Method and system for mode-based intelligent ranging and connectivity feedback for wireless systems

Abstract

Methods and systems for mode-based intelligent ranging and connectivity feedback for wireless systems. A method for operating a digital key configured to wirelessly connect to a vehicle includes receiving wireless communication signal data and vehicle data. The method further includes rating a plurality of wireless communication technologies for wirelessly connecting the mobile device to the vehicle using the machine learning model, and selecting one of the plurality of wireless communication technologies based on the rating of the plurality of wireless communication technologies. Further, the method includes establishing wireless communication between the mobile device and the vehicle using the selected wireless communication technology. The digital key application is running on the mobile device, thereby allowing the digital key application to operate as a digital key for the vehicle after wireless communication between the mobile device and the vehicle has been established.

Description

Technical Field

[0001] The present disclosure relates to systems and methods for establishing wireless communication between wireless devices such as smartphones and vehicle systems. More particularly, the present disclosure relates to methods and systems for pattern-based intelligent ranging and connectivity for wireless systems and feedback to wireless devices. Background Art

[0002] This introduction generally presents the context of the present disclosure. To the extent described in this introduction, aspects of the work of the presently named inventors and of the description that may otherwise not be eligible as prior art at the time of filing are not, either expressly or impliedly, admitted to be prior art against the present disclosure.

[0003] Sometimes, it is useful to wirelessly connect a mobile device such as a smartphone or a table to a vehicle. For example, the mobile device can operate a digital key that allows a vehicle operator to lock and unlock their vehicle. In such cases, it is desirable to establish a strong wireless connection between the vehicle and the mobile device. Summary of the Invention

[0004] The present disclosure describes a method for operating a digital key configured to be wirelessly connected to a vehicle. The method includes receiving wireless communication signal data. The wireless communication signal data includes information about a plurality of wireless communication signals forming a network pattern within a predetermined distance from the vehicle. The wireless communication signal data includes a received signal strength indicator (RSSI) for each of the plurality of wireless communication signals within a predetermined distance from the vehicle. The method further includes receiving vehicle data. The vehicle data includes information about the vehicle and the parking location of the vehicle. The method further includes triggering the digital key system of the vehicle to establish a wireless connection with the digital key of the mobile device in response to matching the network pattern and determining that the distance between the mobile devices is less than a predetermined distance threshold. The method further includes rating a plurality of wireless communication technologies (i.e., wireless communication networks) for wirelessly connecting the mobile device to the vehicle using a machine learning model (e.g., a deep neural network). The vehicle data and the wireless communication signal data are inputs to the machine learning model. The method further includes selecting one of the plurality of wireless communication technologies (i.e., wireless communication networks) based on the rating of the plurality of wireless communication technologies as determined by the machine learning model. The method further includes establishing wireless communication between the mobile device and the vehicle using one of the selected wireless communication technologies of the plurality of wireless communication technologies (i.e., wireless communication networks). The wireless communication technology may be referred to as a wireless communication network (e.g., a cellular network, a Wi-Fi network, a Bluetooth network, a UWB network, etc.). A digital key application is running on the mobile device, thereby allowing the digital key application to operate as a mobile digital key of the vehicle after the wireless communication between the mobile device and the vehicle is established. The method described in this paragraph improves vehicle technology by establishing a strong wireless connection between the mobile device and the vehicle, thereby minimizing the number of times the digital key operation fails.

[0005] The implementation may include one or more of the following features. The method may include receiving navigation data. The navigation data includes the vehicle position when the vehicle is moving towards a parking location. The method further includes using the navigation data to determine the distance from the vehicle position to the parking location, comparing the distance from the vehicle position to the parking location with a predetermined distance threshold to determine whether the distance from the vehicle position to the parking location is less than the predetermined distance threshold, and collecting wireless communication signal data in response to determining that the distance from the vehicle position to the parking location is less than the predetermined distance threshold. The wireless communication signal data includes the RSSI of each of a plurality of wireless communication signals at a plurality of positions when the vehicle is moving towards the parking location, and the plurality of positions are spaced apart from each other at a predetermined distance for constructing a network map or pattern. The method further includes collecting parking infrastructure data only in response to determining that the distance from the vehicle position to the parking location is less than the predetermined distance threshold. The parking infrastructure data is information about the parking infrastructure in the parking location. Additionally, the method may include uploading the wireless communication signal data and the surrounding network pattern to a digital key application running on a mobile device, and uploading the wireless communication signal data to a remote server. The wireless communication signal data includes the RSSI of each of a plurality of wireless communication signals at a plurality of positions when the vehicle operator is moving away from the vehicle after the vehicle has parked at the parking location. The plurality of positions are spaced apart from each other at a predetermined distance. The method further includes uploading the wireless communication signal data and the network pattern to the vehicle, and uploading the wireless communication signal data to a remote server. The wireless communication signal data includes the RSSI of each of a plurality of wireless communication signals at a plurality of positions when the vehicle operator is moving away from the vehicle after the vehicle has parked at the parking location. The plurality of positions are spaced apart from each other at a predetermined distance. The method may include an analysis of the observed and current network patterns, determining that the vehicle operator is continuously moving towards the vehicle, determining the distance from the vehicle operator to the vehicle when the vehicle operator is continuously moving towards the vehicle to determine whether the distance from the vehicle operator to the vehicle is less than a predetermined proximity threshold, and in response to determining that the distance from the vehicle operator to the vehicle is less than the predetermined proximity threshold when the vehicle operator is continuously moving towards the vehicle. The method may include triggering the vehicle wireless system to establish a wireless connection, and the triggering may be P2P communication or through a remote server, and in response, rating the available communication technologies using a machine learning (ML) model, the plurality of wireless communication technologies for wirelessly connecting the mobile device to the vehicle. The user profile mode should be part of the ML training and it serves as one of the inputs to the machine learning model. The plurality of wireless communication technologies includes near field communication (NFC), ultra-wideband (UWB), Bluetooth, Wi-Fi, and cellular networks.The method may include various digital key functionalities, such as operating a digital key to perform vehicle operations (e.g., locking or unlocking doors, opening a trunk, enabling ambient lighting, customizing an infotainment system, etc.) after establishing wireless communication between a mobile device and a vehicle using one of a plurality of selected wireless communication technologies. The method may include selecting the wireless communication technology with the highest rating.

