Vehicle signal directory

Abstract

Embodiments of the present disclosure relate to a vehicle signal directory. The signal directory defines one or more vehicle signals selected from a vehicle signal related to a speed limit, a vehicle signal related to a travel time, or a vehicle signal related to regenerative braking of the vehicle. The processing resource processes the one or more vehicle signals to generate an indication related to operation of the vehicle.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of U.S. Provisional Application No. 63 / 557,034, filed on February 23, 2024, entitled “Traffic Signal Catalog,” and U.S. Application No. 18 / 956,130, filed on November 22, 2024, entitled “Traffic Signal Catalog,” under 35 U.S.C. §119(e), the entire contents of which are incorporated herein by reference. Background Art

[0003] Vehicles can include various electronic components capable of performing corresponding tasks. Examples of electronic components include controllers and sensors. Programs can also be executed in the vehicle. Examples of programs include synthetic sensors, application programs, and other types of programs. BRIEF DESCRIPTION OF THE DRAWINGS

[0004] Some implementations of the present disclosure are described with reference to the following figures.

[0005] Figure 1 is a block diagram of an example arrangement including a vehicle according to some implementations of the present disclosure.

[0006] Figure 2 is a block diagram for generating a custom signal catalog according to some implementations of the present disclosure.

[0007] Figure 3 is a block diagram of a custom signal catalog according to some implementations of the present disclosure.

[0008] Figure 4 is a flow diagram of a process according to some implementations of the present disclosure.

[0009] Figure 5 is a block diagram of a system according to some implementations of the present disclosure.

[0010] Throughout the drawings, the same reference numerals designate similar, but not necessarily identical, elements. The drawings are not necessarily drawn to scale, and the size of some parts may be exaggerated to more clearly illustrate the examples shown. In addition, the drawings provide examples and / or implementations consistent with the description; however, the description is not limited to the examples and / or implementations provided in the drawings. DETAILED DESCRIPTION

[0011] Vehicle signals used for communication in a vehicle can be based on a signal protocol, such as the Vehicle Signal Specification (VSS) from the Connected Vehicle Systems Alliance (COVESA). The VSS provides a directory of signals (referred to as a "VSS directory") that represent various features of a vehicle, including components, actuators, sensors, and attributes. The VSS directory is a hierarchical representation that includes nodes (arranged in a hierarchical form) that represent corresponding features of the vehicle. For example, the nodes of the VSS directory may include branch nodes, actuator nodes, sensor nodes, and attribute nodes. A branch node represents a group of nodes, an actuator node represents a read-write signal (such as a read-write signal obtained from or sent to an actuator of the vehicle), a sensor node represents a read-only signal (such as a signal obtained from a sensor), and an attribute node represents an attribute whose value will generally not be changed more than once per ignition cycle.

[0012] While reference is made to VSS in some examples, it should be noted that the techniques or mechanisms according to some implementations may be applied to other types of signal catalogs for vehicles.

[0013] The VSS catalog may not define certain vehicle signals that may be useful for operations associated with a vehicle. Note that different vehicle models from one or more vehicle manufacturers may include different sets of vehicle features. As newly developed vehicles introduce new vehicle features, the set of vehicle signals defined by the VSS catalog may not be sufficient to support or utilize these new vehicle features.

[0014] Some examples of vehicle signals that may be missing from the VSS catalog include vehicle signals related to vehicle speed limits, vehicle travel times, or vehicle regenerative braking. In some examples, a machine learning model can use these vehicle signals (and other vehicle signals) to predict the range of an electric vehicle or provide vehicle speed recommendations. A "machine learning model" can refer to a model that is updated based on the training of the model and / or based on previous predictions generated by the model. Updating a model is part of how a model learns from training or past operation.

