System and method for auditing assets

Abstract

To provide a system to verify a property 154 in a spatial model without manual measurements on or near the railway, improving safety and efficiency of property verification.SOLUTION: A system and a method for inspecting a property comprises the steps of: receiving first light detection and ranging (LiDAR) data associated with the railway environment; extracting a property 154 from the first LiDAR data associated with the railway environment; and superimposing the property onto a spatial model 156a. It also comprises the steps of: receiving a field display 158 associated with the modification to the railway environment; and modifying the spatial model 156b in response to the step of receiving the field display associated with the modification to the railway environment. It further comprises the steps of receiving second LiDAR data 152b associated with the railway environment and comparing the second LiDAR data with the modified spatial model 156c.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] FIELD OF THE DISCLOSURE The present disclosure relates generally to auditing assets, and more particularly to systems and methods for auditing assets. [Background technology]

[0002] Positive train control (PTC) is a communications-based train control system used to prevent train-related accidents. PTC improves rail traffic safety by auditing railroad track data. However, the track data used in PTC systems can misrepresent the actual location of assets associated with the railroad, which can negatively impact the performance of the PTC system. Summary of the Invention [Means for solving the problem]

[0003] According to one embodiment, a method includes receiving first Light Detection and Ranging (LDR) data associated with a railroad environment, extracting assets from the first LiDAR data associated with the railroad environment, and overlaying the assets on a spatial model. The method also includes receiving field indications associated with modifications to the railroad environment and modifying the spatial model in response to receiving the field indications associated with the modifications to the railroad environment. The method further includes receiving second LiDAR data associated with the railroad environment and comparing the second LiDAR data with the modified spatial model.

[0004] According to another embodiment, a system includes one or more processors and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations including receiving first LiDAR data associated with a rail environment, extracting assets from the first LiDAR data associated with the rail environment, and overlaying the assets on a spatial model. The operations also include receiving field indications associated with modifications to the rail environment and modifying the spatial model in response to receiving the field indications associated with the modifications to the rail environment. The operations further include receiving second LiDAR data associated with the rail environment and comparing the second LiDAR data to the modified spatial model.

[0005] According to yet another embodiment, one or more computer-readable storage media embodying instructions that, when executed by a processor, cause the processor to perform operations including receiving first LiDAR data associated with a rail environment; extracting assets in the first LiDAR data associated with the rail environment; and overlaying the assets onto a spatial model.

[0006] The operations also include receiving a field indication associated with a modification to the rail environment and modifying the spatial model in response to receiving the field indication associated with the modification to the rail environment. The operations further include receiving second LiDAR data associated with the rail environment and comparing the second LiDAR data with the modified spatial model.

[0007] Technical advantages of certain embodiments of the present disclosure may include one or more of the following: Certain systems and methods described herein identify and verify PTC critical assets without manual measurements on or near railways, thereby improving the safety and efficiency of asset identification and verification. Certain systems and methods described herein utilize LiDAR to identify and verify PTC critical assets, thereby improving the accuracy of asset identification and verification.

[0008] Other technical advantages will be readily apparent to those skilled in the art from the following figures, descriptions, and claims. Additionally, while certain advantages have been enumerated above, various embodiments may include all, some, or none of the enumerated advantages.

[0009] For an improved understanding of the present disclosure, reference is made to the following description taken in conjunction with the accompanying drawings. [Brief explanation of the drawings]

[0010] [Figure 1] 1 illustrates an exemplary system for auditing assets. [Figure 2] 1 illustrates another exemplary system for auditing assets. [Figure 3] 1 illustrates an exemplary method for auditing assets. [Figure 4] An example of the output generated by the audit module is shown below. [Figure 5] 1 illustrates an exemplary computer system that may be used by the systems and methods described herein. DETAILED DESCRIPTION OF THE INVENTION

[0011] Certain embodiments of the present disclosure include systems and methods for auditing assets by comparing data (e.g., LiDAR data and field data) captured at different times. The assets may be PTC critical assets associated with a railway environment that are audited for PTC compliance.

[0012] Figures 1-5 illustrate exemplary systems and methods for auditing assets. Figure 1 illustrates an exemplary system for auditing assets, and Figure 2 illustrates another exemplary system for auditing assets. Figure 3 illustrates an exemplary method for auditing assets. Figure 4 illustrates example output generated by an audit module. Figure 5 illustrates an exemplary computer system that may be used by the systems and methods described herein.

[0013] FIG. 1 illustrates an exemplary system 100 for auditing assets. The system 100 of FIG. 1 includes a network 110, an auditing module 120, a LiDAR vehicle 170, and an observer 180. The system 100, or portions thereof, may include or be associated with any entity, such as a business, a company (e.g., a railroad company, a transportation company, etc.), or a government agency (e.g., a transportation department, a public safety department, etc.) that audits assets. Elements of the system 100 may be implemented using any suitable combination of hardware, firmware, and software.

[0014] Network 110 may be any type of network that facilitates communication between components of system 100. Network 110 may connect audit module 120 to LiDAR vehicle 170 of system 100. Although this disclosure describes network 110 as a particular type of network, this disclosure contemplates any suitable network. One or more portions of network 110 may include an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), a metropolitan area network (MAN), a portion of the Internet, a portion of the public switched telephone network (PSTN), a cellular network, a 3G network, a 4G network, a 5G network, a Long Term Evolution (LTE) cellular network, a combination of two or more of these, or any other suitable type of network. One or more portions of network 110 may include one or more access (e.g., mobile access), core, and / or edge networks. The network 110 may be any communication network, such as a private network, a public network, a connection via the Internet, a mobile network, a Wi-Fi network, a Bluetooth network, etc. The network 110 may include cloud computing capabilities. One or more components of the system 100 may communicate via the network 110. For example, the audit module 120 may include receiving information from the LiDAR vehicle 170 and communicate via the network 110.