[0006] The present disclosure further describes a vehicle. The vehicle includes a body, a plurality of vehicle transceivers coupled to the body, a plurality of sensors coupled to the body, and a vehicle controller in communication with the vehicle transceivers and the sensors. The vehicle controller is programmed to perform the method described above.

[0007] The present disclosure also describes a tangible non-transitory machine-readable medium including machine-readable instructions that, when executed by a processor, cause the processor to perform the method described above.

[0008] The present disclosure may also include the following technical solutions:

[0009] 1. A method for operating a digital key configured to be wirelessly connected to a vehicle, comprising:

[0010] Receiving wireless communication signal data, wherein the wireless communication signal data includes information about a plurality of wireless communication signals within a predetermined distance from the vehicle, and the wireless communication signal data includes a received signal strength indicator (RSSI), channel state information (CSI), and channel quality information (CQI) for each of the plurality of wireless communication signals forming a network pattern within a predetermined distance from the vehicle;

[0011] Receiving vehicle data, wherein the vehicle data includes information about the vehicle, and the vehicle data includes the parking location of the vehicle;

[0012] Triggering a digital key system of the vehicle to establish a wireless connection with the digital key of the mobile device in response to matching the network pattern and determining that the distance between the mobile devices is less than a predetermined distance threshold;

[0013] Rating a plurality of wireless communication technologies for wirelessly connecting the mobile device to the vehicle using a machine learning model, wherein the vehicle data and the wireless communication signal data are inputs to the machine learning model;

[0014] Selecting one of the plurality of wireless communication technologies based on the rating of the plurality of wireless communication technologies as determined by the machine learning model; and

[0015] Use one of the selected multiple wireless communication technologies to establish wireless communication between a mobile device and a vehicle, where a digital key application is running on the mobile device, thereby allowing the digital key application to operate as a digital key of the vehicle after the wireless communication between the mobile device and the vehicle is established.

[0016] 2. The method according to claim 1, further comprising:

[0017] Receiving navigation data, where the navigation data includes the vehicle position when the vehicle is moving towards a parking position;

[0018] Using the navigation data to determine the distance from the vehicle position to the parking position;

[0019] Comparing the distance from the vehicle position to the parking position with a predetermined distance threshold to determine whether the distance from the vehicle position to the parking position is less than the predetermined distance threshold; and

[0020] In response to determining that the distance from the vehicle position to the parking position is less than the predetermined distance threshold, collecting wireless communication signal data, where the wireless communication signal data includes the RSSI, channel state information (CSI), and channel quality information (CQI) of each of the multiple wireless communication signals at multiple positions when the vehicle is moving towards the parking position, and the multiple positions are spaced apart from each other by a predetermined distance; and

[0021] Collecting parking infrastructure data only in response to determining that the distance from the vehicle position to the parking position is less than the predetermined distance threshold, where the parking infrastructure data is information about the parking infrastructure in the parking position.

[0022] 3. The method according to claim 2, further comprising:

[0023] Uploading the wireless communication signal data to the digital key application running on the mobile device; and

[0024] Uploading the wireless communication signal data to a remote server.

[0025] 4. The method according to claim 3, further comprising:

[0026] Determining that the vehicle has been parked at the parking position;

[0027] In response to determining that the vehicle has been parked at a parking position, when the vehicle operator is moving away from the vehicle after the vehicle has been parked at the parking position, collect wireless communication signal data using a mobile device, where the wireless communication signal data includes the RSSI, channel state information (CSI), and channel quality information (CQI) of each of a plurality of wireless communication signals at a plurality of positions when the vehicle operator is moving away from the vehicle after the vehicle has been parked at the parking position, and the plurality of positions are spaced apart from each other by a predetermined distance;

[0028] Upload the wireless communication signal data to the vehicle; and

[0029] Upload the wireless communication signal data to a remote server.

[0030] 5. The method according to claim 3, further comprising:

[0031] Determine that the vehicle has been parked at a parking position;

[0032] In response to determining that the vehicle has been parked at a parking position, when the vehicle operator is moving away from the vehicle after the vehicle has been parked at the parking position, collect wireless communication signal data from a plurality of remote vehicles, where the wireless communication signal data includes the RSSI, channel state information (CSI), and channel quality information (CQI) of each of a plurality of wireless communication signals at a plurality of positions when the vehicle operator is moving away from the vehicle after the vehicle has been parked at the parking position, and the plurality of positions are spaced apart from each other by a predetermined distance;

[0033] Upload the wireless communication signal data to the vehicle; and

[0034] Upload the wireless communication signal data to a remote server.

[0035] 6. The method according to claim 3, further comprising:

[0036] Determine that the vehicle operator is continuously moving towards the vehicle;

[0037] Determine the distance from the vehicle operator to the vehicle when the vehicle operator is continuously moving towards the vehicle to determine whether the distance from the vehicle operator to the vehicle is less than a predetermined proximity threshold; and

[0038] In response to determining that the distance from the vehicle operator to the vehicle is less than a predetermined proximity threshold when the vehicle operator is continuously moving towards the vehicle, use a machine learning model to rate a plurality of wireless communication technologies for wirelessly connecting the mobile device to the vehicle.

