Fog lamp control method, system and device of vehicle and electronic equipment

Abstract

The invention discloses a fog lamp control method, system and device for a vehicle and electronic equipment. The method comprises the steps that vehicle information of a target vehicle and rain and fog information of the target vehicle in the current driving environment are obtained, the vehicle information is used for representing the driving state of surrounding vehicles in the driving process of the target vehicle, and the surrounding vehicles are other vehicles except the target vehicle in the current driving environment; based on the vehicle information, determining a target number of surrounding vehicles and a target position corresponding to the target number; in response to a fog lamp control request of a target vehicle, a fog lamp control instruction of the vehicle is generated based on the target number, the target position and the rain and fog information, and the fog lamp control request is used for controlling the fog lamp state of the vehicle; and in response to the fog lamp control instruction, executing control operation on a fog lamp of the vehicle. According to the invention, the technical problems of low real-time performance and low accuracy of adjustment of the vehicle fog lamp in the prior art are solved.

Description

Technical Field

[0001] This invention relates to the technical field of vehicle control, and more specifically, to a method, system, device, and electronic device for controlling fog lights in a vehicle. Background Technology

[0002] Currently, with the advancement of technology, intelligent driving assistance systems for vehicles are increasingly becoming key elements in improving driving safety and experience. Among these systems, fog lights, as an important lighting device for vehicles, play a crucial role in rainy and foggy weather. However, traditional fog light control methods mainly rely on manual operation by the driver, resulting in technical problems such as low real-time performance and accuracy in fog light adjustment.

[0003] There is currently no effective solution to the technical problems of low real-time performance and accuracy of vehicle fog light adjustment. Summary of the Invention

[0004] This invention provides a method, system, device, and electronic device for controlling vehicle fog lights, to at least solve the technical problems of low real-time performance and accuracy of vehicle fog light adjustment in the prior art.

[0005] According to one aspect of the present invention, a fog light control method for a vehicle is provided, comprising: acquiring vehicle information of a target vehicle and rain / fog information of the target vehicle in the current driving environment, wherein the vehicle information is used to characterize the driving state of surrounding vehicles during the driving of the target vehicle, and the surrounding vehicles are other vehicles besides the target vehicle in the current driving environment; determining a target number of surrounding vehicles and a target position corresponding to the target number based on the vehicle information, wherein the target position is the relative position of the surrounding vehicles and the target vehicle; generating a fog light control command for the vehicle based on the target number, the target position, and the rain / fog information in response to a fog light control request from the target vehicle, wherein the fog light control request is used to control the fog light state of the vehicle; and performing a control operation on the fog lights of the vehicle in response to the fog light control command.

[0006] Furthermore, the fog light control command includes at least: a first control command, a second control command, and a third control command. The first control command controls the on / off state of the fog lights, the second control command controls the height of the fog lights, and the third control command controls the brightness of the fog lights. In response to a fog light control request from a target vehicle, based on the target number, target location, and rain / fog information, a fog light control command for the vehicle is generated, including: in response to an automatic control request for the fog light control request, controlling the automatic setting of the fog lights to be in the on state, and determining the rain / fog level of the target vehicle in the current driving environment based on the rain / fog information, wherein the rain / fog level is used to characterize the severity of rain / fog weather; in response to the rain / fog level meeting the fog light on-condition, generating an on-state command of the first control command, wherein the on-state command controls the fog lights to be in the on state; and in response to the on-state command, generating the second and third control commands based on the target number and target location.

[0007] Furthermore, in response to the activation command, based on the target quantity and target location, a second control command and a third control command are generated, including: in response to the activation command, generating a second control command based on the target quantity and target location; in response to the activation command, obtaining a rain and fog level strategy corresponding to the rain and fog level, and generating a third control command based on the rain and fog level strategy, wherein the rain and fog level strategy is used to characterize the control rules of the target vehicle for the fog lights in the current driving environment.

[0008] Furthermore, in response to a fog light control command, control operations are performed on the vehicle's fog lights, including: in response to a second control command, performing a height control operation on the fog lights; and in response to a third control command, performing a brightness control operation on the fog lights according to a rain / fog level strategy.

[0009] Furthermore, in response to the third control command, according to the rain and fog level strategy, a brightness control operation is performed on the fog lights, including: in response to the third control command, determining the fog light duty cycle of the target vehicle based on the rain and fog level, wherein the fog light duty cycle is used to characterize the ratio of the fog light's illumination time to the total time within a preset time period; and performing a brightness control operation on the fog lights based on the fog light duty cycle.

[0010] Further, the vehicle information of the target vehicle and the rain and fog information of the target vehicle in the current driving environment are obtained, including: obtaining a first image and a second image of the target vehicle in the current driving environment, wherein the image quality of the first image is higher than that of the second image, and the first image is identified as the target image; the target image is input into the image processing model of the target vehicle for analysis to obtain vehicle information and rain and fog information, wherein the image processing model is obtained by training on historical target image samples, and the historical target image samples are historical images of the target image.

[0011] Furthermore, the image processing model includes a backbone network, an aggregation network, and a prediction network. The backbone network is used to extract features from the target image, the aggregation network is constructed through the path aggregation network, and the prediction network is used to output vehicle information and rain / fog information.

[0012] According to another aspect of the present invention, a fog light control system for a vehicle is also provided, comprising: a camera for acquiring vehicle information of a target vehicle and rain / fog information of the target vehicle in the current driving environment, wherein the vehicle information is used to characterize the driving state of surrounding vehicles during the driving of the target vehicle, and the surrounding vehicles are other vehicles besides the target vehicle in the current driving environment; a visual detection module for determining the target number of surrounding vehicles and the target position corresponding to the target number based on the vehicle information, wherein the target position is the relative position of the surrounding vehicles and the target vehicle; a vehicle body electronic controller for generating a fog light control command for the vehicle based on the target number, target position, and rain / fog information in response to a fog light control request from the target vehicle, wherein the fog light control request is used to control the fog light state of the vehicle; and a fog light controller for performing control operations on the fog lights of the vehicle in response to the fog light control command.

[0013] According to another aspect of the present invention, a fog light control device for a vehicle is also provided, comprising: an acquisition unit, configured to acquire vehicle information of a target vehicle and rain / fog information of the target vehicle in the current driving environment, wherein the vehicle information is used to characterize the driving state of surrounding vehicles during the driving of the target vehicle, and the surrounding vehicles are other vehicles besides the target vehicle in the current driving environment; a determination unit, configured to determine a target number of surrounding vehicles and a target position corresponding to the target number based on the vehicle information, wherein the target position is the relative position of the surrounding vehicles and the target vehicle; a generation unit, configured to generate a fog light control command for the vehicle based on the target number, target position, and rain / fog information in response to a fog light control request from the target vehicle, wherein the fog light control request is used to control the fog light state of the vehicle; and an execution unit, configured to perform control operations on the fog lights of the vehicle in response to the fog light control command.