[0015] Audit module 120 of system 100 represents any suitable computing component that may be used to audit asset 154. Audit module 120 may be communicatively coupled to LiDAR vehicle 170 via network 110. Audit module 120 includes an interface 122, a memory 124, and a processor 126.

[0016] Interface 122 of audit module 120 represents any suitable computing element capable of receiving information from network 110, transmitting information over network 110, and performing appropriate processing of the information to communicate with other components of system 100 of Figure 1 (e.g., components of LiDAR vehicle 170) or any combination of the above. Interface 122 represents any port or connection, actual or virtual, including any suitable combination of hardware, firmware, and software, including protocol conversion and data processing capabilities, that communicates over a LAN, WAN, or other communication system that enables components of system 100 of Figure 1 to exchange information.

[0017] The memory 124 of the audit module 120 permanently and / or temporarily stores received and transmitted information, as well as system software, control software, other software for the audit module 120, and various other information. The memory 124 may store information for execution by the processor 126. The memory 124 may include any one or combination of volatile or non-volatile local or remote devices suitable for storing information. The memory 124 may include random access memory (RAM), read-only memory (ROM), magnetic storage, optical storage, or any other suitable information storage device, or combination of these devices. The memory 124 may contain any suitable information for use in the operation of the audit module 120. Furthermore, the memory 124 may be a component external (or partially external) to the audit module 120. The memory 124 may be located in any location suitable for the memory 124 to communicate with the audit module 120. 1, memory 124 of audit module 120 stores data collection engine 130, model modification engine 132, comparison engine 134, reporting engine 136, and database 150. In particular embodiments, data collection engine 130, model modification engine 132, comparison engine 134, reporting engine 136, and / or database 150 may be external to memory 124 and / or audit module 120.

[0018] The data collection engine 130 of the audit module 120 is an application that collects data from one or more components of the system 100. The data collection engine 130 may collect data from the LiDAR vehicle 170. For example, the data collection engine 130 may collect LiDAR data 152 (e.g., digital images) and / or GPS data from one or more components of the LiDAR vehicle 170 via the network 110. The data collection engine 130 may collect data from the observer 180. For example, the data collection engine 130 may collect one or more field representations 158 from a user device (e.g., a smartphone, a tablet, a laptop computer, etc.) associated with the observer 180.

[0019] The data collection engine 130 may use one or more programs to generate the spatial model 156. For example, the data collection engine 130 may use geographic information system (GIS) and / or LiDAR visualization software to generate the spatial model 156.

[0020] A GIS integrates various types of data. For example, a GIS may analyze spatial locations and organize layers of information into a spatial model 156 using maps, two-dimensional (2D) scenes, and / or three-dimensional (3D) scenes. The 2D scenes may include orthoimages generated from LiDAR point cloud data. LiDAR visualization software may be used by the data collection engine 130 to read and interpret the LiDAR data 152. The data collection engine 130 may generate the spatial model 156 using the LiDAR data 152, GPS data, one or more field representations 158, one or more images (e.g., LiDAR images), one or more point clouds, any other suitable data, or any suitable combination of the foregoing. The data collection engine 130 may extract one or more assets 154 from the LiDAR data 152. The data collection engine 130 may overlay the assets 154 onto the spatial model 156.

[0021] The data collection engine 130 may use machine learning to intelligently and automatically identify assets 154. In particular embodiments, the data collection engine 130 may use machine learning to extract assets 154 from the LiDAR data 152. One or more machine learning algorithms may identify the assets 154 and compare the assets 154 to a database to audit the presence, location, and / or other characteristics of the assets 154 within the environment captured by the LiDAR data 152.

[0022] The model modification engine 132 of the audit module 120 is an application that modifies the spatial model 156. The model modification engine 132 may modify the spatial model 156 in response to one or more conditions. For example, the model modification engine 132 may modify the spatial model 156 in response to receiving a field indication 158 that the environment captured by the LiDAR data 152 is or has been modified. The field indication 158 may indicate that the asset 154 is or has been physically moved from a first location to a second location within the environment captured by the LiDAR data 152. The field indication 158 may indicate that the asset 154 is or has been physically removed from the environment captured by the LiDAR data 152. The field indication 158 may indicate that the asset 154 is or has been added to the environment captured by the LiDAR data 152.

[0023] The comparison engine 134 of the audit module 120 is an application that compares data. For example, the spatial model 156 may include spatial models 156a-n (where n represents any suitable integer), and the comparison engine 134 may compare data in a first spatial model 156a generated at time T1 with data in a second spatial model 156b generated at time T2, where time T2 is any time after time T1. The comparison engine 134 may determine whether an anomaly exists between the two or more spatial models 156 based on a comparison of the two or more spatial models 156. For example, the comparison engine 134 may determine that the location of the asset 154 in the first spatial model 156a is different from the location of the asset 154 in the second spatial model 156b. As another example, the comparison engine 134 may determine that the asset 154 in the first spatial model 156a does not exist in the second spatial model 156b.

[0024] The comparison engine 134 may confirm that the information in the compared two or more spatial models 156 is identical based on a comparison of the two or more spatial models 156. For example, the comparison engine 134 may confirm that the location of the asset 154 in the first spatial model 156a matches the location of the asset 154 in the second spatial model 156b. Confirmation by the comparison engine 134 that the location of the asset 154 in the first spatial model 156a matches the location of the asset 154 in the second spatial model 156b may be based on a predetermined tolerance. For example, the comparison engine 134 may confirm that the location in the first spatial model 156a matches the location of the asset 154 in the second spatial model 156b if the locations are determined to be within 2.2 meters of each other.