[0039] 7. The method according to claim 6 further includes determining a user profile mode of a vehicle operator when the vehicle operator is moving relative to the vehicle, wherein the user profile mode is used as one of the inputs to a machine learning model.

[0040] 8. The method according to claim 1, wherein the plurality of wireless communication technologies includes near field communication (NFC), ultra-wideband (UWB), Bluetooth, Wi-Fi, and a cellular network.

[0041] 9. The method according to claim 1 further includes, after establishing wireless communication between a mobile device and a vehicle using one of the selected wireless communication technologies among the plurality of wireless communication technologies, operating a digital key to effect vehicle operation.

[0042] 10. The method according to claim 1, wherein one of the selected wireless communication technologies among the plurality of wireless communication technologies has the highest rating.

[0043] 11. A vehicle, comprising:

[0044] A vehicle body;

[0045] A plurality of vehicle transceivers coupled to the vehicle body;

[0046] A plurality of sensors coupled to the vehicle body;

[0047] A vehicle controller in communication with the plurality of sensors and the plurality of vehicle transceivers, wherein the vehicle controller is programmed to:

[0048] Receive wireless communication signal data, wherein the wireless communication signal data includes information about a plurality of wireless communication signals within a predetermined distance from the vehicle, and the wireless communication signal data includes a received signal strength indicator (RSSI), channel state information (CSI), and channel quality information (CQI) for each of the plurality of wireless communication signals within a predetermined distance from the vehicle;

[0049] Receive vehicle data, wherein the vehicle data includes information about the vehicle, and the vehicle data includes the parking position of the vehicle;

[0050] Use a machine learning model to rate a plurality of wireless communication technologies for wirelessly connecting a mobile device to the vehicle, wherein the vehicle data and the wireless communication signal data are inputs to the machine learning model;

[0051] Select one of the plurality of wireless communication technologies based on the rating of the plurality of wireless communication technologies as determined by the machine learning model; and

[0052] Establish wireless communication between a mobile device and a vehicle using one of the selected multiple wireless communication technologies, where a digital key application is running on the mobile device, thereby allowing the digital key application to operate as a digital key for the vehicle after the wireless communication between the mobile device and the vehicle is established.

[0053] 12. The vehicle according to claim 11, wherein the controller is further programmed to:

[0054] Receive navigation data, where the navigation data includes the vehicle position when the vehicle is moving towards a parking position;

[0055] Use the navigation data to determine the distance from the vehicle position to the parking position;

[0056] Compare the distance from the vehicle position to the parking position with a predetermined distance threshold to determine whether the distance from the vehicle position to the parking position is less than the predetermined distance threshold; and

[0057] In response to determining that the distance from the vehicle position to the parking position is less than the predetermined distance threshold, collect wireless communication signal data, where the wireless communication signal data includes the RSSI, channel state information (CSI), and channel quality information (CQI) of each of the multiple wireless communication signals at multiple positions when the vehicle is moving towards the parking position, and the multiple positions are spaced apart from each other by a predetermined distance; and

[0058] Collect parking infrastructure data only in response to determining that the distance from the vehicle position to the parking position is less than the predetermined distance threshold, where the parking infrastructure data is information about the parking infrastructure in the parking position.

[0059] 13. The vehicle according to claim 12, wherein the controller is further programmed to:

[0060] Upload the wireless communication signal data to a digital key application running on a mobile device; and

[0061] Upload the wireless communication signal data to a remote server.

[0062] 14. The vehicle according to claim 13, wherein the controller is further programmed to:

[0063] Determine that the vehicle has been parked at the parking position;

[0064] In response to determining that the vehicle has been parked at a parking position, when the vehicle operator is moving away from the vehicle after the vehicle has been parked at the parking position, collect wireless communication signal data using a mobile device, where the wireless communication signal data includes the RSSI, channel state information (CSI), and channel quality information (CQI) of each of a plurality of wireless communication signals at a plurality of positions when the vehicle operator is moving away from the vehicle after the vehicle has been parked at the parking position, and the plurality of positions are spaced apart from each other at a predetermined distance;

[0065] Upload the wireless communication signal data to the vehicle; and

[0066] Upload the wireless communication signal data to a remote server.

[0067] 15. The vehicle according to claim 13, further comprising:

[0068] Determine that the vehicle has been parked at a parking position;

[0069] In response to determining that the vehicle has been parked at a parking position, when the vehicle operator is moving away from the vehicle after the vehicle has been parked at the parking position, collect wireless communication signal data from a plurality of remote vehicles, where the wireless communication signal data includes the RSSI of each of a plurality of wireless communication signals at a plurality of positions when the vehicle operator is moving away from the vehicle after the vehicle has been parked at the parking position, and the plurality of positions are spaced apart from each other at a predetermined distance;

[0070] Upload the wireless communication signal data to the vehicle; and

[0071] Upload the wireless communication signal data to a remote server.

[0072] 16. The vehicle according to claim 13, wherein the controller is further programmed to:

[0073] Determine that the vehicle operator is continuously moving towards the vehicle;

[0074] Determine the distance from the vehicle operator to the vehicle when the vehicle operator is continuously moving towards the vehicle to determine whether the distance from the vehicle operator to the vehicle is less than a predetermined proximity threshold; and

[0075] In response to determining that the distance from the vehicle operator to the vehicle is less than a predetermined proximity threshold when the vehicle operator is continuously moving towards the vehicle, use a machine learning model to rate a plurality of wireless communication technologies for wirelessly connecting the mobile device to the vehicle.

[0076] 17. The vehicle according to aspect 16, wherein the controller is further programmed to determine a user profile mode of a vehicle operator when the vehicle operator is moving relative to the vehicle, and wherein the user profile mode serves as one of the inputs to a machine learning model.

[0077] 18. The vehicle according to aspect 11, wherein the plurality of wireless communication technologies includes near field communication (NFC), ultra-wideband (UWB), Bluetooth, Wi-Fi, and a cellular network.