[0014] According to another aspect of the present invention, an electronic device is also provided, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods of various embodiments of the present invention during runtime.

[0015] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is executed, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of the present invention.

[0016] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0017] According to another aspect of the present invention, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0018] According to another aspect of the present invention, a computer program is also provided, which, when executed by a processor, implements the methods of the various embodiments of the present invention.

[0019] According to another aspect of the present invention, a vehicle is also provided that implements the methods of the various embodiments of the present invention when executed.

[0020] In this embodiment of the invention, vehicle information of the target vehicle and rain / fog information of the target vehicle in the current driving environment are obtained. The vehicle information is used to characterize the driving status of surrounding vehicles during the driving of the target vehicle. Surrounding vehicles are other vehicles besides the target vehicle in the current driving environment. Based on the vehicle information, the target number of surrounding vehicles and the target position corresponding to the target number are determined. The target position is the relative position of the surrounding vehicles and the target vehicle. In response to the fog light control request of the target vehicle, a fog light control command for the vehicle is generated based on the target number, target position, and rain / fog information. The fog light control request is used to control the fog light status of the vehicle. In response to the fog light control command, a control operation is performed on the fog lights of the vehicle. In other words, in this embodiment of the invention, vehicle information of the target vehicle and rain / fog information of the target vehicle in the current driving environment can be obtained first. The vehicle information is used to characterize the driving status of surrounding vehicles during the target vehicle's driving process. Then, based on the vehicle information obtained above, the target number of surrounding vehicles and the target position corresponding to the target number can be determined. In response to the target vehicle's fog light control request, a fog light control command for the vehicle can be generated based on the target number, target position, and rain / fog information. Finally, the fog light control operation of the vehicle can be performed according to the fog light control command. By taking into account the vehicle information and rain / fog information of the target vehicle during its driving process, the on / off state, height, and brightness of the fog lights can be intelligently adjusted to adapt to different weather and visibility conditions, thereby improving driving safety. This solves the technical problem of low real-time performance and accuracy of vehicle fog light adjustment and achieves the technical effect of improving the real-time performance and accuracy of vehicle fog light adjustment. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0022] Figure 1 This is a hardware structure block diagram of a computer terminal (or mobile device) for implementing a fog light control method for a vehicle according to an embodiment of the present invention.

[0023] Figure 2 This is a flowchart of a vehicle fog light control method according to an embodiment of the present invention;

[0024] Figure 3 This is a flowchart of a method for adjusting vehicle fog lights according to an embodiment of the present invention;

[0025] Figure 4 This is a schematic diagram of the model structure of an image processing model according to an embodiment of the present invention;

[0026] Figure 5 This is a schematic diagram of an aggregated network structure according to an embodiment of the present invention;

[0027] Figure 6 This is a schematic diagram of a vehicle fog light control system according to an embodiment of the present invention;

[0028] Figure 7 This is a schematic diagram of another vehicle fog light control system according to an embodiment of the present invention;

[0029] Figure 8 This is a schematic diagram of a fog light control device for a vehicle according to an embodiment of the present invention. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] According to an embodiment of the present invention, an embodiment of a fog light control method for a vehicle is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0033] Figure 1 This is a hardware structure block diagram of a computer terminal (or mobile device) for implementing a fog light control method for a vehicle according to an embodiment of the present invention. Figure 1 As shown, a computer terminal (or mobile device) may include one or more processors 102 (processors may include, but are not limited to, central processing units, graphics processors, digital signal processing chips, microprocessors, programmable logic devices, neural network processors, tensor processors, artificial intelligence type processors, etc.) and a memory 104 for storing data. In addition, it may include a transmission device 106 for communication functions, an input / output device 108, and a display 110. Those skilled in the art will understand that... Figure 1 The structures shown are for illustrative purposes only and do not limit the structure of the computer terminal (or mobile device) described above. For example, a computer terminal may include more or fewer components than those described above, or have a different configuration than those described above.

[0034] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the vehicle fog light control method in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the aforementioned vehicle fog light control method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to a mobile terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0035] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0036] Display 110 may be a touchscreen liquid crystal display (LCD). This LCD allows a user to interact with the user interface of the mobile terminal. In some embodiments, the mobile terminal has a graphical user interface (GUI), which allows the user to interact with the GUI via finger contact and / or gestures on a touch-sensitive surface. Optional human-computer interaction functions include: creating web pages, drawing, word processing, creating electronic documents, playing games, video conferencing, instant messaging, sending and receiving emails, a call interface, playing digital video, playing digital music, and / or web browsing. Executable instructions for performing these human-computer interaction functions are configured / stored in one or more processor-executable computer program products or readable storage media.

[0037] Figure 2 This is a flowchart of a fog light control method for a vehicle according to an embodiment of the present invention, such as... Figure 2 As shown, the method includes the following steps:

[0038] Step S201: Obtain vehicle information of the target vehicle and rain / fog information of the target vehicle in the current driving environment.

[0039] In the technical solution provided by step S201 in this embodiment of the invention, vehicle information is used to characterize the driving status of surrounding vehicles during the driving of the target vehicle. Surrounding vehicles are other vehicles besides the target vehicle in the current driving environment.

[0040] Optionally, the target vehicle can be a vehicle currently traveling on the road, and can be referred to as the current vehicle.

[0041] Optionally, at least two cameras can be installed on the target vehicle to capture multiple images of the target vehicle; based on the multiple images, vehicle information of the target vehicle and rain / fog information of the target vehicle in the current driving environment can be obtained.

[0042] Optionally, multiple images captured by the camera can be used to characterize the road conditions of the target vehicle in the current driving environment. The camera can be referred to as an in-vehicle camera.

[0043] For example, multiple cameras can be installed on a vehicle to capture multiple images that characterize the road conditions on which the vehicle is currently traveling. Based on these multiple images, vehicle information and rain / fog information can be obtained to determine the visibility in rain / fog and the vehicle's condition.

[0044] It should be noted that this is only a preferred implementation method for obtaining vehicle information of the target vehicle and rain / fog information of the target vehicle in the current driving environment, and does not specifically limit the process and method of obtaining vehicle information of the target vehicle and rain / fog information of the target vehicle in the current driving environment.

[0045] Step S202: Based on vehicle information, determine the target number of surrounding vehicles and the target location corresponding to the target number.

[0046] In the technical solution provided by step S202 in this embodiment of the invention, the target position is the relative position of the surrounding vehicles and the target vehicle.

[0047] Optionally, based on the vehicle information obtained in the above steps, the visual detection module in the vehicle's fog light control system can determine the number of vehicles surrounding the target vehicle, the target location corresponding to the number of vehicles, and the visibility level. The visibility level can be referred to as the rain / fog level.

[0048] Optionally, surrounding vehicles may include vehicles in front of the target vehicle and oncoming vehicles. The number of targets may include the number of vehicles in front and the number of oncoming vehicles. The target location may include the relative positions of the vehicles in front and the target vehicle, as well as the relative positions of the oncoming vehicles and the target vehicle.