[0025] The report engine 136 of the audit module 120 is an application that generates one or more reports 160. The report engine 136 may generate the report 160 in response to the comparison engine 134 making one or more determinations. For example, the report engine 136 may generate the report 160 in response to the comparison engine 134 determining that an anomaly exists between two or more spatial models 156. As another example, the report engine 136 may generate the report 160 in response to the comparison engine 134 determining that information between two or more datasets (e.g., two or more spatial models 156) is identical.

[0026] The database 150 of the audit module 120 may store specific types of information for the audit module 120. For example, the database 150 may store LiDAR data 152, one or more assets 154, one or more spatial models 156, one or more field representations 158, and one or more reports 160. The LiDAR data 152 is any data generated using a LiDAR. The LiDAR data 152 may include one or more digital images. In particular embodiments, the digital images in the LiDAR data 152 may be 360-degree images with a range of approximately 600 feet on each side of the centerline of a railroad track in a railroad environment. In the embodiment shown in FIG. 1 , the LiDAR data 152 is communicated from a LiDAR vehicle 170 over the network 110 to the audit module 120 of the system 100.

[0027] Assets 154 are data extracted from LiDAR data 152 that represent physical objects in the environment. For example, assets 154 may be images extracted from LiDAR data 152 that represent physical objects in a rail environment. In certain embodiments, assets 154 may be key assets of a PTC. PTC is a system of functional requirements for monitoring and controlling train movement. Each asset 154 may represent one or more of the following physical objects in a rail environment: train control signals (e.g., signals that control train movement), switch points, crossings at grade, mile post signs, speed signs, clearance points, etc.

[0028] The spatial models 156 are 2D and 3D models representing one or more environments. Each spatial model 156 may include vector data and / or raster data. The vector data of the spatial model 156 may represent one or more assets 154 as discrete points, lines, and / or polygons. The raster data of the spatial model 156 may represent one or more assets 154 as a rectangular matrix of square cells. The spatial models 156 may be stored in a GIS database. One or more spatial models 156 may include LiDAR vector data and / or LiDAR raster data. One or more spatial models 156 may include LiDAR point cloud data. The LiDAR point cloud data may be converted to vector and / or raster format. One or more spatial models 156 may include one or more assets 154. Each asset 154 has a location within the spatial model 156. Each asset 154 in the spatial model 156 may include one or more attributes. Asset attributes specify characteristics (eg, quality, state, version, etc.) that can be applied to an asset 154 .

[0029] Field display 158 is a representation of changes to physical objects in the environment. Field display 158 may include a representation of expected changes to physical objects in the environment. Field display 158 may indicate that assets 154 are changing or have changed position within the environment captured by LiDAR data 152.

[0030] For example, the field display 158 may indicate that a speed sign is scheduled to be moved 20 feet within the railroad environment. As another example, the field display 158 may indicate that a speed sign has moved 20 feet within the railroad environment. The field display 158 may indicate that an asset 154 is being or has been removed from the environment captured by the LiDAR data 152. For example, the field display 158 may indicate that a grade crossing is being removed from the railroad environment. As another example, the field display 158 may indicate that a grade crossing has been removed from the railroad environment. The field display 158 may indicate that an asset 154 is being or has been added to the environment captured by the LiDAR data 152. For example, the field display 158 may indicate that a distance sign is scheduled to be added to the railroad environment. As another example, the field display 158 may indicate that a distance sign has been added to the railroad environment.

[0031] A report 160 is a communication generated in response to a determination made by the audit module 120 (e.g., the comparison engine 134). One or more reports 160 may be oral and / or written communications. One or more reports 160 may be generated electronically by a machine and / or physically by a human. A report 160 may include information indicating an anomaly exists between two or more spatial models 156. A report 160 may include information confirming that information between two or more spatial models 156 is identical. A report 160 may include a list, chart, table, diagram, etc. For example, a report 160 may include table 410 of FIG. 4 illustrating example audit results generated by the audit module 120.

[0032] Database 150 may be any one or combination of volatile or non-volatile local or remote devices suitable for storing information. Database 150 may include RAM, ROM, magnetic storage, optical storage, or any other suitable information storage device, or a combination of these devices. Database 150 may be a component external to audit module 120. Database 150 may be located in any location suitable for database 150 to store information related to audit module 120. For example, database 150 may be located in a cloud environment.

[0033] The processor 126 of the audit module 120 controls certain operations of the audit module 120 by processing information received from or accessed by the interface 122 and memory 124. The processor 126 is communicatively coupled to the interface 122 and the memory 124. The processor 126 may include any hardware and / or software operable to control and process information. The processor 126 may be a programmable logic device, a microcontroller, a microprocessor, any suitable control device, or any suitable combination thereof. Additionally, the processor 126 may be a component external to the audit module 120. The processor 126 may be located in any location suitable for the processor 126 to communicate with the audit module 120. The processor 126 of the audit module 120 controls the data collection engine 130, the model correction engine 132, the comparison engine 134, and the report engine 136.

[0034] The LiDAR vehicle 170 of the system 100 refers to a vehicle (e.g., a van, truck, automobile, railcar, etc.) that collects LiDAR data 152 (e.g., digital images). The LiDAR vehicle 170 may include one or more scanning and / or imaging sensors. The sensors may generate one or more images (e.g., a 3D point cloud) that facilitate the audit module 120 in detecting the assets 154. The LiDAR vehicle 170 may collect GPS data. The LiDAR and GPS data may be used to generate a 360-degree real-world view of the railroad environment. In certain embodiments, the LiDAR vehicle communicates the data (e.g., the LiDAR data 152 and / or the GPS data) to the audit module 120.

