Display method and device of vehicle environment image, storage medium and vehicle

Abstract

This application provides a method, apparatus, storage medium, and vehicle for displaying vehicle environmental images, applied in the field of vehicle technology. The method includes: acquiring driving data and external environment detection data, including precipitation level, ambient light intensity, and environmental image data; determining the target driving scenario currently in which the vehicle is located based on the driving data and external environment detection data; determining a target image enhancement strategy corresponding to the target driving scenario from a preset set of image enhancement strategies; processing the environmental image data according to the target image enhancement strategy; and displaying the processed environmental image data. This method can perform scene-adaptive image enhancement processing, taking into account both weather complexity and differences in vehicle operating states, significantly improving the detail recognition and penetration enhancement effect of snow and fog environment images.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and more specifically, to a method, apparatus, storage medium, and vehicle for displaying vehicle environment images in the field of vehicle technology. Background Technology

[0002] With the rapid development of automotive technology, streaming rearview mirror systems (Camera Monitor Systems, CMS) are widely used in vehicles. These systems capture real-time images from external cameras and transmit them to an in-vehicle screen for display, replacing traditional optical mirror reflections and effectively expanding the driver's field of vision and reducing blind spots.

[0003] Typically, to ensure driving safety, images captured by a CMS (Content Management System) need to be enhanced before display in low-visibility weather conditions such as snow and fog. However, existing image enhancement technologies do not perform well in snow and fog environments. Over-enhancement can result in images with a plastic-like appearance, while under-enhancement can leave the image appearing washed out. Consequently, these technologies fail to present clear road conditions to users, creating driving safety hazards. Summary of the Invention

[0004] This application provides a method, apparatus, storage medium, and vehicle for displaying vehicle environment images. The method can dynamically select image enhancement strategies for targeted image processing based on actual driving scenarios, effectively improving the enhancement effect of snow and fog environment images.

[0005] In a first aspect, this application provides a method for displaying a vehicle environment image, the method comprising: Acquire driving data and external environment detection data while the vehicle is in motion, including precipitation level, ambient light intensity, and environmental image data; Based on the driving data and the external environment detection data, the target driving scenario in which the vehicle is currently located is determined; Determine the target image enhancement strategy corresponding to the target driving scenario from a preset set of image enhancement strategies; The environmental image data is processed according to the target image enhancement strategy, and the processed environmental image data is displayed.

[0006] In conjunction with the first aspect, in some possible implementations, the driving data includes vehicle speed, driving gear, and the operating status of the electronic stability control system. Determining the target driving scenario currently in which the vehicle is located based on the driving data and the external environment detection data includes: The driving state of the vehicle is determined based on the vehicle speed, the driving gear, and the operating status of the electronic stability control system. From multiple preset scene discrimination conditions, a target scene discrimination condition that matches the driving state, the precipitation level, and the ambient light intensity is determined, and different scene discrimination conditions correspond to different preset driving scenarios; The preset driving scenario corresponding to the target scenario discrimination condition is taken as the target driving scenario in which the vehicle is currently located.

[0007] In conjunction with the first aspect, in some possible implementations, determining the target scene discrimination conditions that match the driving state, the precipitation level, and the ambient light intensity from a plurality of preset scene discrimination conditions includes: Based on the precipitation level and the ambient light intensity, determine the weather visibility level of the driving environment; The scene discrimination condition that includes the driving state and the weather visibility level is determined from multiple preset scene discrimination conditions and used as the target scene discrimination condition.

[0008] In conjunction with the first aspect, in some possible implementations, the driving data further includes the windshield wiper setting, and determining the weather visibility level of the driving environment based on the precipitation level and the ambient light intensity includes: The visibility level of the driving environment is determined based on the wiper setting, the environmental image data, the precipitation level, and the ambient light intensity.

[0009] In conjunction with the first aspect, in some possible implementations, determining the weather visibility level of the driving environment based on the wiper operating setting, the environmental image data, the precipitation level, and the ambient light intensity includes: The environmental image data is processed using a trained image classification model to obtain a visibility prediction value. The image classification model is trained based on a set of environmental image samples collected under different weather visibility conditions. The wiper settings, rainfall levels, and ambient light intensity are normalized to obtain multiple normalized values. The visibility prediction value and the multiple normalized values ​​are weighted and summed based on preset weights, and the level corresponding to the preset numerical range of the sum is taken as the weather visibility level of the driving environment.

