Fog lamp control method and device and vehicle

Abstract

The invention relates to the technical field of vehicles, in particular to a fog lamp control method and device and a vehicle. The method comprises the following steps: determining an index degradation degree of at least one environment index based on multi-modal environment information of a vehicle; determining whether the vehicle is in a target scene based on the index degradation degree of the at least one environment index; wherein the judgment condition that the vehicle is located in the target scene is that the target scene belongs to one of a plurality of preset scenes influencing the environmental visibility, and the proportion of a target environment index in a plurality of environment indexes bound with the target scene is greater than a preset proportion; the target environment index is an environment index of which the index deterioration degree is greater than a preset index deterioration degree; and when the vehicle is in the target scene, the fog lamp of the vehicle is controlled to be turned on, so that the low-visibility driving scene can be identified more accurately, and the intelligent control performance of the fog lamp is improved.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and more particularly to the field of lighting control technology, specifically to a fog light control method, device, and vehicle. Background Technology

[0002] Currently, vehicles have become an indispensable part of people's daily lives, and people's requirements for vehicle safety are getting higher and higher. Especially in foggy weather, the ability to control the fog lights to turn on and off in a timely manner, and to transmit vehicle information more clearly and accurately, in order to ensure the safe operation of vehicles, has become a key focus of people's attention.

[0003] Existing fog light control methods generally rely on the driver to determine whether to turn the fog lights on or off based on the vehicle's driving conditions. However, this manual judgment and activation of the fog lights not only cause inconvenience but may also lead to traffic accidents or unnecessary safety hazards due to delays or distraction. Therefore, this application proposes a more effective fog light control method. Summary of the Invention

[0004] This invention provides a fog light control method, device, and vehicle to at least solve the technical problem in related technologies where untimely or inaccurate fog light control affects vehicle driving safety. The technical solution adopted in this application is as follows: Firstly, this application provides a fog light control method, comprising: determining the degradation degree of at least one environmental indicator based on the vehicle's multimodal environmental information; determining whether the vehicle is in a target scenario based on the degradation degree of the at least one environmental indicator; wherein the determination condition for the vehicle being in the target scenario is: the target scenario belongs to one of multiple preset scenarios affecting environmental visibility and the proportion of the target environmental indicator among the multiple environmental indicators bound to the target scenario is greater than a preset proportion; the target environmental indicator is an environmental indicator whose degradation degree is greater than a preset degradation degree; and controlling the vehicle's fog lights to turn on when the vehicle is in the target scenario.

[0005] Based on the aforementioned technical means, this application comprehensively analyzes multimodal environmental information during vehicle driving, enabling more accurate quantification of the degradation degree of different environmental indicators during vehicle driving. By considering the degradation degree of multiple environmental indicators and pre-set judgment conditions corresponding to low-visibility driving scenarios, it can more comprehensively and effectively identify whether a vehicle is in a low-visibility driving scenario, avoiding misjudgments of the vehicle's environmental state caused by the collection of environmental information from a single sensor. In low-visibility driving scenarios, it promptly controls the fog lights to activate, providing auxiliary lighting to avoid driving inconvenience caused by untimely manual fog light activation, thus improving the vehicle's adaptability to different scenarios and enhancing driving safety.

[0006] In one possible implementation, the method further includes: controlling the vehicle's fog lights to turn on when the vehicle is in the target scene and the vehicle meets preset conditions; wherein the preset conditions are that the tracking stability of the vehicle in target tracking is lower than a preset tracking stability threshold.

[0007] Based on the aforementioned technical means, the tracking stability of the vehicle tracking target in this application characterizes the vehicle's tracking perception capability for the tracking target. Since the vehicle's tracking perception capability for the tracking target is significantly reduced when driving in low-visibility conditions, the fog lights are only activated after recognizing that the vehicle is in a low-visibility driving scene and its tracking perception capability for the tracking target is low. This reduces the occurrence of fog lights being mistakenly activated due to errors in recognizing low-visibility driving scenes.

[0008] In one possible implementation, the method further includes: controlling the vehicle's fog lights to turn on when the vehicle is in the target scene and the duration of the vehicle meeting the preset conditions is longer than the preset duration.

[0009] Based on the above technical means, this application turns on the fog lights when the vehicle is in a low visibility driving scenario and the vehicle's tracking perception ability for the target is low for a continuous period of time, thereby reducing misjudgments caused by instantaneous fluctuations in tracking perception ability and further reducing the occurrence of fog lights being turned on accidentally.

[0010] In one possible implementation, determining the degree of degradation of at least one environmental indicator based on the vehicle's multimodal environmental information includes: for any one of the at least one environmental indicators, determining the actual value of the environmental indicator based on the multimodal environmental information; and determining the degree of degradation of the environmental indicator based on the actual value and the calibration value of the environmental indicator; wherein the calibration value is the value of the environmental indicator in a scenario where the environmental visibility is higher than a preset environmental visibility threshold.

[0011] Based on the aforementioned technical means, this application compares the actual value of each environmental indicator with the calibrated value under high visibility driving scenarios, and quantifies the degree of degradation of the environmental indicators by comparing the difference, so as to more intuitively and accurately reflect the changes in environmental indicators, thereby providing a basis for the identification of low visibility driving scenarios.

[0012] In one possible implementation, the tracking stability of the vehicle in target tracking includes: determining the tracking fluctuation of the vehicle in target tracking; wherein the tracking fluctuation includes the fluctuation of the vehicle tracking the target, and / or the fluctuation of the distance between the vehicle and the target; and determining the tracking stability based on the tracking fluctuation.

[0013] Based on the aforementioned technical means, this application comprehensively and holistically reflects the dynamic change characteristics between the vehicle and the tracked target by using fluctuation information such as the fluctuation of the tracked target within the vehicle's perception range and the fluctuation of the distance between the vehicle and the tracked target, thereby improving the accuracy of determining the tracking stability of the vehicle in target tracking.

