The present disclosure relates generally to the automotive field. More particularly, the present disclosure relates to a method and system for vehicle unique signal to vehicle general signal matching.
Modern connected vehicle networks handle several thousand different data signals, such as wheel pulses, battery levels, locations, window positions etc. Vehicle data and the corresponding identifications have often been a proprietary original equipment manufacturer (OEM) concern and anyone trying to interface vehicle data across OEMs has had to confront this situation. New use cases stemming from autonomous vehicles, smart cities, and various connected services raise a daunting matching task across OEMs. Various web and vehicle-focused groups and consortiums have together attempted to resolve this by proposing web standards for vehicle signal data. These standards facilitate data handling and promote innovation in the vehicle data space. However, it is still necessary to map OEM signals to these standards, such as the Vehicle Signal Specification (VSS).
The present background is provided as illustrative environmental context only and should not be construed to be limiting in any manner. It will be readily apparent to those of ordinary skill in the art that the principles and concepts of the present disclosure may be applied in other environments and applications equally, without limitation.
The present disclosure generally provides a method and system for vehicle unique signal to vehicle general signal matching, such that proprietary vehicle signals can be mapped and translated to corresponding VSS signals or the like, facilitating all types of connected vehicle functionalities. The present disclosure provides a candidate ranking algorithm based on the lexical elements of signal names and their semantic descriptions. Furthermore, units and data types may also be used to further enhance candidate suggestions. For candidate storage and generation, a knowledge graph using a graph database is suggested. Methods for automatic translation code generation are also suggested, which occurs when a one-to-one mapping is lacking. The algorithms of the present disclosure may be implanted in vehicle or in the cloud, and mappings may be constrained to a schema dictated by a given use case, or may be more extensive and cached for future use.
In one illustrative embodiment, the present disclosure provides a method for vehicle unique signal to vehicle general signal matching, the method including: using translation instructions stored in a memory and executed by a processor of a vehicle or resident in a cloud network, receiving a vehicle signal identifier associated with a signal of a vehicle and mapping the vehicle signal identifier to a corresponding specification signal identifier associated with an external standard, where the mapping is performed using a matching and scoring algorithm utilizing multiple attributes of the vehicle signal identifier and the specification signal identifier selected from signal name, signal descriptive text, signal units, and signal data type. The vehicle signal is received based on a vehicle identification that defines a plurality of extracted candidate vehicle signals. The vehicle signal is also received based on a specified use case providing a schema associated with the vehicle signal that defines a plurality of extracted candidate vehicle signals. The method also includes caching a result of the mapping in a database stored in the memory of the vehicle or resident in the cloud for future use. The method further includes, based on a result of the mapping, providing the vehicle signal to an external entity identifying the vehicle signal using the specification signal identifier. The matching and scoring algorithm is operable for assessing a Levenshtein or similar distance modified to fit signal name mapping between a signal name of the vehicle signal identifier and a signal name of the specification signal identifier. The matching and scoring algorithm is operable for assessing a similarity between signal descriptive text of the vehicle signal identifier and signal descriptive text of the specification signal identifier using a machine learning model trained with automotive language and terminology, for example. The matching and scoring algorithm is operable for assessing a similarity between signal units of the vehicle signal identifier and signal units of the specification signal identifier using a heuristic matching algorithm. The matching and scoring algorithm is operable for assessing a similarity between a signal data type of the vehicle signal identifier and a signal data type of the specification signal identifier using a data structure matching algorithm. Data types are matched based on information such as bit length, integers, floating point numbers, and strings. The matching and scoring algorithm is operable for weighting mapping results associated with each of the multiple attributes of the vehicle signal identifier and the specification signal identifier.
In another illustrative embodiment, the present disclosure provides a system for vehicle unique signal to vehicle general signal matching, the system including: a memory of a vehicle or resident in a cloud network; a processor of the vehicle or resident in the cloud network; and translation instructions stored in the memory and executed by the processor of the vehicle or resident in the cloud network operable for receiving a vehicle signal identifier associated with a signal of a vehicle and mapping the vehicle signal identifier to a corresponding specification signal identifier associated with an external standard, where the mapping is performed using a matching and scoring algorithm utilizing multiple attributes of the vehicle signal identifier and the specification signal identifier selected from signal name, signal descriptive text, signal units, and signal data type. The vehicle signal is received based on a vehicle identification that defines a plurality of extracted candidate vehicle signals. The vehicle signal is also received based on a specified use case providing a schema associated with the vehicle signal that defines a plurality of extracted candidate vehicle signals. The memory further caches a result of the mapping in a database stored in the memory of the vehicle or resident in the cloud for future use. Based on a result of the mapping, the processor further provides the vehicle signal to an external entity identifying the vehicle signal using the specification signal identifier. The matching and scoring algorithm is operable for assessing a Levenshtein or similar distance modified to fit signal name mapping between a signal name of the vehicle signal identifier and a signal name of the specification signal identifier. The matching and scoring algorithm is operable for assessing a similarity between signal descriptive text of the vehicle signal identifier and signal descriptive text of the specification signal identifier using a machine learning model trained with automotive language and terminology, for example. The matching and scoring algorithm is operable for assessing a similarity between signal units of the vehicle signal identifier and signal units of the specification signal identifier using a heuristic matching algorithm. The matching and scoring algorithm is operable for assessing a similarity between a signal data type of the vehicle signal identifier and a signal data type of the specification signal identifier using a data structure matching algorithm. Data types are matched based on information such as bit length, integers, floating point numbers, and strings. The matching and scoring algorithm is operable for weighting mapping results associated with each of the multiple attributes of the vehicle signal identifier and the specification signal identifier.
