System and method for vehicle license plate cleaning

Abstract

The present disclosure provides "Systems and methods for vehicle license plate cleaning." A vehicle is disclosed that includes a license plate, a transceiver, and a processor. The transceiver can receive an image of the license plate from an external device. The processor can obtain the image of the license plate from the transceiver and determine whether the license plate is dirty based on the image. In response to determining that the license plate is dirty, the processor can perform a predetermined action to clean the license plate.

Description

Technical Field

[0001] This disclosure relates to vehicles, and more specifically to systems and methods for license plate cleaning. Background Technology

[0002] Vehicle license plates easily accumulate dust and debris from the external environment, especially for users who frequently drive on unpaved or dirty roads. Dust, mud, and other elements can obscure the visibility of the license plate, making it difficult for users to read. The problem of dirty license plates becomes even more relevant for self-driving or autonomous vehicles, where no human is present to notice or address them. In human-driven vehicles, users rely on their vision to spot dirty or obscured license plates and take action to clean them. However, users may sometimes not even notice that their license plates may be dirty. Maintaining clean license plates is expected. Summary of the Invention

[0003] This disclosure describes systems and methods for facilitating the cleaning of license plates associated with vehicles (e.g., "first vehicle"). The system can first determine that the license plate may be dirty. In response to determining that the license plate may be dirty, the system can perform predetermined actions to clean the license plate. In an exemplary aspect, the license plate may become dirty when the vehicle has been driving (or is driving) in an off-road area (or a dirty area / road), when the vehicle has been driving (or is driving) in a storm such as a sandstorm, blizzard, etc., when the vehicle may be parked in a storm area, etc.

[0004] To determine if the license plate may be dirty, the system can obtain a license plate image (or "first image") from an external device. The external device can be, for example, a second vehicle located near the first vehicle, a drone, a smart infrastructure unit (e.g., a camera), etc. In an exemplary aspect, the system can obtain the license plate image from the second vehicle via vehicle-to-vehicle (V2V) communication, from the drone via vehicle-to-drone (V2D) communication, and from the smart infrastructure unit via vehicle-to-the-world (V2X) communication.

[0005] In some aspects, before the system obtains the license plate image from the external device, the system may first determine the probability that the license plate is dirty based on route information associated with a vehicle route that the first vehicle may be traveling on (or a route that the first vehicle may have recently traveled). When the probability that the license plate may be dirty is greater than a predefined threshold, the system may transmit a request to the external device (e.g., the second vehicle) to capture the license plate image and provide the license plate image to the system. The probability may be greater than the predefined threshold when the first vehicle has already driven (or is driving) through the dirty area, when the first vehicle has already driven (or is driving) during the storm, when the first vehicle may be parked in the storm area, etc. In response to transmitting the request, the system can obtain the license plate image from the external device.

[0006] In response to obtaining the license plate image from the external device, the system can determine, based on the obtained license plate image, that the license plate may be dirty. Furthermore, in response to determining that the license plate may be dirty, the system can perform the predetermined action to clean the license plate. The predetermined action may include outputting a notification to a user (e.g., a vehicle operator) on a user interface associated with a vehicle human-machine interface (HMI) or user device. The notification may indicate to the user that the license plate is dirty and needs cleaning. Alternatively, the predetermined action may include activating a sprayer installed in the first vehicle that can spray a cleaning liquid (e.g., water) onto the license plate to clean it (e.g., remove dust, snow, etc. from the license plate). Yet another way, the predetermined action may include transmitting a request to the drone to spray the cleaning liquid onto the license plate (e.g., when the first vehicle may be parked in a parking area). In another way, the system may schedule a car wash to facilitate license plate cleaning. Still another way, the system may perform timely cleaning using rainwater based on a rain forecast.

[0007] In response to performing the predetermined action to clean the license plate as described above, the system can verify whether the license plate is clean or still dirty. In some aspects, the system can perform the verification after a predefined duration (e.g., 10 minutes, 30 minutes, 1 hour, 3 hours, etc.) of performing the predetermined action. To perform the verification, the system can request the external device to capture another image (or a second image) of the license plate and provide the second image to the system. The system can obtain the second image and determine, based on the second image, whether the license plate is clean (or still dirty). In response to determining that the license plate is clean, the system can output another notification (or a second notification) to the user indicating that the license plate is clean. Alternatively, the system can repeat and continue performing the predetermined action described above until the license plate is clean.

[0008] This disclosure discloses a vehicle that uses existing vehicle components to determine whether a license plate is dirty or clean and facilitates license plate cleaning. The vehicle utilizes its ability to communicate with external devices to detect a dirty license plate and perform a cleaning operation accordingly. In some scenarios, the vehicle may not require a human presence to keep the license plate clean.

[0009] These and other advantages of this disclosure are provided in detail herein. Attached Figure Description

[0010] Specific embodiments are illustrated with reference to the accompanying drawings. The same reference numerals may be used to indicate similar or identical items. Various embodiments may utilize elements and / or components other than those shown in the drawings, and some elements and / or components may not be present in various embodiments. Elements and / or components in the drawings are not necessarily drawn to scale. Throughout this disclosure, singular and plural terms may be used interchangeably, depending on the context.

[0011] Figure 1 An example environment is described in which the techniques and structures used to provide the systems and methods disclosed herein can be implemented.

[0012] Figure 2 A block diagram of a system for promoting the cleanliness of vehicle license plates according to this disclosure is depicted.

[0013] Figure 3 A first example scenario is depicted that uses vehicle-to-vehicle (V2V) communication to facilitate the cleaning of vehicle license plates according to this disclosure.

[0014] Figure 4 An example cleaning mechanism for promoting the cleaning of vehicle license plates according to this disclosure is described.