[0010] In conjunction with the first aspect, in some possible implementations, different scenario discrimination conditions include different precipitation level thresholds and / or light intensity thresholds, and the method further includes: Monitor whether the geographical location of the vehicle has changed; If a change occurs, obtain the historical precipitation data and / or historical sunshine data for the changed geographical location; The precipitation level threshold is updated based on the historical precipitation data, and / or the light intensity threshold is updated based on the historical sunshine data.

[0011] In conjunction with the first aspect, in some possible implementations, after displaying the processed environmental image data, the method further includes: Monitor whether the user adjusts the image parameters of the displayed environmental image data; If so, record the corresponding image parameter adjustment information; When the preset period of time is reached, the target image enhancement strategy is updated according to all the image parameter adjustment information recorded under the target driving scenario.

[0012] Secondly, this application provides a display device for vehicle environment images, the device comprising: The acquisition unit is used to acquire driving data and external environment detection data when the vehicle is in motion. The external environment detection data includes precipitation level, ambient light intensity and environmental image data. The first determining unit is used to determine the target driving scenario currently in which the vehicle is located based on the driving data and the external environment detection data. The second determining unit is used to determine the target image enhancement strategy corresponding to the target driving scenario from a preset set of image enhancement strategies; The processing unit is used to process the environmental image data according to the target image enhancement strategy and display the processed environmental image data.

[0013] Thirdly, this application provides a vehicle, the vehicle comprising: Memory, used to store executable program code; A processor is configured to call and run the executable program code from the memory, causing the vehicle to perform the method described in the first aspect or any possible implementation thereof.

[0014] Fourthly, this application provides a computer program product comprising computer program code, which, when executed on a computer, causes the computer to perform the method described in the first aspect or any possible implementation thereof.

[0015] Fifthly, this application provides a computer storage medium storing computer program code, which, when executed on a computer, causes the computer to perform the method described in the first aspect or any possible implementation thereof.

[0016] The beneficial effects of the technical solutions provided in some embodiments of this application include at least the following: In one or more embodiments of this application, driving data and external environment detection data are acquired during vehicle operation. The external environment detection data includes precipitation level, ambient light intensity, and environmental image data. Based on the driving data and external environment detection data, the target driving scenario in which the vehicle is currently located is determined. A target image enhancement strategy corresponding to the target driving scenario is determined from a preset set of image enhancement strategies. The environmental image data is processed according to the target image enhancement strategy, and the processed environmental image data is displayed. That is, the actual driving scenario is identified and determined by combining multi-source sensor signals from two dimensions: driving state and weather state. Different image enhancement strategies are adapted to different driving scenarios to perform targeted processing of environmental images. This allows for scene-adaptive image enhancement processing that takes into account the complexity of weather and the differences in vehicle operating state. This not only avoids the problem of over-enhancement or under-enhancement of snow and fog environment images caused by using a fixed image enhancement strategy, but also significantly improves the detail recognition and penetration enhancement effect of snow and fog environment images, which is beneficial to improving driving safety.

[0017] The above description is merely an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is an exemplary system architecture diagram of a method for displaying vehicle environment images provided in an embodiment of this application; Figure 2 This is a schematic flowchart of a method for displaying vehicle environment images provided in an embodiment of this application; Figure 3 This is a flowchart illustrating another method for displaying vehicle environment images provided in an embodiment of this application; Figure 4 This is a flowchart illustrating another method for displaying vehicle environment images provided in an embodiment of this application; Figure 5 This is a schematic diagram of the structure of a vehicle environment image display device provided in an embodiment of this application; Figure 6This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0021] In the description of this application, it should be understood that the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances. Furthermore, in the description of this application, unless otherwise stated, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship.

[0022] This application provides a method, apparatus, vehicle, storage medium, and computer program product for displaying vehicle environment images.

[0023] Please see Figure 1 , Figure 1 This is an exemplary system architecture diagram of a method for displaying vehicle environment images provided in an embodiment of this application.