[0015] According to some implementations of the present disclosure, a custom signal catalog is defined that includes additional vehicle signals that are not present in the initial signal catalog. The initial signal catalog may refer to a signal catalog that conforms to a version of a signal protocol, such as the VSS protocol. Additional vehicle signals may be added using one or more separate update data structures, such as overlay files according to the VSS protocol. Overlay files allow new vehicle signals to be added without changing the data structure(s), such as file(s), of the initial signal catalog. In some examples of the present disclosure, the additional vehicle signals may include any or some combination of the following: vehicle signals related to speed limits, vehicle signals related to travel time, or vehicle signals related to regenerative braking.

[0016] Techniques or mechanisms utilizing custom signal catalogs according to some examples can improve the operation of a vehicle, for example, by controlling the speed of the vehicle, applying a degree of regenerative braking to control the speed of the vehicle or to charge the vehicle's battery, or improving other aspects of the vehicle's operation.

[0017] Note that although additional vehicle signals may not be defined by a given version of a signaling protocol, such as the VSS protocol, one or more of the additional vehicle signals may be added to subsequent versions of the signaling protocol.

[0018] Examples of vehicles include motor vehicles (e.g., automobiles, cars, trucks, buses, motorcycles, etc.), aircraft (e.g., airplanes, unmanned aerial vehicles, unmanned aircraft systems, drones, helicopters, etc.), spacecraft (e.g., space planes, space shuttles, space capsules, space stations, satellites, rockets, etc.), watercraft (e.g., ships, boats, hovercrafts, submarines, etc.), rail vehicles (e.g., trains and trams, etc.), or other types of vehicles, including any combination of any of the foregoing, whether currently existing or later appearing.

[0019] Figure 1 1 is a block diagram of an example vehicle 100 including various components. Vehicle 100 includes sensors 102, which may include hardware sensors or software sensors, or both. Examples of hardware sensors include temperature sensors, pressure sensors, humidity sensors, speed sensors, accelerometers, clocks, or other types of physical sensors. Examples of software sensors include monitoring agents that can monitor data from subsystems in a vehicle, such as a monitoring agent in a navigation system; a monitoring agent in a driver assistance system, such as an advanced driver assistance system (ADAS); a monitoring agent in a vehicle controller; or any other type of software sensor.

[0020] Sensors (hardware sensors or software sensors) can acquire measurement data including one or more metrics. Some of the sensors 102 can be coupled to one or more buses (not shown) in the vehicle. An example of a bus in the vehicle includes a controller area network (CAN) bus. In other examples, other types of communication links can be used. The communication link can include a physical link, including a wired or wireless link. The communication link can also include an inter-process link, through which programs can communicate with each other.

[0021] The measurement data from the sensors 102 is provided to a traffic signal service 104, which includes machine-readable instructions for deriving a traffic signal 106 based on the measurement data from the sensors 102. Note that there may be one or more additional components between the sensors 102 and the traffic signal service 104. The traffic signal 106 derived by the traffic signal service 104 is defined by a custom signal catalog 108 stored in a memory 110 of the vehicle 100. In some examples, the traffic signal service 104 may include a signal processor that operates on the input measurement data from the sensors 102 to apply a transformation or other operation to the measurement data from the sensors 102 to generate the traffic signal 106.

[0022] The vehicle signal 106 may be provided to a signal interface 130, which includes a cache 132 to store the vehicle signal 106. Note that the signal interface 130 may be implemented in hardware or software, and the cache 132 may be a hardware cache or a software cache.

[0023] The vehicle signals 106 stored in the cache 132 can be accessed by the synthetic sensors 112 and 114 executing in the vehicle 100. The synthetic sensors are implemented as programs (including machine-readable instructions). The synthetic sensors can apply calculations to the vehicle signals 106 to produce outputs. The synthetic sensors 112 and 114 can operate on respective different subsets of the vehicle signals 106. Although Figure 1 Two synthetic sensors are depicted in FIG, but in other examples, a different number (one or more) of synthetic sensors may be used.