[0014] In one possible implementation, the fluctuation of the vehicle's tracking target is characterized by the consistency ratio of the tracking targets within the vehicle's tracking range at adjacent acquisition times; the fluctuation of the distance between the vehicle and the tracking target is characterized by the volatility of the average tracking distance between the vehicle and the tracking targets within the vehicle's tracking range at adjacent acquisition times; based on the tracking fluctuation, tracking stability is determined, including: determining tracking stability based on the consistency ratio and / or volatility; wherein, the consistency ratio is directly proportional to tracking stability; and the volatility is inversely proportional to tracking stability.

[0015] Based on the above technical means, this application quantifies the fluctuation of the vehicle tracking target by the consistency ratio of the tracking target within the vehicle tracking range at adjacent acquisition times; it quantifies the distance fluctuation between the vehicle and the tracking target by the fluctuation rate of the average tracking distance between the vehicle and the tracking target within the vehicle tracking range at adjacent acquisition times; and it accurately calculates the tracking stability of the target tracking by combining the consistency ratio and the fluctuation rate.

[0016] In one possible implementation, the above method further includes turning off the fog lights after the fog lights are turned on and the vehicle leaves the target scene.

[0017] Based on the aforementioned technical means, this application continuously determines whether the vehicle is still in a low-visibility driving scenario after the fog lights are turned on. When the vehicle leaves the low-visibility driving scenario, the fog lights are turned off to avoid wasting resources and to reduce glare interference to other vehicles caused by the fog lights being on in high-visibility driving scenarios, thereby reducing the driving risk of the vehicle.

[0018] In one possible implementation, environmental metrics include: point cloud detection distance reduction rate, point cloud density reduction rate, point cloud noise ratio, image sharpness reduction rate, image contrast reduction rate, and atmospheric light intensity value.

[0019] Based on the aforementioned technical means, this application pre-sets multiple environmental indicators affected by changes in ambient visibility, so as to accurately identify whether a vehicle is in a low-visibility driving scenario through multiple environmental indicators.

[0020] Secondly, this application provides a fog light control device, comprising: an indicator acquisition module, used to determine the indicator degradation degree of at least one environmental indicator based on the vehicle's multimodal environmental information; a scene judgment module, used to determine whether the vehicle is in a target scene based on the indicator degradation degree of at least one environmental indicator; wherein, the determination condition for the vehicle being in the target scene is: the target scene belongs to one of multiple preset scenes affecting environmental visibility and the proportion of the target environmental indicator among the multiple environmental indicators bound to the target scene is greater than a preset proportion; the target environmental indicator is an environmental indicator whose indicator degradation degree is greater than a preset indicator degradation degree; and a fog light control module, used to control the vehicle's fog lights to turn on when the vehicle is in the target scene.

[0021] In one possible implementation, the device is further configured to control the vehicle's fog lights to turn on when the vehicle is in the target scene and the vehicle meets preset conditions; wherein the preset conditions are that the tracking stability of the vehicle in target tracking is lower than a preset tracking stability threshold.

[0022] In one possible implementation, the device is further configured to control the vehicle's fog lights to turn on when the vehicle is in the target scene and the duration of the vehicle meeting the preset conditions is longer than the preset duration.

[0023] In one possible implementation, the indicator acquisition module is used to determine the actual indicator value of any environmental indicator based on multimodal environmental information for at least one environmental indicator; and to determine the indicator degradation degree of the environmental indicator based on the actual indicator value and the calibration indicator value of the environmental indicator; wherein the calibration indicator value is the indicator value of the environmental indicator in a scenario where the environmental visibility is higher than a preset environmental visibility threshold.

[0024] In one possible implementation, the above-mentioned device is further used to determine the tracking fluctuation of the vehicle in tracking the target; wherein the tracking fluctuation includes the fluctuation of the vehicle tracking the target, and / or the distance fluctuation between the vehicle and the tracking target; and based on the tracking fluctuation, the tracking stability is determined.

[0025] In one possible implementation, the fluctuation of the vehicle's tracking target is characterized by the consistency ratio of the tracking targets within the vehicle's tracking range at adjacent acquisition times; the fluctuation of the distance between the vehicle and the tracking target is characterized by the volatility of the average tracking distance between the vehicle and the tracking targets within the vehicle's tracking range at adjacent acquisition times; the aforementioned device is specifically used to determine tracking stability based on the consistency ratio and / or volatility; wherein, the consistency ratio is directly proportional to tracking stability; and the volatility is inversely proportional to tracking stability.

[0026] In one possible implementation, the device is also used to turn off the fog lights after the vehicle has left the target scene, following the initial activation of the fog lights.

[0027] In one possible implementation, environmental metrics include: point cloud detection distance reduction rate, point cloud density reduction rate, point cloud noise ratio, image sharpness reduction rate, image contrast reduction rate, and atmospheric light intensity value.

[0028] Thirdly, this application provides a vehicle including a fog light, which is controlled to be turned on or off using the fog light control method described in the first aspect.

[0029] Fourthly, this application provides an electronic device, including: a processor and a memory, wherein the memory stores at least one computer program, and the at least one computer program is loaded and executed by the processor to implement the method described in the first aspect and any possible implementation thereof.

[0030] Fifthly, this application provides a computer-readable storage medium that, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform the methods described in the first aspect and any possible implementation thereof.

[0031] In a sixth aspect, this application provides a computer program product comprising computer instructions that, when executed on an electronic device, cause the electronic device to perform the method described in the first aspect and any of its possible implementations.

[0032] It should be noted that the technical effects of any of the implementation methods in aspects two through six can be found in the technical effects of the corresponding implementation methods in aspect one, and will not be repeated here.

[0033] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0034] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application, and do not constitute an undue limitation of this application.

[0035] Figure 1 This is a schematic diagram of the structure of a vehicle shown in an embodiment of this application; Figure 2 This is a flowchart illustrating a fog light control method according to an embodiment of this application; Figure 3 This is a flowchart illustrating yet another fog light control method according to an embodiment of this application; Figure 4 This is a schematic diagram illustrating an embodiment of the present application for determining tracking stability; Figure 5 This is a flowchart illustrating another fog light control method according to an embodiment of this application; Figure 6 This is a block diagram illustrating a fog light control device according to an embodiment of this application; Figure 7 This is a block diagram illustrating an electronic device according to an embodiment of this application. Detailed Implementation

[0036] To enable those skilled in the art to better understand the technical solutions of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0037] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application 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 this application described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0038] In the embodiments of this application, the words "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the words "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a specific manner.