[0078] 19. The vehicle according to aspect 11, wherein the controller is further programmed to operate a digital key to effect vehicle operation after establishing wireless communication between a mobile device and the vehicle using one of the plurality of wireless communication technologies selected.

[0079] 20. The vehicle according to aspect 11, wherein one of the plurality of wireless communication technologies selected has the highest rating.

[0080] Other applicable fields of the present disclosure will become apparent from the detailed description provided below. It should be understood that the detailed description and specific examples are intended for illustrative purposes only and are not intended to limit the scope of the present disclosure.

[0081] The above features and advantages of the presently disclosed systems and methods, as well as other features and advantages, are readily apparent from the detailed description (including the claims) and the exemplary embodiments (when understood in conjunction with the accompanying drawings). BRIEF DESCRIPTION OF THE DRAWINGS

[0082] The present disclosure will be more fully understood from the detailed description and the accompanying drawings, in which:

[0083] Figure 1 is a schematic diagram of a system for operating a digital key configured to be wirelessly connected to a vehicle.

[0084] Figure 2 is a flowchart of a method for operating a digital key configured to be wirelessly connected to a vehicle.

[0085] Figure 3 is a flowchart of a method for rating wireless communication technologies. DETAILED DESCRIPTION

[0086] Reference will now be made in detail to several examples of the present disclosure illustrated in the accompanying drawings. Whenever possible, the same or similar reference numerals will be used in the drawings and the description to refer to the same or like components or steps.

[0087] Figure 1Shown is a system 20 for operating a digital key configured to wirelessly connect to a vehicle 10. The system 20 may be referred to as a digital key system. The vehicle 10 generally includes a body 12 and a plurality of wheels 14 coupled to the body 12. The vehicle 10 may be an autonomous vehicle. In the depicted embodiment, the vehicle 10 is depicted as a sedan in the illustrated embodiment, but it should be appreciated that other vehicles may also be used, including trucks, coupes, sport utility vehicles (SUVs), boats, airplanes, recreational vehicles (RVs), unmanned aerial vehicles, electric bicycles, and also any security systems, such as home security systems, computer security systems, locker systems, etc.

[0088] The vehicle 10 further includes one or more sensors 24 coupled to the body 12. The sensors 24 sense observable conditions of the external environment and / or the internal environment of the vehicle 10. As a non-limiting example, the sensors 24 may include one or more cameras, one or more light detection and ranging (LIDAR) sensors, one or more proximity sensors, one or more ultrasonic sensors, one or more thermal imaging sensors, a global positioning system (GPS) transceiver, and / or other sensors. Each sensor 24 is configured to generate a signal that indicates the sensed observable conditions of the external environment and / or the internal environment of the vehicle 10 (i.e., sensor data). The signal indicates the sensor data collected by the sensor 24.

[0089] The vehicle 10 includes a vehicle controller 34 that communicates with the sensors 24. The vehicle controller 34 includes at least one vehicle processor 44 and a vehicle non-transitory computer-readable storage device or medium 46. The vehicle processor 44 may be a custom or commercially available processor, a central processing unit (CPU), a graphics processing unit (GPU), an auxiliary processor among several processors associated with the vehicle controller 34, a semiconductor-based microprocessor (in the form of a microchip or chipset), a macroprocessor, a combination thereof, or generally a device for executing instructions. The vehicle-readable storage device or medium 46 may include, for example, volatile and non-volatile storage devices such as read-only memory (ROM), random access memory (RAM), and keep-alive memory (KAM). KAM is a permanent or non-volatile memory that may be used to store various operating variables when the vehicle processor 44 is powered off. The vehicle computer-readable storage device or medium 46 may be implemented using multiple memory devices such as PROM (programmable read-only memory), EPROM (electric PROM), EEPROM (electrically erasable PROM), flash memory, or other electrical, magnetic, optical, or combined memory devices capable of storing data, some of which represent executable instructions used by the vehicle controller 34 in controlling the vehicle 10. The vehicle controller 34 is specifically programmed to execute method 300 ( Figure 2 ).

[0090] The instructions can include one or more individual programs, each of which includes an ordered list of executable instructions for implementing a logical function. When executed by the vehicle processor 44, the instructions receive and process signals from sensors, execute logic, computations, methods, and / or algorithms for automatically controlling components of the vehicle 10, and generate control signals based on the logic, computations, methods, and / or algorithms to automatically control components of the vehicle 10. Although Figure 1 a single vehicle controller 34 is shown, embodiments of the vehicle 10 can include multiple vehicle controllers 34 that communicate via a suitable communication medium or combination of communication mediums and cooperate to process sensor signals, execute logic, computations, methods, and / or algorithms, and generate control signals to automatically control features of the vehicle 10.

[0091] The vehicle 10 further includes one or more actuators 26 that communicate with the vehicle controller 34. The actuators 26 control one or more vehicle features, such as but not limited to the propulsion system, transmission system, steering system, radio, air conditioning system, and braking system of the vehicle 10. In various embodiments, the vehicle features can further include interior and / or exterior vehicle features, such as but not limited to doors, trunks, and cabin features such as air, music, lighting, etc.

[0092] The host vehicle 10 further includes one or more vehicle transceivers 36 that communicate with the vehicle controller 34. Each of the vehicle transceivers 36 is configured to wirelessly transmit information to and receive information from other entities using, for example, one or more wireless communication technologies. As non-limiting examples, the wireless communication technologies include near field communication (NFC), ultra-wideband (UWB), Bluetooth, and Wi-Fi, as well as cellular networks. As non-limiting examples, the vehicle transceivers 36 can transmit and / or receive information from other vehicles (“V2V” communication), infrastructure (“V2I” communication), remote systems at a remote call center (e.g., GM's ON-STAR), and / or personal electronic devices such as mobile phones. In certain embodiments, the vehicle transceivers 36 can be configured to communicate using the IEEE 802.11 standard or via a wireless local area network (WLAN) using cellular data communication. However, additional or alternative communication methods are also contemplated within the scope of the present disclosure, such as dedicated short range communication (DSRC) channels. DSRC channels refer to one-way or two-way short-range to medium-range wireless communication channels and corresponding protocol and standard sets designed specifically for automotive use.

