GB2640261A - A computing system for generating a connectivity map for a vehicle and method - Google Patents

Abstract

A computing system (1) for generating a connectivity map for a vehicle, comprising a control circuit (2) configured to obtain data indicative of one or more connection attributes (3) describing a network connectivity of a vehicle (10), to determine contextual data (4) associated with the one or more connection attributes (3), wherein the contextual data (4) describes a context of the vehicle (10) associated with obtaining the connection attributes (3), to generate a vehicle connectivity map (5) for the vehicle (10) based on the one or more connection attributes (3) and the contextual data (4), wherein the vehicle connectivity map (5) is indicative of a plurality of locations (6) and at least one connection attribute (3a), of the one or more connection attributes (3), for each respective location (6a) of the plurality of locations (6), and to output the vehicle connectivity map (5) for performance of a vehicle function (7).

Description

A Computing System for generating a Connectivity Map for a Vehicle and Method

FIELD OF THE INVENTION

[0001] The invention relates to the field of automobiles. More specifically, the present invention relates to a computing system for generating a connectivity map according to claim 1. Furthermore, the present invention relates to a method for operating the computing system, a corresponding computer program product, and a corresponding non-transitory computer-readable storage medium.

BACKGROUND INFORMATION

[0002] Telecommunication is indispensable for automobiles nowadays and must continually be developed to provide the best reception.

SUMMARY OF THE INVENTION

[0003] The objective of the present invention is to improve the connectivity for autonomous or semi-autonomous automobiles while enabling a better wireless communication.

[0004] This objective is accomplished through a computing system with the features of claim 1, as well as by a method according to the invention and by utilizing a corresponding computer program product and a corresponding non-transitory computer-readable storage medium. Advantageous embodiments of the invention can be found in the dependent claims.

[0005] One aspect of the invention pertains to a computing system comprising a control circuit configured to acquire data representing one or more connection attributes that describe the network connectivity of an autonomous or semi-autonomous vehicle.

Additionally, it determines contextual data associated with the one or more connection attributes, with this contextual data describing the context of the vehicle concerning the acquisition of these connection attributes. Furthermore, the system generates a vehicle connectivity map for the vehicle based on the one or more connection attributes on the contextual data, wherein the vehicle connectivity map is indicative of a plurality of locations and at least one connection attribute, of the one or more connection attributes, for each respective location of the plurality of locations. Finally, the system outputs the vehicle connectivity map to facilitate the execution of a vehicle function. The objective is to create connectivity maps, consisting of both on-board and off-board maps depicting a matrix relating to connectivity strength, such as latency and throughput. These maps empower the vehicle to make more rational choices when it comes to sending and/or receiving data. For instance, it can prioritize bandwidth intensive critical machine tasks but postpone the transfer of large files, thereby optimizing the utilization of its network resources. Therefore one benefit of this system is the capacity to offer a comprehensive and contextual overview of a vehicle network connectivity, thereby improving the efficiency and effectiveness of vehicle-related functions and operations.

[0006] In an advantageous embodiment of the invention, it is provided that the one or more connection attributes comprise at least one of a network latency and/or a network throughput and/or a signal strength and/or a network reliability. In particular, the configuration is specified so that one or more connection attributes encompass a range of critical factors essential for evaluating network performance. These attributes may include, but are not limited to, the network latency, which measures the time it takes for data to travel from source to destination, the network throughput, which quantifies the rate at which data can be transmitted over the network, the signal strength, indicating the power of the signal being received and network reliability, which assesses the stability and consistency of the network connection. The inclusion of these connection attributes ensures an accurate assessment of the vehicle's network connectivity, facilitating decision making and optimization of vehicle-related functions and operations.

[0007] In yet another advantageous embodiment of the invention, it is provided that the contextual data comprises at least one location coordinate and/or an accuracy associated with the location coordinates and/or a time stamp and/or a communication hardware type and/or a communication hardware position and/or a communication hardware status and/or a connectivity protocol and/or a network protocol and/or a network provider and/or an equipment identifier and/or an antenna type and/or a network roaming status and/or weather data and/or a vehicle speed. Additional options are also possible, depending on the configuration of the invention.

[0008] It is also possible to configure the computing system in such a way that the communication hardware type is indicative of at least one of a communication model type or an antenna type. Further options may be considered. For instance, the system may be designed to enable user-defined adjustments to the parameter of contextual data, allowing adaptability to diverse scenarios and specific needs. Furthermore, the integration of machine-learning algorithms can be used to refine a position of contextual data, thereby continuously optimizing its performance.