[0039] The technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0040] The vehicle provided in this application embodiment can also be referred to as a vehicle, mobile carrier, electric vehicle (EV), hybrid electric vehicle (HEV), plug-in hybrid electric vehicle (PHEV), fuel cell vehicle (FCV), autonomous vehicle, intelligent and connected vehicle (ICV), driverless vehicle, etc.

[0041] In this application, the vehicle can be a sedan, a sport utility vehicle (SUV), a truck, a special vehicle (such as an ambulance, fire truck, police car, etc.), a driverless taxi, a smart connected bus, an autonomous logistics vehicle, an electric truck, etc. Furthermore, this method is also applicable to various special-purpose vehicles, such as agricultural vehicles, mining vehicles, forestry vehicles, airport vehicles, and port vehicles. This application does not impose specific limitations in this regard.

[0042] like Figure 1 As shown, the vehicle provided in this application embodiment specifically includes: fog lights 101 and fog light control device 102; wherein, the fog light control device 102 controls the fog lights 101 to turn on or off by sending fog light control commands to the fog lights 101.

[0043] The aforementioned vehicles may be those with intelligent driving functions enabled, or those without intelligent driving functions enabled or equipped with them.

[0044] The aforementioned fog lights can include front fog lights and rear fog lights. The front fog lights typically emit a wide, flat, diffused beam of light, primarily used to illuminate a wide area of ​​the road surface in front of the vehicle while avoiding upward glare. The rear fog lights emit a very bright, stable red light with strong penetrating power, used to ensure that following vehicles can clearly see the vehicle and remind them to maintain a safe distance.

[0045] As one possible implementation, the vehicle also includes a multimodal environmental information acquisition device 103, which is used to acquire multimodal environmental information around the vehicle and transmit the multimodal environmental information to the fog light control device 102.

[0046] The aforementioned multimodal environmental information acquisition device 103 includes a lidar 103-1, a camera 103-2, and a millimeter-wave radar 103-3.

[0047] The aforementioned multimodal environmental information acquisition device 103 also includes various types of sensors such as infrared sensors, ultrasonic sensors, and radar, to acquire visual images around the vehicle through cameras, point cloud images around the vehicle through lidar, thermal imaging images around the vehicle through infrared sensors, and distribution information of objects around the vehicle through radar and ultrasonic sensors, thereby reflecting weather information, road condition information, etc. around the vehicle through the acquired multimodal environmental information.

[0048] The aforementioned fog light control device 102 is specifically used to determine the degree of degradation of environmental indicators based on multimodal environmental information. These environmental indicators are at least one pre-set environmental indicator affected by changes in ambient visibility. For example, environmental indicators include point cloud detection distance, point cloud density, point cloud noise, image clarity, image contrast, and atmospheric light intensity. The degree of degradation of these environmental indicators includes the rate of decrease in point cloud detection distance, the rate of decrease in point cloud density, the point cloud noise ratio, the rate of decrease in image clarity, the rate of decrease in image contrast, and atmospheric light intensity. Then, based on the degree of degradation of at least one environmental indicator, it is determined whether the vehicle is in a target scene. The criteria for determining whether the vehicle is in a target scene are: the target scene belongs to one of multiple pre-set scenes affecting ambient visibility, and the proportion of the target environmental indicator among the multiple environmental indicators bound to the target scene is greater than a pre-set proportion; the target environmental indicator is an environmental indicator whose degree of degradation is greater than a pre-set degree of degradation. When the vehicle is in the target scene, a fog light activation command is sent to the fog light 101 to control the fog light 101 to activate.

[0049] The aforementioned preset scenarios include: driving in foggy / smoggy weather, driving in rainy / snowy weather, driving in sandstorm / dust storm weather, driving in smog, and driving at night. Among these, driving in smog and driving at night are caused by human factors. Driving in smog includes situations such as fire, and driving at night includes situations such as oncoming headlights.

[0050] For example, the current scenario is a foggy driving scenario in a preset scenario; the degradation of environmental indicators bound to the foggy driving scenario includes: a 20% decrease in point cloud detection distance, a 15% decrease in point cloud density, an 8% decrease in point cloud noise ratio, an 18% decrease in image clarity, and a 25% decrease in image contrast; and a preset degradation of 14%, with a preset proportion of 75%; since the actual proportion of 80% is greater than the preset proportion of 75%, it is determined that the vehicle is in the target scenario.

[0051] The aforementioned fog light control device 102 is also used to control the fog lights 101 to turn off when the vehicle leaves the target scene.

[0052] In practical applications, the fog light control device 102 is connected to the multimodal environmental information acquisition device 103, and the fog light control device 102 can be connected to one or more fog lights 101.

[0053] For ease of understanding, this application uses the example of a fog light 101 communicating with a fog light control device 102 and a multimodal environmental information acquisition device 103.

[0054] As a feasible approach, Figure 1 The fog lights 101, fog light control device 102, and multimodal environmental information acquisition device 103 are installed in the vehicle. The fog lights 101 and fog light control device 102 can be functional modules integrated into the same device, or they can be independently installed devices. This application does not impose any limitations on comparison.

[0055] It is easy to understand that when the fog light 101 and the fog light control device 102 are functional modules integrated within the same device, the communication method between the fog light 101 and the fog light control device 102 is the same as the communication method between internal modules of the device. In this case, the communication process between the two is the same as the communication process when the fog light 101 and the fog light control device 102 are set up independently. For ease of understanding, this application mainly uses the example of the fog light 101 and the fog light control device 102 being set up independently for explanation.

[0056] As a feasible approach, Figure 1 The fog light control device 102 can be installed in an in-vehicle or remote electronic device; the aforementioned electronic device can be a terminal, a server, or other types of electronic devices.