[0024] like Figure 1 As shown, the system architecture may include a vehicle processor 110, a network 120, and an underlying control system 130. The network 120 serves as a medium for providing a communication link between the vehicle processor 110 and the underlying control system 130. The network 120 may include various types of wireless and wired communication links. Wireless communication links include Bluetooth and Wireless-Fidelity (Wi-Fi) links, while wired communication links include Controller Area Network (CAN), LIN bus, FlexRay bus, MOST bus, and in-vehicle Ethernet bus.

[0025] The vehicle processor 110 can interact with the underlying control system 130 via the network 120 to receive or send messages to the underlying control system 130. Specifically, the vehicle processor 110 is generally used in the vehicle to execute various user commands, receive user interaction information, and send commands, signals, and other messages to the underlying control system 130, so that specific underlying functional modules in the underlying control system 130 operate and adjust according to the control messages. The underlying control system 130 can receive commands, signals, and other messages sent by the vehicle processor 110, and operate and adjust various underlying functional modules in the vehicle that support the operation of various functions based on the control messages, such as the streaming rearview mirror system (Camera Monitor System, CMS), rain sensor, ambient light sensor, vehicle speed sensor, etc. Each underlying functional module jointly implements vehicle function support based on hardware and software.

[0026] In this embodiment, when the vehicle is in motion, the underlying control system 130 sends messages to the CMS, rain sensor, and ambient light sensor, triggering each module to collect corresponding external environment detection data. For example, the CMS collects environmental images around the vehicle, the rain sensor collects real-time precipitation, and the ambient light sensor collects ambient light intensity. The collected external environment detection data is then fed back to the vehicle processor 110. Simultaneously, the vehicle processor 110 acquires various driving data of the vehicle via the CAN bus, such as vehicle speed, driving gear (e.g., forward, reverse), and the operating status of the Electronic Stability Program (ESP) (e.g., active, inactive). Then, based on the driving data and external environment detection data, the vehicle processor 110 determines the current target driving scenario (e.g., high-speed driving in moderate snow, reversing at night in a blizzard, emergency stability driving, etc.), and determines the target image enhancement strategy corresponding to the target driving scenario from a preset set of image enhancement strategies. Finally, the environmental image data is processed based on the target image enhancement strategy and displayed to the user through the in-vehicle display screen to present a clear road condition.

[0027] It should be understood that Figure 1 The number of vehicle processors 110, networks 120 and underlying control systems 130 is only illustrative and can be any number of vehicle processors 110, networks 120 and underlying control systems 130 as needed.

[0028] Based on the above system architecture, this application provides a method for displaying vehicle environment images. Please refer to [link to relevant documentation]. Figure 2 , Figure 2This is a flowchart illustrating a method for displaying a vehicle environment image according to an embodiment of this application. The executing entity of this method can be a vehicle executing the method, a processor within the vehicle executing the method, or a vehicle environment image display service within the vehicle executing the method. For ease of description, the following uses a processor within the vehicle as an example to illustrate the specific execution process of the method. The method for displaying a vehicle environment image includes the following steps S202-S208, wherein: S202. Acquire driving data and external environment detection data while the vehicle is in motion, including precipitation level, ambient light intensity and environmental image data.

[0029] The driving data is obtained through the CAN bus, which is the core communication network inside the vehicle. It can acquire key driving data in real time, including but not limited to vehicle speed, driving gear (such as common gears like P / R / N / D), turn signal status (such as left turn activated / deactivated), and whether ESP is activated (such as outputting a high level when activated and a low level when deactivated).

[0030] Precipitation levels are determined by a rain sensor. Optionally, the rain sensor is installed on the inside of the vehicle's windshield and detects the precipitation (intensity) level using the principle of infrared light reflection. Specifically, the rain sensor's detection end continuously emits infrared light of a specific wavelength onto the inside of the windshield. After penetrating the glass, the infrared light is reflected off its outer surface. The rain sensor's receiving end collects the reflected light signal and calculates the reflectivity in real time. The precipitation level is determined based on the gradient of reflectivity changes. Typically, when there is no precipitation on the glass surface, the infrared reflectivity is stable. When raindrops or snowflakes adhere to the glass surface, the infrared light is scattered, and the reflectivity decreases as the amount of adhering raindrops or snowflakes increases. The rain sensor can output several precipitation levels: no rain (snow), light rain (snow), moderate rain (snow), and heavy rain (snow), for example, 0 = no precipitation, 1 = light rain (snow), 2 = moderate rain, and 3 = heavy rain.

