A computing system for generating a function execution plan for the vehicle to perform the one or more vehicle functions

Abstract

The invention relates to a computing system (10) comprising: a control circuit (12) configured to: obtain data (14) indicative of one or more vehicle functions (22) to be performed by a vehicle (16); obtain a connectivity map (18) associated with the vehicle (16), wherein the connectivity map (18) is indicative of a plurality of locations (L) and at least one connection attribute for each respective location (L) of the plurality of locations (L), wherein the at least one connection attribute describes a network connectivity (20) of the vehicle (16) at the respective location (L); generate, based on the connectivity map (18), a function execution plan for the vehicle (16) to perform the one or more vehicle functions (22); and output one or more control signals (24) to initiate the performance of the one or more vehicle functions (22) based on the function execution plan.

Description

Applicant’s Ref.: 2023P02005WQAttorney’s Ref.: 941101Mercedes-Benz Group AG KrishnamurthyprakashA computing system for generating a function execution plan for the vehicle to perform the one or more vehicle functionsFIELD OF THE INVENTION

[0001] The present invention relates to the field of automobiles. More specifically, the present invention relates to a computing system for generating a function execution plan for the vehicle to perform the one or more vehicle functions.BACKGROUND INFORMATION

[0002] According to the state of the art it is known, that different data packages, for example for digital maps or for example communication inside the motor vehicle can be downloaded from different backend servers. Up to now, there is no prioritization of that data packages, and therefore, in particular depending on a bandwidth of the communication link, some of these data packages cannot be downloaded.SUMMARY OF THE INVENTION

[0003] It is an object of the present invention to provide a computing system, by which a function execution plan can be generated in an improved manner.

[0004] This object is solved by a computing system according to the independent claims. Advantageous embodiments are presented in the dependent claims.

[0005] One aspect of the invention relates to a computing system comprising a control circuit configured to obtain data indicative of one or more vehicle functions to be performed by a vehicle, and obtaining a connectivity map associated with the vehicle, wherein the connectivity map is indicative of a plurality of locations and at least oneApplicant’s Ref.: 2023P02005WOAttorney’s Ref.: 941102 connection attribute for each respective location of the plurality of locations, wherein the at least one connection attribute describes a network connectivity of the vehicle and the respective location. Furthermore, the control circuit is configured for generating, based on the connectivity map, a function execution plan for the vehicle to perform the one or more vehicle functions and outputting one of the more control signals to initiate the performance of the one or more vehicle function based on the function execution plan.

[0006] According to an embodiment to obtain the data indicative of the one or more vehicle functions to be performed by the vehicle the control circuit is configured to obtain data indicative of a current vehicle function being performed by the motor vehicle.

[0007] In another embodiment to obtain the data indicative of the one or more vehicle functions to be performed by the vehicle control circuit is configured to obtain user history data indicative of a historic usage of the one or more vehicle functions by a current user of the vehicle and based on the user history data, predicting the one or more vehicle functions to be performed by the vehicle.

[0008] In another embodiment to obtain the data indicative of the one or more vehicle functions to be performed by the motor vehicle the control circuit is configured to determine a presence of a non-driver occupant of the vehicle and based on the presence of the non-driver occupant of the vehicle, predicting the one or more vehicle functions are to be performed by the vehicle.

[0009] In another embodiment the one or more vehicle functions comprises at least one of obtaining map data, obtaining multi-media content and obtaining a software update or autonomously driving the motor vehicle.

[0010] In another embodiment the function execution plan comprises a plurality of message actions and a respective location for executing each respective message action.

[0011] In another embodiment, to generate the function execution plan, the control circuit is configured to determine, based on the connectivity map, at least one of a location constraint or a timing constraint, for performing a respective vehicle function, of the one or more vehicle functions, wherein the function execution plan is indicative of at least one of the timing constraint or the location constraint for performing the respective vehicle function.Applicant’s Ref.: 2023P02005WOAttorney’s Ref.: 941103

[0012] According an another embodiment, the one or more vehicle functions comprise a plurality of vehicle functions, and to generate the function execution plan, the control circuit is configured to determine, based on the data indicative of the one or more vehicle functions to be performed by the vehicle and the connectivity map, a prioritization for performing the plurality of vehicle functions and generating, based on the prioritization for performing the plurality of vehicle functions, the function execution plan.

[0013] In another embodiment, the prioritization is based on a respective criticality level associated with the respective vehicle functions.