[0093] System 20 includes a mobile device 100 that communicates with a vehicle 10. In the present disclosure, the term "mobile device" is a piece of portable electronic equipment that can communicate with another device at least via a wireless signal. As a non-limiting example, the mobile device 100 can be a smartphone or a smart tablet, or a smartwatch or an embedded chip that can communicate with the vehicle 10. The mobile device 100 is running a digital key application and includes one or more device transceivers 136 that communicate with a vehicle controller 34. Each device transceiver 36 is configured to wirelessly transmit information from and to other entities using, for example, one or more wireless communication technologies. As non-limiting examples, the wireless communication technologies include Near Field Communication (NFC), Ultra Wideband (UWB), Bluetooth, and Wi-Fi, as well as cellular networks. The mobile device 100 includes a device controller 134. The device controller 134 includes at least one device processor 144 and a device non-transitory computer-readable storage device or medium 146. The device processor 144 can be a custom or commercially available processor, a central processing unit (CPU), a graphics processing unit (GPU), an auxiliary processor among several processors associated with the device controller 134, a semiconductor-based microprocessor (in the form of a microchip or a chipset), a macroprocessor, a combination thereof, or generally a device for executing instructions. The device-readable storage device or medium 146 can include, for example, volatile and non-volatile storage devices such as read-only memory (ROM), random access memory (RAM), and keep-alive memory (KAM). KAM is a permanent or non-volatile memory that can be used to store various operating variables when the device processor 144 is powered off. The device computer-readable storage device or medium 146 can be implemented using multiple memory devices such as PROM (programmable read-only memory), EPROM (electric PROM), EEPROM (electrically erasable PROM), flash memory, or other electrical, magnetic, optical, or combined storage devices capable of storing data, some of which represent executable instructions used by the device controller 134 in controlling the vehicle 10. For example, the digital key application runs on the mobile device 100. The digital key application operates a digital key that allows a vehicle operator to remotely actuated one or more actuators 26 (e.g., vehicle doors) of the vehicle 10 from the mobile device 100. For example, a vehicle operator can use the mobile device 100 to lock or unlock the vehicle doors via the digital key application. In another example, a vehicle operator can start the internal combustion engine of the vehicle 10 via the mobile device 100 by using the digital key application.

[0094] System 20 further includes a remote server 200 that communicates with vehicle 10 and mobile device 100. As a non-limiting example, remote server 200 can be a cloud-based system or an edge-based system. Remote server 200 includes a server controller 234. Server controller 234 includes at least one server processor 244 and a server non-transitory computer-readable storage device or medium 246. Server processor 244 can be a custom or commercially available processor, a central processing unit (CPU), a graphics processing unit (GPU), a secondary processor among several processors associated with server controller 234, a semiconductor-based microprocessor (in the form of a microchip or chipset), a macroprocessor, a combination thereof, or generally a device for executing instructions. Server-readable storage device or medium 246 can include, for example, volatile and non-volatile storage devices such as read-only memory (ROM), random access memory (RAM), and keep-alive memory (KAM). KAM is a permanent or non-volatile memory that can be used to store various operating variables when server processor 244 is powered down. Server computer-readable storage device or medium 246 can be implemented using multiple storage devices such as PROM (programmable read-only memory), EPROM (electrically PROM), EEPROM (electrically erasable PROM), flash memory, or other electrical, magnetic, optical, or combined memory devices capable of storing data, some of which represent executable instructions used by server controller 234 in controlling vehicle 10.

[0095] Figure 2It is a flowchart of a method 300 for operating a digital key configured to be wirelessly connected to a vehicle 10. The method 300 starts at block 302. At block 302, a trigger event occurs to start the method 300. The trigger event can occur in different ways. The vehicle controller 34 can determine that the trigger event has occurred based on the received vehicle data (e.g., navigation data). The vehicle data can include, among other data, navigation data, map data, GPS data, navigation destinations, and the navigation data further includes the current position of the vehicle 10 when the vehicle 10 is moving towards a parking position. To this end, the vehicle controller 34 uses the navigation data or the vehicle data to determine the distance from the current position of the vehicle 10 to the parking position, such as a position where the vehicle often parks or a navigation destination. The vehicle controller 34 then compares the distance from the current position of the vehicle 10 to the parking position with a predetermined distance threshold (e.g., two hundred meters or three hundred and fifty meters) to determine whether the distance from the current position of the vehicle 10 to the parking position is less than the predetermined distance threshold. If the distance from the current position of the vehicle 10 to the parking position is less than the predetermined distance threshold, the vehicle controller 34 determines that the trigger event has occurred. The vehicle operator can also initiate the trigger event by a manual request (e.g., pressing a button). In response to determining that the trigger event has occurred, the method 300 proceeds to block 304.