[0049] Optionally, the number of vehicles in front can be simply referred to as the number of vehicles in front. The number of vehicles traveling in the opposite direction can be simply referred to as the number of vehicles in the opposite direction. The relative position of the vehicle in front and the target vehicle can be simply referred to as the position of the vehicle in front. The relative position of the vehicle traveling in the opposite direction and the target vehicle can be simply referred to as the position of the vehicles in the opposite direction.

[0050] Optionally, the vision detection module can perform real-time environmental analysis based on image information acquired by the onboard camera. This involves identifying whether rain or fog exists in the vehicle's current driving environment and the severity of the rain or fog. This is crucial for determining whether fog lights need to be turned on and for determining the brightness and height of the fog lights. In addition to detecting rain and fog conditions, the vision detection module also needs to identify vehicle information on the road, including the number, position, and driving status of vehicles ahead.

[0051] For example, the visual detection module analyzes and processes vehicle information and rain / fog information, enabling it to identify the number of vehicles ahead, their position, the number of oncoming vehicles, their position, and visibility levels.

[0052] It is understood that this is only a preferred embodiment for determining the target quantity and target location, and the process and method for determining the target quantity and target location are not specifically limited. As long as the process and method for determining the target quantity of surrounding vehicles and the target location corresponding to the target quantity are based on vehicle information, they are all within the protection scope of this invention, and will not be listed here.

[0053] Step S203: In response to the fog light control request from the target vehicle, generate a fog light control command for the vehicle based on the number of targets, the target location, and rain / fog information.

[0054] In the technical solution provided by step S203 in this embodiment of the invention, the fog light control request is used to control the fog light status of the vehicle.

[0055] Optionally, the fog light control request from the target vehicle can be obtained through the human-machine interface module in the vehicle's fog light control system. This fog light control request, also known as a user request, can include both automatic and manual control requests.

[0056] Optionally, the automatic control request is used to characterize the automatic determination of rain and fog visibility and vehicle information based on image data collected by the vehicle-mounted camera and computer vision algorithms, thereby deciding whether to turn on the fog lights and whether to adjust the brightness and height of the fog lights.

[0057] Optionally, a manual control request is used to indicate that the driver actively selects to turn the fog lights on or off, or manually adjusts the brightness and height of the fog lights, directly through the vehicle's control interface (such as a button, touchscreen, or knob).

[0058] In this embodiment, after obtaining the target quantity, target location, and rain / fog information, in response to the fog light control request from the target vehicle, the vehicle's fog light control command can be generated using the Body Control Module (BCM) in the vehicle's fog light control system, based on the target quantity, target location, and rain / fog information. The Body Control Module can be referred to as the Body Control Module.

[0059] For example, through the human-machine interaction module, the current user request (fog light control request) of the vehicle can be obtained. In response to the user request, based on the number of vehicles in front, the position of the vehicles in front, the number of vehicles opposite, the position of vehicles opposite, and the rain and fog information obtained in the above steps, fog light control commands can be generated to control the fog lights.

[0060] It should be noted that this is only a preferred embodiment for generating fog light control commands for vehicles, and the process and method for generating fog light control commands for vehicles are not specifically limited. As long as it is in response to the fog light control request of the target vehicle, and is based on the target number, target location, and rain and fog information, the process and method for generating fog light control commands for vehicles are within the protection scope of this invention, and will not be listed here.

[0061] Step S204: In response to the fog light control command, perform control operation on the vehicle's fog lights.

[0062] In the technical solution provided by step S204 in the embodiment of the present invention, in response to the fog light control command obtained in the above steps, the fog light controller in the vehicle's fog light control system is used to perform control operations on the vehicle's fog lights.

[0063] Optionally, the control operations include height control operations, brightness control operations, and switch control operations, wherein the height control operation characterizes the action of controlling the fog light height; the height control operation characterizes the action of controlling the fog light brightness; and the switch control operation characterizes the action of controlling the fog light to switch on or off.

[0064] For example, the vehicle's electronic controller, as the core of the vehicle's fog light control system, integrates information from the vision detection module and the human-machine interaction module. Based on the analysis results and user instructions, it makes decisions and sends fog light control commands to the fog light controller to achieve automatic control of the fog lights and ensure vehicle driving safety under different weather conditions.

[0065] In steps S201 to S204 of this embodiment of the invention, vehicle information of the target vehicle and rain / fog information of the target vehicle in the current driving environment can be obtained first. The vehicle information is used to characterize the driving status of surrounding vehicles during the driving process of the target vehicle. Then, based on the vehicle information obtained above, the target number of surrounding vehicles and the target position corresponding to the target number can be determined. In response to the fog light control request of the target vehicle, a fog light control command for the vehicle can be generated based on the target number, target position and rain / fog information. Finally, the fog light control operation of the vehicle can be performed according to the fog light control command. By taking into account the vehicle information and rain / fog information of the target vehicle during the driving process, the on / off state, height and brightness of the fog lights can be intelligently adjusted to adapt to different weather and visibility conditions, improve driving safety, and thus solve the technical problem of low real-time performance and accuracy of vehicle fog light adjustment, and achieve the technical effect of improving the real-time performance and accuracy of vehicle fog light adjustment.

[0066] The method described in this embodiment will be further described below.

[0067] As an optional embodiment, the fog light control command includes at least: a first control command, a second control command, and a third control command. The first control command controls the on / off state of the fog lights, the second control command controls the height of the fog lights, and the third control command controls the brightness of the fog lights. In response to a fog light control request from a target vehicle, based on the target number, target location, and rain / fog information, a fog light control command for the vehicle is generated, including: responding to an automatic control request for the fog light control request, controlling the automatic setting of the fog lights to be in an on state, and determining the rain / fog level of the target vehicle in the current driving environment based on the rain / fog information, wherein the rain / fog level is used to characterize the severity of rain / fog weather; in response to the rain / fog level meeting the fog light on-time conditions, generating an on-time command of the first control command, wherein the on-time command controls the fog lights to be in an on state; and in response to the on-time command, generating the second and third control commands based on the target number and target location.

[0068] In this embodiment of the invention, in response to the automatic control request of the fog light control request, the automatic setting of the fog lights is controlled to be in the on state, and based on the rain and fog information obtained above, the rain and fog level of the target vehicle in the current driving environment is determined, and the rain and fog level is used to characterize the severity of rain and fog weather. When the rain and fog level meets the fog light on-state conditions, the first control command on-state instruction is generated to control the fog lights to be in the on state. Then, in response to the on-state instruction, based on the target quantity and target position, the second control command and the third control command can be generated.

[0069] Optionally, the fog light control commands include at least: a first control command, a second control command, and a third control command, wherein the first control command can be called an on / off command, the second control command can be called a height adjustment command, and the third control command can be called a brightness adjustment command. The automatic setting can be called an automatic fog light setting.