[0009] In yet another advantageous embodiment of the invention, it is provided that the communication hardware position is indicative of a position of an antenna on and/or of the vehicle, additional considerations are allowed. Specifically, the system may be designed to continuously monitor and update a communication hardware position data, thereby maintaining precise information regarding the antenna's location on the vehicle. It is also possible to configure the computing system in such a way that the communication hardware status is indicative of at least one of an age of the communication hardware and/or damage of the communication hardware and/or other issues. Therefore, additionally, the computing system could be configured to allow for real-time monitoring of a reporting of communication hardware status, indicating factors, enabling proactive maintenance and replacement strategies to ensure a better network performance and reliability.

[0010] It is also possible to configure the computing system in such a way that the plurality of locations of the vehicle connectivity map comprise a plurality of waypoints or segments of a road network, wherein the vehicle connectivity map is indicative of at least one connection attribute of at least one connection attribute for each waypoint or segment. This configuration enables the system's ability to finally analyze and optimize the network connectivity allowing specific rules of the vehicle.

[0011] In yet another advantageous embodiment of the invention, it is provided that the control circuit is further configured to transmit the data indicative of the one or more connection attributes and the contextual data to a remote computing system that is remote from the vehicle. This remote computing system serves for example as a central hub for collecting and analyzing connectivity information, enabling comprehensive monitoring and management of the vehicle's network performance. This arrangement facilitates data-driven decision-making, remote diagnostics, and real-time adjustments to enhance the vehicle's connectivity and operational efficiency.

[0012] In yet another advantageous embodiment of the invention, it is provided that the control circuit is further configured to determine, based on the vehicle's connectivity map, at least one of a location constraint or a timing constraint for transmitting the data indicative of the one or more connection attributes and the contextual data to the remote computing system. This determination may involve factors such as network conditions and a vehicle's route.

[0013] Furthermore, the control circuit is configured to transmit the data indicative of the one or more connection attributes and the contextual data to the remote computing system based on at least one of the location constraint or the timing constraint. This approach ensures that data transmission aligns with specific criteria, optimizing digitalization of network resources and ensuring that the most relevant information is sent to the remote computing system at the right time and location for efficient data analysis and for decision-making examples.

[0014] In yet another advantageous embodiment of the invention it is provided that to generate the vehicle connectivity map, the control circuit is configured to access a machine-learned model trained to generate the vehicle connectivity map and also to provide, as an input to the machine-learned model, the data indicative of the one or more connection attributes and contextual data. Therefore, the computing system receives the vehicle connectivity map as an output of the machine-learned model. Inputting any needed information into the machine learning allows for learning from this data and developing models based on that knowledge. This arrangement leverages machine-learning techniques to create the vehicle connectivity map, enhancing its accuracy and adaptability by utilizing advanced data analysis and modeling capabilities.

[0015] In yet another advantageous embodiment of the invention, it is specified that the control circuit is additionally configured to obtain a general connectivity map. This general connectivity map provides information about at least one location among the plurality of locations and includes at least one general connection attribute for the at least one location. The last one general connection attribute is based on an aggregation of one or more connection attributes and contextual data acquired from a plurality of other vehicles.

This approach allows for creating a comprehensive overview of a network connectivity across various locations, based on the collective data from multiple vehicles. This enables the system's ability to analyze and optimize connectivity while considering the broader network context and performance data from a range of connected vehicles.

[0016] It is also possible to configure the computing system in such a way that the general connectivity map is associated with a manufacturer and/or a model of the vehicle. The system enables offering more precise insights and optimizations for the specific vehicle, for example for its network connectivity.

[0017] In yet another advantageous embodiment of the invention, it is provided that the control circuit is configured to determine a comparison between the vehicle's connectivity map and the general connectivity map and to generate feedback data based on the comparison between the vehicle's connectivity map and the general connectivity map. Therefore, this feedback data serves to inform the vehicle about how its network connectivity performance compares to the general network connectivity condition, also providing valuable insights for optimizing a data transmission and network-related decision-making. This functionality enables therefore their system's ability to adapt and improve network performance by providing real-time feedback based on the comparison between the vehicle-specific connectivity and the broader network environment.

[0018] In yet another advantageous embodiment of the invention, it is provided that the control circuit is configured to update the vehicle connectivity map based on the feedback data. This optimization ensures that the vehicle's connectivity map remains current.

[0019] It is also possible to configure the computing system in such a way that the vehicle is operable in autonomous driving mode. For autonomous vehicles, this configuration opens up possibilities for the system to make real-time decisions about data transmission, ensuring that network connectivity is optimized to support autonomous driving functions, such as navigation, sensor data sharing and vehicle-to-vehicle communication. This integration, for example, enhances the safety and efficiency of autonomous vehicles by enabling them to adapt to varying network conditions and to prioritize critical tasks while in the autonomous mode.