[0015] Figure 5A second example scenario is depicted, illustrating the use of vehicle-to-drone (V2D) communication to facilitate the cleaning of vehicle license plates, according to this disclosure.

[0016] Figure 6 A third example scenario is described, illustrating the use of vehicle-to-infrastructure (V2X) communication to facilitate the cleaning of vehicle license plates according to this disclosure.

[0017] Figure 7 A flowchart depicts an example method for promoting the cleaning of vehicle license plates according to this disclosure. Detailed Implementation

[0018] The present disclosure will be described more fully below with reference to the accompanying drawings, which illustrate exemplary embodiments of the present disclosure and are not intended to be limiting.

[0019] Figure 1 An example environment 100 is depicted in which the techniques and structures for providing the systems and methods disclosed herein can be implemented. Environment 100 may include a vehicle 102 (hereinafter referred to as the first vehicle 102) that may be traveling on an off-road area 104. Alternatively, the first vehicle 102 may be traveling on a road that may be dirty (e.g., covered with dust, snow, etc.), or the first vehicle 102 may be traveling on a road (or off-road area 104) during a storm such as a sandstorm, blizzard, etc.

[0020] The first vehicle 102 can take the form of any passenger or commercial vehicle, such as an automobile, work vehicle, crossover, truck, van, minivan, taxi, bus, etc. The first vehicle 102 can be a manually driven vehicle or can be configured to operate in a partially / fully autonomous mode. Furthermore, the first vehicle 102 can include any powertrain system, such as a gasoline engine, one or more electric actuators, or a hybrid system.

[0021] The first vehicle 102 may include a license plate 106 for identifying the first vehicle 102. The license plate 106 may be a rectangular metal plate having a unique alphanumeric code for identifying the first vehicle 102. In some aspects, the first vehicle 102 may have two license plates, one attached to the front portion 108 of the vehicle and the other attached to the rear portion 110 of the vehicle, both of which may be mounted on the interior or exterior of the vehicle. Alternatively, the first vehicle 102 may have only one license plate that can be attached to the rear portion 110 of the vehicle. In some aspects, the license plate 106 may be made of a metal sheet or the like. In other aspects, the license plate 106 may be in the form of a digital display.

[0022] The first vehicle 102 may also include a license plate cleaning unit (in Figure 2The license plate cleaning unit (shown as license plate cleaning unit 210) can determine the state of the license plate and perform one or more predefined actions based on the determined state. For example, the license plate cleaning unit (or "unit") can determine that the license plate is dirty and perform a predetermined action to clean the license plate 106 in response to this determination. In an exemplary aspect, the license plate 106 may become dirty when the first vehicle 102 is traveling in an off-road area 104 (or a dirty area / road), when the first vehicle 102 is traveling in a storm (e.g., a sandstorm, blizzard, etc.), when the first vehicle 102 may be parked in a storm area, etc. In some aspects, the unit can determine that both the license plates located at the front portion 108 and the rear portion 110 of the vehicle may be dirty (e.g., when the first vehicle 102 includes two license plates). Alternatively, the unit can determine that one license plate may be dirty (e.g., when the first vehicle 102 may include a single license plate at the rear portion 110 of the vehicle).

[0023] In some respects, the first vehicle 102 can be communicatively coupled to an external device. The external device could be, for example, a second vehicle located near the first vehicle 102 (in... Figure 2 The image shows the second vehicle 202a and the drone (in...). Figure 2 The diagram shows a drone (202b), and smart infrastructure units such as infrastructure cameras (in...). Figure 2 (As shown in the image, camera 202c), etc. The first vehicle 102 can be accessed via a network (in...). Figure 2 The first vehicle 102 is shown as network 204) and is communicatively coupled to an external device. In an exemplary aspect, the first vehicle 102 may communicate with the second vehicle via vehicle-to-vehicle (V2V) communication, the first vehicle 102 may communicate with the drone via vehicle-to-drone (V2D) communication, and the first vehicle 102 may communicate with the smart infrastructure unit via vehicle-to-the-world (V2X) communication.

[0024] In some aspects, to determine the license plate status, the unit may obtain a license plate image (or a first image) associated with license plate 106 from an external device via a network. In some aspects, the unit may initially request the external device to capture a license plate image and provide the captured license plate image to the unit. In response to transmitting the request, the unit may obtain the license plate image from the external device. In an exemplary aspect, the unit may request the external device to capture a license plate image located at the front portion 108 and / or the rear portion 110 of the vehicle.

[0025] In some aspects, before transmitting the request to the external device, the unit may first determine the probability that the license plate 106 is dirty based on route information associated with the vehicle route that the first vehicle 102 may be traveling on (or the route that the first vehicle 102 may have recently traveled). Route information may include weather conditions along the vehicle route, road conditions along the vehicle route, information associated with the existence of off-road areas along the vehicle route, etc. For example, based on the route information, the unit may estimate that the first vehicle 102 may be moving through an off-road area between 9:00 AM and 10:00 AM, and may estimate that it may be raining in the off-road area between 9:00 AM and 10:00 AM. Based on this estimate, the unit may determine that the probability that the license plate 106 is dirty is high or higher than a predefined threshold (because the surface of the off-road area may be muddy). In an exemplary aspect, the unit may correlate road condition information with weather condition information and calculate the probability based on the correlation. For example, whenever road condition information indicates the presence of an off-road area in the vehicle's route and weather condition information indicates rain, snow, etc. (when vehicle 102 may be traveling through an off-road area), the unit can correlate these two conditions to calculate a high probability that license plate 106 will become dirty. On the other hand, if road condition information indicates the road may be a high-grade concrete road, the unit can calculate a low probability that license plate 106 will become dirty.