[0096] At block 304, the vehicle 10 uses the sensor 24 to collect wireless communication signal data within a predetermined distance of the vehicle 10. The wireless communication signal data includes the signal strength (e.g., received signal strength indicator (RSSI)) and channel condition information (such as channel state information (CSI) and channel quality information (CQI)) of each of a plurality of wireless communication signals (e.g., NFC, Wi-Fi, Bluetooth, and / or cellular signals) at a plurality of positions when the vehicle 10 is moving towards the parking point. These plurality of positions are spaced apart from each other by a predetermined distance (e.g., ten meters), and the wireless communication signal data at each position is captured over a moving window of a defined size (e.g., ten windows). Once the vehicle is parked, the moving window information is obtained as part of a network mode map. Further, at block 304, the vehicle 10 uses the sensor 24 to collect parking infrastructure data that becomes part of the mode map information only in response to the trigger event as discussed above. The parking infrastructure data is information about the parking infrastructure in the parking position. For example, the parking infrastructure includes data on whether the parking area is a rooftop parking lot, a multi-level parking garage, an underground parking lot, a roadside parking lot, a public parking lot, a valet parking lot, etc. Then, the method 300 proceeds to block 306.

[0097] At block 306, vehicle controller 34 uses sensors 24 (e.g., cameras), automatic parking assist (APA), steering of vehicle 10, drive mode, GPS, etc. to detect a parking location. To this end, vehicle controller 34 can detect the activation and completion of APA, detect parking markings, the speed, steering, and movement of vehicle 10, detect drive mode changes and / or manual requests. Once the parking location is detected and confirmed, vehicle controller 34 can analyze, save, and filter the wireless communication signal data to data relevant only to the most recent slots (e.g., five locations) of a moving window closest to the parking location. Then, method 300 proceeds to block 308.

[0098] At block 308, vehicle controller 34 evaluates, authenticates, classifies, ranks, and uploads the filtered wireless communication signal data to a digital key application running on mobile device 100 and / or remote server 200. Next, method 300 proceeds to block 310. Specifically, at block 308, vehicle controller 34 evaluates the wireless communication signal data of signals of multiple wireless communication technology formats (e.g., Bluetooth Low Energy, Wi-Fi, UWB, NFC, cellular). For each wireless communication technology, vehicle controller 34 rates the wireless communication technology (i.e., network) with a separate technology rating score. The scores can have yellow metric ratings, green metric ratings, and red metric ratings. Each wireless communication technology can be rated based on metrics specific to that technology. For example, UWB signals can be rated (green, red, or yellow) based on angle of arrival accuracy, signal strength, distance accuracy, communication range, whether ranging is secure, and energy consumption. NFC signals can be rated based on uptime, signal strength, Rayleigh distance, spatial effects, grid alignment and size, and near-field beam separation. Wi-Fi signals can be rated based on uptime, signal strength, packet loss and retransmission, latency, bandwidth and throughput, and jitter. Vehicle controller 34 then considers environmental, vehicle, and user factors to determine dynamic and historical patterns. These dynamic and historical patterns are then provided as feedback to remote server 200 and the digital key application running on mobile device 100. The execution of block 308 can include Figure 3 the method 400 shown in

[0099] Figure 3It is a flowchart of a method 400 for rating wireless communication technologies. Method 400 begins at block 402 and then proceeds to block 404. At block 404, as vehicle 10 approaches a parking position, vehicle controller 34 monitors the signal health state (SOH) of each available wireless communication technology (e.g., Bluetooth, Wi-Fi, NFC, UWB) for establishing communication between mobile device 100 and vehicle 10. Then, method 400 proceeds to block 406 and is thus used to operate the digital key.

[0100] At block 406, vehicle controller 34 determines whether there has been any change in the SOH state of the signals of each available wireless communication technology as vehicle 10 moves towards the parking position. If there has been no change in the SOH state of the signals of each available wireless communication technology yet, method 400 proceeds to block 408. At block 408, no action is taken. If there has been any change in the SOH state of the signals of each available wireless communication technology as vehicle 10 moves towards the parking position, method 400 proceeds to block 410. At block 410, vehicle controller 34 determines whether any wireless communication signal of the wireless communication technology has been rated red as explained above. A red rating indicates that the signal is not available for operating the digital key. If any wireless communication signal of the wireless communication technology has been rated red, method 400 proceeds to block 412.

[0101] At block 412, vehicle controller 34 eliminates the wireless communication signals of the wireless communication technology rated red from the priority rating matrix. Then, method 400 proceeds to block 414. At block 414, method 400 ends.

[0102] If at block 410, vehicle controller 34 determines that no wireless communication signal of the wireless communication technology has been rated red, method 400 proceeds to block 416. At block 416, vehicle controller 34 determines whether the change in the SOH state of the wireless communication signal is equal to or greater than a predetermined threshold. If the change in the SOH state of the wireless communication signal is not equal to or not greater than the predetermined threshold, method 400 proceeds to block 418. At block 418, no action is taken.

[0103] If the change in the SOH state of the wireless communication signal is equal to or greater than the predetermined threshold, method 400 proceeds to block 420. At block 420, the wireless communication technology with the change in SOH is re-evaluated. That is, this wireless communication technology is re-prioritized and re-scored in the priority rating matrix. The new priority rating matrix is uploaded to the digital key applications running on mobile device 100 and remote server 200. Then, method 400 proceeds to block 414.

[0104] Return to Figure 2 , at block 310, the device controller 134 determines that the vehicle operator holding the mobile device 100 is moving away from the vehicle 10. By detecting that the mobile device 100 is moving away from the parking location, the device controller 134 can determine that the vehicle operator is moving away from the vehicle 10. At this juncture, the digital key application running on the mobile device 100 collects wireless communication signal data at multiple locations while the vehicle operator is moving away from the vehicle 10. As discussed above, the wireless communication signal data includes the signal strength (e.g., RSSI) of each of the wireless communication signals at multiple locations while the vehicle operator is moving away from the vehicle 10 after the vehicle 10 has been parked at the parking location. These multiple locations are spaced apart from each other by a predetermined distance (e.g., ten meters). Alternatively or additionally, the remote server 200 can collect wireless communication signal data from one or more remote vehicles approaching the parking location from different directions while the vehicle operator is moving away from the vehicle 10 after the vehicle 10 has been parked at the parking location. This wireless communication signal data is uploaded to the vehicle 10 and the remote server 200. Then, method 300 proceeds to block 312.