In a further illustrative embodiment, the present disclosure provides a non-transitory computer-readable medium including a memory storing translation instructions executed by a processor of a vehicle or resident in a cloud network for carrying out steps for vehicle unique signal to vehicle general signal matching, the steps including: receiving a vehicle signal identifier associated with a signal of a vehicle and mapping the vehicle signal identifier to a corresponding specification signal identifier associated with an external standard, where the mapping is performed using a matching and scoring algorithm utilizing multiple attributes of the vehicle signal identifier and the specification signal identifier selected from signal name, signal descriptive text, signal units, and signal data type.
The present disclosure is illustrated and described herein with reference to the various drawings, in which like reference numbers are used to denote like system components/method steps, as appropriate, and in which:
It will be readily apparent to those of ordinary skill in the art that aspects of any disclosed embodiment may be included, omitted, and/or combined in any other disclosed embodiment as desired in a given application.
Again, the present disclosure generally provides a method and system for vehicle unique signal to vehicle general signal matching, such that proprietary vehicle signals can be mapped and translated to corresponding VSS signals or the like, facilitating all types of connected vehicle functionalities. The present disclosure provides a candidate ranking algorithm based on the lexical elements of signal names and their semantic descriptions. Furthermore, units and data types may also be used to further enhance candidate suggestions. For candidate storage and generation, a knowledge graph using a graph database is suggested. Methods for automatic translation code generation are also suggested, which occurs when a one-to-one mapping is lacking. The algorithms of the present disclosure may be implanted in vehicle or in the cloud, and mappings may be constrained to a schema dictated by a given use case, or may be more extensive and cached for future use.
In general, the names 14a and 24a include simple one word/phrase or title labels for the associated signals 12 and 22. An illustrative example includes the use of VehSpd for a vehicle speed signal for the vehicle 10, with the use of Vehicle. Speed for the corresponding vehicle speed signal for the external system 20. The descriptive texts 14b and 24b include simple text passages for the associated signals 12 and 22. The units 14c and 24c include the nature of the units used for the associated signals 12 and 22. An illustrative example includes the use of m/s for the vehicle speed signal for the vehicle 10, with the use of km/h for the corresponding vehicle speed signal for the external system 20. Finally, the data types 14d and 24d include the data structures used for the associated signals 12 and 22. An illustrative example includes the use of (time):(speed) for the vehicle speed signal for the vehicle 10, with the use of (time-speed) for the corresponding vehicle speed signal for the external system 20. Another illustrative example includes the use of a string for the vehicle speed signal for the vehicle 10, with the use of an integer for the corresponding vehicle speed signal for the external system 20. It will be readily apparent to those of ordinary skill in the art that these are very simple illustrative examples only, intended to clarify the functionalities of the present disclosure, without being limiting in any manner.
Optionally, the memory 34 caches a result of the mapping in a database 42 stored in the memory 34 of the vehicle 10 or resident in the cloud 32 for future use. In this manner, mapping can be performed on an as needed basis or on a preparatory basis. Based on a result of the mapping, the processor 36 further provides the vehicle signal 12 to the external entity 20 identifying the vehicle signal 12 using the specification signal identifier 24, for example.
The matching and scoring algorithm 40 is operable for parsing or assessing a Levenshtein or similar distance modified to fit signal name mapping between a signal name 14a of the vehicle signal identifier 14 and a signal name 24a of the specification signal identifier 24. The matching and scoring algorithm 40 is also operable for assessing a similarity between signal descriptive text 14b of the vehicle signal identifier 14 and signal descriptive text 24b of the specification signal identifier 24 using a machine learning model trained with automotive language and terminology, for example. The matching and scoring algorithm 40 is further operable for assessing a similarity between signal units 14c of the vehicle signal identifier 14 and signal units 24c of the specification signal identifier 24 using a heuristic matching algorithm. The matching and scoring algorithm 40 is still further operable for assessing a similarity between a signal data type 14d of the vehicle signal identifier 14 and a signal data type 24d of the specification signal identifier 24 using a data structure matching algorithm. Data types are matched based on information such as bit length, integers, floating point numbers, and strings. It will be readily apparent to those of ordinary skill in the art that a multitude of tools could be used to perform each of these operations and all are contemplated herein, without limitation.