[0057] When the fog light control device 102 is located in an in-vehicle terminal, the terminal can be a device installed or configured inside the vehicle to provide data connectivity to vehicle users or vehicle owners, a handheld device with wireless connectivity, or other processing devices connected to a wireless modem. The terminal can communicate with one or more core networks via a radio access network (RAN).

[0058] When the fog light control device 102 is located at a remote terminal, the terminal can be a device that provides data connectivity to vehicle users or vehicle owners, a handheld device with wireless connectivity, or other processing devices connected to a wireless modem. The terminal can communicate with one or more core networks via a radio access network (RAN). The terminal can be a mobile terminal, such as a computer with a mobile terminal, or a mobile device that exchanges voice and / or data with the radio access network, such as a mobile phone, tablet, laptop, netbook, or personal digital assistant (PDA). This application does not impose any limitations on this.

[0059] When the fog light control device 102 is located on a server mounted on a vehicle or remotely, the server can be a single server or a server cluster consisting of multiple servers. In some embodiments, the server cluster can also be a distributed cluster. This application does not impose any limitations on this.

[0060] It should be noted that the structure illustrated in the embodiments of this application does not constitute a limitation on the fog light control device 102. It may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of both.

[0061] For ease of understanding, the fog light control method provided in this application will be described in detail below with reference to the accompanying drawings.

[0062] Figure 2 This is a flowchart illustrating a fog light control method according to an embodiment of this application, with reference to... Figure 2 The fog light control method includes: S201. Based on the vehicle's multimodal environmental information, determine the degree of degradation of at least one environmental indicator.

[0063] The degradation of the above indicators includes the rate of decrease in point cloud detection distance, the rate of decrease in point cloud density, the point cloud noise ratio, the rate of decrease in image sharpness, the rate of decrease in image contrast, and atmospheric light intensity.

[0064] The aforementioned point cloud detection range reduction rate is determined by using the furthest detection distance in an environment with visibility greater than a preset level as the baseline detection distance, and then comparing the furthest detection distance in the current frame's point cloud with the baseline detection distance. As one possible implementation, the point cloud detection range reduction rate = (baseline distance - current furthest stable detection distance) / baseline distance. 100%.

[0065] The aforementioned point cloud density reduction rate is determined by comparing the actual point cloud density in the vehicle's detection area with the baseline point cloud density in an environment where the environmental visibility is greater than the preset environmental visibility.

[0066] The aforementioned point cloud noise ratio is the ratio of noise points in the vehicle's environmental information identified by a preset algorithm to the total number of point clouds. The preset algorithm includes clustering algorithms, spatial feature analysis algorithms, etc.

[0067] The aforementioned image sharpness reduction rate is determined by comparing the image sharpness of the acquired image information with the baseline sharpness of a reference image in an environment where the environmental visibility is greater than a preset environmental visibility.

[0068] The aforementioned image contrast reduction rate is determined by comparing the average grayscale value of the acquired image information with the average grayscale value of a reference image in an environment where the visibility is greater than a preset level.

[0069] The above atmospheric light intensity values ​​are used to reflect the radiation intensity of light in the atmosphere. The atmospheric light intensity values ​​are dynamically adjusted with changes in atmospheric composition and wavelength.

[0070] The atmospheric light intensity values ​​mentioned above were determined using a dark channel prior model, which is a conventional technique for determining atmospheric light intensity values ​​and will not be elaborated upon here.

[0071] As an achievable method, determining the degree of degradation of an environmental indicator includes: for any environmental indicator of at least one environmental indicator, determining the actual indicator value of the environmental indicator based on multimodal environmental information; and determining the degree of degradation of the environmental indicator based on the actual indicator value and the calibration indicator value of the environmental indicator; wherein the calibration indicator value is the indicator value of the environmental indicator in a scenario where the environmental visibility is higher than a preset environmental visibility threshold.

[0072] The aforementioned preset environmental visibility threshold is a critical threshold used to quantify whether the vehicle's environmental indicators meet the requirements for vehicle operation without the need to turn on fog lights.

[0073] As another feasible approach, determining the degree of degradation of environmental indicators further includes: for any environmental indicator of at least one environmental indicator, determining the actual indicator value of the environmental indicator based on multimodal environmental information; and determining the degree of degradation of the environmental indicator based on the actual indicator value and the calibration range of the environmental indicator; wherein, the calibration range is the range of environmental indicators in scenarios where environmental visibility is higher than a preset environmental visibility threshold. The process of determining the degree of degradation includes: determining the difference between the actual indicator value and the upper and lower limits of the calibration range, respectively, and selecting the smaller difference as the degree of degradation of the environmental indicator.

[0074] As another feasible method, determining the degree of degradation of environmental indicators also includes: for any environmental indicator of at least one environmental indicator, determining the actual value of the environmental indicator based on multimodal environmental information; and determining the degree of degradation of the environmental indicator based on the ratio of the actual value to the calibrated value of the environmental indicator.

[0075] As another feasible method, determining the degree of degradation of environmental indicators also includes: determining the difference between the actual indicator value and the calibrated indicator value of the environmental indicator, and determining the degree of degradation of the environmental indicator based on the ratio of the indicator difference to the calibrated indicator value.

[0076] S202. Based on the degree of degradation of at least one environmental indicator, determine whether the vehicle is in the target scenario.

[0077] The criteria for determining whether a vehicle is in a target scenario are as follows: the target scenario belongs to one of several preset scenarios that affect environmental visibility, and the proportion of the target environmental indicator among the multiple environmental indicators bound to the target scenario is greater than the preset proportion; the target environmental indicator is an environmental indicator whose indicator degradation degree is greater than the preset indicator degradation degree.

[0078] As an achievable method, determining whether a vehicle is within the target scenario can also be based on factors such as the vehicle's operating status and the quantity of the target environmental indicator among multiple environmental indicators bound to the target scenario.

[0079] As another feasible method, determining whether a vehicle is within the target scenario can also be based on weather conditions or road information reflected in high-precision maps, GPS, and other information as auxiliary information.