[0014] In another embodiment, to generate the function execution plan, the control circuit is configured to address a machine-learned model trained to generate the function execution plan and providing, as input into the machine-learned model, the data indicative of the one or more vehicle functions and the connectivity map, and receiving, as an output of the machine-learned model, the function execution plan for the vehicle to perform the one or more vehicle functions.

[0015] In another embodiment, the machine-learned model is trained to generate the function execution plan to reduce a probability that the performance of the one or more vehicle functions may be disrupted to the network connectivity.

[0016] In another embodiment, the control circuit is further configured to obtain data indicative of a route for the vehicle and generate the function execution plan based on the route for the vehicle, wherein the function execution plan indicates one or more locations along the route for performing the one or more vehicle functions.

[0017] In another embodiment, the control circuit is configured to generate, based on the at least one of the connectivity map or the function execution plan, content for a display via a display device within the vehicle, wherein the content comprises a map overlay for a map interface, the map overlay indicating at least one of an unavailable vehicle function or an available vehicle functions with a geographic area represented in the map interface and output data indicative of the content for display via the display device within the vehicle.

[0018] Another aspect of the invention relates to a computer-implemented method comprising obtaining data indicative of one or more vehicle functions to be performed by a vehicle, obtaining a connectivity map associated with a vehicle, wherein the connectivityApplicant’s Ref.: 2023P02005WOAttorney’s Ref.: 941104 map is indicative of a plurality of locations and at least one connection attribute for each respective location of the plurality of locations, wherein the at least one connection attribute describes a network connectivity of the vehicle at the respective location, generating, based on the connectivity map, a function execution plan for the vehicle to perform the one or more vehicle functions and outputting one or more control signals to initiate the performance of the one or more vehicle functions based on the function execution plan.

[0019] According to an embodiment generating the function execution plan comprises determining, based on the connectivity map at least one of a location constraint or a timing constraint, for performing a respective vehicle function, of the one or more vehicle functions, wherein the function execution plan is indicative of at least one of the timing constraint or the location constraint for performing the respective vehicle function.

[0020] In another embodiment, the one or more vehicle functions comprise a plurality of vehicle functions, and generating the function execution plan comprises determining, based on the data indicative of the one or more vehicle functions to be performed by the vehicle and the connectivity map, a prioritization for performing the plurality of vehicle functions and generating, based on the prioritization for performing the plurality of vehicle functions, the function execution plan.

[0021] In another embodiment, generating the function execution plan comprises assessing a machine-learning model trained to generate the function execution plan, providing, as an input into the machine-learned model, the data indicative of the one or more vehicle functions and the connectivity map and receiving as an output of the machine-learned model, the function execution plan for the vehicle to perform the one or more vehicle functions.

[0022] According to another embodiment, the machine-learned model is trained to generate the function execution plan to reduce a probability that the performance of the one or more vehicle functions may be disrupted to the network connectivity.

[0023] Another aspect of the invention relates to one or more non-transitory computer- readable media that store instructions that are executed by the control circuit to obtain data indicative of one or more vehicle functions to be performed by a vehicle, obtain a connectivity map associated with the vehicle, wherein the connectivity map is indicative ofApplicant’s Ref.: 2023P02005WQAttorney’s Ref.: 941105 a plurality of locations and at least one connection attribute for each respective location of the plurality of locations, wherein the at least one connection attribute describes a network connectivity of the vehicle and the respective location, generate, based on the connectivity map, a function execution plan for the vehicle to perform the one or more vehicle functions, and output one of the more control signals to initiate the performance of the one or more vehicle functions based on the function execution plan.

[0024] According to an embodiment to obtain the data indicative of the one or more vehicle functions to be performed by the vehicle the control circuit is configured to obtain data indicative of a current vehicle functions being performed by the vehicle, obtain user history data indicative of a historic usage of the one or more vehicle functions by a current user of the vehicle, and generate the data indicative of the one or more vehicle functions to be performed by the vehicle based on the data indicative of the current vehicle functions being performed by the vehicle and the user history data.

[0025] A computing unit / electronic computing device / Computing system may in particular be understood as a data processing device, which comprises processing circuitry. The computing unit can therefore in particular process data to perform computing operations. This may also include operations to perform indexed accesses to a data structure, for example a look-up table, LUT.

[0026] In particular, the computing unit may include one or more computers, one or more microcontrollers, and / or one or more integrated circuits, for example, one or more application-specific integrated circuits, ASIC, one or more field-programmable gate arrays, FPGA, and / or one or more systems on a chip, SoC. The computing unit may also include one or more processors, for example one or more microprocessors, one or more central processing units, CPU, one or more graphics processing units, GPU, and / or one or more signal processors, in particular one or more digital signal processors, DSP. The computing unit may also include a physical or a virtual cluster of computers or other of said units.

