in the image. The system issues forward collision warnings but makes no assumptions about lane position, thus failing safely by reducing its operational scope.

stateDiagram-v2
    state "Full Functionality" as Full {
      [*] --> Normal
      Normal: Horizon & Lanes Detected
      Normal: Full ADAS enabled
    }
    state "Limited Mode" as Limited {
      [*] --> BumperRelative
      BumperRelative: No features detected
      BumperRelative: Lane Keep OFF
      BumperRelative: FCW only
    }
    Full --> Limited: Low Confidence Score
    Limited --> Full: Features Detected

5.2. Failsafe Cross-Validation with Physical Sensor

• Enabling Description: A secondary, low-cost physical sensor (e.g., an ultrasonic or single-point LiDAR sensor) is mounted with a fixed downward orientation to directly measure the distance to the road surface. The main system operates as described in the patent. A separate "Validator" module continuously compares the camera height calculated by the CNN (H_cnn) with the height measured by the physical sensor (H_phys). If |H_cnn - H_phys| > Threshold (e.g., > 10 cm) for a sustained period, the system flags the vision-based calibration as untrustworthy, disables dependent ADAS features, and logs a maintenance alert for the operator.

flowchart TD
    A[Camera] --> B(CNN);
    B -- Calculated Height (H_cnn) --> D{Validator};
    C[Ultrasonic Sensor] -- Measured Height (H_phys) --> D;
    D -- |H_cnn - H_phys| > Threshold? --> E{Decision};
    E -- No --> F[Enable ADAS];
    E -- Yes --> G[Disable ADAS & Alert];

---

6. Combination Prior Art with Open-Source Standards

6.1. Combination with ROS (Robot Operating System)

• Enabling Description: The camera initialization system is packaged as a standard ROS 2 node named camera_calibrator. This node subscribes to a sensor_msgs/Image topic for video frames and a sensor_msgs/Imu topic. Upon initialization, it computes the extrinsic parameters and publishes them as a tf2_msgs/TFMessage transform between the base_link and camera_link frames. It also publishes the full camera intrinsic and extrinsic details on a sensor_msgs/CameraInfo topic. This allows any other ROS-compliant node, such as a path planner or object detector, to immediately use the calibrated camera data without custom integration.

6.2. Combination with OpenCV and ONNX Runtime

• Enabling Description: The trained CNN model is exported to the open standard ONNX (Open Neural Network Exchange) format. An on-vehicle C++ application uses the OpenCV library for video capture and pre-processing (e.g., cv::VideoCapture, cv::resize). The core inference is then performed using the ONNX Runtime, which is optimized for cross-platform execution on various hardware accelerators. The output tensors from the ONNX Runtime, representing the horizon/lane coordinates, are then fed into OpenCV's geometry functions (e.g., cv::solvePnP or custom homography calculations) to derive the final extrinsic parameters. This decouples the model training framework (e.g., PyTorch) from the deployment environment.

6.3. Combination with AUTOSAR (Automotive Open System Architecture)

• Enabling Description: The initialization logic is encapsulated within an AUTOSAR Adaptive Platform Application. It defines its service interfaces using the ARA::COM API. It provides a "CameraCalibrationService" that other applications (e.g., a LaneKeepingApplication) can discover and consume. The service exposes methods like get_camera_height() and get_road_normal_vector(). The application runs in its own sandboxed execution context, managed by the AUTOSAR Execution Management functional cluster, ensuring it meets automotive safety and real-time constraints (e.g., freedom from interference). Video data is received via a SOME/IP binding from a lower-level camera driver.