[0024] In some examples of the present disclosure, the combined sensor 112 is a combined vehicle range and speed management sensor capable of determining the expected range of the vehicle 100 and providing speed recommendations for the vehicle 100. For example, the vehicle 100 is an electric vehicle that includes a vehicle battery 122 that provides energy to an electric motor 124 of the vehicle 100. As the vehicle 100 operates, the charge in the vehicle battery 122 is continuously depleted. The remaining charge in the vehicle battery 122 determines the remaining range of the vehicle 100. The combined vehicle range and speed management sensor 112 includes a machine learning model 116 that receives various inputs and makes predictions based on the inputs. Some inputs to the machine learning model 116 include a speed limit vehicle signal (indicating the speed limit of the vehicle 100), a trip time vehicle signal (indicating the time elapsed since the start of the current trip of the vehicle 100), and a regenerative braking vehicle signal (indicating characteristics associated with regenerative braking of the vehicle 100). Regenerative braking refers to the use of the electric motor 124 (rather than the vehicle brakes) to slow the vehicle 100. Regenerative braking charges the vehicle battery 122 when activated.

[0025] Another input to the machine learning model 116 is a vehicle signal representing the remaining battery charge of the vehicle battery 122. The vehicle battery 122 may include a sensor (not shown) that indicates the remaining battery charge. The remaining battery charge data from the vehicle battery sensor is provided to the vehicle signal service 104, which outputs a vehicle signal 106 representing the remaining battery charge. The vehicle signal 106 representing the remaining battery charge is a vehicle signal defined by the custom signal catalog 108.

[0026] The machine learning model 116 may receive another input to perform its predictions.

[0027] The output of the vehicle range and speed management composite sensor 112 can be provided to an insight interface 134. The insight interface 134 can provide insights to other modules in the vehicle 100 based on the output of the composite sensors 112 and 114. "Insights" can include data derived by processing vehicle signals, such as those obtained by the composite sensors 112 and 114. The insight interface 134 can forward insights from the composite sensors 112 and 114 to other modules, including the application 118 and the user interface (UI) subsystem 120. In some examples, the insight interface 134 can apply further calculations based on the output of the composite sensors to generate insights.

[0028] The UI subsystem 120 can present a UI on a display device of the vehicle 100. The presented UI can include information related to insights derived from the output of the vehicle range and speed management composite sensor 112. For example, the information in the UI can include information about the remaining range of the vehicle 100 and a speed recommendation at which speed the vehicle 100 should travel to best utilize the vehicle battery 122. The driver of the vehicle 100 can choose to accept or ignore the recommended speed displayed in the UI by the UI subsystem 120.

[0029] Application 118 may be a control program for vehicle 100 that is capable of controlling the operation of vehicle 100. For example, application 118 may be part of an autonomous driving system for vehicle 100, which may control various operational aspects of vehicle 100, including the speed of vehicle 100, as well as other operational aspects of vehicle 100. In some examples, application 118 may receive insights including remaining range and speed recommendations based on output from a combined vehicle range and speed management sensor 112. Application 118 may interact with vehicle controller 136 of vehicle 100 to control the speed of vehicle 100 based on the remaining range and speed recommendations. For example, if the driver of vehicle 100 has enabled an autonomous driving feature or adaptive cruise control feature of vehicle 100, application 118 may cause vehicle 100 to adjust the vehicle's speed based on the recommended speed from the combined vehicle range and speed management sensor 112.

[0030] The vehicle 100 can also communicate with a remote server 142 via the network 140. For example, the vehicle 100 can include a communication subsystem 144 that includes a signal transceiver and protocol layers to communicate via the network 140. The server 142 can be part of a cloud computing environment, a data center, or any other type of computing environment.

[0031] although Figure 1 An example of a synthetic sensor in vehicle 100 consuming vehicle signal 106 is shown, but in other examples, other types of programs in the vehicle can consume vehicle signal 106. In yet other examples, an external entity, such as a program in server 142, can consume vehicle signal 106. Vehicle signal 106 can be communicated to server 142 via network 140.