[0035] The observer 180 of the system 100 is any human or machine that observes the environment captured by the LiDAR data 152. The observer 180 may be an inspector (e.g., a railroad inspector), an engineer (e.g., a railroad field engineer or safety engineer), a passerby (e.g., a pedestrian, a driver, etc.), a law enforcement officer (e.g., a police officer), a camera (e.g., a video camera), etc. The observer 180 may communicate information (e.g., field display 158) to the audit module 120 via a web application (e.g., a work order application), a phone call, a text message, an email, a report, etc. The observer 180 may communicate information to the audit module 120 using a phone (e.g., a smartphone), a tablet, a laptop computer, or any other suitable device.

[0036] Although FIG. 1 illustrates a particular arrangement of network 110, audit module 120, interface 122, memory 124, processor 126, data collection engine 130, model correction engine 132, comparison engine 134, report engine 136, database 150, LiDAR data 152, assets 154, spatial model 156, field display 158, reports 160, LiDAR vehicles 170, and observers 180, this disclosure contemplates any suitable arrangement of network 110, audit module 120, interface 122, memory 124, processor 126, data collection engine 130, model correction engine 132, comparison engine 134, report engine 136, database 150, LiDAR data 152, assets 154, spatial model 156, field display 158, reports 160, LiDAR vehicles 170, and observers 180. The network 110, audit module 120, interface 122, memory 124, processor 126, data collection engine 130, model correction engine 132, comparison engine 134, report engine 136, database 150, and LiDAR vehicle 170 may be, in whole or in part, physically or logically co-located with one another.

[0037] Although FIG. 1 illustrates a particular number of networks 110, audit modules 120, interfaces 122, memory 124, processors 126, data collection engines 130, model correction engines 132, comparison engines 134, report engines 136, databases 150, LiDAR data 152, assets 154, spatial models 156, field displays 158, reports 160, LiDAR vehicles 170, and observers 180, this disclosure contemplates any suitable number of networks 110, audit modules 120, interfaces 122, memory 124, processors 126, data collection engines 130, model correction engines 132, comparison engines 134, report engines 136, databases 150, LiDAR data 152, assets 154, spatial models 156, field displays 158, reports 160, LiDAR vehicles 170, and observers 180. One or more components of the audit module 120 and / or the LiDAR vehicle 170 may be implemented using one or more components of the computer system of FIG. 5 .

[0038] 1 illustrates a system 100 for auditing assets 154, one or more components of the system 100 may be applied to other implementations. For example, one or more components of the audit module 120 may be used for asset identification and / or inventory.

[0039] During operation, the data collection engine 130 of the audit module 120 of the system 100 receives LiDAR data 152 from a LiDAR vehicle 170 via the network 110 at time T1. The LiDAR data 152 is associated with a railway environment. The data collection engine 130 extracts assets 154 from the LiDAR data 152 associated with the railway environment and overlays the assets 154 on a spatial model 156. The data collection engine 130 receives a field indication 158 from an observer 180 (e.g., a railway field engineer) at time T2 that the railway environment is or has been modified. The model modification engine 132 modifies the spatial model 156 in response to receiving the field indication 158 that the railway environment is or has been modified. The data collection engine 130 receives the LiDAR data 152 at time T3. The LiDAR data 152 received at time T3 is associated with the railway environment. Comparison engine 134 compares LiDAR data 152 received at time T3 to revised spatial model 156. Comparison engine 134 determines that the location of asset 154 in revised spatial model 146 is identical to the location of asset 154 in LiDAR data 152 received at time T3. Report engine 136 generates a report confirming that the location of asset 154 in revised spatial model 146 is accurate.

[0040] Thus, the system 100 of FIG. 1 verifies assets 154 in a spatial model 156 without manual measurements on or near railroad tracks, improving the safety and efficiency of asset verification.

[0041] FIG. 2 illustrates an exemplary system 200 for auditing assets using the audit module 120 of FIG. 1. The system 200 of FIG. 2 includes a railroad environment 210 and the audit module 120. The railroad environment 210 is an area including one or more railroad tracks 220. The railroad environment 210 may be associated with divisions and / or subdivisions. A division is a portion of a railroad under the supervision of a supervisor. A subdivision is a smaller portion of a division. A subdivision may be a crew section and / or a branch line. In the embodiment illustrated in FIG. 2, the railroad environment 210 includes a physical object 230. The physical object 230 may be any tangible component within the railroad environment 210, such as a train control signal, a switch point, a grade crossing, a distance sign, a speed sign, a vehicle contact limit, etc. In the embodiment illustrated in FIG. 2, the physical object 230 is a sign.

[0042] The system 200 of Figure 2 includes a diagram of a railroad environment 210 at three times: T1, T2, and T3. At time T1 in the embodiment shown in Figure 2, the railroad environment 210 includes a LiDAR vehicle 170. The LiDAR vehicle 170 captures LiDAR data 152a at time T1. Time T1 represents the time required for the LiDAR vehicle 170 to capture the LiDAR data 152a. Time T1 may vary depending on the speed at which the LiDAR vehicle 170 is moving as the LiDAR vehicle 170 captures the LiDAR data 152a. The LiDAR data 152a indicates that the physical object 230 is at physical object location X at time T1. In certain embodiments, the LiDAR vehicle 170 communicates the LiDAR data 152a to the audit module 120 of the system 200.