[0105] At block 312, the device controller 134 determines whether the vehicle operator is continuously moving towards the vehicle 10 by detecting and monitoring the position of the mobile device 100. Further, the device controller 134 determines the distance from the vehicle operator to the vehicle 10 while the vehicle operator is continuously moving towards the vehicle to determine whether the distance from the vehicle operator to the vehicle 10 is less than a predetermined proximity threshold (e.g., ten meters). If the distance from the vehicle operator to the vehicle 10 is determined to be less than the predetermined proximity threshold while the vehicle operator is continuously moving towards the vehicle 10, the digital key application is triggered. Then, method 300 proceeds to block 314.

[0106] At block 314, as the vehicle operator is moving away from and / or towards vehicle 10, mobile device 100 uses the digital key application to estimate a user profile mode (i.e., the mode of the vehicle operator) during each interaction of the digital key for each wireless communication technology. The user profile mode estimation includes wireless communication signal data (e.g., signal strength) of each wireless communication technology each time the vehicle operator (while holding mobile device 100) moves away from or towards vehicle 10 towards a known frequent location. Based on known historical locations (e.g., the movement of the vehicle operator between frequent parking locations and frequent venues), a user profile mode is estimated for each movement towards or away from vehicle 10. The user profile estimation is partitioned based on the direction of movement of the vehicle operator (e.g., movement towards vehicle 10 and movement away from vehicle 10). Then, each wireless communication technology (i.e., wireless communication network) is rated for each direction of movement of the vehicle operator in a priority rating matrix. Then, the priority rating matrix is used as an input to a machine learning model (e.g., a deep neural network), and method 300 proceeds to block 316.

[0107] At block 316, vehicle controller 34 uses a machine learning model (such as a deep neural network) to rate the wireless communication technologies for wirelessly connecting mobile device 100 to vehicle 10 and operating the digital key. The machine learning model can perform vectorization of different dynamic factors for location segments. As stated above, wireless communication signal data is collected at multiple locations (i.e., location segments) spaced apart from each other. These dynamic factors can include traffic, location, time of day, seasonality, range of the wireless communication technology, number of users, etc. These different dynamic factors can be obtained from the user profile mode and previously collected wireless communication signal data. The machine learning model (e.g., a deep neural network) then rates the available wireless communication technologies or networks (e.g., Bluetooth, Wi-Fi, cellular, UWB, etc.) using, for example, the priority rating matrix. As a non-limiting example, the inputs to the machine learning model include wireless communication signal data, a vector representation of dynamic external factors, a best network vector sample, intelligent context awareness data, and a tolerance window. The tolerance window is the average information from the moving window described above. The output of the machine learning model is a priority rating matrix that includes confidence scores and ratings for each wireless communication technology (i.e., wireless communication networks such as NFC, UWB, Bluetooth, Bluetooth Low Energy, and Wi-Fi, as well as cellular networks). The priority matrix and the digital key can be shared from the vehicle operator's (i.e., the vehicle owner's) mobile device 100 to the mobile devices of family members, friends, or any other entity. Some other mobile devices can be part of a dynamic whitelist, and these other mobile devices 100 can provide dynamic feedback to the technology matrix, just as would be provided to the vehicle owner's mobile device 100. Then, method 400 proceeds to block 318.

[0108] At block 318, the digital key application then selects and searches for the wireless communication technology (i.e., wireless communication network) with the highest rating. Further, information about the selected wireless communication technology is uploaded to the remote server 200 and transmitted to other vehicles. Then, method 300 proceeds to block 320.

[0109] At block 320, the digital key application commands vehicle 10 to search for the wireless communication technology (i.e., wireless communication network) with the highest rating via the remote server 200. Next, wireless communication is established between the mobile device 100 and vehicle 10 using the selected wireless communication technology (i.e., network), thereby allowing the digital key application to operate as a digital key for vehicle 10. Then, the vehicle operator can operate the digital key of the digital key application to actuate one or more actuators 26 (e.g., vehicle doors) of vehicle 10. For example, after wireless communication is established between the mobile device 100 and vehicle 10 using the selected wireless communication technology (i.e., wireless communication network), the digital key can be operated to unlock the vehicle doors of vehicle 10.

[0110] The figures are in simplified form and are not drawn to exact scale. For convenience and clarity only, directional terms such as top, bottom, left, right, up, above, over, down, below, rear, and front may be used with respect to the figures. These and similar directional terms should not be construed to limit the scope of the disclosure in any way.

[0111] Embodiments of the disclosure are described herein. However, it should be understood that the disclosed embodiments are merely examples, and other embodiments may take various and alternative forms. The figures are not necessarily to scale; some features may be enlarged or reduced to show details of particular components. Thus, the specific structural and functional details disclosed herein should not be construed as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the presently disclosed systems and methods. As will be understood by one of ordinary skill in the art, the various features illustrated and described with reference to any one of the figures may be combined with features illustrated in one or more other figures to produce embodiments not explicitly illustrated or described. Combinations of the illustrated features provide representative embodiments for typical applications. However, various combinations and modifications of the features consistent with the teachings of the disclosure may be desired for a particular application or implementation.

[0112] Embodiments of the present disclosure may be described herein in terms of functional and / or logical block components and various processing steps. It should be appreciated that such block components may be implemented by a number of hardware, software, and / or firmware components configured to perform the specified functions. For example, embodiments of the present disclosure may employ various integrated circuit components such as memory elements, digital signal processing elements, logic elements, or look-up tables, among others, which may perform various functions under the control of one or more microprocessors or other control devices. Additionally, those skilled in the art will appreciate that embodiments of the present disclosure may be practiced in conjunction with multiple systems, and the systems described herein are merely exemplary embodiments of the present disclosure.