[0070] For example, when the automatic fog light setting is enabled, the system can detect road conditions (rain and fog) for the target vehicle, obtain rain and fog information, and then determine the rain and fog level based on this information. When the detected rain and fog level meets the activation conditions, the vehicle's fog light control system will default to turning on the fog lights, i.e., it will generate fog light on / off commands. And while the fog lights are on, it will generate height adjustment commands and brightness adjustment commands based on the road conditions the target vehicle is traveling on.

[0071] Optionally, the priority of the manual control request for fog light control is set to the highest. In response to the manual control request for fog light control, the fog lights are turned on directly, and while the fog lights are on, the fog light brightness and fog light height are adjusted directly. The manual control request can be referred to as a user-initiated adjustment request.

[0072] For another example, the fog light control system prioritizes user-initiated adjustment requests to the highest level. If it receives a request to manually turn on the fog lights or an instruction to adjust their height and brightness, the fog light control system will directly turn on the fog lights, adjust their brightness and height, and then exit. If no manual adjustment request is received, the fog light control system will check if the automatic fog light setting is enabled.

[0073] As an optional embodiment, in response to the activation command, a second control command and a third control command are generated based on the target quantity and the target location, including: in response to the activation command, generating a second control command based on the target quantity and the target location; in response to the activation command, obtaining a rain and fog level strategy corresponding to the rain and fog level, and generating a third control command based on the rain and fog level strategy, wherein the rain and fog level strategy is used to characterize the control rules of the target vehicle for the fog lights in the current driving environment.

[0074] In this embodiment of the invention, in response to an activation command, i.e., when the fog lights are on, a second control command can be generated based on the target number and target location. For example, when the fog lights are already on, it is necessary to detect the road conditions on which the current vehicle is traveling, thereby generating a height adjustment command.

[0075] Furthermore, in response to the activation command (i.e., when the fog lights are on), the system acquires the rain / fog level strategy corresponding to the current rain / fog level, and generates a third control command based on this strategy. This rain / fog level strategy can be referred to as the rain / fog level decision. For example, when the fog lights are already on, the system can determine the rain / fog level strategy corresponding to the current rain / fog level, and then generate a brightness adjustment command based on that strategy.

[0076] For another example, when the rain and fog level is detected to meet the activation conditions, the fog light control system will turn on the fog lights by default and perform a global road condition detection. At the same time, it will execute a rain and fog level decision based on the rain and fog detection data from the previous level to generate brightness adjustment commands and height adjustment commands.

[0077] As an optional embodiment, in response to a fog light control command, a control operation is performed on the vehicle's fog lights, including: in response to a second control command, performing a height control operation on the fog lights; and in response to a third control command, performing a brightness control operation on the fog lights according to a rain / fog level strategy.

[0078] In this embodiment of the invention, in response to a second control command, a height control operation is performed on the fog lights. For example, after receiving a height adjustment command, a height control action can be performed on the fog lights according to the height adjustment command to achieve height control of the fog lights.

[0079] Optionally, in response to a third control command, the fog lights can be controlled according to a rain / fog level strategy. For example, after receiving a brightness control command, the fog lights can be controlled according to a rain / fog level strategy to achieve brightness control of the fog lights.

[0080] For example, when the fog level detects that the activation conditions are met, the fog light control system will automatically turn on the fog lights and perform a global road condition check. Simultaneously, it will make a fog level decision based on the previous level's fog detection, and finally, based on the global road condition and fog level decision, execute fog light height and brightness commands. While the fog lights are automatically on, the fog light control system continues to monitor road conditions in real time. If environmental conditions change, the fog light control system will determine whether to turn off the fog lights or further adjust their brightness and height based on the new detection results. This design ensures that fog light control can flexibly adapt to constantly changing weather and road conditions, ensuring that fog light control is always in optimal condition, thereby improving vehicle driving safety.

[0081] As an optional embodiment, in response to a third control command, a brightness control operation is performed on the fog lights according to a rain and fog level strategy, including: in response to the third control command, determining the fog light duty cycle of the target vehicle based on the rain and fog level, wherein the fog light duty cycle is used to characterize the ratio of the fog light's illumination time to the total time within a preset time period; and performing a brightness control operation on the fog lights based on the fog light duty cycle.

[0082] In this embodiment of the invention, in response to a third control command, the fog light duty cycle of the target vehicle is determined according to the rain and fog level. The fog light duty cycle is used to characterize the ratio of the fog light's illumination time to the total time within a preset time period. Then, based on the fog light duty cycle, a brightness control operation is performed on the fog light.

[0083] Optionally, the rain and fog levels can be divided into six types, and the fog light duty cycles corresponding to the six rain and fog levels are 0, 20%, 40%, 60%, 80%, and 100%, respectively.

[0084] For example, considering the high safety requirements of vehicle automatic fog lights, it is necessary to differentiate between rain and fog levels to control the brightness of the fog lights. That is, there are six rain and fog levels, from 0 to 5, which correspond to fog light duty cycles of 0, 20%, 40%, 60%, 80%, and 100%, respectively. Based on the fog light duty cycles obtained above, the brightness of the fog lights can be adjusted.

[0085] As an optional embodiment, obtaining vehicle information of the target vehicle and rain / fog information of the target vehicle in the current driving environment includes: obtaining a first image and a second image of the target vehicle in the current driving environment, wherein the image quality of the first image is higher than that of the second image, and determining the first image as the target image; inputting the target image into the image processing model of the target vehicle for analysis to obtain vehicle information and rain / fog information, wherein the image processing model is obtained by training on historical target image samples, and the historical target image samples are historical images of the target image.

[0086] In this embodiment of the invention, at least two vehicle-mounted cameras can be used to acquire a first image and a second image of the target vehicle under the current driving environment. The first image, with higher image quality, is selected from the first and second images and designated as the target image. The target image is then input into an image processing model for analysis to obtain vehicle information and rain / fog information. The first and second images are obtained from two different vehicle-mounted cameras.

[0087] For example, in the brightness processing of fog lights, in order to ensure that the fog lights provide the best lighting effect in rainy and foggy weather, the fog light control system will select the image with better image quality from the two on-board cameras as the basis for judging the rain and fog level, and then use the image processing model to analyze and process the image to obtain vehicle information and rain and fog information.

[0088] Furthermore, by selecting high-quality image data, the fog light control system can process the data more precisely, thereby improving the accuracy and efficiency of fog light brightness adjustment. The above method simplifies the algorithm because it does not require synchronizing and fusing data from two different cameras, thus reducing computational complexity and the computing power required. Based on this information, the fog light control system will synchronously adjust the brightness of the two fog lights to ensure that the driver has a clear view in any environment.

[0089] As an optional implementation, the image processing model includes a backbone network, an aggregation network, and a prediction network, wherein the backbone network is used to extract features from the target image, the aggregation network is constructed through the path aggregation network, and the prediction network is used to output vehicle information and rain / fog information.

[0090] In this embodiment of the invention, the backbone network is used to extract features from the target image, the aggregation network is used to construct the network through the path aggregation network, and the prediction network is used to output vehicle information and rain / fog information.