[0080] As an achievable approach, environmental indicators also include those that are related to environmental visibility, such as oncoming headlight glare angle, road surface moisture, water depth, snow cover thickness, and traffic volume.

[0081] The aforementioned preset percentage refers to the percentage of environmental indicators that are pre-set to determine whether a vehicle is in the target scenario when the vehicle belongs to one of the multiple preset scenarios that affect environmental visibility. For example, if the preset percentage is 80%, it means that if the target environmental indicator accounts for more than 80% of the environmental indicators and the vehicle belongs to one of the multiple preset scenarios that affect environmental visibility, then the vehicle is determined to be in the target scenario.

[0082] As an achievable approach, different vehicle types have different preset percentages. For example, the preset percentage of environmental indicators for passenger cars can be 70% of the target environmental indicators, while the preset percentage of environmental indicators for trucks can be 80%.

[0083] As a feasible approach, the pre-defined degradation levels of different environmental indicators can be the same or different.

[0084] As an achievable approach, when the preset degradation levels of different environmental indicators are the same, the environmental indicator with a degradation level greater than the preset degradation level is taken as the target environmental indicator. For example, if the preset degradation levels of the point cloud detection distance reduction rate, point cloud density reduction rate, and point cloud noise ratio are all 20%, and the degradation levels of the point cloud detection distance reduction rate, point cloud density reduction rate, and point cloud noise ratio are all 25%, then the environmental indicators corresponding to the point cloud detection distance reduction rate, point cloud density reduction rate, and point cloud noise ratio are taken as the target environmental indicators.

[0085] For example, when the preset degradation of the point cloud detection distance reduction rate, the point cloud density reduction rate, and the point cloud noise ratio is all 20%, and the point cloud detection distance reduction rate is 21%, the point cloud density reduction rate is 15%, and the point cloud noise ratio is 25%, the environmental indicators corresponding to the point cloud detection distance reduction rate and the point cloud noise ratio are used as the target environmental indicators.

[0086] As another feasible approach, when different environmental indicators correspond to different preset indicator degradation degrees, the environmental indicator with a degradation degree greater than the corresponding preset indicator degradation degree can be used as the target environmental indicator. For example, when the point cloud detection distance decreases by 21%, the point cloud density decreases by 15%, and the point cloud noise ratio is 25%, the preset indicator degradation degree corresponding to the point cloud detection distance decrease rate is 20%, the preset indicator degradation degree corresponding to the point cloud density decrease rate is 18%, and the preset indicator degradation degree corresponding to the point cloud noise ratio is 22%. Therefore, the point cloud detection distance decrease rate is greater than the corresponding preset indicator degradation degree, and the point cloud noise ratio is greater than the corresponding preset indicator degradation degree. Based on this, the environmental indicators corresponding to the point cloud detection distance decrease rate and the point cloud noise ratio can be used as the target environmental indicators.

[0087] S203. When the vehicle is in the target scenario, control the vehicle's fog lights to turn on.

[0088] As an feasible approach, when the vehicle is in the target scenario, the brightness of the vehicle's fog lights can be adjusted based on the proportion of the target environmental indicator among multiple environmental indicators bound to the target scenario.

[0089] As another feasible approach, when the vehicle is in the target scenario, the brightness of the vehicle's fog lights can be adjusted based on the proportion of the target environmental indicator among the multiple environmental indicators bound to the target scenario. The higher the proportion of the target environmental indicator among the multiple environmental indicators bound to the target scenario, the brighter the fog lights will be.

[0090] As another feasible approach, when the vehicle is in a target scenario and the proportion of the target environmental indicators among the multiple environmental indicators bound to the target scenario is greater than the preset proportion, the brightness of the vehicle's fog lights can be adjusted based on the difference between the index degradation degree corresponding to each environmental indicator bound to the target scenario and the preset index degradation degree. When the index degradation degree is greater than the preset index degradation degree, the higher the index degradation degree, the brighter the fog lights will be.

[0091] As another feasible approach, vehicle fog lights can be switched between multiple modes, such as low beam, standard beam, and high beam.

[0092] As one feasible approach, the fog lights can be turned on and then turned off once the vehicle leaves the target area.

[0093] As another possible implementation, after the fog lights are turned on, they are turned off if the duration of the vehicle leaving the target scene is longer than the preset duration.

[0094] Based on the aforementioned technical means, this application comprehensively analyzes multimodal environmental information during vehicle driving, enabling more accurate quantification of the degradation degree of different environmental indicators during vehicle driving. By considering the degradation degree of multiple environmental indicators and pre-set judgment conditions corresponding to low-visibility driving scenarios, it can more comprehensively and effectively identify whether a vehicle is in a low-visibility driving scenario, avoiding misjudgments of the vehicle's environmental state caused by the collection of environmental information from a single sensor. In low-visibility driving scenarios, it promptly controls the fog lights to activate, providing auxiliary lighting to avoid driving inconvenience caused by untimely manual fog light activation, thus improving the vehicle's adaptability to different scenarios and enhancing driving safety.

[0095] As a feasible approach, Figure 3This is a flowchart illustrating another fog light control method according to an embodiment of this application, see below. Figure 3 The fog light control method includes: S301. Based on the vehicle's multimodal environmental information, determine the degree of degradation of at least one environmental indicator.

[0096] S302. Determine whether the vehicle is in the target scenario based on the degree of degradation of at least one environmental indicator.

[0097] The criteria for determining whether a vehicle is in a target scenario are as follows: the target scenario belongs to one of several preset scenarios that affect environmental visibility, and the proportion of the target environmental indicator among the multiple environmental indicators bound to the target scenario is greater than the preset proportion; the target environmental indicator is an environmental indicator whose indicator degradation degree is greater than the preset indicator degradation degree.

[0098] The schemes for S301 and S302 are the same as those for S201 and S202 mentioned above, and will not be repeated here.

[0099] S303. When the vehicle is in the target scenario and the vehicle meets the preset conditions, control the vehicle's fog lights to turn on.

[0100] The preset condition is that the tracking stability of the vehicle in target tracking is lower than a preset tracking stability threshold.