[0031] Ambient light intensity is detected by an ambient light sensor, and its unit is lux. This ambient light sensor can be installed in an unobstructed area above the vehicle's dashboard. Ambient light intensity reflects the lighting conditions of the external environment and provides accurate data support for functions such as automatic headlight switching, adaptive adjustment of instrument panel backlight brightness, and adaptive adjustment of in-vehicle screen brightness.

[0032] Environmental image data is acquired by high-definition cameras in the CMS system and is used to capture the vehicle's surrounding environment in real time. Optionally, the CMS system includes at least a rear-view camera mounted at the rear of the vehicle, and may also include left-view and right-view cameras mounted on the left and right sides of the vehicle, so that the acquired environmental image data can cover key information such as roads, obstacles, and traffic signs to the left rear, right rear, and rear of the vehicle.

[0033] S204. Based on the driving data and external environment detection data, determine the target driving scenario in which the vehicle is currently located.

[0034] The driving data reflects the vehicle's current driving status, including but not limited to emergency stability (ESP activated), reversing, low-speed driving, high-speed driving, and turning / lane-changing. External environment detection data reflects the current weather conditions. Specifically, multiple driving scenarios can be pre-defined based on a combination of features from both the driving status and weather conditions dimensions. Each driving scenario corresponds to a unique scenario discrimination condition. Driving scenarios include, but are not limited to, high-speed driving in moderate snow, reversing at night in a blizzard, and emergency stability driving.

[0035] The scenario discrimination criteria are logical combinations of driving state features and weather state features. For example, "high-speed driving state (drive gear is forward, vehicle speed ≥ 80km / h) + moderate snow daytime (ambient light intensity > 1000 lux, precipitation level = 2)" corresponds to a moderate snow high-speed driving scenario; "reversing state (drive gear is reverse) + blizzard nighttime (ambient light intensity < 50 lux, precipitation level = 3)" corresponds to a blizzard nighttime reversing scenario; and "ESP activated state + any weather state" corresponds to an emergency stable driving scenario. In actual judgment, real-time collected driving data and external environment detection data can be compared with preset feature thresholds. When all discrimination criteria in a certain scenario are met, the driving scenario corresponding to that discrimination criterion can be taken as the target driving scenario.

[0036] It should be noted that this embodiment identifies and determines driving scenarios by combining multi-source sensor signals from two dimensions: driving state and weather state. This overcomes the limitations of single-dimensional scenario division and can take into account the complexity of weather and the differences in vehicle operating status under different driving scenarios. This provides accurate and reliable scenario basis for subsequent differentiated and adaptive image enhancement processing (such as image denoising in blizzard weather, low-light enhancement in nighttime reversing scenarios, and dynamic calling and parameter adaptation of algorithms for image stabilization in emergency stable driving scenarios).

[0037] S206. Determine the target image enhancement strategy corresponding to the target driving scenario from the preset set of image enhancement strategies.

[0038] The image enhancement strategy set includes image enhancement strategies corresponding to various driving scenarios. A mapping library of driving scenarios and image enhancement strategies can be pre-built based on different driving scenarios (such as highway driving in moderate snow, reversing at night in heavy snow, and emergency stable driving) through a combination of real-vehicle road tests and simulation tests. Differentiated image enhancement strategies are configured for the weather and driving state characteristics and pain points of each type of driving scenario. Different image enhancement strategies can be configured with different image processing algorithms and / or parameter combinations to specifically address the pain points of each scenario and achieve precise adaptation between scenario and strategy.

[0039] For example, in high-speed driving scenarios with moderate snow, the core challenges are reduced image clarity due to snowflakes adhering to the lens and image shake caused by vehicle bumps at high speeds. Based on this, image enhancement strategies can employ multi-frame fusion denoising algorithms to filter out snowflake noise, combined with adaptive contrast enhancement and defogging algorithms (e.g., setting the defogging intensity to medium-high) to improve image clarity, and overlaid with electronic image stabilization (EIS) algorithms to compensate for image shifts caused by vehicle bumps, ensuring clear identification of distant obstacles.