[0091] Optionally, the image processing model (e.g., the PP-YOLOE model) may include a backbone network, an aggregation network, and a prediction network, wherein the prediction network includes a detection head structure. By analyzing the input image using the image processing model, the category and location can be output, thereby achieving the purpose of obtaining vehicle information and rain / fog information.

[0092] Alternatively, the PP-YOLOE model achieves high-precision and robust target detection by using an anchor-free design, combining a more powerful backbone network and neck network, and then performing target detection through a detection head.

[0093] Optionally, the input image is first fed into the backbone network of the PP-YOLOE model to obtain its feature information. This feature information is then fed into the aggregation network to obtain the processed detection features. After the detection head receives the detection features from the previous part, a feature enhancement module improves the network's ability to perceive target edge features, resulting in feature information with a fixed size (length * width * dimension). Then, using regression and classification branches, and employing convolution operations and feature fusion techniques, bounding box positions and class predictions are performed respectively. Through multi-layer feature extraction and fusion, multi-scale detection, and an efficient detection head, the PP-YOLOE model can accurately identify rain and fog conditions and targets such as vehicles on the road, providing crucial environmental information for automatic fog light control systems, thereby achieving intelligent control of fog lights.

[0094] Optionally, the Path Aggregation Network (PANet) can be constructed from the aggregation network structure. The PANet algorithm can be used to improve detection accuracy through multi-scale feature fusion. In fog light control systems, the introduction of the PANet algorithm significantly improves the accuracy of target detection tasks, especially for small targets and targets in complex scenes. This means that even in low visibility conditions, fog light control systems can accurately identify small obstacles or distant vehicles on the road, providing better visual assistance to the driver.

[0095] Optionally, PANet's overall structure can extract features layer by layer through a bottom-up path. Meanwhile, the path aggregation network also employs a top-down path and lateral connections to achieve multi-scale feature fusion. The top-down path allows high-level features to be combined with low-level features, thus enriching feature information and enhancing feature representation capabilities, enabling more accurate localization and recognition in fog light control detection.

[0096] In embodiments of the present invention, vehicle information of the target vehicle and rain / fog information of the target vehicle in the current driving environment can be obtained first. The vehicle information is used to characterize the driving status of surrounding vehicles during the driving process of the target vehicle. Then, based on the vehicle information obtained above, the target number of surrounding vehicles and the target position corresponding to the target number can be determined. In response to the fog light control request of the target vehicle, a fog light control command for the vehicle can be generated based on the target number, target position, and rain / fog information. Finally, the fog light control operation of the vehicle can be performed according to the fog light control command. By taking into account the vehicle information and rain / fog information of the target vehicle during the driving process, the on / off state, height, and brightness of the fog lights can be intelligently adjusted to adapt to different weather and visibility conditions, thereby improving driving safety. This solves the technical problem of low real-time performance and accuracy of vehicle fog light adjustment and achieves the technical effect of improving the real-time performance and accuracy of vehicle fog light adjustment.

[0097] The technical solutions of the embodiments of the present invention will be illustrated below with reference to preferred embodiments.

[0098] Currently, with the rapid development of vehicle control technology, intelligent driving assistance systems are increasingly becoming key elements in improving driving safety and experience. Among these systems, fog lights, as an important lighting device, play a crucial role in rainy and foggy weather. However, current passenger vehicles generally require users to actively request fog lights via a switch, making it easy for drivers to overlook their use and creating safety hazards. This also presents technical problems with the real-time performance and accuracy of fog light adjustments.

[0099] Therefore, to solve the above problems, the present invention provides a fog light control method for a vehicle. This method includes first acquiring vehicle information of the target vehicle and rain / fog information of the target vehicle in the current driving environment. The vehicle information is used to characterize the driving status of surrounding vehicles during the target vehicle's operation. Then, based on the acquired vehicle information, the target number of surrounding vehicles and the target positions corresponding to that number can be determined. In response to a fog light control request from the target vehicle, a fog light control command can be generated based on the target number, target position, and rain / fog information. Finally, based on this fog light control command, control operations can be performed on the vehicle's fog lights. By considering the vehicle information and rain / fog information of the target vehicle during its operation, the method intelligently adjusts the on / off state, height, and brightness of the fog lights to adapt to different weather and visibility conditions, improving driving safety. This solves the technical problem of low real-time performance and accuracy in vehicle fog light adjustment, achieving the technical effect of improving the real-time performance and accuracy of vehicle fog light adjustment.

[0100] Figure 3 This is a flowchart of a vehicle fog light adjustment method according to an embodiment of the present invention, such as... Figure 3As shown, the method includes:

[0101] Step S301: Wait for fog light control command.

[0102] Step S302: Manually turn on the fog lights.

[0103] Step S303: Adjust the fog light height and brightness.

[0104] In this embodiment, the fog light control system prioritizes user-initiated adjustment requests to the highest level. If it receives a request to manually turn on the fog lights or an instruction to adjust their height and brightness, the fog light control system will directly turn on the fog lights, adjust their brightness and height, and then exit. If no manual adjustment request is received, the fog light control system will check whether the automatic fog light setting is enabled.

[0105] Step S304: Is the automatic fog light setting enabled?

[0106] In this embodiment, it is necessary to determine whether the fog light setting is turned on. If it is turned on, step S305 is executed; if it is not turned on, step S301 is executed.

[0107] Step S305: Detect road rain and fog conditions.

[0108] In this embodiment, when the automatic fog light setting is enabled, the fog light control system will detect road rain and fog conditions.

[0109] Step S306: Are the fog lights on?

[0110] In this embodiment, it is necessary to determine whether the fog lights are on. If they are on, steps S307 and S308 are executed; if they are not on, step S305 is executed.

[0111] Step S307: Detect the condition of vehicles on the road.

[0112] Step S308, Rain / Fog Level Decision.

[0113] In this embodiment, when the rain and fog level is detected to meet the activation conditions, the fog light control system will turn on the fog lights by default and perform a global road condition detection, while making a rain and fog level decision based on the rain and fog detection results from the previous level.

[0114] Step S309: Perform height adjustment.

[0115] Step S310: Perform brightness adjustment.

[0116] In this embodiment, fog light height and brightness commands are executed based on the overall road conditions and the level of rain and fog.

[0117] Furthermore, when the fog lights are automatically activated, the fog light control system continues to monitor road conditions in real time. If environmental conditions change, the fog light control system will determine whether to turn off the fog lights or further adjust their brightness and height based on the new detection results. This design ensures that the fog light control can flexibly adapt to constantly changing weather and road conditions, ensuring that the fog light control is always in optimal condition, thereby improving vehicle driving safety.

[0118] Figure 4 This is a schematic diagram of the model structure of an image processing model according to an embodiment of the present invention, as shown below. Figure 4 As shown, the specific model structure of the image processing model, namely the specific structure of the PP-YOLOE model, can be obtained. The PP-YOLOE model includes: a backbone network (including C1, C2, and C3 layers), an aggregation network (including P3, P4, and P5), and a prediction network.