[0026] In response to determining a probability, the unit can transmit a request to an external device to provide a license plate image based on that probability. In some aspects, the unit can transmit the request to an external device when the probability that license plate 106 is dirty is greater than a predefined threshold. The probability may be greater than the predefined threshold when the first vehicle 102 has already driven (or is driving) through the dirty area, when the first vehicle 102 has already driven (or is driving) during the storm, when the first vehicle 102 is parked in the storm area, etc.

[0027] In response to obtaining a license plate image from an external device, the unit can determine, based on the obtained license plate image, that the license plate 106 may be dirty. The unit can perform image processing on the obtained license plate image to determine whether the license plate 106 may be dirty or obscured. In some aspects, the unit can compare the obtained license plate image with a pre-stored image (stored in the vehicle's memory) and can determine, based on the comparison, that the license plate 106 may be dirty (e.g., when the license plate 106 is unreadable).

[0028] In response to determining that license plate 106 may be dirty, the unit may perform a predetermined action to clean license plate 106. The predetermined action may include, for example, outputting a notification to a user (e.g., a vehicle operator) via a user interface (associated with a vehicle human-machine interface (HMI) or user device). The notification may indicate that license plate 106 is dirty and needs cleaning. Alternatively, the predetermined action may include activating a sprayer (in...) Figure 2 The unit, shown as a sprayer 238, can spray a cleaning liquid (e.g., water) onto license plate 106 to clean it (e.g., remove dust, snow, etc. from license plate 106). The sprayer can be an integral part of the first vehicle 102 or can be removably attached to it. In another aspect, the predetermined action may include transmitting a request to a drone to spray cleaning liquid onto license plate 106 (e.g., when the first vehicle 102 may be parked in a parking area). In another aspect, in response to determining that license plate 106 is dirty, the unit can automatically schedule a car wash to facilitate license plate cleaning. In yet another aspect, the unit can use rainwater based on a rain forecast to perform timely cleaning. This will be discussed later in conjunction with... Figure 4 Describe this aspect.

[0029] In response to performing the predetermined action as described above, the unit can verify whether the license plate 106 is clean or still dirty. In some aspects, the unit can perform the verification after a predefined duration (e.g., 5 minutes, 10 minutes, 30 minutes, 1 hour, 3 hours, etc.) of performing the predetermined action. To perform the verification, the first vehicle 102 can request the external device to capture another image (or "second image") of the license plate 106 and provide the second image to the unit. The unit can obtain the second image and can determine, based on the second image, whether the license plate 106 is clean (or still dirty). In response to determining that the license plate 106 is clean, the unit can output another notification (or second notification) to the user indicating that the license plate 106 is now clean. Alternatively, the unit can reactivate the sprayer to clean the license plate 106. The unit can repeat the predetermined action as described above until the license plate 106 is clean.

[0030] This disclosure describes the cleaning of a license plate 106 that can be mounted on an external part of a vehicle; however, such description should not be construed as limiting the applicability of this disclosure to license plates mounted only externally. In some aspects, the license plate 106 may be mounted inside the first vehicle 102. In some aspects, the license plate 106 may be a paper license plate (e.g., when the license plate 106 may be temporary). In such scenarios, the unit can obtain an image of the license plate from an external device and determine, based on the obtained image (as described above), that the license plate 106 may be covered with dust / snow. The license plate 106 (mounted inside the vehicle 102) may be covered with dust / snow if the rear windshield may be covered with dust, snow, etc. In response to this determination, the unit can perform an in-vehicle cleaning to expose the license plate 106. For example, the unit can activate a rear wiper, a fluid sprayer, a defroster, etc., to expose the license plate 106.

[0031] The following text combines Figure 2 Describe additional vehicle details.

[0032] The first vehicle 102 implements and / or performs the operations described herein in accordance with the owner's manual and safety guidelines. Furthermore, any actions taken by the user based on notifications provided by the first vehicle 102 shall comply with all rules specific to the location and operation of the first vehicle 102 (e.g., federal, state, national, city, etc.). Notifications provided by the first vehicle 102 shall be considered advice and shall be followed only in accordance with any rules specific to the location and operation of the first vehicle 102.

[0033] Figure 2 A block diagram of a system 200 for facilitating the cleaning of license plate 106 according to this disclosure is depicted. In the description... Figure 2 At that time, will refer to Figure 3 , Figure 4 , Figure 5 and Figure 6 .

[0034] System 200 may include a first vehicle 102 and external devices (including a second vehicle 202a, a drone 202b and / or smart infrastructure units such as a camera 202c), which may be communicatively coupled to each other via one or more networks 204.

[0035] Network 204 illustrates example communication infrastructure in which connected devices discussed in various embodiments of this disclosure may communicate. Network 204 may be and / or include the Internet, a private network, a public network, or other configurations operating using any one or more known communication protocols such as Transmission Control Protocol / Internet Protocol (TCP / IP), Bluetooth, etc. ®Bluetooth Low Energy (BLE), Wi-Fi based on the IEEE standard 802.11, Ultra Wideband (UWB), and cellular technologies such as Time Division Multiple Access (TDMA), Code Division Multiple Access (CDMA), High-Speed ​​Packet Access (HSPDA), Long Term Evolution (LTE), Global System for Mobile Communications (GSM), and 5G, to name just a few.

[0036] The first vehicle 102 may include multiple units, including but not limited to an automotive computer 206, a vehicle control unit (VCU) 208, and a license plate cleaning unit 210 (or unit 210). The VCU 208 may include multiple electronic control units (ECUs) 212 that communicate with the automotive computer 206.