[0040] For reversing scenarios during blizzards at night, the core challenges are severe overexposure / underexposure caused by the blizzard, low signal-to-noise ratio in low-light conditions, and high accuracy requirements for near-field obstacle edge recognition. Additionally, falling snowflakes cause dense noise and blurred details in the far field. To address these challenges, an image enhancement strategy can employ a zoned dynamic exposure control algorithm. This algorithm applies differentiated exposure parameters to the near field (e.g., 0-5m behind the vehicle) and the far field (e.g., 5-15m behind the vehicle). For example, it increases exposure gain in the near field to enhance brightness and edge sharpness, while reducing exposure time in the far field to suppress snowflake noise and reduce far-field noise. Combined with an infrared and visible light image fusion algorithm, infrared thermal imaging features compensate for detail loss in visible light images under low light, improving the distinction between obstacles and background. Edge detection enhancement algorithms (such as Canny operator optimization) strengthen the contour features of near-field obstacles, while high-sharpening is disabled to prevent sharpening algorithms from amplifying far-field noise and causing image distortion, ultimately reducing the risk of reversing collisions.

[0041] For emergency stable driving scenarios, the core challenges are the geometric distortion caused by drastic changes in vehicle attitude during ESP activation, the high timeliness requirements for driver recognition of dangerous targets ahead during emergency braking / steering, and the potential for complex image enhancement processing to exacerbate image judder. Therefore, the image enhancement strategy can employ a real-time distortion correction algorithm. This algorithm dynamically adjusts distortion correction parameters by collecting real-time data from vehicle attitude sensors (such as yaw rate and pitch angle) to accurately correct the geometric distortion caused by the superposition of lens optical distortion and vehicle attitude changes. Combined with a fast target area sharpening algorithm, it focuses on sharpening only the core visual area 5-30m in front of the vehicle, enhancing obstacle edge features. Simultaneously, unnecessary complex post-processing steps (such as multi-frame fusion and color enhancement) are disabled to minimize algorithm latency and ensure rapid detection of dangerous targets. Furthermore, dynamic enhancement is temporarily disabled, retaining only basic brightness correction to prevent inter-frame parameter fluctuations in the dynamic enhancement algorithm from exacerbating image judder and reducing visual interference and psychological stress for the driver.

[0042] S208. Process the environmental image data according to the target image enhancement strategy, and display the processed environmental image data.

[0043] Once the target image enhancement strategy is determined, the real-time acquired environmental image data can be processed according to the preset algorithm flow and parameter configuration of the strategy, ensuring that the processing process accurately matches the pain points and needs of the target driving scenario. For example, for high-speed driving scenarios in moderate snow, multi-frame fusion denoising, adaptive contrast enhancement, defogging, and electronic image stabilization are performed simultaneously, focusing on optimizing image clarity and stability to ensure that distant obstacles are clearly identifiable; for reversing scenarios at night in blizzards, the brightness of the image is first balanced through zoned dynamic exposure control, and then infrared and visible light image fusion and near-field edge enhancement are completed, while avoiding high-sharpening operations and focusing on near-field obstacle recognition to provide clear guidance for reversing operations; for emergency stable driving scenarios, real-time distortion correction and rapid sharpening of the core field of view are performed, along with basic brightness correction, while the dynamic enhancement function is temporarily turned off to ensure rapid detection of dangerous targets while avoiding image shaking that exacerbates driver tension.

[0044] After image processing is complete, the processed environmental image data will be displayed synchronously in real time on the in-vehicle display screen. In addition, the display brightness can be dynamically adjusted according to the ambient light intensity inside the vehicle to avoid visual fatigue caused by the screen being too dark in strong light or too bright in low light.