[0043] The audit module 120 of the system 200 extracts assets 154 from the LiDAR data 152a. The assets 154 represent physical objects 230 in the railroad environment 210 at time T1. The audit module 120 then overlays the assets 154 on the spatial model 156a so that the spatial model 156a represents the railroad environment 210 at time T1. In particular embodiments, the audit module 120 generates a railroad track centerline 222 in the spatial model 156a. The audit module 120 may extract one or more assets corresponding to the railroad track 220 from the LiDAR data 152a, overlay the one or more assets corresponding to the railroad track 220 on the spatial model 156a, determine a railroad track centerline 222 based on the assets corresponding to the railroad track 220, and generate the railroad track centerline 222 for the spatial model 156a. In certain embodiments, the railroad track centerline 222 is the line centered between the two outer rails of the railroad track 220. The audit module 120 may use the railroad track centerline 222 as a reference line for the position of the asset 154. In the spatial model 156a, the audit module 120 transforms the position of the asset 154 from its actual location to a corresponding position along the railroad track centerline 222, as shown by asset position X.

[0044] At time T2 in the embodiment shown in Figure 2, the rail environment 210 includes an observer 180. The observer 180 perceives a modification of the rail environment 210 at time T2. Time T2 represents the time required for the observer 180 to capture the modification in the rail environment 210. The modification may be a rearrangement of a physical object 230 within the rail environment 210, a removal of a physical object 230 from the rail environment 210, an addition of a second physical object 230 to the rail environment 210, etc. The observer 180 may communicate a field indication 158 that the rail environment 180 is or has been modified to the audit module 120.

[0045] In the embodiment shown in FIG. 2, field display 158 represents an observation by observer 180 that physical object 230 has physically moved from physical object location X to physical object location Y.

[0046] The audit module 120 of the system 200 receives the field representation 158 from the observer 180. For example, the audit module 120 of the system 200 may receive the field representation 158 from the observer 180 via a web application (e.g., a work order application), email, phone call, text message, fax, report, etc. In response to receiving the field representation 158, the audit module 120 modifies the spatial model 156a to generate the spatial model 156b. For example, a user (e.g., a manager) may edit the spatial model 156a to move the asset 154 from asset location X to asset location Y, as shown in the spatial model 156b. In the spatial model 156b, the audit module 120 converts the position of the asset 154 from its actual location to a corresponding position along the railroad track centerline 222, as indicated by asset location Y.

[0047] 2 , LiDAR vehicle 170 captures LiDAR data 152b. Time T3 represents the time required for LiDAR vehicle 170 to capture LiDAR data 152b. Time T3 may vary depending on the speed at which LiDAR vehicle 170 is traveling as LiDAR vehicle 170 captures LiDAR data 152b. LiDAR data 152b indicates that physical object 230 is at physical object position Y at time T3. In particular embodiments, LiDAR vehicle 170 communicates LiDAR data 152b to audit module 120 of system 200.

[0048] The audit module 120 of the system 200 extracts the asset 154 from the LiDAR data 152b. The asset 154 represents a physical object 230 in the rail environment 210 at time T3. The audit module 120 then overlays the asset 154 onto the spatial model 156c, such that the spatial model 156c represents the rail environment 210 at time T3. In the spatial model 156c, the audit module 120 transforms the actual location of the asset 154 into a corresponding position along the rail track centerline 222, as indicated by asset position Y. The audit module 120 may then compare the spatial model 156c with the spatial model 156b to verify that changes to the rail environment 210 have been accurately captured.

[0049] 2 illustrates a particular arrangement of the components of system 200, but this disclosure contemplates any suitable arrangement of the components of system 200. While FIG. 2 illustrates a particular number of audit modules 120, spatial models 156a, 156b, 156c, assets 154, asset locations X and Y, LiDAR vehicles 170, time periods T1, T2, and T3, physical objects 230, physical object locations X and Y, railroad tracks 220, and railroad track centerlines 222, this disclosure contemplates any suitable number of audit modules 120, spatial models 156a, 156b, 156c, assets 154, asset locations X and Y, LiDAR vehicles 170, time periods T1, T2, and T3, physical objects 230, physical object locations X and Y, railroad tracks 220, and railroad track centerlines 222. For example, the system 200 of FIG. 2 may include multiple physical objects 230a-n and multiple assets 154a-n, where n represents any suitable integer.

[0050] FIG. 3 shows an example method 300 for auditing assets. Method 300 begins at step 305. In step 310, an audit module (e.g., audit module 120 of FIG. 2 ) receives first LiDAR data (e.g., LiDAR data 152 a of FIG. 2 ) associated with a railroad environment (e.g., railroad environment 210 of FIG. 2 ) from a LiDAR vehicle (e.g., LiDAR vehicle 170 of FIG. 2 ). The railroad environment includes a railroad track (e.g., railroad track 220 of FIG. 2 ). Method 300 then moves from step 310 to step 320. In step 320, the audit module extracts assets (e.g., assets 154 of FIG. 2 ) from the LiDAR data. The assets may represent physical objects (e.g., physical objects 230 of FIG. 2 ), such as train control signals, switch points, grade crossings, distance signs, speed signs, vehicle contact limits, etc. Method 300 then moves from step 320 to step 330.

[0051] In step 330, the audit module overlays the asset onto a spatial model (e.g., spatial model 156a in FIG. 2). The asset's location in the spatial model may be transformed from its actual location in the railroad environment to a location along the centerline of the railroad track (e.g., railroad track centerline 222 in FIG. 2). Method 300 then moves from step 330 to step 340.