[0027] In various embodiments, the computing unit includes one or more hardware and / or software interfaces and / or one or more memory units.

[0028] A memory unit may be implemented as a volatile data memory, for example a dynamic random access memory, DRAM, or a static random access memory, SRAM, orApplicant’s Ref.: 2023P02005WQAttorney’s Ref.: 941106 as a non-volatile data memory, for example 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, a flash memory or flash EEPROM, a ferroelectric random access memory, FRAM, a magnetoresistive random access memory, MRAM, or a phase-change random access memory, PCRAM.

[0029] Further advantages, features, and details of the invention derive from the following description of preferred embodiments as well as from the drawings. The features and feature combinations previously mentioned in the description as well as the features and feature combinations mentioned in the following description of the figures and / or shown in the figures alone can be employed not only in the respectively indicated combination but also in any other combination or taken alone without leaving the scope of the invention.BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The novel features and characteristic of the disclosure are set forth in the appended claims. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and together with the description, serve to explain the disclosed principles. The same numbers are used throughout the figures to reference like features and components. Some embodiments of system and / or methods in accordance with embodiments of the present subject matter are now described below, by way of example only, and with reference to the accompanying figures.

[0031] The drawings show in:

[0032] Fig. 1 a schematic side view according to an embodiment of a motor vehicle comprising an embodiment of an electronic computing device; and

[0033] Fig. 2 a schematic flow chart according to an embodiment of the method.

[0034] In the figures the same elements or elements having the same function are indicated by the same reference signs.DETAILED DESCRIPTIONApplicant’s Ref.: 2023P02005WOAttorney’s Ref.: 941107

[0035] In the present document, the word "exemplary" is used herein to mean "serving as an example, instance, or illustration". Any embodiment or implementation of the present subject matter described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.

[0036] While the disclosure is susceptible to various modifications and alternative forms, specific embodiments thereof have been shown by way of example in the drawing and will be described in detail below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.

[0037] The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion so that a setup, device or method that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus preceded by “comprises” or “comprise” does not or do not, without more constraints, preclude the existence of other elements or additional elements in the system or method.

[0038] In the following detailed description of the embodiment of the disclosure, reference is made to the accompanying drawing that forms part hereof, and in which is shown by way of illustration a specific embodiment in which the disclosure may be practiced. This embodiment is described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

[0039] Fig. 1 shows a schematic view according to an embodiment of a computing system comprising 10 comprising a control circuit 12 configured to obtaining data 14 indicative of one or more vehicle functions 22 to be performed by a vehicle 16. A connectivity map 18 associated with the vehicle 16 is obtained, wherein the connectivity map 18 is indicative of a plurality of locations L and at least one connection attribute for each respective location L of the plurality of locations L, wherein the at least one connection attribute describes a network connectivity 20 of the vehicle 16 at the respective location L. Based on the connectivity map 18, a function execution plan for theApplicant’s Ref.: 2023P02005WOAttorney’s Ref.: 941108 vehicle 16 to perform the one or more vehicle functions 22 is generated. One or more control signals 24 are outputted to initiate the performance of the one or more vehicle functions 22 based on the function execution plan.

[0040] Furthermore, for obtaining the data 14 indicative of the one or more vehicle functions 22 to be performed by the vehicle 16 the control circuit 12 is configured to obtain data 14 indicative of a current vehicle function 22 being performed by the vehicle 16. In another embodiment, to obtain the data 14 indicative of the one or more vehicle functions 22 to be performed by the vehicle 10 the control circuit 12 is configured to obtain user history data 14 indicative of a historic usage of the one or more vehicle functions 22 by a current user of the vehicle 16, and based on the user history data 14, predicting the one or more vehicle functions 22 to be performed by the vehicle 16 is performed.

[0041] According to another embodiment to obtain the data 14 indicative of the one or more vehicle functions 22 to be performed by the vehicle 16 the control circuit 12 is configured to determine a presence of a non-driver occupant of the vehicle 16, and based on the presence of the non-driver occupant of the vehicle 16, predicting the one or more vehicle functions 22 to be performed by the vehicle 16 is provided.

[0042] In another embodiment the function execution plan comprises a plurality of message actions and a respective location L for executing each respective message action. Furthermore, to generate the function execution plan, the control circuit 12 is configured to determine, based on the connectivity map 18, at least one of a location constraint or a timing constraint, for performing a respective vehicle function 22, of the one or more vehicle functions 22, wherein the function execution plan is indicative of at least one of the timing constraint or the location constraint for performing the respective vehicle function 22.