[0032] like Figure 2As shown, the custom signal catalog 108 is derived based on an initial signal catalog 202 and an update data structure 204 (or multiple update data structures). The initial signal catalog 202 may conform to a given version of a signal protocol, such as the VSS protocol. When applied to the initial signal catalog 202, the update data structure(s) 204 add additional vehicle signals not defined by the initial signal catalog 201. The update data structure 204 may be an overlay structure, such as an overlay file according to the VSS protocol. The update data structure(s) 204 and the initial signal catalog 202 may be combined to generate the custom signal catalog 108. The combination of the initial signal catalog 202 and one or more update data structures 204 may be performed by an entity external to the vehicle 100. The external entity (e.g., server 142) may generate the custom signal catalog 108 and transmit the custom signal catalog 108 to the vehicle 100 (e.g., via network 140) for storage in the memory 110. In other examples, an entity within the vehicle 100 (eg, a program or hardware processing resources) may combine the initial signal catalog 202 and one or more updated data structures 204 to generate the customized signal catalog 108 .

[0033] As used herein, a "processing resource" may refer to one or more hardware processors. A hardware processor may include a microprocessor, a core of a multi-core microprocessor, a microcontroller, a programmable integrated circuit, a programmable gate array, or another hardware processing circuit.

[0034] In the example where the initial signal directory 202 is a VSS directory, the VSS directory can be implemented using one or more specification files. The specification files of a VSS directory are called "VSPEC files." For example, the VSPEC files are based on the YAML non-markup language (YAML).

[0035] Making changes to the source material (one or more VSPEC files) of a VSS directory can trigger the requirements in the Mozilla Public License Version 1 (MPL V1) for distributing the modified source material to others. In some cases, this may be undesirable.

[0036] Instead of making changes to the source material of the VSS directory, VSS also provides an overlay mechanism that allows changes or additions to be made to the VSS directory without having to change the source material of the VSS directory. The overlay mechanism supports the creation of one or more overlay files (which are themselves VSPEC files) that contain instructions for specifying changes to nodes in the VSS directory or for specifying the addition of nodes that do not exist in the VSS directory. Overlay files can be used to specify the addition of new nodes representing new traffic signals.

[0037] A converter (e.g., within the server 142 or within the vehicle 100) can combine the VSS catalog with one or more overlay files to produce an output representation (e.g., the custom signal catalog 108) in a different format, such as any of the following: JavaScript Object Notation (JSON) format, Interface Definition Language (IDL) format, or any other format. The converter can be a VSS tool provided as one of the tools associated with VSS.

[0038] Figure 3 An example of a custom signal catalog 108 is shown, which includes a hierarchical arrangement of nodes representing respective features of a vehicle 100. A root node 302 represents the vehicle 100. In the hierarchical arrangement of nodes, various nodes may descend from the root node 302. A first node "descends" from a second node in the hierarchical arrangement of nodes if the first node is connected to the second node and is at a lower hierarchical level in the hierarchical arrangement than the second node. The root node 302 is at the highest hierarchical level in the hierarchical arrangement.

[0039] Nodes descending from the root node 302 include branch nodes 304, 306, and 308, as well as a sensor node 303. The sensor node 303 represents a signal from a sensor (a "travel time vehicle signal") that measures the travel time of the vehicle 100. An example travel time vehicle signal may have the following form: Vehicle.TripTime. The travel time vehicle signal may indicate the time that has elapsed since the start of the current trip of the vehicle 100.

[0040] Branch node 304 represents the powertrain of vehicle 100. Additional nodes depend from powertrain branch node 304, including branch node 310 representing the electric motor. Branch node 312 depends from electric motor branch node 310. Branch node 312 represents the regenerative braking feature of vehicle 100. Nodes depending from regenerative braking branch node 312 include sensor node 314, which indicates whether the regenerative braking feature is active (IsActive). If IsActive sensor node 314 is set to a first value (e.g., "0" or false), it indicates that the regenerative braking feature is disabled, and if it is set to a different second value (e.g., "1" or true), it indicates that the regenerative braking feature is enabled. IsActive sensor node 314 can be represented as the following example vehicle signal: Vehicle.PowerTrain.ElectricMotor.RegenerativeBraking.IsActive. In some cases, node 314 can be an actuator or attribute node.