[0052] At step 340, the audit module determines whether a field indication (e.g., field indication 158 of FIG. 2 ) indicating the rail environment is or has been modified is received. For example, the audit module may receive a field indication indicating that a physical object is or has been moved within the rail environment captured by the first LiDAR data, that a physical object is or has been removed from the rail environment captured by the first LiDAR data, or that a new physical object is or has been added to the rail environment captured by the first LiDAR data. If the audit module determines that a field indication indicating the rail environment is or has been modified is not received, method 300 proceeds from step 340 to step 360. If the audit module determines that a field indication indicating the rail environment is or has been modified is received, method 300 proceeds from step 340 to step 350.

[0053] In step 350, the audit module modifies the spatial model according to the field representation. For example, the audit module may move the location of the asset from position X along the centerline of the railroad track to position Y along the centerline of the railroad track. As another example, the audit module may remove an asset from the spatial model. As yet another example, the audit module may add an asset to the spatial model. Modifying the spatial model generates a modified spatial model (e.g., spatial model 156b in FIG. 2 ). Next, method 300 moves from step 350 to step 360.

[0054] In step 360, the audit module receives second LiDAR data associated with the railway environment (e.g., LiDAR data 152b in FIG. 2 ). The second LiDAR data is captured later than the first LiDAR data. For example, the second LiDAR data may be captured by the LiDAR vehicle one month, one year, or five years after the first LiDAR data is captured by the LiDAR vehicle. Method 300 then moves from step 360 to step 370. In step 370, the audit module compares the second LiDAR data with the spatial model. For example, the audit module may compare the location of asset 154 in the modified spatial model with the location of asset 154 indicated by the second LiDAR data. Method 300 then moves from step 370 to step 380.

[0055] In step 380, the audit module determines whether an anomaly exists between the second LiDAR data and the spatial model. For example, the audit module may determine that the location of the asset in the modified spatial model differs from the location of the asset in the second LiDAR data. If the audit module determines that an anomaly exists between the second LiDAR data and the spatial model, method 300 moves from step 380 to step 385, where the audit module generates a report indicating the anomaly. If the audit module determines that an anomaly does not exist between the second LiDAR data and the spatial model, method 300 moves from step 380 to step 390, where the audit module validates the asset data in the spatial model. Next, method 300 moves from step 385 and step 390 to step 395, where method 300 ends.

[0056] Modifications, additions, or omissions may be made to the method 300 shown in FIG. 3 . Method 300 may include more, fewer, or other steps. For example, method 300 may include generating a report in response to verifying the asset data in step 390. Steps may be performed in parallel or in any suitable order. Although discussed as specific components completing steps of method 300, any suitable component may perform any step of method 300. For example, an audit module may receive first LiDAR data from a first LiDAR vehicle and second LiDAR data from a second LiDAR vehicle different from the first LiDAR vehicle.

[0057] FIG. 4 illustrates an example of an output 400 that may be generated by the audit module 120 of FIG. 1 . The output 400 of FIG. 4 may be in any form, such as a table, chart, list, etc. In the embodiment illustrated in FIG. 4 , the output 400 is a chart 410. The chart 410 illustrates audit results 420 from auditing assets 154 using the audit module 120 of FIG. 1 . The audit process includes comparing assets 154 determined using LiDAR data with previously determined assets 154. For example, the audit process may include comparing assets 154 in spatial model 156c of FIG. 2 that integrates LiDAR data 152b with assets 154 in spatial model 156b of FIG. 2 that integrates LiDAR data 152a and one or more field representations 158.

[0058] Chart 410 includes ten columns labeled 430 through 448. Column 430 indicates the assets 154 analyzed by audit module 120 of FIG. 1. Assets 154 represent physical objects associated with the rail environment, such as: vehicle contact limits, grade crossings, distance signs, signals, speed signs, and switches. Column 432 of table 410 indicates the total number of assets 154 analyzed by audit module 120 of FIG. 1. In the embodiment shown in FIG. 4, audit module 120 analyzed 117 vehicle contact limits, 269 intersections, 27 distance signs, 199 signals, 98 speed signs, and 84 switches, for a total of 974 assets 154.

[0059] Column 434 of table 410 indicates a pass determined by audit module 120 of FIG. 1. A pass represents a match between the data within a predetermined tolerance. For example, of 117 vehicle contact limits analyzed in a first model (e.g., spatial model 156c of FIG. 2), the locations of the 117 vehicle contact limits match, within a predetermined tolerance (e.g., 2.2 meters), the locations of the 117 vehicle contact limits in a second model (e.g., spatial model 156b of FIG. 2). Column 436 of table 410 indicates an exception determined by audit module 120 of FIG. 1. An exception represents an anomaly between the data. For example, of 207 distance markers analyzed in a first model (e.g., spatial model 156c of FIG. 2), four distance markers are missing or in a different position in a second model (e.g., spatial model 156b of FIG. 2).

[0060] Column 438 of table 410 shows the percentage of assets 154 that passed, which is the number of assets 154 that passed, shown in column 434, divided by the total number of assets 154, shown in column 432, shown as a percentage. Column 440 shows exceptions discovered without a change management process (CMP). A CMP represents a change (e.g., a field display) shown in the audit module 120. Column 442 shows exceptions discovered with an incorrect CMP. An incorrect CMP indicates a CMP that was incorrectly implemented in the spatial model. For example, an incorrect CMP may indicate a distance marker that was relocated 20 feet in the spatial model, but the CMP indicates that the distance marker should be relocated 10 feet in the spatial model.

[0061] Column 444 of table 410 indicates that CMPs were discovered but no Geographic Information System (GIS) edits were made, indicating changes that are visible in the audit module 120 (e.g., in the field display) but have not yet been implemented in the spatial model. Column 446 of table 410 indicates exceptions discovered due to office errors, and column 448 of table 410 indicates assets 154 that could not be verified.