[0101] As one possible approach, the preset conditions also include: when the distance between the vehicle and the tracked target is less than a safe distance, controlling the vehicle's fog lights to turn on or controlling the brightness of the vehicle's fog lights to increase.

[0102] As another feasible approach, the preset conditions also include: when the vehicle's deceleration is greater than a preset deceleration threshold, controlling the vehicle's fog lights to turn on or controlling the vehicle's fog light brightness to increase, wherein the vehicle's deceleration can be 4 m / s².

[0103] As another possible approach, the preset conditions also include: if the number of lane changes performed by the vehicle exceeds the preset number of lane changes within the first preset duration, the vehicle's fog lights are turned on or the fog light brightness is increased. The first preset duration is 30 minutes and the preset number of lane changes is 10.

[0104] The aforementioned target tracking refers to the tracking of a vehicle for a target. Other tracked targets include pedestrians, vehicles, non-motorized vehicles, animals, road signs and markings, obstacles, and infrastructure. Among these, vehicles include vehicles in front, vehicles behind, vehicles to the side, and vehicles in the oncoming lane.

[0105] As an achievable approach, tracking stability can also be evaluated based on the vehicle's confidence in the target detection, trajectory continuity, position error, speed error, and angle measurement error. If the stability of the target detection confidence, trajectory continuity, position error, speed error, and angle measurement error are all below a preset tracking stability threshold, the vehicle's fog lights can be turned on.

[0106] For example, if the target confidence is 90%, the trajectory continuity is 5 consecutive frames without loss, and the preset tracking stability threshold has a preset confidence of 85% and a preset number of consecutive trajectory frames of 3, then the vehicle's fog lights will be turned on.

[0107] As an feasible approach, the tracking stability of a vehicle for a target can also be determined based on the vehicle's speed and the road conditions. For example, when a vehicle is driving in a congested urban area, the traffic density is high and the target being tracked changes frequently. Therefore, it is necessary to lower the preset tracking stability threshold. That is, if the preset tracking stability threshold for a vehicle is 90% when driving on a highway at a speed greater than 80 km / h, then the preset tracking stability threshold for a vehicle driving in a congested urban area is 80%.

[0108] The higher the tracking stability, the stronger the vehicle's ability to track and perceive the target.

[0109] The aforementioned preset tracking stability threshold is the minimum tracking stability threshold set in advance for the vehicle to track the target. The higher the tracking stability threshold, the higher the requirements are placed on the vehicle's ability to perceive and track the target.

[0110] As a feasible approach, Figure 4 This is a schematic diagram illustrating the determination of tracking stability in an embodiment of this application, with reference to... Figure 4 The tracking stability of the vehicle in target tracking includes: S401. Determine the tracking fluctuation of the vehicle in target tracking; wherein, the tracking fluctuation includes the fluctuation of the vehicle tracking the target, and / or the fluctuation of the distance between the vehicle and the tracking target.

[0111] S402. Determine tracking stability based on tracking fluctuations.

[0112] The aforementioned tracking fluctuation refers to the tracking fluctuation caused by the vehicle tracking the target when the target is in a low-visibility environment within a preset time period. This is due to the vehicle's significantly reduced ability to perceive the target.

[0113] As an achievable approach, the fluctuation of the vehicle's tracking target is characterized by the consistency ratio of the tracking target within the vehicle's tracking range at adjacent acquisition times.

[0114] The aforementioned consistency ratio refers to the ratio between the number of tracking targets that did not fluctuate within the vehicle's tracking range at adjacent acquisition times and the total number of tracking targets identified within the vehicle's tracking range.

[0115] As another possible approach, the fluctuation of the vehicle's tracking target can also be characterized by the smoothness of the track of the target within the vehicle's tracking range at adjacent acquisition times.

[0116] The aforementioned trajectory smoothness is determined based on the rate of change of the acceleration of the tracked target. The greater the rate of change of acceleration, the smaller the trajectory smoothness. The trajectory smoothness is directly proportional to the tracking stability.

[0117] As an achievable approach, the distance fluctuation between the vehicle and the tracked target is characterized by the volatility of the average tracking distance between the vehicle and the tracked target within the vehicle's tracking range at adjacent acquisition times.

[0118] The aforementioned volatility is determined based on the difference in tracking distance between the same tracking target within the tracking range of vehicles at adjacent data collection times.

[0119] As another feasible approach, the process of determining the distance fluctuation between the vehicle and the tracked target also includes: determining the distance fluctuation of each tracked target based on the standard deviation of the distance between the same tracked target and the vehicle within the tracking range of the vehicle at multiple consecutive acquisition times, and using the average of the distance fluctuations corresponding to all tracked targets within the tracking range of the vehicle as the distance fluctuation between the vehicle and the tracked target.

[0120] As another feasible approach, the process of determining the distance fluctuation between the vehicle and the tracked target also includes: determining the distance fluctuation of each tracked target based on the distance range between the same tracked target and the vehicle within the tracking range of the vehicle at multiple consecutive acquisition times, and using the average of the distance fluctuations of all tracked targets within the tracking range of the vehicle as the distance fluctuation between the vehicle and the tracked target.

[0121] The aforementioned distance standard deviation refers to the standard deviation of the distance between the vehicle and the tracked target at multiple consecutive data collection times. The larger the standard deviation, the greater the fluctuation in the distance between the vehicle and the tracked target.

[0122] The aforementioned distance range refers to the difference between the maximum and minimum distances between the vehicle and the tracked target at multiple consecutive data collection times. The larger the range, the wider the distance fluctuation range.

[0123] The aforementioned consistency ratio is directly proportional to tracking stability; the aforementioned volatility is inversely proportional to tracking stability.

[0124] As an achievable approach, the tracking stability of vehicle target tracking is determined based on the consistency ratio.

[0125] As another feasible approach, the tracking stability of a vehicle for target tracking can be determined based on volatility.

[0126] As another feasible approach, the consistency ratio and volatility are normalized, and the tracking stability is determined based on the consistency impact weight of the consistency ratio on tracking stability, the volatility impact weight of the volatility on tracking stability, and the normalized consistency ratio and volatility.