[0119] Optionally, since the fog light control method of a vehicle needs to monitor rain and fog information and vehicle information in real time, it is necessary to select an algorithm with low latency and high accuracy to reflect the vehicle control status in real time, so as to ensure that the fog lights can be adjusted in time under low visibility conditions to improve driving safety.

[0120] Optionally, the overall structure of the model is as follows: Figure 4 As shown, by using an anchor-free design, a more powerful backbone network and neck network are combined to achieve high-precision and high-robustness target detection, which is then performed by a detection head.

[0121] Optionally, the input image is first fed into the backbone network of the PP-YOLOE model to obtain its feature information. This feature information is then fed into the aggregation network to obtain the processed detection features. After the detection head receives the detection features from the previous part, a feature enhancement module improves the network's ability to perceive target edge features, resulting in feature information with a fixed size (length × width × dimension). Then, using regression and classification branches, and employing convolution operations and feature fusion techniques, bounding box positions and category predictions are performed respectively. Through multi-layer feature extraction and fusion, multi-scale detection, and an efficient detection head, the PP-YOLOE model can accurately identify rain and fog conditions and targets such as vehicles on the road, providing crucial environmental information for automatic fog light control systems, thereby achieving intelligent control of fog lights.

[0122] Furthermore, Figure 5 This is a schematic diagram of an aggregation network structure according to an embodiment of the present invention. The aggregation network structure in the above PP-YOLOE model uses the PANet algorithm, i.e., as shown below. Figure 5As shown, multi-scale feature fusion is used to improve detection accuracy. In fog light control systems, the introduction of the PANet algorithm significantly improves the accuracy of target detection, especially for small targets and targets in complex scenes. This means that for fog light control systems, even in low visibility conditions, they can accurately identify small obstacles on the road or distant vehicles, providing better visual assistance to the driver.

[0123] Optionally, the overall structure of PANet is as follows: Figure 5 As shown, feature extraction can be performed layer by layer through a bottom-up path. The network also employs a top-down path and lateral connections to achieve multi-scale feature fusion. The top-down path allows high-level features to be combined with low-level features, which enriches feature information and enhances feature representation. Figure 5 The dashed arrows in the diagram indicate the flow direction of feature maps within the network, while solid arrows indicate the transfer of feature maps between different layers. Through this structure, the PANet algorithm can effectively aggregate feature information from different layers, thereby achieving more accurate localization and recognition in fog light control detection.

[0124] In this embodiment, vehicle information of the target vehicle and rain / fog information of the target vehicle in the current driving environment can be obtained first. The vehicle information is used to characterize the driving status of surrounding vehicles during the target vehicle's driving process. Then, based on the obtained vehicle information, the target number of surrounding vehicles and the target positions corresponding to the target number can be determined. In response to the target vehicle's fog light control request, a fog light control command can be generated based on the target number, target position, and rain / fog information. Finally, the fog light control operation can be performed on the vehicle's fog lights according to the fog light control command. By taking into account the vehicle information and rain / fog information of the target vehicle during driving, the on / off state, height, and brightness of the fog lights can be intelligently adjusted to adapt to different weather and visibility conditions, thereby improving driving safety. This solves the technical problem of low real-time performance and accuracy of vehicle fog light adjustment and achieves the technical effect of improving the real-time performance and accuracy of vehicle fog light adjustment.

[0125] This invention also provides a fog light control system for a vehicle. Figure 6 This is a schematic diagram of a vehicle fog light control system according to an embodiment of the present invention, as shown below. Figure 6 As shown, the vehicle's fog light control system 600 includes: a camera 601, a vision detection module 602, a body electronic controller 603, and a fog light controller 604.

[0126] Camera 601 is used to acquire vehicle information of the target vehicle and rain / fog information of the target vehicle in the current driving environment. The vehicle information is used to characterize the driving status of surrounding vehicles while the target vehicle is driving. Surrounding vehicles are other vehicles besides the target vehicle in the current driving environment.

[0127] Optionally, the fog light control system can be adapted to control either a single fog light or two fog lights. In a dual fog light configuration, the fog light control system can process vehicle information about road conditions in parallel and independently adjust the height of each fog light to adapt to different road conditions.

[0128] Optionally, in fog light brightness processing, to ensure optimal illumination in rainy or foggy weather, the system selects the image with better image quality from the two onboard cameras as the basis for judging the rain / fog level. By carefully selecting the data with better image quality, the fog light control system's centralized processing becomes more precise, thereby improving the accuracy and efficiency of brightness adjustment. This method simplifies the algorithm because it does not require synchronizing and fusing data from two different cameras, thus reducing computational complexity and the required computing power. Based on this information, the fog light control system will synchronously adjust the brightness of the two fog lights to ensure a clear view for the driver in any environment.

[0129] The visual detection module 602 is used to determine the number of surrounding vehicles and the corresponding target positions based on vehicle information, wherein the target position is the relative position of the surrounding vehicles and the target vehicle.

[0130] Optionally, the visual detection module requires a large amount of data for model training. Therefore, it is necessary to select an appropriate dataset and perform accurate data annotation. Since it needs to detect rain / fog information and vehicle information, two datasets (Outdoor-Rain dataset and BIT-Vehicle dataset) need to be selected. The Outdoor-Rain dataset is used for rain / fog, and the BIT-Vehicle dataset is used for vehicle data. The datasets are then populated using network resources. Considering the high safety requirements of automatic fog lights, it is necessary to differentiate between rain / fog levels to control the fog light brightness. Specifically, six rain / fog levels are used, from 0 to 5, corresponding to fog light duty cycles of 0, 20%, 40%, 60%, 80%, and 100%, respectively.

[0131] The vehicle body electronic controller 603 is used to respond to a fog light control request from a target vehicle and generate a fog light control command for the vehicle based on the number of targets, the target location, and rain / fog information. The fog light control request is used to control the fog light status of the vehicle.

[0132] Fog light controller 604 is used to perform control operations on the fog lights of a vehicle in response to fog light control commands.

[0133] Optionally, the fog light control command includes at least: a first control command, a second control command, and a third control command. The first control command controls the on / off state of the fog lights, the second control command controls the height of the fog lights, and the third control command controls the brightness of the fog lights. The system is also used to: respond to an automatic control request for fog light control, control the automatic setting of the fog lights to be in the on state, and determine the rain and fog level of the target vehicle in the current driving environment based on rain and fog information, wherein the rain and fog level is used to characterize the severity of rain and fog weather; generate an on command of the first control command in response to the rain and fog level meeting the conditions for fog light on, wherein the on command is used to control the fog lights to be in the on state; and generate the second and third control commands based on the target number and target location in response to the on command.