Optionally, the memory 34 caches a result of the mapping in a database 42 stored in the memory 34 of the vehicle 10 or resident in the cloud 32 for future use (step 60). In this manner, mapping can be performed on an as needed basis or on a preparatory basis. Based on a result of the mapping, the processor 36 further provides the vehicle signal 12 to the external entity 20 identifying the vehicle signal 12 using the specification signal identifier 24, for example (step 62).
The matching and scoring algorithm 40 is operable for parsing or assessing a Levenshtein or similar distance modified to fit signal name mapping between a signal name 14a of the vehicle signal identifier 14 and a signal name 24a of the specification signal identifier 24 (step 58a). The matching and scoring algorithm 40 is also operable for assessing a similarity between signal descriptive text 14b of the vehicle signal identifier 14 and signal descriptive text 24b of the specification signal identifier 24 using a machine learning model trained with automotive language and terminology, for example (step 58b). The matching and scoring algorithm 40 is further operable for assessing a similarity between signal units 14c of the vehicle signal identifier 14 and signal units 24c of the specification signal identifier 24 using a heuristic matching algorithm (step 58c). The matching and scoring algorithm 40 is still further operable for assessing a similarity between a signal data type 14d of the vehicle signal identifier 14 and a signal data type 24d of the specification signal identifier 24 using a data structure matching algorithm (step 58d). Data types are matched based on information such as bit length, integers, floating point numbers, and strings. It will be readily apparent to those of ordinary skill in the art that a multitude of tools could be used to perform each of these operations and all are contemplated herein, without limitation.
It is to be recognized that, depending on the example, certain acts or events of any of the techniques described herein can be performed in a different sequence, may be added, merged, or left out altogether (e.g., not all described acts or events are necessary for the practice of the techniques). Moreover, in certain examples, acts or events may be performed concurrently, e.g., through multi-threaded processing, interrupt processing, or multiple processors, rather than sequentially.
The cloud-based system 100 can provide any functionality through services such as software-as-a-service (SaaS), platform-as-a-service, infrastructure-as-a-service, security-as-a-service, Virtual Network Functions (VNFs) in a Network Functions Virtualization (NFV) Infrastructure (NFVI), etc. to the locations 110, 120, and 130 and devices 140 and 150. Previously, the Information Technology (IT) deployment model included enterprise resources and applications stored within an enterprise network (i.e., physical devices), behind a firewall, accessible by employees on site or remote via Virtual Private Networks (VPNs), etc. The cloud-based system 100 is replacing the conventional deployment model. The cloud-based system 100 can be used to implement these services in the cloud without requiring the physical devices and management thereof by enterprise IT administrators.
Cloud computing systems and methods abstract away physical servers, storage, networking, etc., and instead offer these as on-demand and elastic resources. The National Institute of Standards and Technology (NIST) provides a concise and specific definition which states cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. Cloud computing differs from the classic client-server model by providing applications from a server that are executed and managed by a client's web browser or the like, with no installed client version of an application necessarily required. Centralization gives cloud service providers complete control over the versions of the browser-based and other applications provided to clients, which removes the need for version upgrades or license management on individual client computing devices. The phrase “software as a service” (SaaS) is sometimes used to describe application programs offered through cloud computing. A common shorthand for a provided cloud computing service (or even an aggregation of all existing cloud services) is “the cloud.” The cloud-based system 100 is illustrated herein as one example embodiment of a cloud-based system, and those of ordinary skill in the art will recognize the systems and methods described herein are not necessarily limited thereby.
The processor 202 is a hardware device for executing software instructions. The processor 202 may be any custom made or commercially available processor, a central processing unit (CPU), an auxiliary processor among several processors associated with the server 200, a semiconductor-based microprocessor (in the form of a microchip or chipset), or generally any device for executing software instructions. When the server 200 is in operation, the processor 202 is configured to execute software stored within the memory 210, to communicate data to and from the memory 210, and to generally control operations of the server 200 pursuant to the software instructions. The I/O interfaces 204 may be used to receive user input from and/or for providing system output to one or more devices or components.