[0113] For simplicity, techniques related to signal processing, data fusion, signaling, control, and other functional aspects of the system (as well as the individual operating components of the system) may not be described in detail herein. Further, the connecting lines shown in the various figures included herein are intended to represent example functional relationships and / or physical couplings between various elements. It should be noted that alternative or additional functional relationships or physical connections may exist in embodiments of the present disclosure.

[0114] The description is merely illustrative in nature and is in no way intended to limit the present disclosure, its applications, or uses. The broad teachings of the present disclosure may be implemented in a variety of forms. Thus, while the present disclosure includes specific examples, the true scope of the present disclosure should not be so limited since other modifications will become apparent upon study of the drawings, the specification, and the following claims.

Claims

1. A method for operating a digital key configured to be wirelessly connected to a vehicle, comprising: Receiving wireless communication signal data, wherein the wireless communication signal data includes information about a plurality of wireless communication signals within a predetermined distance from the vehicle, and the wireless communication signal data includes a received signal strength indicator (RSSI), channel state information (CSI), and channel quality information (CQI) for each of the plurality of wireless communication signals forming a network pattern within a predetermined distance from the vehicle; Receiving vehicle data, wherein the vehicle data includes information about the vehicle, and the vehicle data includes the parking location of the vehicle; Triggering the digital key system of the vehicle to establish a wireless connection with the digital key of the mobile device in response to matching the network pattern and determining that the distance between the mobile devices is less than a predetermined distance threshold; Rating a plurality of wireless communication technologies for wirelessly connecting the mobile device to the vehicle using a machine learning model, wherein the vehicle data and the wireless communication signal data are inputs to the machine learning model; Selecting one of the plurality of wireless communication technologies based on the rating of the plurality of wireless communication technologies determined by the machine learning model; And Establishing wireless communication between the mobile device and the vehicle using one of the plurality of wireless communication technologies selected, wherein a digital key application is running on the mobile device, thereby allowing the digital key application to operate as the digital key of the vehicle after the wireless communication between the mobile device and the vehicle is established.

2. The method according to claim 1, further comprising: Receiving navigation data, wherein the navigation data includes the vehicle position when the vehicle is moving towards the parking location; Determining the distance from the vehicle position to the parking location using the navigation data; Comparing the distance from the vehicle position to the parking location with a predetermined distance threshold to determine whether the distance from the vehicle position to the parking location is less than the predetermined distance threshold; And Collecting wireless communication signal data in response to determining that the distance from the vehicle position to the parking location is less than the predetermined distance threshold, wherein the wireless communication signal data includes the RSSI, channel state information (CSI), and channel quality information (CQI) for each of the plurality of wireless communication signals at a plurality of positions when the vehicle is moving towards the parking location, and the plurality of positions are spaced apart from each other by a predetermined distance; And Collecting parking infrastructure data only in response to determining that the distance from the vehicle position to the parking location is less than the predetermined distance threshold, wherein the parking infrastructure data is information about the parking infrastructure at the parking location.

3. The method according to claim 2, further comprising: Uploading the wireless communication signal data to a digital key application running on the mobile device; And Uploading the wireless communication signal data to a remote server.

4. The method according to claim 3, further comprising: Determining that the vehicle has been parked at the parking location; In response to determining that the vehicle has been parked at a parking location, when the vehicle operator is moving away from the vehicle after the vehicle has been parked at the parking location, collect wireless communication signal data using a mobile device, wherein the wireless communication signal data includes the RSSI, channel state information (CSI), and channel quality information (CQI) of each of a plurality of wireless communication signals at a plurality of locations when the vehicle operator is moving away from the vehicle after the vehicle has been parked at the parking location, and the plurality of locations are spaced apart from each other by a predetermined distance; Upload the wireless communication signal data to the vehicle; and Upload the wireless communication signal data to a remote server.

5. The method according to claim 3, further comprising: Determine that the vehicle has been parked at a parking location; In response to determining that the vehicle has been parked at a parking location, when the vehicle operator is moving away from the vehicle after the vehicle has been parked at the parking location, collect wireless communication signal data from a plurality of remote vehicles, wherein the wireless communication signal data includes the RSSI, channel state information (CSI), and channel quality information (CQI) of each of a plurality of wireless communication signals at a plurality of locations when the vehicle operator is moving away from the vehicle after the vehicle has been parked at the parking location, and the plurality of locations are spaced apart from each other by a predetermined distance; Upload the wireless communication signal data to the vehicle; and Upload the wireless communication signal data to a remote server.

6. The method according to claim 3, further comprising: Determine that the vehicle operator is continuously moving towards the vehicle; Determine the distance from the vehicle operator to the vehicle when the vehicle operator is continuously moving towards the vehicle to determine whether the distance from the vehicle operator to the vehicle is less than a predetermined proximity threshold; and In response to determining that the distance from the vehicle operator to the vehicle is less than a predetermined proximity threshold when the vehicle operator is continuously moving towards the vehicle, use a machine learning model to rate a plurality of wireless communication technologies for wirelessly connecting the mobile device to the vehicle.

7. The method according to claim 6, further comprising determining the user profile mode of the vehicle operator when the vehicle operator is moving relative to the vehicle, wherein the user profile mode is used as one of the inputs to the machine learning model.

8. The method according to claim 1, wherein the plurality of wireless communication technologies includes near field communication (NFC), ultra-wideband (UWB), Bluetooth, Wi-Fi, and cellular networks.

9. The method according to claim 1, further comprising operating a digital key to implement vehicle operations after establishing wireless communication between the mobile device and the vehicle using one of the selected wireless communication technologies among the plurality of wireless communication technologies.

10. The method according to claim 1, wherein one of the selected wireless communication technologies among the plurality of wireless communication technologies has the highest rating.

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