[0037] In some respects, according to this disclosure, the vehicle computer 206 and / or unit 210 can be installed anywhere within the first vehicle 102. Furthermore, the vehicle computer 206 can operate as a functional part of unit 210. The vehicle computer 206 can be or include an electronic vehicle controller having one or more processors 214 and memory 216. Additionally, unit 210 can be separate from the vehicle computer 206 (e.g., Figure 2 (as shown in the diagram), or it can be integrated as part of the vehicle computer 206.

[0038] Processor 214 can communicate with one or more memory devices (e.g., memory 216 and / or memory) of a corresponding computing system. Figure 2 The processor 214 may communicate with one or more external databases (not shown). The processor 214 may utilize the memory 216 to store programs in code form and / or store data to execute aspects of this disclosure. The memory 216 may be a non-transitory computer-readable medium or memory that stores license cleaning program code. The memory 216 may include any or a combination of volatile memory elements (e.g., dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), etc.) and may include any one or more non-volatile memory elements (e.g., erasable programmable read-only memory (EPROM), flash memory, electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), etc.).

[0039] According to some aspects, VCU 208 may share a power bus with vehicle computer 206 and may be configured and / or programmed to coordinate data between the systems of first vehicle 102, a connected server (not shown), and other vehicles operating as part of a vehicle fleet (including second vehicle 202a). VCU 208 may include or communicate with any combination of ECUs 212, such as Body Control Module (BCM) 218, Engine Control Module (ECM) 220, Transmission Control Module (TCM) 222, Telematics Control Unit (TCU) 224, Driver Assistance Technology (DAT) Controller 226, etc.

[0040] VCU 208 may also include and / or communicate with a vehicle perception system (VPS) 228, which has connectivity with and / or controls one or more vehicle sensing systems 230 (or “sensor units”). The vehicle sensing system 230 may include one or more vehicle sensors, including but not limited to radio detection and ranging (radar) sensors, seating area latch sensors, seating area sensors, light detection and ranging (LiDAR) sensors, door sensors, proximity sensors, temperature sensors, wheel sensors, ambient weather sensors, ambient light sensors, vehicle interior and exterior cameras, one or more rain sensors, humidity sensors, tire pressure sensors, ultrasonic sensors, etc., configured to detect and locate objects inside and outside the first vehicle 102 using radio waves.

[0041] In some respects, VCU 208 can control vehicle operation aspects and implement one or more sets of instructions received from user equipment associated with a user (or vehicle operator) and one or more sets of instructions stored in memory 216, including instructions that operate as part of unit 210.

[0042] TCU 224 can provide vehicle connectivity to wireless computing systems on and outside the first vehicle 102, and may include a navigation (NAV) receiver 232 for receiving and processing GPS signals, a BLE module (BLEM) 234, a Wi-Fi transceiver, a UWB transceiver, and / or other wireless transceivers that can be configured for wireless communication (including cellular communication) between the first vehicle 102 and other systems (e.g., user devices, key fobs, NFC devices, etc.), computers, and modules. Figure 2 (Not shown in the image). NAV receiver 232 can be configured to determine the real-time geographical location of the vehicle. TCU 224 can communicate with ECU 212 via bus.

[0043] ECU 212 can control various aspects of vehicle operation and communication using inputs from the human driver, inputs from the autonomous vehicle controller, unit 210, and / or wireless signal inputs received via wireless connection from other connected devices (such as user devices associated with the vehicle user, servers, etc.).

[0044] The BCM 218 typically includes the integration of sensors, vehicle performance indicators, and variable reactors associated with vehicle systems. It may also include processor-based power distribution circuitry that controls functions associated with the vehicle body, such as lights, windows, safety devices, cameras, fans, headlights, audio systems, speakers, wipers, door locks and entry controls, mirrors, various comfort controls, sprinkler 238 (described below), one or more shock absorbers 240, etc. The BCM 218 can also operate as a gateway for bus and network interfaces to communicate with remote ECUs ( Figure 2 Interact with (not shown in the image).

[0045] The DAT controller 226 can provide Level 1 to Level 3 automated driving and driver assistance functionality, which may include features such as active parking assist, vehicle reversing assist, and adaptive cruise control. The DAT controller 226 can also provide various aspects of user and environmental inputs that can be used for user authentication.

[0046] In some respects, the vehicle computer 206 may connect to the infotainment system 236 (or the vehicle human-machine interface (HMI) 236). The infotainment system 236 may include a touchscreen interface portion and may include voice recognition features, and the ability to identify users based on facial recognition, voice recognition, fingerprint recognition, or other biometric methods. In other respects, the infotainment system 236 may also receive user commands / inputs via the touchscreen interface portion, and / or display notifications / recommendations, navigation maps, etc., on the touchscreen interface portion.

[0047] The first vehicle 102 may also include a sprayer 238 that can spray a cleaning liquid (e.g., water) onto the license plate 106 to clean it. The sprayer 238 may be an integral part of the first vehicle 102 or may be removably attached to it. The sprayer 238 may be located near the license plate 106. For example, the sprayer 238 may be located directly above the license plate 106. The first vehicle 102 may also include one or more shock absorbers 240, which may be part of a vehicle suspension system designed to absorb and dampen shock pulses. The shock absorbers 240 may suppress the movement of springs, which may be part of the vehicle suspension system.

[0048] The computing system architecture of the automotive computer 206, VCU 208, and / or unit 210 may omit certain computing modules. This should be easily understood. Figure 2 The computing environment depicted herein is an example of possible implementations according to this disclosure and should therefore not be considered limiting or exclusive.

[0049] Depending on some aspects, unit 210 may be integrated with and / or performed as part of ECU 212. Unit 210 may include transceiver 242, processor 244, and computer-readable storage 246, whether it is integrated with vehicle computer 206 or ECU 212, or whether it operates as a stand-alone computing system in the first vehicle 102.