[0043] Furthermore, the one or more vehicle functions 22 comprise a plurality of vehicle functions 22, and to generate the function execution plan, the control circuit 12 is configured to determine, based on the data 14 indicative of the one or more vehicle functions 22 to be performed by the vehicle 16 and the connectivity map 18, a prioritization for performing the plurality of vehicle functions 22, and to generate, based on the prioritization for performing the plurality of vehicle functions 22, the function execution plan.Applicant’s Ref.: 2023P02005WOAttorney’s Ref.: 941109

[0044] The control circuit 12 is further configured to access a machine-learned model trained to generate the function execution plan, to provide, as an input into the machine- learned model, the data 14 indicative of the one or more vehicle functions 22 and the connectivity map 18, and to receive, as an output of the machine-learned model, the function execution plan for the vehicle 16 to perform the one or more vehicle functions 22. The machine-learned model is in particular trained to generate the function execution plan to reduce a probability that the performance of the one or more vehicle functions 22 will be disrupted due to the network connectivity 20.

[0045] In another embodiment the control circuit 12 is configured to generate, based on at least one of the connectivity map 18 or the function execution plan, content for display via a display device within the vehicle 16, wherein the content comprises a map overlay for a map interface, the map overlay indicating at least one of an unavailable vehicle function 22 or an available vehicle function 22 within a geographic area represented in the map interface, and to output data 14 indicative of the content for display via the display device within the vehicle 16.

[0046] Fig. 2 shows a schematic flow chart according to an embodiment a corresponding method. In a first step S1 obtaining data 14 indicative of one or more vehicle functions 22 to be performed by the vehicle 16 is performed. In a second step S2, obtaining the connectivity map 18 associated with the vehicle 16, wherein the connectivity map 18 is indicative of the plurality of locations L and at least one connection attribute for each respective location L of the plurality of locations L, wherein the at least one connection attribute describes the network connectivity 20 of the vehicle 16 at the respective location L is performed. According to a third step S3, generating, based on the connectivity map 18, a function execution plan for the vehicle 16 to perform the one or more vehicle functions 22 is provided. In a fourth step S4 outputting the one or more control signals 24 to initiate the performance of the one or more vehicle functions 22 based on the function execution plan is provided.

[0047] In particular, for example, if a driver of the motor vehicle 16 is using a fully autonomous driving while making a voice call, the motor vehicle 16 needs to download for example a new automatic driving map tile to remain in the fully automatic driving mode. The motor vehicle 16 interacts with for example the computing system 10 on board andApplicant’s Ref.: 2023P02005WOAttorney’s Ref.: 9411010 may get the probability to download this automatic drive map tile. The model may produce the probability that the map tile may be downloaded, and the computing system 10 may decide what / how to manage these downloads.

[0048] A second example may be, that a series of map tiles need to be downloaded. Therefore, a download plan is generated. The computing system 10 may produce a dictionary of locations to message actions, essentially a map of when to execute the given sizes. This model is an optimization model. When to download these messages while keeping the probability of disruption to the reserved bandwidth below some threshold. This model may even potentially be off-board, for example on the backend.

[0049] Different types of downloads may be categorized based on their priorities, for example safety critical downloads are the highest priority, luxury features are the lowest priority, or custom priorities, for example work-related apps are highest priority and furthermore. For example, when a new automatic driving map tile and an over-the-air update download is needed. If the map tile is in a jeopardy, the over-the-air update should not be started.

[0050] For example, the automated driving map tile may have a high priority class, the over-the-air update software package may have a low priority class and for example music streaming may have a middle priority class.

[0051] Download plans might change depending on several factors. For example, changes to route, on-road conditions, or the tile has been missed but the motor vehicle 16 drives further. Furthermore, pre-checked-ins may be performed if not to get a new download plan every time but to ensure the available plan is still good enough to be used.

[0052] The machine learning algorithm may be a forecast model, using a loss function defined for each use case. For example, one, in particular single probability, as the difference between the predicted download probability in a geo location whether the aggregated actual successful connection. For the download plan, the loss function is defined as how much of the planned download was able to be successfully downloaded.