[0062] Modifications, additions, or omissions may be made to output 400 shown in FIG. 4. For example, output 400 may be presented in a format other than chart 410, such as a list or graph. Table 410 may include more, fewer, or other columns and / or rows. For example, table 410 may include more or fewer than 10 columns and six assets. In certain embodiments, output 400 may be included in one or more reports (e.g., report 160 of FIG. 1).

[0063] 5 illustrates an exemplary computer system that may be used by the systems and methods described herein. For example, network 110, audit module 120, and LiDAR vehicle 170 of FIG. 1 may include one or more interfaces 510, processing circuits 520, memory 530, and / or other suitable elements. Interface 510 (e.g., interface 122 of FIG. 1) receives input, sends output, processes the input and / or output, and / or performs other suitable operations. Interface 510 may include hardware and / or software.

[0064] Processing circuitry 520 (e.g., processor 126 of FIG. 1 ) performs or manages the operation of the components. Processing circuitry 520 may include hardware and / or software. Examples of processing circuitry include one or more computers, one or more microprocessors, one or more applications, etc. In particular embodiments, processing circuitry 520 executes logic (e.g., instructions) to perform actions (e.g., operations) such as generating output from input. The logic executed by processing circuitry 520 may be encoded on one or more tangible, non-transitory computer-readable media (e.g., memory 530). For example, logic may include computer programs, software, computer-executable instructions, and / or instructions that can be executed by a computer. In particular embodiments, operations of the embodiments may be performed by one or more computer-readable media having a computer program stored thereon, one or more computer-readable media embodied with a computer program, and / or one or more computer-readable media encoded with a computer program, and / or one or more computer-readable media having a computer program stored and / or encoded thereon.

[0065] The memory 530 (or memory unit) stores information. The memory 530 (e.g., memory 124 of FIG. 1) may include one or more non-transitory, tangible, computer-readable, and / or computer-executable storage media. Examples of the memory 530 include computer memory (e.g., RAM or ROM), mass storage media (e.g., hard disks), removable storage media (e.g., compact discs (CDs) or digital video discs (DVDs)), databases, and / or network storage (e.g., servers), and / or other computer-readable media.

[0066] As used herein, a computer-readable non-transitory storage medium or media may include one or more semiconductor-based or other integrated circuits (ICs) (e.g., field programmable gate arrays (FPGAs) or application-specific ICs (ASICs)), hard disk drives (HDDs), hybrid hard drives (HHDs), optical disks, optical disk drives (ODDs), magneto-optical disks, magneto-optical drives, floppy disks, floppy disk drives (FDDs), magnetic tapes, solid-state drives (SSDs), RAM drives, secure digital cards or drives, other suitable computer-readable non-transitory storage media, or, where appropriate, a suitable combination of two or more of these. Computer-readable non-transitory storage media may, where appropriate, be volatile, non-volatile, or a combination of volatile and non-volatile.

[0067] As used herein, "or" is inclusive and not exclusive, unless expressly indicated otherwise or the context otherwise indicates. Furthermore, "and" is jointly and severally, unless expressly indicated otherwise or the context otherwise indicates. Thus, as used herein, "A and B" means "A and B, jointly or severally," unless expressly indicated otherwise or the context otherwise indicates.

[0068] The scope of the present disclosure includes all changes, substitutions, variations, changes, and modifications to the exemplary embodiments described or illustrated herein that would be comprehensible to one skilled in the art. The scope of the present disclosure is not limited to the exemplary embodiments described or illustrated herein. Furthermore, although the present disclosure describes and illustrates each embodiment herein as including particular components, elements, features, functions, operations, or steps, any of these embodiments may include any combination or permutation of any of the components, elements, features, functions, operations, or steps described or illustrated herein that would be comprehensible to one skilled in the art. Furthermore, references in the appended claims to a device or system, or components thereof, that are suitably configured and arranged, and operative or operable to perform a particular function, include that device, system, or component, regardless of whether the device, system, or component is suitably configured and arranged, and operative or operable, or whether the particular function is activated or deactivated. Furthermore, although the present disclosure describes or illustrates particular embodiments as providing certain advantages, certain embodiments may provide none, some, or all of those advantages.

Claims

1. 1. A method comprising: receiving, by a computer system, first LiDAR data associated with a railway environment; a computer system extracting assets from the first LiDAR data associated with the rail environment; a computer system superimposing the asset onto a location on a centerline of a railroad track in a spatial model, the centerline of the railroad track being a line centered between two outer rails of the railroad track, the location on the centerline of the railroad track in the spatial model being transformed from a physical location of the asset in the railroad environment, the physical location of the asset in the railroad environment being outside the railroad track in the railroad environment; receiving, by a computer system, field indications associated with modifications to the railway environment; a computer system modifying the spatial model in response to receiving the field representation associated with the modification to the railway environment; receiving, by a computer system, second LiDAR data associated with the railway environment; a computer system comparing the second LiDAR data with a modified spatial model; A method comprising:

2. the computer system further comprising generating a report in response to comparing the second LiDAR data with the modified spatial model; The report states: an anomaly between the location of the asset determined by the modified spatial model and the location of the asset determined by the second LiDAR data; and verifying that the location of the asset determined by the modified spatial model is consistent with the location of the asset determined by the second LiDAR data; The method of claim 1 , comprising one or more of:

3. The field representation associated with the modification to the railway environment comprises: a physical object representing said asset is physically moved or physically moved from a first location to a second location within said railway environment; the physical object representing the asset is or has been physically removed from the railway environment; and new physical objects are or have been added to the railway environment; The method of claim 1 , wherein the first and second inputs represent one of:

4. the first LiDAR data and the second LiDAR data are captured using a LiDAR vehicle, the LiDAR vehicle including one or more scanning and imaging sensors; the first LiDAR data representing a first 360 degree image; the second LiDAR data representing a second 360 degree image; the first 360-degree image and the second 360-degree image each have a range showing approximately 600 feet on each side of a centerline of the railroad tracks within the railroad environment. The method of claim 1.