[0041] Another node that hangs down from the regenerative braking branch node 312 is a sensor node 316, which indicates the level of regenerative braking to be applied. The regenerative braking level sensor node 316 can be represented as the following example vehicle signal: Vehicle.Powertrain.ElectricMotor.RegenerativeBraking.Level, which can have several discrete values, such as off (OFF), low (LOW), normal (NORMAL), and strong (STRONG). If it is set to off (OFF), it indicates that regenerative braking is not being used. However, regenerative braking can have a low level, a normal level, or a strong level, which can be indicated using the low (LOW), normal (NORMAL), and strong (STRONG) values ​​assigned to Vehicle.Powertrain.ElectricMotor.RegenerativeBraking.Level. In some cases, node 316 can be an actuator or attribute node.

[0042] Branch node 306 represents an advanced driver assistance system (ADAS), which can assist the driver in safely operating vehicle 100. A sensor node 320 hangs down from branch node 306. Sensor node 320 represents a speed limit obtained by the ADAS. For example, the ADAS can obtain the speed limit for the current road of vehicle 100 based on reading road signs on the side of the road. A camera of vehicle 100 can capture images of the road signs, and the ADAS can obtain the speed limit based on image analysis of the captured images of the road signs. The ADAS speed limit sensor node 320 can be represented as the following example vehicle signal: Vehicle.ADAS.SpeedLimit.

[0043] Branch node 308 represents a current location determination system for vehicle 100. An example of a current location determination system is a navigation system, which may be based on Global Positioning System (GPS) coordinates obtained by a GPS receiver of vehicle 100. A sensor node 322 descends from branch node 308. Sensor node 322 represents a speed limit obtained by the current location determination system. For example, the current location determination system may determine the current location of vehicle 100, and based on the current location, the current location determination system may access a speed limit repository to retrieve a speed limit applicable to the current location of vehicle 100. Current location speed limit sensor node 322 may be represented by the following example vehicle signal: Vehicle.CurrentLocation.SpeedLimit.

[0044] The recommended speed can be based on the values ​​of the ADAS speed limit node 320 and the current location speed limit node 322. In one example, the recommended speed is the smaller value of nodes 320 and 322. In another example, if the quality of the road sign image is below a threshold, or if the confidence score of the speed limit estimate from the image analysis is below a threshold, the ADAS speed limit node 320 may have no value, and the recommended speed is based on the value of the current location speed limit node 322. In some other examples, the recommended speed is the value of node 320 or 322 multiplied by a factor, and the factor is based on the environment surrounding the vehicle. For good road conditions and during the day, the factor can be one, and for poor road conditions (e.g., flooded / icy roads or bumpy roads), at night, or when there are emergency sirens around, the factor can be a value less than one. The factor can be determined by a machine learning model. For example, based on images captured by a camera or sounds captured by a microphone, the machine learning model can analyze the images or sounds, determine how poor the road conditions are or whether there are sirens, and select a value for the factor. In some cases, the factor can depend on the vehicle load. If the vehicle is heavily loaded, the factor may be a value less than one.

[0045] Although the above references examples of various traffic signals and nodes of the custom signal catalog 108 that represent the traffic signals, in other examples, traffic signals may have different forms and different nodes may be used to represent the traffic signals.

[0046] Figure 4 is a flow diagram of a process 400 that may be performed by a vehicle (eg, vehicle 100 ) or by a computer system separate from the vehicle (eg, server 142 ).

[0047] The process 400 includes storing (at 402) in a memory a signal catalog that defines one or more vehicle signals selected from a vehicle signal related to a speed limit, a vehicle signal related to travel time, or a vehicle signal related to regenerative braking of a vehicle. The memory may be Figure 1 Alternatively, the memory may be external to the vehicle 100, such as in a server 142.