[0050] Transceiver 242 can receive information / input from one or more external devices or systems (e.g., user devices associated with the vehicle user, the aforementioned external devices, etc.) via network 204. For example, transceiver 242 can receive license plate images from external devices via network 204. Furthermore, transceiver 242 can transmit notifications / data / signals (including request / command signals) to external devices. Additionally, transceiver 242 can receive information / input from components of the first vehicle 102 (such as infotainment system 236, VCU 208, etc.). Furthermore, transceiver 242 can transmit notification / command signals to components of the first vehicle 102 (such as VCU 208, infotainment system 236, BCM 218, etc.).

[0051] Processor 244 and memory 246 may be the same as or similar to processor 214 and memory 216, respectively. In some aspects, processor 244 may utilize memory 246 to store programs in code form and / or store data for execution of aspects according to this disclosure. Memory 246 may be a non-transitory computer-readable medium or memory storing license plate cleaning procedure code. In some aspects, memory 246 may store (obtained from an external device) a license plate image and a pre-stored image of the first vehicle 102 having license plate 106 (as well as other vehicle attributes / details).

[0052] In operation, processor 244 can acquire a license plate image (or "first image") associated with license plate 106 from an external device via transceiver 242. In response to acquiring the license plate image, processor 244 can determine, based on the license plate image (e.g., by performing an image processing algorithm on the acquired license plate image), that license plate 106 may be dirty. In response to determining that license plate 106 is dirty, processor 244 can perform a first predetermined action to clean license plate 106.

[0053] In a first exemplary aspect, the external device may be a second vehicle 202a that can be located near the first vehicle 102. For example, the second vehicle 202a may be located near the rear portion 110 of the vehicle. The first vehicle 102 may communicate with the second vehicle 202a via V2V communication. When the first vehicle 102 may be idling near a traffic light 302 (e.g.) Figure 3 (As shown) or when traveling at a steady speed on a highway during daytime hours, the processor 244 associated with the first vehicle 102 may transmit a request to the second vehicle 202a to capture a license plate image associated with license plate 106. The second vehicle 202a may receive the request and, in response, capture the license plate image via a second vehicle camera (located in the front portion of the second vehicle). In response to capturing the license plate image, the second vehicle 202a may transmit the license plate image to the processor 244. In some aspects, the second vehicle 202a may perform cleaning operations on the camera lens (and / or windshield) associated with the second vehicle 202a, or perform camera lens adjustments to optimally capture the license plate image.

[0054] In some aspects, before transmitting the request for image capture to the second vehicle 202a as described above, the processor 244 may determine the probability that the license plate 106 is dirty, and transmit the request to the second vehicle 202 based on the probability (e.g., when the probability is likely to be high) (e.g., during daytime hours). In an exemplary aspect, to determine the probability, the processor 244 may obtain route information associated with a vehicle route that the first vehicle 102 may be traveling (or a route that the first vehicle 102 may have recently traveled) from an external server. The route information may include weather conditions along the vehicle route, road conditions along the vehicle route, information associated with the presence or absence of the off-road area 104 along the vehicle route, etc. In response to obtaining the route information, the processor 244 may determine the probability based on the route information. For example, based on the route information, the processor 244 may estimate that the first vehicle 102 may move through the off-road area between 9:00 AM and 10:00 AM, and estimate that it may rain in the off-road area between 9:00 AM and 10:00 AM. Based on this estimation, processor 244 can determine that the probability that license plate 106 is dirty is high or higher than a predefined threshold (because the surface of the off-road area may be muddy). Processor 244 can then compare the probability to the predefined threshold. When the probability that license plate 106 is dirty is greater than the predefined threshold, processor 244 can transmit a request to the second vehicle 202. In an exemplary aspect, the probability may be greater than the predefined threshold when processor 244 determines that the first vehicle 102 has already driven (or is driving) through a dirty area, storm, etc.

[0055] Processor 244 can also determine that the probability of license plate 106 being dirty is greater than a predefined threshold based on other conditions, not limited to those described above. For example, when processor 244 (e.g., based on input obtained from vehicle sensing system 230) determines that the first vehicle 102 is parked in a storm area, processor 244 can determine that the probability is greater than a predefined threshold. In another example, when processor 244 determines that a third vehicle 304 moving in front of the first vehicle 102 has a dirty license plate (and the first vehicle 102 and the third vehicle 304 are driving / have been driving on the same road / off-road area), processor 244 can determine that the probability is greater than a predefined threshold.

[0056] Processor 244 can also determine, based on the factors mentioned above (including route information), which license plate might be dirty (in addition to determining the probability that license plate 106 is dirty). For example, processor 244 can determine whether both license plates located on the front portion 108 and the rear portion 110 of the vehicle are likely dirty, or whether only one license plate located on the rear portion 110 is likely dirty (e.g., when the first vehicle 102 has two license plates, one on the front portion 108 and the other on the rear portion 110). Based on this determination, processor 244 can select a vehicle located in front of or behind the first vehicle 102 to capture the license plate image and transmit the request to the selected vehicle. For example, when processor 244 determines that the license plate located on the rear portion 110 of the vehicle is likely dirty, processor 244 can select a second vehicle 202a located behind the first vehicle 102 (and may not select a third vehicle 304), and transmit the request to the selected second vehicle 202a. In a scenario where the first vehicle 102 may only have a license plate at the rear portion 110 (and may not have any license plate at the front portion 108), the processor 244 may only request license plate images from vehicles behind the first vehicle 102 (because license plate images from vehicles moving in front are not required).