[0134] Optionally, the system is also used to: in response to the activation command, generate a second control command based on the target number and target location; in response to the activation command, obtain a rain and fog level strategy corresponding to the rain and fog level, and generate a third control command based on the rain and fog level strategy, wherein the rain and fog level strategy is used to characterize the control rules of the target vehicle for the fog lights in the current driving environment.

[0135] Optionally, the system is also used to perform height control operation on the fog lights in response to a second control command; and to perform brightness control operation on the fog lights according to a rain / fog level strategy in response to a third control command.

[0136] Optionally, the system is also used to, in response to a third control command, determine the fog light duty cycle of the target vehicle based on the rain / fog level, wherein the fog light duty cycle is used to characterize the ratio of the fog light's illumination time to the total time within a preset time period; and to perform brightness control operations on the fog lights based on the fog light duty cycle.

[0137] Optionally, the system is also used to acquire a first image and a second image of the target vehicle under the current driving environment, wherein the image quality of the first image is higher than that of the second image, and to identify the first image as the target image; input the target image into the image processing model of the target vehicle for analysis to obtain vehicle information and rain / fog information, wherein the image processing model is obtained by training on historical target image samples, and the historical target image samples are historical images of the target image.

[0138] Optionally, the image processing model includes a backbone network, an aggregation network, and a prediction network, wherein the backbone network is used to extract features from the target image, the aggregation network is constructed through the path aggregation network, and the prediction network is used to output vehicle information and rain / fog information.

[0139] In an embodiment of the present invention, Figure 7This is a schematic diagram of another vehicle fog light control system according to an embodiment of the present invention. Figure 6 Based on this, the vehicle's fog light control system 600 also includes a human-machine interaction module 605.

[0140] The human-computer interaction module 605 is used to obtain user requests.

[0141] In this embodiment, computer vision algorithms are used to analyze road conditions captured by the camera, including vehicle information and rain / fog information, to determine visibility and vehicle status. The vision detection module analyzes and processes the vehicle and rain / fog information, enabling it to identify the number of vehicles ahead, their positions, the number and positions of oncoming vehicles, and visibility levels. The human-machine interface module allows users to input requests for automatic fog light settings or manual fog light control. The vehicle's electronic controller, as the core of the fog light control system, integrates information from the vision detection module and the human-machine interface module. Based on the analysis results and user commands, it makes decisions and sends fog light control commands to the fog light controller to achieve automatic fog light control and ensure vehicle safety under different weather conditions.

[0142] In this embodiment, vehicle information of the target vehicle and rain / fog information of the target vehicle in the current driving environment are acquired through a camera. The vehicle information is used to characterize the driving status of surrounding vehicles during the target vehicle's operation. Surrounding vehicles refer to other vehicles besides the target vehicle in the current driving environment. Based on the vehicle information, a visual detection module determines the target number of surrounding vehicles and the target positions corresponding to the target number. The target positions are the relative positions of the surrounding vehicles and the target vehicle. In response to the fog light control request from the target vehicle, the vehicle's electronic controller generates fog light control commands based on the target number, target positions, and rain / fog information. The fog light control request is used to control the fog light status of the vehicle. In response to the fog light control commands, the fog light controller performs control operations on the vehicle's fog lights. This solves the technical problem of low real-time performance and accuracy of vehicle fog light adjustment, and achieves the technical effect of improving the real-time performance and accuracy of vehicle fog light adjustment.

[0143] Embodiments of the present invention also provide a fog light control device for a vehicle. Figure 8 This is a schematic diagram of a fog light control device for a vehicle according to an embodiment of the present invention, as shown below. Figure 8 As shown, the vehicle fog light control device 800 includes: an acquisition unit 801, a determination unit 802, a generation unit 803, and an execution unit 804.

[0144] The acquisition unit 801 is used to acquire vehicle information of the target vehicle and rain and fog information of the target vehicle in the current driving environment. The vehicle information is used to characterize the driving status of surrounding vehicles during the driving of the target vehicle. The surrounding vehicles are other vehicles besides the target vehicle in the current driving environment.

[0145] The determining unit 802 is used to determine the target number of surrounding vehicles and the target position corresponding to the target number based on vehicle information, wherein the target position is the relative position of the surrounding vehicles and the target vehicle.

[0146] The generation unit 803 is used to respond to the fog light control request of the target vehicle and generate the fog light control command of the vehicle based on the number of targets, the target location, and rain and fog information. The fog light control request is used to control the fog light status of the vehicle.

[0147] The execution unit 804 is used to perform control operations on the vehicle's fog lights in response to fog light control commands.

[0148] Optionally, the fog light control command includes at least: a first control command, a second control command, and a third control command. The first control command controls the on / off state of the fog lights, the second control command controls the height of the fog lights, and the third control command controls the brightness of the fog lights. The generation unit 803 may include: a first determining module, used to respond to the automatic control request of the fog light control request, control the automatic setting of the fog lights to be in the on state, and determine the rain and fog level of the target vehicle in the current driving environment based on rain and fog information, wherein the rain and fog level is used to characterize the severity of rain and fog weather; a first generating module, used to generate an opening command of the first control command in response to the rain and fog level meeting the fog light opening conditions, wherein the opening command is used to control the fog lights to be in the on state; and a second generating module, used to generate the second control command and the third control command in response to the opening command, based on the target quantity and the target location.

[0149] Optionally, the second generation module may include: a first generation submodule, used to generate a second control command based on the target number and target location in response to the activation command; and a second generation submodule, used to obtain a rain and fog level strategy corresponding to the rain and fog level in response to the activation command, and generate a third control command based on the rain and fog level strategy, wherein the rain and fog level strategy is used to characterize the control rules of the target vehicle for the fog lights in the current driving environment.

[0150] Optionally, the second generation module may further include: a first execution submodule, used to perform height control operation on the fog lights in response to the second control command; and a second execution submodule, used to perform brightness control operation on the fog lights according to the rain and fog level strategy in response to the third control command.

[0151] Optionally, the second execution submodule is also configured to respond to the third control command, determine the fog light duty cycle of the target vehicle based on the rain / fog level, wherein the fog light duty cycle is used to characterize the ratio of the fog light's illumination time to the total time within a preset time period; and perform brightness control operation on the fog lights based on the fog light duty cycle.

[0152] Optionally, the acquisition unit 801 may include: a second determining module, used to acquire a first image and a second image of the target vehicle under the current driving environment, wherein the image quality of the first image is higher than that of the second image, and to determine the first image as the target image; and an acquisition module, used to input the target image into the image processing model of the target vehicle for analysis to obtain vehicle information and rain / fog information, wherein the image processing model is obtained by training on historical target image samples, and the historical target image samples are historical images of the target image.

[0153] Optionally, the image processing model includes a backbone network, an aggregation network, and a prediction network, wherein the backbone network is used to extract features from the target image, the aggregation network is constructed through the path aggregation network, and the prediction network is used to output vehicle information and rain / fog information.