[0048] The signal directory can be Figure 1 The custom signal catalog 108. The process 400 includes receiving (at 404) one or more vehicle signals defined by the signal catalog.

[0049] Process 400 includes processing (at 406) one or more received vehicle signals by a processing resource including one or more hardware processors to generate an indication related to the operation of the vehicle. In some examples, a machine learning model (e.g., Figure 1 116) to perform processing on the received one or more vehicle signals.

[0050] In some examples, the instructions are generated by a machine learning model and include a predicted range for the vehicle and / or a speed recommendation regarding the speed of the vehicle.

[0051] In some examples, a vehicle may use speed recommendations to control its speed, such as those provided by Figure 1 Application 118.

[0052] In some examples, the vehicle signal related to the speed limit is based on the output of the vehicle's driver assistance system (e.g., ADAS). The vehicle signal related to the speed limit can be generated by the driver assistance system branch of the signal directory (e.g., Figure 3 306) in the node to represent.

[0053] In some examples, the vehicle signal related to the speed limit is based on the output of the vehicle's navigation system. The vehicle signal related to the speed limit can be derived from the navigation system branch of the signal catalog (e.g., Figure 3 308) in the node to represent.

[0054] In some examples, a travel time-related vehicle signal provides an indication of the travel time that has elapsed since the start of the current trip.

[0055] In some examples, the vehicle signal related to regenerative braking includes an indication of whether regenerative braking in the vehicle is active (eg, an IsActive indication). In some examples, the vehicle signal related to regenerative braking includes an indication of a level of regenerative braking to apply.

[0056] In some examples, vehicle signals related to regenerative braking are represented by the electric motor branch of the signal directory (e.g., Figure 3 310) in the node to represent.

[0057] In some examples, the signal catalog is based on an initial signal catalog and an update structure that adds one or more vehicle signals not present in the initial signal catalog. The initial signal catalog may be a Vehicle Signal Specification (VSS) catalog, and the overlay data structure may include an overlay file.

[0058] Figure 5is a block diagram of a system 500 according to some examples. System 500 can be part of a vehicle or can be separate from the vehicle. System 500 includes processing resources 502, which include one or more hardware processors. System 500 also includes non-transitory machine-readable or computer-readable storage media 504 that stores machine-readable instructions 506 that are executable by processing resources 502.

[0059] Machine-readable instructions 506 may access a custom signal catalog 508 stored in memory 510 of system 500. Custom signal catalog 508 defines one or more vehicle signals selected from a vehicle signal related to speed limits, a vehicle signal related to travel time, or a vehicle signal related to regenerative braking of a vehicle.

[0060] The machine-readable instructions 506 may receive one or more vehicle signals defined by the custom signal catalog 508 and may process the received one or more vehicle signals to generate an indication related to the operation of the vehicle.

[0061] Storage medium 504 may include any one or some combination of the following: semiconductor memory devices, such as dynamic or static random access memory (DRAM or SRAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and flash memory or other types of non-volatile memory devices; magnetic disks, such as fixed disks, floppy disks, and removable disks; other magnetic media, including magnetic tape; optical media, such as compact disks (CDs) or digital video disks (DVDs); or another type of storage device. Note that the instructions discussed above may be provided on one computer-readable or machine-readable storage medium, or alternatively, may be provided on multiple computer-readable or machine-readable storage media distributed across a large system, possibly with multiple nodes. Such computer-readable or machine-readable storage medium(s) are considered part of an article (or product). An article or product may refer to any manufactured component or components. The storage medium(s) may be located in the machine that runs the machine-readable instructions, or may be located at a remote site from which the machine-readable instructions may be downloaded over a network for execution.

[0062] In this disclosure, the use of the terms "a", "an", or "the" is also intended to include plural forms, unless the context clearly indicates otherwise. Similarly, when the terms "comprises", "comprising", "containing", "including", "having", or "having" are used in this disclosure, these terms specify the presence of the elements described, but do not exclude the presence or addition of other elements.