[0045] As described above, the vehicle environment image display method provided in this embodiment acquires driving data and external environment detection data, including precipitation level, ambient light intensity, and environmental image data. Based on the driving data and external environment detection data, it determines the target driving scenario where the vehicle is currently located. It then determines a target image enhancement strategy corresponding to the target driving scenario from a preset set of image enhancement strategies. The environmental image data is processed according to the target image enhancement strategy, and the processed environmental image data is displayed. In other words, it combines multi-source sensor signals from two dimensions—driving state and weather state—to identify and determine the actual driving scenario, and adapts different image enhancement strategies for different driving scenarios to perform targeted processing of the environmental image. This allows for scene-adaptive image enhancement processing that takes into account both the complexity of the weather and the differences in the vehicle's operating state. This not only avoids the problem of over-enhancement or under-enhancement of snow and fog environment images caused by using a fixed image enhancement strategy, but also significantly improves the detail recognition and penetration enhancement effect of snow and fog environment images, which is beneficial to improving driving safety.

[0046] Based on the methods described in the above embodiments, this application also provides another method for displaying vehicle environment images. Please refer to... Figure 3 , Figure 3 This is a flowchart illustrating another method for displaying a vehicle environment image provided in this application embodiment. The method for displaying the vehicle environment image includes the following steps S302-S320, wherein: S302. Acquire driving data and external environment detection data while the vehicle is in motion. The external environment detection data includes precipitation level, ambient light intensity and environmental image data. The driving data includes vehicle speed, driving gear and the operating status of the electronic stability control system.

[0047] The steps S302 and S202 are the same as those described above, and will not be repeated here.

[0048] S304. Determine the vehicle's driving status based on the vehicle speed, driving gear, and the operating status of the electronic stability control system.

[0049] The driving status determination logic adopts a hierarchical priority rule: the ESP operating status is used as the core determination basis, combined with the driving gear and vehicle speed parameters for further subdivision. For example, when the ESP operating status is ESP activated, the vehicle is determined to be in an emergency stability control state, which has the highest priority and is not affected by the gear and vehicle speed parameters. When the ESP operating status is ESP deactivated, the driving status is further determined in conjunction with the driving gear. If the driving gear is reverse, the vehicle is determined to be in reverse; if the driving gear is drive, it is further subdivided in conjunction with the vehicle speed threshold. For example, when the vehicle speed is greater than the first preset speed threshold (e.g., 80 km / h), the vehicle is determined to be in a high-speed driving state; when the vehicle speed is less than or equal to the second preset speed threshold (e.g., 30 km / h), the vehicle is determined to be in a low-speed driving state; when the vehicle speed is greater than the second preset speed threshold and less than or equal to the first preset speed threshold, the vehicle is determined to be in a medium-speed driving state.

[0050] S306. Determine the target scene discrimination conditions that match the driving state, precipitation level and ambient light intensity from multiple preset scene discrimination conditions. Different scene discrimination conditions correspond to different preset driving scenarios.

[0051] The different scene discrimination conditions include different precipitation level thresholds and / or light intensity thresholds, and all scene discrimination conditions are adapted to the driving state determined in step S304 (such as emergency stability control state, reversing state, high-speed driving state, medium-speed driving state, low-speed driving state, etc.). By combining the threshold ranges of precipitation level and ambient light intensity, the driving scene can be finely discerned, avoiding scene misjudgment caused by single parameter discrimination.

[0052] Specifically, a scene discriminator can be designed based on rules or lightweight decision logic to identify the specific driving scenario in which the vehicle is currently located. For example, the scene discriminator's discrimination logic is based on a three-dimensional parameter combination of "driving state + precipitation level threshold + light intensity threshold," and provides clear discrimination examples in conjunction with actual working conditions. This ensures that for each driving state, different precipitation levels and different light conditions have corresponding scene discrimination conditions, achieving comprehensive coverage and accurate matching of driving scenarios, and providing a clear basis for subsequent image enhancement strategies.

[0053] For example, if the precipitation level is 2, the light intensity is >1000 lux, and the vehicle speed is >80 km / h (corresponding to high-speed driving), it is determined as a "moderate snow daytime high-speed driving scenario"; if the precipitation level is 3, the light intensity is <50 lux, and the driving gear is R (corresponding to reversing), it is determined as a "blizzard nighttime reversing scenario"; if ESP is activated (corresponding to emergency stability control), regardless of the precipitation level and ambient light intensity threshold range, it is preferentially determined as an "emergency stability driving scenario" to ensure the smoothness of subsequent image processing. The above examples all correspond to unique scene discrimination conditions to ensure that the discrimination logic is feasible and reusable.