5. The asset is a key asset of positive train control (PTC), The assets are: Train control signals, Switch point, Level crossing, distance sign, Speed ​​signs, and Vehicle contact limit, The method of claim 1 , wherein the physical object represents one of:

6. the spatial model is stored in a geographic information system (GIS) database; The spatial model is Vector data, and Raster data, The method of claim 1 , comprising one or more of:

7. 2. The method of claim 1, further comprising: in response to comparing the second LiDAR data to the modified spatial model, a computer system auditing the railway environment for PTC compliance.

8. 1. A system including one or more processors and a memory for storing instructions, The instructions, when executed by the one or more processors, cause the one or more processors to: receiving first LiDAR data associated with a railway environment; extracting assets from the first LiDAR data associated with a rail environment; superimposing the asset to a position on a centerline of a railroad track in a spatial model, the centerline of the railroad track being a line centered between two outer rails of the railroad track, the position on the centerline of the railroad track in the spatial model being transformed from a physical position of the asset in the railroad environment, the physical position of the asset in the railroad environment being outside the railroad track in the railroad environment; receiving a field representation associated with a modification to the railway environment; modifying the spatial model in response to receiving the field representation associated with the modification to the railway environment; receiving second LiDAR data associated with the railway environment; comparing the second LiDAR data with a modified spatial model; A system that performs operations including:

9. The operations further include generating a report in response to comparing the second LiDAR data to the modified spatial model; The report: an anomaly between the location of the asset determined by the modified spatial model and the location of the asset determined by the second LiDAR data; and verifying that the location of the asset determined by the modified spatial model is consistent with the location of the asset determined by the second LiDAR data; The system of claim 8 , comprising one or more of:

10. A field representation associated with the modification to the railway environment is: a physical object representing said asset is physically moved or physically moved from a first location to a second location within said railway environment; the physical object representing the asset is or has been physically removed from the railway environment; and new physical objects are or have been added to the railway environment; 9. The system of claim 8, wherein the system exhibits one of:

11. the first LiDAR data and the second LiDAR data are captured using a LiDAR vehicle including one or more scanning and imaging sensors; the first LiDAR data representing a first 360 degree image; the second LiDAR data representing a second 360 degree image; the first 360-degree image and the second 360-degree image each have a range representing approximately 600 feet on each side of a centerline of the railroad tracks within the railroad environment. The system of claim 8.

12. The asset is a key asset of positive train control (PTC), The assets are: Train control signals, Switch point, Level crossing, distance sign, Speed ​​signs, and Vehicle contact limit, 9. The system of claim 8, wherein the physical object is one of:

13. the spatial model is stored in a geographic information system (GIS) database; The spatial model is Vector data, and Raster data, The system of claim 8 , comprising one or more of:

14. 10. The system of claim 8, wherein the operations further include auditing the second LiDAR data in response to the modified spatial model, comparing the second LiDAR data with the railway environment for PTC compliance.

15. One or more computer-readable storage media embodying instructions, The instructions, when executed by a processor, cause the processor to: receiving first LiDAR data associated with a railway environment; extracting assets from the first LiDAR data associated with the rail environment; superimposing the asset to a position on a centerline of a railroad track in a spatial model, the centerline of the railroad track being a line centered between two outer rails of the railroad track, the position on the centerline of the railroad track in the spatial model being transformed from a physical position of the asset in the railroad environment, the physical position of the asset in the railroad environment being outside the railroad track in the railroad environment; receiving a field representation associated with a modification to the railway environment; modifying the spatial model in response to receiving the field representation associated with the modification to the railway environment; receiving second LiDAR data associated with the railway environment; comparing the second LiDAR data with a modified spatial model; One or more computer-readable storage media configured to perform operations including:

16. The operations further include generating a report in response to comparing the second LiDAR data to the modified spatial model; The report: an anomaly between the location of the asset determined by the modified spatial model and the location of the asset determined by the second LiDAR data; and verifying that the location of the asset determined by the modified spatial model is consistent with the location of the asset determined by the second LiDAR data; 16. One or more computer-readable storage media according to claim 15, comprising one or more of:

17. The field representation associated with the modification to the railway environment comprises: a physical object representing said asset is physically moved or physically moved from a first location to a second location within said railway environment; the physical object representing the asset is or has been physically removed from the railway environment; and new physical objects are or have been added to the railway environment; 16. One or more computer-readable storage media according to claim 15, representing one of:

18. the first LiDAR data and the second LiDAR data are captured using a LiDAR vehicle including one or more scanning and imaging sensors; the first LiDAR data representing a first 360 degree image; the second LiDAR data representing a second 360 degree image; the first 360-degree image and the second 360-degree image each have a range showing approximately 600 feet on each side of a centerline of the railroad tracks within the railroad environment.

16. One or more computer-readable storage media according to claim 15.

19. the asset is a positive train control (PTC) critical asset; The assets are: Train control signals, Switch point, Level crossing, distance sign, Speed ​​signs, and Vehicle contact limit, 16. The one or more computer-readable storage media of claim 15, illustrating one of the physical objects of

20. the spatial model is stored in a geographic information system (GIS) database; The spatial model is Vector data, and Raster data, 16. One or more computer-readable storage media according to claim 15, comprising one or more of:

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