[0057] When processor 244 transmits a request to second vehicle 202a, second vehicle 202a may use its built-in camera (e.g., an exterior camera located in the front portion of the second vehicle) to capture a license plate image associated with license plate 106 and transmit the license plate image to processor 244. Processor 244 may obtain the license plate image from second vehicle 202a. Upon receiving the license plate image, processor 244 may perform image processing on the obtained license plate image to determine whether license plate 106 may be dirty or obscured. In some aspects, processor 244 may compare the obtained license plate image with a pre-stored image (stored in memory 246) of first vehicle 102 having license plate 106 and determine, based on the comparison, that license plate 106 may be dirty / obscured (e.g., when license plate 106 is unreadable). In some aspects, processor 244 may use a machine learning model to determine whether license plate 106 is dirty based on the obtained license plate image.

[0058] In response to determining that license plate 106 may be dirty, processor 244 may perform a first predetermined action to clean license plate 106. In some aspects, processor 244 may transmit / output a first notification as part of the first predetermined action on a user interface associated with infotainment system 236 and / or user device. The first notification may indicate to the user that license plate 106 (e.g., rear and / or front license plate) is dirty and needs cleaning. The user / vehicle operator can see / hear the notification and accordingly perform manual cleaning of license plate 106.

[0059] In another aspect, as part of the first predetermined action, processor 244 may activate (or turn on) sprayer 238 for a first predetermined duration (e.g., 30 seconds) to spray cleaning fluid onto the dirty license plate 106 to remove snow / dust from the license plate 106. The cleaning fluid may be water or any other fluid capable of cleaning the license plate 106. In another aspect, as part of the first predetermined action, processor 244 may schedule a car wash to clean the license plate 106. For example, when the first vehicle 102 may be an autonomous vehicle, processor 244 may automatically schedule a car wash for the first vehicle 102.

[0060] In another aspect, as part of the first predetermined action, processor 244 can perform timely license plate cleaning by using rainwater based on a rain forecast. To perform this cleaning, processor 244 can first obtain information from an external server (not shown) related to weather conditions in the area where the first vehicle 102 may be located. Then, processor 244 can predict the likelihood of rainfall in the area based on the obtained weather condition information. In response to the rainfall forecast (i.e., when processor 244 determines the probability is likely to be high), processor 244 can adjust the vehicle's position so that rainwater can fall on license plate 106. For example, when the first vehicle 102 is parked in an open parking area (or a partially open area) and processor 244 predicts that it may rain soon (or is raining), processor 244 can adjust the vehicle to facilitate license plate cleaning by rainwater. Processor 244 can perform vehicle adjustments to increase the likelihood of rain hitting license plate 106 (and thus cleaning license plate 106).

[0061] In this configuration, processor 244 can transmit command signals to BCM 218 to adjust shock absorber 240 and vehicle orientation to facilitate license plate cleaning. For example, processor 244 can position the first vehicle 102 in a first orientation (e.g., the front portion 108 of the vehicle faces a predefined structure, such as a user's residence) and adjust shock absorber 240 to move the first vehicle 102 into a nose-down orientation, such as... Figure 4 As shown in view 402. In the nose-down configuration, the front shock absorber can be lowered and the rear shock absorber can be raised to increase the likelihood of rain hitting the license plate 106 located at the rear portion 110 of the vehicle (because the surface area associated with the license plate 106 facing the rain is increased in this location).

[0062] In a scenario where a dirty license plate is located at the front portion 108 of the vehicle, the processor 244 can reposition the vehicle to facilitate cleaning of other license plates located at the front portion 108. For example, in this case, the processor 244 can change the vehicle orientation from a first orientation to a second orientation (e.g., the rear portion 110 of the vehicle facing a predefined structure) after a predetermined duration (e.g., 10-15 minutes). Additionally, the processor 244 can adjust the shock absorber 240 to move the first vehicle 102 into a nose-up orientation, such as... Figure 4 As shown in view 404. In the nose-up configuration, the front shock absorbers can be raised and the rear shock absorbers can be lowered to increase the likelihood of rain impacting the license plate 106 located at the front portion 108 of the vehicle.

[0063] In response to performing a license plate cleaning operation using any of the methods described above, processor 244 can verify whether license plate 106 is clean or still dirty. Processor 244 can perform this verification after a predetermined duration following the execution of the first predetermined action described above (e.g., after 1-3 hours). To perform the verification, processor 244 can obtain a second image associated with license plate 106 from an external device (in the same manner as the first image described above). Based on the second image, processor 244 can verify whether license plate 106 is clean or still dirty (e.g., using the image processing methods described above). Processor 244 can then perform a second predetermined action based on the verification.

[0064] For example, when processor 244 verifies that license plate 106 is clean, processor 244 can output a second notification indicating that license plate 106 is clean on the user interface. On the other hand, when processor 244 determines that license plate 106 is still dirty, processor 244 can repeat the first predetermined action until license plate 106 is clean. For example, processor 244 can output a third notification indicating that license plate 106 is still dirty on the user interface. In another case, when license plate 106 is still dirty, processor 244 can activate sprayer 238 for a second predetermined duration (e.g., an additional 30 seconds or 1 minute).

[0065] In another aspect, the aforementioned external device may include a drone 202b. In this case, the processor 244 can utilize the drone 202b via V2D communication to facilitate license plate cleaning, such as... Figure 5 As shown. For example, when the first vehicle 102 is part of a fleet and is parked in a parking area where other vehicles (e.g., vehicles 502a, 502b, ..., 502n, collectively referred to as other vehicles 502) are also parked, the fleet manager can use drone 202b to facilitate license plate cleaning.

[0066] In some aspects, the drone 202b can fly behind and in front of parked vehicles (including the first vehicle 102 and other vehicles 502) and capture images of the license plates of the respective vehicles. The drone 202b can transmit the license plate image associated with the first vehicle 102 to the processor 244 via transceiver 242. In some aspects, the drone 202b can transmit the license plate image when the processor 244 transmits a request to the drone 202b to capture the license plate image. As described above, when the processor 244 determines that the probability of license plate 106 being dirty is greater than a predefined threshold, the processor 244 can transmit a request to the drone 202b to capture the license plate image. The processor 244 can obtain the license plate image from the drone 202b and determine, based on the obtained license plate image, that license plate 106 may be dirty (in the same manner as described above).