[0020] Another aspect of the invention comprises a computer-implemented method comprising obtaining data indicative of one or more connection attributes describing a network connectivity of a vehicle, determining contextual data associated with the one or more connection attributes, wherein the contextual data describes a context of the vehicle associated with obtaining their connection attributes, generating a vehicle connectivity map for the vehicle based on the one or more connection attributes and the contextual data, wherein the vehicle connectivity map is indicative of a plurality of locations and at least one connection attribute, of the one or more connection attributes, for each respective location of the plurality of locations, and outputting the vehicle connectivity map for performance of a vehicle function.

[0021] 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

[0022] 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. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. 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.

[0023] The drawings show in: [0024] Fig. 1 a diagram showing the architecture and functionalities of a computing system; and [0025] Fig. 2 a block diagram with a comparison between the IT infrastructure of an autonomous vehicle and an IT infrastructure of a backend to demonstrate a method for creating connectivity maps.

DETAILED DESCRIPTION

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Fig. 1 shows a diagram showing the architecture and functionalities of a computing system 1. This computing system 1 features a control circuit 2 that serves several roles. Firstly, a data acquisition process is established, in which the control circuit 2 is engineered to gather data that defines one or more connection attributes 3 representing the network connectivity of a vehicle 10.

[0031] Next is the contextual data determination phase, during which it further evaluates its contextual data 4 associated with these connection attributes 3. This contextual data 4 offers valuable insights into the context surrounding the acquisition of these attributes.

[0032] Following that, the process involves generating a connectivity map. During this phase, the control circuit 2 utilizes the connection attributes 3 and contextual data 4 to generate a vehicle connectivity map 5 for the vehicle 10. This connectivity map 5 provides detailed information about multiple locations 6 and includes specific connection attributes 3a associated with each of these locations 6a of the multiple locations 6. The next step is providing the output for a vehicle function. Ultimately, the vehicle connectivity map 5 is generated to facilitate the execution of the vehicle function 7.

[0033] The connection attributes 3 encompass essential network performance matrix, including a network latency 21, network throughput 22, signal strength 23, and network reliability 24.

[0034] Comprehensive control data 4 comprises a wide range of information, such as location coordinates 25, location accuracy 26, time stamps 27, communication hardware details 28, communication hardware position 29, communication hardware status 30, connectivity and network protocols 31, 32, network provider information 33, equipment identifiers 34, antenna types 35, network roaming status 36, weather data 37, and vehicle speed 38.

[0035] Furthermore, the communication hardware position 29 indicates the placement of an antenna 12 on the vehicle 10.

[0036] The control circuit 2 also incorporates functionality to transmit the connection attribute data and contextual data 4 to a remote computing system 11 situated at a distance from a vehicle 10. It can assess and implement location and timing constraints for data transmission, guided by insights from the vehicle connectivity map 5.

[0037] To generate the vehicle connectivity map 5, the control circuit 2 taps into a machine-learned model 8, specialized for this purpose. It supplies the model with input data consisting of connection attributes and contextual data, receiving the generated vehicle connectivity map 5 as an output. Furthermore, the control circuit 2 can acquire a general connectivity map 5b derived from aggregated data collected from various vehicles 10a. This general connectivity map 5b provides insight into locations and general connection attributes 3b applicable to these locations 6 and 6a.

[0038] For improved decision-making, the control circuit evaluates the comparison between the vehicle connectivity map 5 and the general connectivity map 5b, deriving feedback data 13 based on the analysis. Finally, it applies the feedback data 13 to update the vehicle connectivity map 5.

[0039] It is also possible for a backend 14 to receive the collected data and facilitate the creation of a vehicle connectivity map 5, providing detailed information about various locations 6 and 6b and the associated connection attributes 3 and 3a. This connectivity map 5 plays a role in enhancing the execution of various vehicle functions 7, thereby optimizing network decisions and data transmission strategies. Additional multiple vehicles 10, 10a can be connected to the backend 14, especially when it is configured as a cloud 15 or a part of an external computing system 11.

[0040] It is also possible that the control circuit 2 is further configured to determine, based on the vehicle's connectivity map 5, at least one of a location constraint 39 or a timing constraint 40 for transmitting the data indicative of the one or more connection attributes 3, 3a and the contextual data 4 for the remote computing system 11. This determination may involve factors such as network conditions and the vehicle's route.

[0041] Fig. 2 illustrates in a block diagram a comparison between an IT infrastructure A and of an autonomous or semi-autonomous vehicle 10 and a second IT infrastructure B of a backend 14 to demonstrate a method for creating connectivity maps 5.

[0042] In the IT infrastructure A of the autonomous vehicle or semi-autonomous vehicle 10, a sequence of steps is shown as an example of a potential implementation. This is merely illustrative and by no means mandatory.

[0043] The initial step S1 labelled as "record connection attributes" is directed to step S2, which is labelled as "send data to the cloud". There is also an alternative path marked as step S1, which is labelled as "collect additional data" and leads to step S2 labelled as "send data to the cloud".