[0054] S308. The preset driving scenario corresponding to the target scenario discrimination condition is taken as the target driving scenario in which the vehicle is currently located.

[0055] The scenario discrimination conditions and preset driving scenarios have a one-to-one correspondence, which is pre-stored in the system. Once the matching target scenario discrimination conditions are determined based on driving data and external environment detection data, the corresponding relationship can be directly invoked to determine the bound preset driving scenario as the vehicle's current target driving scenario.

[0056] For example, when the target scenario discrimination conditions determined in step S306 are "precipitation level = 2, light intensity > 1000 lux, vehicle speed > 80 km / h", the system directly uses the "moderate snow daytime highway scenario" bound to the condition as the current target driving scenario; when the target scenario discrimination conditions are "precipitation level = 3, light intensity < 50 lux, driving gear is R", the system directly uses the "blizzard nighttime reversing scenario" as the current target driving scenario; when the target scenario discrimination conditions are "ESP is activated", the system directly uses the "emergency stable driving scenario" as the current target driving scenario.

[0057] S310. Determine the target image enhancement strategy corresponding to the target driving scenario from the preset set of image enhancement strategies.

[0058] The steps S310 and S206 are the same as those described above, and will not be repeated here.

[0059] S312. Process the environmental image data according to the target image enhancement strategy, and display the processed environmental image data.

[0060] The steps S312 and S208 are the same as those described above, and will not be repeated here.

[0061] S314. Monitor whether the user adjusts the image parameters of the displayed environmental image data. If so, record the corresponding image parameter adjustment information.

[0062] S316. When the preset cycle duration is reached, update the target image enhancement strategy according to the adjustment information of all image parameters recorded under the target driving scenario.

[0063] Different image enhancement strategies involve different combinations of processing parameters, which can be personalized and optimized according to user preferences. These parameters include core parameters such as image contrast, brightness, saturation, sharpness, and noise reduction intensity. The initial processing parameter combinations for image enhancement strategies corresponding to different target driving scenarios are preset based on the environmental characteristics of that scenario (e.g., higher noise reduction intensity and contrast are preset for rain and fog scenarios, and higher brightness and sharpness are preset for low-light scenarios). The recorded image parameter adjustment information is bound to the specific driving scenario and stored accordingly. This includes information such as the specific parameter type adjusted by the user, the parameter values ​​before and after the adjustment, and the adjustment time. The preset cycle length can be flexibly set according to the actual operating requirements of the in-vehicle system (e.g., 24 hours, 7 days) to avoid setting the cycle too long or too short. An excessively long cycle will lead to a lag in strategy updates, while an excessively short cycle may cause strategy fluctuations due to occasional user adjustments.

[0064] Specifically, the system can record each image parameter adjustment operation by the user in the target driving scenario, eliminate abnormal adjustment records caused by erroneous operations (such as frequently and repeatedly adjusting the same parameter in a short period of time), calculate the average or optimal adjustment value of each image parameter in the scenario, and integrate it into the parameter configuration of the original target image enhancement strategy to achieve iterative optimization of the strategy. For example, in the "medium snow daytime highway driving scenario," if the user adjusts the image contrast from the default value of 50 to 70 and the brightness from the default value of 40 to 60 multiple times within a preset period, the system updates the target image enhancement strategy corresponding to this scenario, adjusting the default parameters of contrast and brightness to 70 and 60 respectively, so that the enhanced image better matches the user's visual needs. At the same time, the basic logic of the original strategy is still retained after the strategy update, only the parameter configuration is optimized, ensuring that the updated target image enhancement strategy is still adaptable to the target driving scenario, taking into account both the user's personalized needs and the core goal of scenario adaptation.

[0065] S318. Monitor whether the geographical location of the vehicle has changed. If it has changed, proceed with step S320 below.

[0066] S320. Obtain historical precipitation data and / or historical illumination data for the changed geographical location, and update the precipitation level threshold in the scene discrimination conditions based on the historical precipitation data, and / or update the illumination intensity threshold in the scene discrimination conditions based on the historical illumination data.