[0053] Both these models may continually ask new data to calculate the loss function, which then becomes part of the training data set for the next iteration of data.Applicant’s Ref.: 2023P02005WOAttorney’s Ref.: 9411011

[0054] An alternative implementation may be for example a map-based implementation. For example, a general red / yellow / green map for the particular motor vehicle 10 with colors may be produced. For example, green means no restrictions, yellow means high bandwidth actions may impact, and red means expect disruptions to basic commands.Applicant’s Ref.: 2023P02005WOAttorney’s Ref.: 9411012Reference signs10 Computing system12 Control circuit14 Data16 Vehicle18 Connectivity map20 Network connection22 Function24 Control signalS1 - S4 Steps of the method

Claims

Applicant’s Ref.: 2023P02005WOAttorney’s Ref.: 9411013Mercedes-Benz Group AG KrishnamurthyprakashCLAIMS1. A computing system (10) comprising: a control circuit (12) configured to: obtain data (14) indicative of one or more vehicle functions (22) to be performed by a vehicle (16); obtain a connectivity map (18) associated with the vehicle (16), wherein the connectivity map (18) is indicative of a plurality of locations (L) and at least one connection attribute for each respective location (L) of the plurality of locations (L), wherein the at least one connection attribute describes a network connectivity (20) of the vehicle (16) at the respective location (L); generate, based on the connectivity map (18), a function execution plan for the vehicle (16) to perform the one or more vehicle functions (22); and output one or more control signals (24) to initiate the performance of the one or more vehicle functions (22) based on the function execution plan.

2. The computing system (10) of claim 1 , wherein to obtain the data (14) indicative of the one or more vehicle functions (22) to be performed by the vehicle (16) the control circuit (12) is configured to: obtain data (14) indicative of a current vehicle function (22) being performed by the vehicle (16).

3. The computing system (10) of claim 1 , wherein to obtain the data (14) indicative of the one or more vehicle functions (22) to be performed by the vehicle (16) the control circuit (12) is configured to:Applicant’s Ref.: 2023P02005WOAttorney’s Ref.: 9411014 obtain user history data (14) indicative of a historic usage of the one or more vehicle functions (22) by a current user of the vehicle (16); and based on the user history data (14), predicting the one or more vehicle functions (22) are to be performed by the vehicle (16).

4. The computing system (10) of claim 1 , wherein to obtain the data (14) indicative of the one or more vehicle functions (22) to be performed by the vehicle (16) the control circuit (12) is configured to: determine a presence of a non-driver occupant of the vehicle (16); and based on the presence of the non-driver occupant of the vehicle (16), predict the one or more vehicle functions (22) are to be performed by the vehicle (16).

5. The computing system (10) of claim 1 , wherein the function execution plan comprises a plurality of message actions and a respective location (L) for executing each respective message action.

6. The computing system (10) of claim 1 , wherein to generate the function execution plan, the control circuit (12) is configured to: determine, based on the connectivity map (18), at least one of a location constraint or a timing constraint, for performing a respective vehicle function (22), of the one or more vehicle functions (22), wherein the function execution plan is indicative of at least one of the timing constraint or the location constraint for performing the respective vehicle function (22).

7. The computing system (10) of claim 1 , wherein the one or more vehicle functions (22) comprise a plurality of vehicle functions (22), and to generate the function execution plan, the control circuit (12) is configured to: determine, based on the data (14) indicative of the one or more vehicle functions(22) to be performed by the vehicle (16) and the connectivity map (18), a prioritization for performing the plurality of vehicle functions (22); and generate, based on the prioritization for performing the plurality of vehicle functions (22), the function execution plan.Applicant’s Ref.: 2023P02005WOAttorney’s Ref.: 94110158. The computing system (10) of claim 1 , wherein to generate the function execution plan, the control circuit (12) is configured to: access a machine-learned model trained to generate the function execution plan; provide, as an input into the machine-learned model, the data (14) indicative of the one or more vehicle functions (22) and the connectivity map (18); and receive, as an output of the machine-learned model, the function execution plan for the vehicle (16) to perform the one or more vehicle functions (22).

9. The computing system (10) of claim 8, wherein the machine-learned model is trained to generate the function execution plan to reduce a probability that the performance of the one or more vehicle functions (22) will be disrupted due to the network connectivity (20).

10. The computing system (10) of claim 1 , wherein the control circuit (12) is configured to: generate, based on at least one of the connectivity map (18) or the function execution plan, content for display via a display device within the vehicle (16), wherein the content comprises a map overlay for a map interface, the map overlay indicating at least one of an unavailable vehicle function (22) or an available vehicle function (22) within a geographic area represented in the map interface; and output data indicative of the content for display via the display device within the vehicle (16).

Images