[0127] Based on the aforementioned technical means, the tracking stability of the vehicle tracking target in this application characterizes the vehicle's tracking perception capability for the tracking target. Since the vehicle's tracking perception capability for the tracking target is significantly reduced when driving in low-visibility conditions, the fog lights are only activated after recognizing that the vehicle is in a low-visibility driving scene and its tracking perception capability for the tracking target is low. This reduces the occurrence of fog lights being mistakenly activated due to errors in recognizing low-visibility driving scenes.

[0128] As another feasible approach Figure 5 This is a flowchart illustrating another fog light control method according to an embodiment of this application, see reference. Figure 5 The fog light control method includes: S501. Based on the vehicle's multimodal environmental information, determine the degree of degradation of at least one environmental indicator.

[0129] S502. Determine whether the vehicle is in the target scenario based on the degree of degradation of at least one environmental indicator.

[0130] The criteria for determining whether a vehicle is in a target scenario are as follows: the target scenario belongs to one of several preset scenarios that affect environmental visibility, and the proportion of the target environmental indicator among the multiple environmental indicators bound to the target scenario is greater than the preset proportion; the target environmental indicator is an environmental indicator whose indicator degradation degree is greater than the preset indicator degradation degree.

[0131] The schemes for S501 and S502 are the same as those for S201 and S202 mentioned above, and will not be repeated here.

[0132] S503. When the vehicle is in the target scenario and the duration of the vehicle meeting the preset conditions is longer than the preset duration, control the vehicle's fog lights to turn on.

[0133] As an feasible approach, the vehicle's fog lights are not turned on when the vehicle is in the target scenario and the duration of the vehicle meeting the preset conditions is no longer than the preset duration.

[0134] The aforementioned preset duration is a pre-set threshold used to determine whether the vehicle's ability to track and perceive the target has decreased to a critical time threshold that requires the fog lights to be turned on. For example, the preset duration can be 3 seconds.

[0135] Based on the above technical means, this application turns on the fog lights when the vehicle is in a low visibility driving scenario and the vehicle's tracking perception ability for the target is low for a continuous period of time, thereby reducing misjudgments caused by instantaneous fluctuations in tracking perception ability and further reducing the occurrence of fog lights being turned on accidentally.

[0136] In one possible implementation, the above-described fog light control method can be applied to a fog light control device. Figure 6 This is a block diagram illustrating a fog light control device according to an embodiment of this application, with reference to... Figure 6 The fog light control device includes: an indicator acquisition module 601, a scene judgment module 602, and a fog light control module 603.

[0137] The indicator acquisition module 601 is used to determine the degree of degradation of at least one environmental indicator based on the vehicle's multimodal environmental information.

[0138] The scene judgment module 602 is used to determine whether a vehicle is in a target scene based on the index deterioration degree of at least one environmental index. The judgment condition for the vehicle to be in the target scene is: the target scene belongs to one of multiple preset scenes that affect environmental visibility and the proportion of the target environmental index among the multiple environmental indexes bound to the target scene is greater than the preset proportion; the target environmental index is an environmental index whose index deterioration degree is greater than the preset index deterioration degree.

[0139] The fog light control module 603 is used to control the fog lights of the vehicle to be turned on when the vehicle is in the target scenario.

[0140] In one possible implementation, the device is further configured to control the vehicle's fog lights to turn on when the vehicle is in the target scene and the vehicle meets preset conditions; wherein the preset conditions are that the tracking stability of the vehicle in target tracking is lower than a preset tracking stability threshold.

[0141] In one possible implementation, the device is further configured to control the vehicle's fog lights to turn on when the vehicle is in the target scene and the duration of the vehicle meeting the preset conditions is longer than the preset duration.

[0142] In one possible implementation, the indicator acquisition module 601 is used to determine the actual indicator value of any environmental indicator based on multimodal environmental information for at least one environmental indicator; and to determine the indicator degradation degree of the environmental indicator based on the actual indicator value and the calibration indicator value of the environmental indicator; wherein the calibration indicator value is the indicator value of the environmental indicator in a scenario where the environmental visibility is higher than a preset environmental visibility threshold.

[0143] In one possible implementation, the above-mentioned device is further used to determine the tracking fluctuation of the vehicle in tracking the target; wherein the tracking fluctuation includes the fluctuation of the vehicle tracking the target, and / or the distance fluctuation between the vehicle and the tracking target; and based on the tracking fluctuation, the tracking stability is determined.

[0144] In one possible implementation, the fluctuation of the vehicle's tracking target is characterized by the consistency ratio of the tracking targets within the vehicle's tracking range at adjacent acquisition times; the fluctuation of the distance between the vehicle and the tracking target is characterized by the volatility of the average tracking distance between the vehicle and the tracking targets within the vehicle's tracking range at adjacent acquisition times; the aforementioned device is specifically used to determine tracking stability based on the consistency ratio and / or volatility; wherein, the consistency ratio is directly proportional to tracking stability; and the volatility is inversely proportional to tracking stability.

[0145] In one possible implementation, the device is also used to turn off the fog lights after the vehicle has left the target scene, following the initial activation of the fog lights.

[0146] In one possible implementation, environmental metrics include: point cloud detection distance reduction rate, point cloud density reduction rate, point cloud noise ratio, image sharpness reduction rate, image contrast reduction rate, and atmospheric light intensity value.

[0147] Regarding the apparatus in the above embodiments, the specific manner in which each step is performed has been described in detail in the embodiments of the fog light control method, and will not be elaborated here.

[0148] Figure 7 This is a block diagram illustrating an electronic device according to an embodiment of this application. Figure 7 As shown, the electronic device includes, but is not limited to, a processor 701 and a memory 702.

[0149] The memory 702 described above is used to store the executable instructions of the processor 701. It is understood that the processor 701 is configured to execute instructions to implement the image control method in the above embodiments.

[0150] It should be noted that those skilled in the art will understand that Figure 7The electronic device structure shown does not constitute a limitation on the electronic device; the electronic device may include, but is not limited to, other electronic devices. Figure 7 This may indicate more or fewer components, or combinations of certain components, or different component arrangements.