[0044] Moving forward from step S2, the method advances to step S3 labelled as "create personalized connectivity map using on-board ML machine-learning", which is followed by step S4 labelled as "download multiple general connectivity maps periodically". Subsequently, step S5 labelled as "compare personalized and general connectivity maps" follows, leading to step S6 labelled as "send feedback (additional training data, e.g. success rate)", which then leads to a step S7 labelled as "send data to the cloud".

[0045] The IT infrastructure B of the backend 14 is also shown as an example of the potential implementation. This is merely illustrative and by no means mandatory.

[0046] The initial step Z1 labelled as "aggregate data from the fleet", leads to step Z2 labelled as "run data through ML models", which subsequently leads to step Z3 labelled as "create multiple general connectivity maps" Finally, the process concludes with step Z4 labelled as "send data to the car".

[0047] In summary, Fig. 2 serves as a comprehensive visual help, providing a clear understanding of the processes between the IT infrastructure A of the autonomous vehicle 10 and the IT infrastructure B of the backend 14.

Reference Signs 1 computing system 2 control circuit 3 connection attributes 3a connection attribute 3b general connection attribute 4 contextual data connectivity map 5b general connectivity map 6 plurality of locations 6a location 7 vehicle function 8 machine learned model vehicle 10a other vehicles 11 remote computing system 12 antenna 13 feedback data 14 back end cloud 21 network latency 22 network throughput 23 signal strength 24 network reliability location coordinates 26 accuracy 27 timestamp 28 communication hardware type 29 communication hardware position communication hardware status 31 connectivity protocol 32 network protocol 33 network provider information 34 equipment identifier antenna type 36 network roaming status 37 weather data 38 vehicle speed 39 location constraint time constraint A IT-infrastructure B IT-infrastructure S1",S1-S7 Steps Z1-Z4 Steps

Claims (10)

1. CLAIMS1. A computing system (1) for generating a connectivity map (5) for a vehicle (10) comprising: a control circuit (2) configured to - obtain data indicative of one or more connection attributes (3) describing a network connectivity of the vehicle (10); - determine contextual data (4) associated with the one or more connection attributes (3), wherein the contextual data (4) describes a context of the vehicle (10) associated with obtaining the connection attributes (3); - generate the vehicle connectivity map (5) for the vehicle (10) based on the one or more connection attributes (3) and the contextual data (4), wherein the vehicle connectivity map (5) is indicative of a plurality of locations (6) and at least one connection attribute (3a), of the one or more connection attributes (3), for each respective location (6a) of the plurality of locations (6); and - output the vehicle connectivity map (5) for performance of a vehicle function (7).
2. 2. The computing system according to claim 1, characterized in that the one or more connection attributes (3) comprise at least one of a network latency (21) and/or a network throughput (22) and/or signal strength (23) and/or a network reliability (24).
3. 3. The computing system according to claim 1 or 2, characterized in that the contextual data (4) comprises at least one of location coordinates (25) and/or an accuracy (26) associated with the location coordinates (25) and/or a timestamp (27) and/or a communication hardware type (28) and/or a communication hardware position (29) and/or a communication hardware status (30) and/or a connectivity protocol (31) and/or a network protocol (32) and/or a network provider information (33) and/or an equipment identifier (34) and/or an antenna type (35) and/or network roaming status (36) and/or weather data (37) and/or a vehicle speed (38).
4. 4. The computing system according to claim 3, characterized in that the communication hardware position (29) is indicative of a position of an antenna (12) of the vehicle (10).
5. 5. The computing system according to any one of the preceding claims, characterized in that to generate the vehicle connectivity map (5), the control circuit (2) is configured to access a machine-learned model (8) trained to generate the vehicle connectivity map (5); to provide, as an input to the machine-learned model (8), the data indicative of the one or more connection attributes (3, 3a) and the contextual data (4); and to receive, as an output of the machine-learned model (8), the vehicle connectivity map (5).
6. 6. The computing system according to any one of the preceding claims, characterized in that the control circuit is further configured to obtain a general connectivity map (5b), wherein the general connectivity map (5b) is indicative of at least one location (6a) of the plurality of locations (6) and at least one general connection attribute (3b) for the at least one location (6a), wherein the at least one general connection attribute (3b) is based on an aggregation of one or more connection attributes (3, 3a) and contextual data (4) acquired from a plurality of other vehicles (10a).
7. 7. The computing system according to any one of the preceding claims, characterized in that the control circuit (2) is configured to - determine a comparison between the vehicle connectivity map (5) and the general connectivity map (5b); and - generate feedback data (13) based on the comparison between the vehicle connectivity map (5) and the general connectivity map (5b).
8. 8. A method for operating a computing system according to claims 1 to 7.
9. 9. A computer program product comprising program code means for performing a method according to claim 8.
10. 10. A non-transitory computer-readable storage medium comprising at least the computer program product according to claim 9.