[0063] In the foregoing description, numerous details have been set forth to provide an understanding of the subject matter disclosed herein. However, these implementations may be practiced without some of these details. Other implementations may include modifications and variations to the details discussed above. The appended claims are intended to cover such modifications and variations.

Claims

1. A method comprising: storing a signal catalog in a memory, the signal catalog defining one or more vehicle signals, the one or more vehicle signals selected from: a vehicle signal related to a speed limit, a vehicle signal related to travel time, or a vehicle signal related to regenerative braking of a vehicle; receiving the one or more vehicle signals defined by the signal catalog; as well as The one or more received vehicle signals are processed by processing resources including a hardware processor in the vehicle to generate an indication related to operation of the vehicle.

2. The method of claim 1 , wherein the processing of the received one or more vehicle signals is performed using a machine learning model.

3. The method of claim 2, wherein the machine learning model is executed in the vehicle.

4. The method of claim 2, wherein the indication is generated by the machine learning model and comprises a predicted range for the vehicle.

5. The method of claim 2, wherein the indication is generated by the machine learning model and the indication includes a speed recommendation regarding a speed of the vehicle.

6. The method according to claim 5, comprising: The speed of the vehicle is controlled by the vehicle according to the speed recommendation. The method of claim 1 , wherein the vehicle signal relating to the speed limit is based on an output of a driver assistance system of the vehicle. 8 . The method of claim 7 , wherein the vehicle signal related to the speed limit based on the output of the driver assistance system is also based on a current road on which the vehicle is traveling. 9 . The method of claim 7 , wherein the vehicle signal related to the speed limit is represented by a node in a driver assistance system branch of the signal catalog.

10. The method of claim 1, wherein the vehicle signal related to the speed limit is based on an output of a navigation system of the vehicle.

11. The method of claim 10, wherein the vehicle signal related to the speed limit is represented by a node in a navigation system branch of the signal catalog.

12. The method of claim 1, wherein the vehicle signal relating to the travel time provides an indication of the travel time elapsed since the start of a current trip.

13. The method of claim 1, wherein the vehicle signal related to the regenerative braking includes an indication of whether the regenerative braking is active in the vehicle.

14. The method of claim 1, wherein the vehicle signal related to the regenerative braking includes an indication of a level of regenerative braking to apply.

15. The method of claim 1, wherein the vehicle signals related to the regenerative braking are represented by nodes in an electric motor branch of the signal catalog.

16. The method of claim 1, wherein the signal catalog is based on an initial signal catalog and an updated structure that adds the one or more vehicle signals not present in the initial signal catalog.

17. The method of claim 16, wherein the initial signal catalog comprises a Vehicle Signal Specification (VSS) catalog and the update structure comprises an overlay file.

18. A means of transport comprising: a memory for storing a signal catalog defining one or more vehicle signals selected from the group consisting of: a vehicle signal related to a speed limit, a vehicle signal related to travel time, or a vehicle signal related to regenerative braking; Multiple sensors; Processing resources; as well as a non-transitory storage medium storing instructions executable by the processing resource to: receiving the one or more vehicle signals defined by the signal catalog, the one or more vehicle signals being based on measurement data from one or more sensors of the plurality of sensors, and The one or more received vehicle signals are processed to generate an indication related to operation of the vehicle.

19. The vehicle of claim 18, wherein the instructions are part of a synthetic sensor in the vehicle.

20. A non-transitory machine-readable storage medium storing instructions that, when executed, cause a system to: accessing a signal catalog stored in a memory, the signal catalog defining one or more vehicle signals, the one or more vehicle signals selected from: a vehicle signal related to a speed limit, a vehicle signal related to travel time, or a vehicle signal related to regenerative braking of a vehicle; receiving the one or more vehicle signals defined by the signal catalog; and The one or more received vehicle signals are processed to generate an indication related to operation of the vehicle.

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