[0154] In this embodiment, the acquisition unit acquires vehicle information of the target vehicle and rain / fog information of the target vehicle in the current driving environment. The vehicle information characterizes the driving status of surrounding vehicles during the target vehicle's operation; surrounding vehicles refer to other vehicles besides the target vehicle in the current driving environment. The determination unit determines the target number of surrounding vehicles and the target positions corresponding to that number based on the vehicle information. The target positions are the relative positions of the surrounding vehicles and the target vehicle. The generation unit, responding to the target vehicle's fog light control request, generates fog light control commands based on the target number, target positions, and rain / fog information. The fog light control request controls the vehicle's fog light status. The execution unit, responding to the fog light control commands, performs control operations on the vehicle's fog lights. This solves the technical problem of low real-time performance and accuracy in vehicle fog light adjustment, achieving the technical effect of improving the real-time performance and accuracy of vehicle fog light adjustment.

[0155] According to embodiments of the present invention, an electronic device is also provided, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods of various embodiments of the present invention during runtime.

[0156] According to embodiments of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is executed, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of the present invention.

[0157] According to embodiments of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0158] According to embodiments of the present invention, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program, which, when executed by a processor, implements the methods of various embodiments of the present invention.

[0159] According to embodiments of the present invention, a computer program is also provided, which, when executed by a processor, implements the methods of the various embodiments of the present invention.

[0160] According to embodiments of the present invention, a vehicle is also provided that implements the methods of various embodiments of the present invention when executed.

[0161] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0162] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0163] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0164] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0165] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0166] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0167] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method of controlling a fog lamp of a vehicle, characterized by, The method comprises: obtaining vehicle information of a target vehicle and rain and fog information of the target vehicle in a current driving environment, wherein the vehicle information is used to represent driving states of surrounding vehicles in a process in which the target vehicle travels, the surrounding vehicles being vehicles other than the target vehicle in the current driving environment; determining a target number of the surrounding vehicles and a target position corresponding to the target number based on the vehicle information, wherein the target position is a relative position of the surrounding vehicles and the target vehicle; in response to a fog lamp control request of the target vehicle, generating a fog lamp control instruction of the vehicle based on the target number, the target position, and the rain and fog information, wherein the fog lamp control request is used to control a fog lamp state of the vehicle; in response to the fog lamp control instruction, performing a control operation on the fog lamp of the vehicle.

2. The method of claim 1, wherein, In the method, the fog lamp control instruction at least comprises a first control instruction, a second control instruction, and a third control instruction, the first control instruction is used to control an on-off state of the fog lamp, the second control instruction is used to control a height of the fog lamp, and the third control instruction is used to control a brightness of the fog lamp, and generating the fog lamp control instruction of the vehicle based on the target number, the target position, and the rain and fog information in response to the fog lamp control request of the target vehicle comprises: in response to an automatic control request of the fog lamp control request, controlling an automatic setting item of the fog lamp to be in an open state, and determining a rain and fog level of the target vehicle in the current driving environment based on the rain and fog information, wherein the rain and fog level is used to represent a severity of rain and fog weather; in response to the rain and fog level satisfying an opening condition of the fog lamp, generating an opening instruction of the first control instruction, wherein the opening instruction is used to control the fog lamp to be in an open state; in response to the opening instruction, generating the second control instruction and the third control instruction based on the target number and the target position.

3. The method of claim 2, wherein, in response to the opening instruction, generating the second control instruction and the third control instruction based on the target number and the target position comprises: in response to the opening instruction, generating the second control instruction based on the target number and the target position; in response to the opening instruction, obtaining a rain and fog level strategy corresponding to the rain and fog level, and generating the third control instruction based on the rain and fog level strategy, wherein the rain and fog level strategy is used to represent a control rule of the fog lamp of the target vehicle in the current driving environment.

4. The method of claim 2, wherein, in response to the fog lamp control instruction, performing the control operation on the fog lamp of the vehicle comprises: in response to the second control instruction, performing a height control operation on the fog lamp; in response to the third control instruction, performing a brightness control operation on the fog lamp according to the rain and fog level strategy.

5. The method of claim 4, wherein, in response to the third control instruction, performing the brightness control operation on the fog lamp according to the rain and fog level strategy comprises: In response to the third control instruction, a fog lamp duty cycle of the target vehicle is determined based on the rain and fog level, where the fog lamp duty cycle is used to represent a ratio of a lighting time to a total time of the fog lamp within a preset time period; The brightness control operation is performed on the fog lamp based on the fog lamp duty cycle.

6. The control method according to claim 1, characterized by Obtain vehicle information of a target vehicle and rain and fog information of the target vehicle in a current driving environment, including: Obtain a first image and a second image of the target vehicle in the current driving environment, where the image quality of the first image is higher than that of the second image, and the first image is determined as a target image; The target image is input into an image processing model of the target vehicle for analysis to obtain the vehicle information and the rain and fog information, where the image processing model is obtained by training historical target image samples.

7. The control method according to claim 6, characterized by The image processing model includes a backbone network, an aggregation network, and a prediction network, where the backbone network is used to extract features from the target image, the aggregation network is constructed by a path aggregation network, and the prediction network is used to output the vehicle information and the rain and fog information.

8. A fog lamp control system of a vehicle, characterized by, Including: A camera is configured to obtain vehicle information of a target vehicle and rain and fog information of the target vehicle in a current driving environment, where the vehicle information is used to represent driving states of surrounding vehicles in a driving process of the target vehicle, and the surrounding vehicles are other vehicles in the current driving environment except the target vehicle; A visual detection module is configured to determine a target number of the surrounding vehicles and target positions corresponding to the target number based on the vehicle information, where the target positions are relative positions of the surrounding vehicles and the target vehicle; A body electronic controller is configured to generate a fog lamp control instruction of the vehicle based on the target number, the target positions, and the rain and fog information in response to a fog lamp control request of the target vehicle, where the fog lamp control request is used to control a fog lamp state of the vehicle; A fog lamp controller is configured to perform a control operation on the fog lamp of the vehicle in response to the fog lamp control instruction.

9. A fog lamp control device of a vehicle, characterized by comprising: Including: An obtaining unit is configured to obtain vehicle information of a target vehicle and rain and fog information of the target vehicle in a current driving environment, where the vehicle information is used to represent driving states of surrounding vehicles in a driving process of the target vehicle, and the surrounding vehicles are other vehicles in the current driving environment except the target vehicle; A determining unit is configured to determine a target number of the surrounding vehicles and target positions corresponding to the target number based on the vehicle information, where the target positions are relative positions of the surrounding vehicles and the target vehicle; A generating unit is configured to generate a fog lamp control instruction of the vehicle based on the target number, the target positions, and the rain and fog information in response to a fog lamp control request of the target vehicle, where the fog lamp control request is used to control a fog lamp state of the vehicle. An execution unit is configured to perform a control operation on the fog lamp of the vehicle in response to the fog lamp control instruction.

10. An electronic device, comprising: The method comprises the following steps: A memory stores an executable program. A processor is configured to run the program, and the program performs the method in any one of claims 1 to 7 when running.

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