[0067] In response to determining that license plate 106 may be dirty, processor 244 may perform a first predetermined action as described above. In some aspects, as part of the first predetermined action, processor 244 may transmit a request to drone 202b to spray cleaning fluid onto license plate 106 to clean it. Drone 202b may receive the request and may spray cleaning fluid onto the first vehicle 102 (specifically, onto license plate 106) via an onboard sprayer associated with drone 202b. Similarly, if one or more of other vehicles 502 may have dirty license plates, drone 202b may also spray cleaning fluid onto these vehicles. Processor 244 may verify that license plate 106 is clean in the same manner as described above (e.g., by obtaining another image from drone 202b) and perform a second predetermined action as described above.

[0068] Alternatively, drone 202b can fly over the first vehicle 102 and other vehicles 502, capture images of the corresponding license plates, and perform image processing on the captured images to determine that license plate 106 may be dirty (and may not transmit the license plate image to processor 244 for analysis). In this case, in response to determining that license plate 106 may be dirty, drone 202b itself can spray cleaning fluid onto license plate 106 via an onboard sprayer associated with drone 202b (and may not require any request or command signal from processor 244 to spray cleaning fluid).

[0069] On another front, the external device can be a smart infrastructure unit, such as camera 202c. In some aspects, camera 202c can be a smart security camera installed outside the user's residence 602, such as... Figure 6 As shown. In some respects, the first vehicle 102 can be parked within the field of view (FOV) of the camera 202c, such as... Figure 6 As shown. In some aspects, processor 244 can acquire a license plate image associated with license plate 106 from camera 202c in the same manner as described above. Also in this case (e.g., when processor 244 determines that the probability of license plate 106 being dirty is greater than a predefined threshold), processor 244 can transmit a request to camera 202c to provide the license plate image. In response to transmitting the request, processor 244 can acquire the license plate image. Based on the acquired image, processor 244 can determine that license plate 106 is dirty and can perform a first predetermined action to clean license plate 106 (e.g., when license plate 106 may be dirty), as described above.

[0070] Alternatively, camera 202c (or any other smart infrastructure unit) can capture a license plate image and compare it with a pre-stored image (which may be stored in the camera's memory), without transmitting the license plate image back to processor 244 for analysis. The pre-stored image may include vehicle type / color / license plate number. In this case, camera 202c can perform image processing to compare the images and determine, based on the comparison, that license plate 106 may be dirty. In response to determining that license plate 106 may be dirty, camera 202c can transmit a notification indicating that license plate 106 may be dirty to the user interface via V2X communication. In another aspect, camera 202c can transmit a command signal to processor 244 to perform the first predetermined action described above. In some aspects, when multiple vehicles may be located within the camera's field of view (FOV), camera 202c can (e.g., based on type / style / color / license plate) determine which vehicle's license plate may be dirty and can accordingly provide a notification to the corresponding user / vehicle processor.

[0071] In another aspect, camera 202c can be mounted at a toll booth capable of capturing images of license plates. Such a toll booth camera can send a notification to the first vehicle 102 via a smart electronic tag indicating that its license plate is dirty. Processor 244 can receive such a notification and can perform a first predetermined action to clean the license plate 106, as described above.

[0072] Figure 7 A flowchart depicts an example method 700 for promoting the cleaning of vehicle license plates according to this disclosure. Further reference can be made to the preceding figures for further description. Figure 7 The following process is exemplary and is not limited to the steps described below. Furthermore, alternative embodiments may include more or fewer steps than shown or described herein, and may include these steps in a different order than that described in the following example embodiments.

[0073] Method 700 begins at step 702. At step 704, method 700 may include obtaining a license plate image from an external device (e.g., a second vehicle 202a, a drone 202b, or a camera 202c) by processor 244. At step 706, method 700 may include determining, based on the obtained image, that license plate 106 may be dirty. At step 708, method 700 may include, in response to determining that license plate 106 is dirty, performing a first predetermined action by processor 244 to clean license plate 106, as described above.

[0074] At step 710, method 700 can be stopped.

[0075] In the foregoing disclosure, reference has been made to the accompanying drawings, which form a part of the foregoing disclosure, illustrating specific embodiments in which the present disclosure may be practiced. It should be understood that other embodiments and structural changes may be utilized without departing from the scope of the present disclosure. References to “an embodiment,” “embodiment,” “example embodiment,” etc., in this specification indicate that the described embodiment may include specific features, structures, or characteristics, but each embodiment may not necessarily include said specific features, structures, or characteristics. Furthermore, such phrases do not necessarily refer to the same embodiment. Moreover, when features, structures, or characteristics are described in connection with embodiments, those skilled in the art will recognize such features, structures, or characteristics in conjunction with other embodiments, whether explicitly described or not.

[0076] Furthermore, where appropriate, the functions described herein may be performed by one or more of the following: hardware, software, firmware, digital components, or analog components. For example, one or more application-specific integrated circuits (ASICs) may be programmed to perform one or more of the systems and programs described herein. Certain terms are used throughout the specification and claims to refer to specific system components. As those skilled in the art will understand, components may be referred to by different names. This document is not intended to distinguish between components with different names but the same function.

[0077] It should also be understood that the term "example" as used herein is intended to be non-exclusive and non-restrictive in nature. More specifically, the term "example" as used herein refers to one of several examples, and it should be understood that there is no undue emphasis or preference on the particular example described.