The network interface 206 may be used to enable the server 200 to communicate on a network, such as the Internet 104 (
The memory 210 may include any of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)), nonvolatile memory elements (e.g., ROM, hard drive, tape, CDROM, etc.), and combinations thereof. Moreover, the memory 210 may incorporate electronic, magnetic, optical, and/or other types of storage media. Note that the memory 210 may have a distributed architecture, where various components are situated remotely from one another but can be accessed by the processor 202. The software in memory 210 may include one or more software programs, each of which includes an ordered listing of executable instructions for implementing logical functions. The software in the memory 210 includes a suitable operating system (O/S) 214 and one or more programs 216. The operating system 214 essentially controls the execution of other computer programs, such as the one or more programs 216, and provides scheduling, input-output control, file and data management, memory management, and communication control and related services. The one or more programs 216 may be configured to implement the various processes, algorithms, methods, techniques, etc. described herein.
It will be appreciated that some embodiments described herein may include one or more generic or specialized processors (“one or more processors”) such as microprocessors; central processing units (CPUs); digital signal processors (DSPs); customized processors such as network processors (NPs) or network processing units (NPUs), graphics processing units (GPUs), or the like; field programmable gate arrays (FPGAs); and the like along with unique stored program instructions (including both software and firmware) for control thereof to implement, in conjunction with certain non-processor circuits, some, most, or all of the functions of the methods and/or systems described herein. Alternatively, some or all functions may be implemented by a state machine that has no stored program instructions, or in one or more application-specific integrated circuits (ASICs), in which each function or some combinations of certain of the functions are implemented as custom logic or circuitry. Of course, a combination of the aforementioned approaches may be used. For some of the embodiments described herein, a corresponding device in hardware and optionally with software, firmware, and a combination thereof can be referred to as “circuitry configured or adapted to,” “logic configured or adapted to,” etc. perform a set of operations, steps, methods, processes, algorithms, functions, techniques, etc. on digital and/or analog signals as described herein for the various embodiments.
Moreover, some embodiments may include a non-transitory computer-readable storage medium having computer-readable code stored thereon for programming a computer, server, appliance, device, processor, circuit, etc. each of which may include a processor to perform functions as described and claimed herein. Examples of such computer-readable storage mediums include, but are not limited to, a hard disk, an optical storage device, a magnetic storage device, a Read-Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), flash memory, and the like. When stored in the non-transitory computer-readable medium, software can include instructions executable by a processor or device (e.g., any type of programmable circuitry or logic) that, in response to such execution, cause a processor or the device to perform a set of operations, steps, methods, processes, algorithms, functions, techniques, etc. as described herein for the various embodiments.
The processor 302 is a hardware device for executing software instructions. The processor 302 can be any custom made or commercially available processor, a CPU, an auxiliary processor among several processors associated with the user device 300, a semiconductor-based microprocessor (in the form of a microchip or chipset), or generally any device for executing software instructions. When the user device 300 is in operation, the processor 302 is configured to execute software stored within the memory 310, to communicate data to and from the memory 310, and to generally control operations of the user device 300 pursuant to the software instructions. In an embodiment, the processor 302 may include a mobile optimized processor such as optimized for power consumption and mobile applications. The I/O interfaces 304 can be used to receive user input from and/or for providing system output. User input can be provided via, for example, a keypad, a touch screen, a scroll ball, a scroll bar, buttons, a barcode scanner, and the like. System output can be provided via a display device such as a liquid crystal display (LCD), touch screen, and the like.
The radio 306 enables wireless communication to an external access device or network. Any number of suitable wireless data communication protocols, techniques, or methodologies can be supported by the radio 306, including any protocols for wireless communication. The data store 308 may be used to store data. The data store 308 may include any of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, and the like)), nonvolatile memory elements (e.g., ROM, hard drive, tape, CDROM, and the like), and combinations thereof. Moreover, the data store 308 may incorporate electronic, magnetic, optical, and/or other types of storage media.
Again, the memory 310 may include any of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)), nonvolatile memory elements (e.g., ROM, hard drive, etc.), and combinations thereof. Moreover, the memory 310 may incorporate electronic, magnetic, optical, and/or other types of storage media. Note that the memory 310 may have a distributed architecture, where various components are situated remotely from one another, but can be accessed by the processor 302. The software in memory 310 can include one or more software programs, each of which includes an ordered listing of executable instructions for implementing logical functions. In the example of
Although the present disclosure is illustrated and described herein with reference to illustrative embodiments and specific examples thereof, it will be readily apparent to those of ordinary skill in the art that other illustrative embodiments and examples may perform similar functions and/or achieve like results. All such equivalent illustrative embodiments and examples are within the spirit and scope of the present disclosure, are contemplated thereby, and are intended to be covered by the following non-limiting claims for all purposes.
The present disclosure claims the benefit of priority of co-pending U.S. Provisional Patent Application No. 63/342,202, filed on May 16, 2022, and entitled “METHOD AND SYSTEM FOR VEHICLE UNIQUE SIGNAL TO VEHICLE GENERAL SIGNAL MATCHING,” the contents of which are incorporated in full by reference herein.
Number | Date | Country | |
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63342202 | May 2022 | US |