[0151] Processor 701 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in memory 702, and by calling data stored in memory 702, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. Processor 701 may include one or more processing units. Processor 701 may integrate an application processor and a modem processor. The application processor mainly handles the operating system, user interface, and applications, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into processor 701.

[0152] The memory 702 can be used to store software programs and various data. The memory 702 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required by at least one functional module (such as deterministic components, integrated components, etc.), etc. Furthermore, the memory 702 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0153] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as a memory 702 including instructions, which can be executed by a processor 701 of an electronic device to implement the methods in the above embodiments.

[0154] In actual implementation, Figure 6 The functions of the indicator acquisition module 601, scene judgment module 602, and fog light control module 603 can all be provided by... Figure 7 The processor 701 calls the computer program stored in the memory 702 to implement the process. The specific execution process can be found in the description of the method section in the previous embodiment, and will not be repeated here.

[0155] Optionally, the computer-readable storage medium may be a non-transitory computer-readable storage medium, such as a read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device. In an exemplary embodiment, this application also provides a computer program product including one or more instructions, which can be executed by the processor 701 of an electronic device to perform the methods in the above embodiments.

[0156] It should be noted that when one or more instructions in the computer-readable storage medium or computer program product are executed by the processor of an electronic device, they implement the various processes of the above method embodiments and achieve the same technical effect as the above method. To avoid repetition, they will not be described again here.

[0157] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0158] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another apparatus, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0159] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0160] Furthermore, the functional units in the various embodiments of this application 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.

[0161] 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 readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, essentially, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0162] This application provides a computer program product containing instructions that, when run on a computer, cause the computer to perform the methods described in the above method embodiments.

[0163] This application also provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method in the method flow shown in the above method embodiments.

[0164] The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, a register, a hard disk, an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof, or any other form of computer-readable storage medium known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an application-specific integrated circuit (ASIC). In embodiments of this application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0165] Since the fog light control device, computer-readable storage medium, and computer program product in the embodiments of this application can adopt the above-described method, the technical effects they can achieve can also be referred to the above-described method embodiments. The embodiments of this application will not be described again here.

[0166] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A fog light control method, characterized in that, The method includes: Based on the vehicle's multimodal environmental information, determine the degree of degradation of at least one environmental indicator. Based on the degradation degree of the at least one environmental indicator, it is determined whether the vehicle is in the target scenario; wherein, the determination condition for the vehicle being in the target scenario is: the target scenario belongs to one of a plurality of preset scenarios affecting environmental visibility and the proportion of the target environmental indicator among the plurality of environmental indicators bound to the target scenario is greater than a preset proportion; the target environmental indicator is an environmental indicator whose degradation degree is greater than a preset degradation degree. When the vehicle is in the target scenario, control the vehicle's fog lights to turn on.

2. The fog light control method according to claim 1, characterized in that, The method further includes: When the vehicle is in the target scenario and the vehicle meets the preset conditions, the fog lights of the vehicle are turned on. The preset condition is that the tracking stability of the vehicle in target tracking is lower than a preset tracking stability threshold.

3. The fog light control method according to claim 2, characterized in that, The method further includes: When the vehicle is in the target scene and the duration of the vehicle meeting the preset conditions is longer than the preset duration, the fog lights of the vehicle are turned on.

4. The fog light control method according to any one of claims 1-3, characterized in that, The determination of the degradation degree of at least one environmental indicator based on the vehicle's multimodal environmental information includes: For any one of the at least one environmental indicators, the actual indicator value of the environmental indicator is determined based on the multimodal environmental information. Based on the actual index value and the calibrated index value of the environmental index, the degree of degradation of the environmental index is determined. The calibration index value is the index value of the environmental index under the scenario where the environmental visibility is higher than the preset environmental visibility threshold.

5. The fog light control method according to any one of claims 2-3, characterized in that, The tracking stability of the vehicle for target tracking includes: Determine the tracking fluctuation of the vehicle in target tracking; wherein the tracking fluctuation includes the fluctuation of the target being tracked by the vehicle, and / or the distance fluctuation between the vehicle and the target being tracked; Based on the tracking fluctuations, the tracking stability is determined.

6. The fog light control method according to claim 5, characterized in that, The fluctuation of the vehicle's tracking target is characterized by the consistency ratio of the tracking targets within the vehicle's tracking range at adjacent acquisition times; the fluctuation of the distance between the vehicle and the tracking target is characterized by the volatility of the average tracking distance between the vehicle and the tracking targets within the vehicle's tracking range at adjacent acquisition times. Determining the tracking stability based on the tracking fluctuations includes: The tracking stability is determined based on the consistency ratio and / or the volatility. The consistency ratio is directly proportional to the tracking stability, and the volatility is inversely proportional to the tracking stability.

7. The fog light control method according to any one of claims 1-3, characterized in that, The method further includes: After the fog lights are turned on, they are turned off when the vehicle leaves the target scene.

8. The fog light control method according to claim 6, characterized in that, The environmental indicators include: point cloud detection distance reduction rate, point cloud density reduction rate, point cloud noise ratio, image sharpness reduction rate, image contrast reduction rate, and atmospheric light intensity value.

9. A fog light control device, characterized in that, The device includes: The indicator acquisition module is used to determine the degree of degradation of at least one environmental indicator based on the vehicle's multimodal environmental information. The scene determination module is used to determine whether the vehicle is in a target scene based on the index degradation degree of the at least one environmental index; wherein, the determination condition for the vehicle to be in the target scene is: the target scene belongs to one of a plurality of preset scenes that affect environmental visibility and the proportion of the target environmental index among the plurality of environmental indicators bound to the target scene is greater than a preset proportion; the target environmental index is an environmental index whose index degradation degree is greater than a preset index degradation degree. A fog light control module is used to control the fog lights of the vehicle to be turned on when the vehicle is in the target scenario.

10. A vehicle, characterized in that, The vehicle includes fog lights, which are controlled to be turned on or off using the fog light control method as described in any one of claims 1-8.

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