[0078] Computer-readable media (also known as processor-readable media) include any non-transitory (e.g., tangible) medium that contributes to providing data (e.g., instructions) that can be read by a computer (e.g., by the computer's processor). Such media can take many forms, including but not limited to non-volatile and volatile media. Computing devices may include computer-executable instructions, wherein the instructions are executable by one or more computing devices (such as those listed above) and stored on a computer-readable medium.

[0079] Regarding the processes, systems, methods, heuristics, etc., described herein, it should be understood that although the steps of such processes, etc., are described as occurring in a certain ordered order, such processes can be practiced by performing the described steps in a different order than that described herein. It should also be understood that some steps may be performed simultaneously, other steps may be added, or some steps described herein may be omitted. In other words, the description of processes herein is provided for the purpose of illustrating various embodiments and should in no way be construed as limiting the claims.

[0080] Therefore, it should be understood that the above description is intended to be illustrative rather than restrictive. Many embodiments and applications beyond the examples provided will become apparent upon reading the above description. The scope should not be determined by reference to the above description, but rather by reference to the appended claims and the full scope of their equivalents. It is anticipated and expected that the techniques discussed herein will evolve in the future, and the disclosed systems and methods will be incorporated into such future embodiments. In conclusion, it should be understood that modifications and changes are possible with this application.

[0081] Unless explicitly indicated otherwise herein, all terms used in the claims are intended to be given their ordinary meaning as understood by one skilled in the art as described herein. Specifically, unless the claims explicitly limit the recitation to the contrary, the use of singular articles such as “a,” “the,” or “the” should be interpreted as one or more of the elements indicated by the recitation. Unless otherwise specifically stated or otherwise understood in the context of use, conditional language such as, in particular, “can,” “may,” “may,” or “may” is generally intended to express that some embodiments may include certain features, elements, and / or steps, while other embodiments may not include certain features, elements, and / or steps. Therefore, such conditional language is generally not intended to imply that one or more embodiments require each feature, element, and / or step in any way.

[0082] According to an embodiment, in order to perform the first predetermined action, the processor is configured to: obtain weather conditions from an external server; predict rainfall based on the weather conditions; and adjust the vehicle position based on the predicted rainfall to facilitate the cleaning of the license plate.

[0083] According to an embodiment, in order to adjust the vehicle position, the processor is configured to adjust the vehicle shock absorbers to facilitate the cleaning of the license plate.

[0084] According to an embodiment, in order to adjust the vehicle position, the processor is configured to reorient the first vehicle between a first orientation and a second orientation, wherein the first orientation is different from the second orientation.

[0085] According to the present invention, a vehicle is provided, the vehicle having: a license plate; a transceiver configured to receive an image of the license plate from an external device; and a processor configured to: transmit a request to the external device to capture the image of the license plate; obtain the image in response to transmitting the request; determine, based on the image, whether the license plate is dirty; and perform a predetermined action to clean the license plate in response to determining that the license plate is dirty.

[0086] According to the present invention, a method includes: obtaining an image of a license plate associated with a vehicle from an external device by a processor; determining, based on the image, that the license plate is dirty; and causing the processor to perform a predetermined action to clean the license plate in response to determining that the license plate is dirty.

Claims

1. A first vehicle, comprising: license; A transceiver configured to receive a first image of the license plate from an external device; as well as Processor, the processor being configured to: The first image is obtained from the transceiver; Determine whether the license plate is dirty based on the first image; as well as In response to determining that the license plate is dirty, a first predetermined action is performed to clean the license plate.

2. The first vehicle as claimed in claim 1, wherein the processor is further configured to transmit a request to the external device via the transceiver to capture the first image.

3. The first vehicle as claimed in claim 2, wherein the processor is further configured to: The probability of the license plate being dirty is determined based on the route information of the vehicle's journey; Compare the probability with a predefined threshold; and When the probability is greater than the predefined threshold, the request is transmitted to the external device.

4. The first vehicle as claimed in claim 3, wherein the probability is greater than the predefined threshold when the first vehicle has already traveled through an area, when the first vehicle has been traveling during a storm, or when the first vehicle is parked in a storm area.

5. The first vehicle as claimed in claim 2, wherein the external device is a second vehicle, and wherein the processor is configured to transmit the request via vehicle-to-vehicle (V2V) communication.

6. The first vehicle as claimed in claim 1, wherein, in order to perform the first predetermined action, the processor is configured to output a first notification on the user interface indicating that the license plate is dirty.

7. The first vehicle as claimed in claim 1, further comprising a sprayer configured to spray a cleaning liquid onto the license plate to clean the license plate.

8. The first vehicle as claimed in claim 7, wherein in order to perform the first predetermined action, the processor is configured to activate the sprayer for a first predetermined duration.

9. The first vehicle as claimed in claim 8, wherein the processor is further configured to: A second image is acquired from the external device after the first predetermined duration; Verify whether the license plate is clean based on the second image; and If the license plate is not clean, perform the second predetermined action.

10. The first vehicle as claimed in claim 9, wherein, in order to perform the second predetermined action, the processor is configured to output a second notification on the user interface indicating that the license plate is dirty.

11. The first vehicle as claimed in claim 9, wherein, in order to perform the second predetermined action, the processor is configured to reactivate the sprayer for a second predetermined duration.

12. The first vehicle as claimed in claim 1, wherein the external device is a drone.

13. The first vehicle of claim 12, wherein, in order to perform the first predetermined action, the processor is configured to request the drone to spray cleaning liquid onto the license plate.

14. The first vehicle as claimed in claim 1, wherein the external device is a smart infrastructure unit.

15. The first vehicle as claimed in claim 1, wherein, in order to perform the first predetermined action, the processor is configured to support arranging a car wash for the first vehicle.

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