[0067] The different scene discrimination conditions include different precipitation level thresholds and / or light intensity thresholds. These thresholds can be dynamically adjusted according to regional climate and light characteristics to ensure that the scene discrimination conditions match the environmental characteristics of the actual location of the vehicle, avoiding scene discrimination deviations caused by regional differences. Monitoring of changes in geographical location can be achieved through the vehicle's GPS positioning module. When the vehicle's positioning coordinates are detected to be outside the preset geographical range (e.g., outside the original city or district / county range), it is determined that a change in geographical location has occurred.

[0068] Specifically, historical precipitation and sunshine data for the geographical location can be obtained from a cloud database via the vehicle network. This includes core data such as the region's annual average precipitation, precipitation intensity distribution, seasonal sunshine duration, peak and trough sunshine intensity, and the frequency and intensity of extreme weather events (such as heavy rain, heavy snow, polar day, and polar night). Based on this historical data, and combined with the three-dimensional discrimination logic of "driving status + precipitation level threshold + sunshine intensity threshold" in step S306, the system will selectively update the precipitation level threshold and sunshine intensity threshold in the original scenario discrimination conditions. This ensures that the updated thresholds are adapted to the new geographical environment without compromising the integrity and feasibility of the original scenario discrimination system. For example, if a vehicle is driving in a low-light city (such as a city with frequent overcast and rainy weather or a high latitude), the system can gradually increase the light threshold for "nighttime," changing the original "light intensity <50 lux is considered nighttime" to "light intensity <80 lux is considered nighttime," adapting to the insufficient daytime light in that region and avoiding misclassifying low-light daytime scenes as nighttime scenes. Simultaneously, the system records threshold updates after each location change, forming a region-threshold mapping library. When the vehicle re-enters that region, historically updated thresholds can be quickly retrieved, shortening adaptation time and improving scene identification efficiency.

[0069] Based on the methods described in the above embodiments, this application also provides another method for displaying vehicle environment images. Please refer to... Figure 4 , Figure 4 This is a flowchart illustrating another method for displaying a vehicle environment image provided in this application embodiment. The method for displaying a vehicle environment image includes the following steps S402-S414, wherein: S402. Acquire driving data and external environment detection data while the vehicle is in motion. The external environment detection data includes precipitation level, ambient light intensity and environmental image data. The driving data includes vehicle speed, driving gear, operating status of electronic stability control system and windshield wiper working gear.

[0070] The steps S402 and S302 are the same as those described above, and will not be repeated here.

[0071] S404. Determine the driving status of the vehicle based on the vehicle speed, the driving gear, and the operating status of the electronic stability control system.

[0072] The steps S402 and S304 are the same as those described above, and will not be repeated here.

[0073] S406. Determine the weather visibility level of the driving environment based on the precipitation level and ambient light intensity.

[0074] Among these methods, a weighted coupling model can be used to perform coupled quantitative analysis of precipitation level and ambient light intensity, enabling accurate quantification of visibility level. Precipitation level and ambient light intensity are the core input parameters for determining the visibility level of the driving environment. The two are strongly coupled; precipitation directly scatters light and reduces visibility, while ambient light intensity determines the basic visual perception conditions.

[0075] In some embodiments, the windshield wiper setting and environmental image data can be further combined to identify and determine the weather visibility level. In this case, step S406 specifically includes: The visibility level of the driving environment is determined based on the wiper setting, environmental image data, precipitation level, and ambient light intensity.

[0076] Among them, the windshield wiper operating speed is an auxiliary verification parameter for the intensity of precipitation, and the speed is positively correlated with the amount of precipitation per unit time (e.g., high speed corresponds to heavy rainfall, and low speed corresponds to light rainfall). Environmental image data is a direct visual representation of visibility. Together with the precipitation level detected by the rain sensor and the ambient light intensity detected by the ambient light sensor, these two data constitute a four-dimensional discrimination criterion. A weighted fusion model is used to improve the robustness of visibility level determination.

[0077] Furthermore, the above step of "determining the visibility level of the driving environment based on the wiper setting, environmental image data, precipitation level, and ambient light intensity" specifically includes: The environmental image data is processed using a trained image classification model to obtain a visibility prediction value. This image classification model is trained based on a set of environmental image samples collected under different weather visibility conditions. The wiper's operating setting, rainfall level, and ambient light intensity were normalized to obtain multiple normalized values. The visibility prediction value and the multiple normalized values ​​are weighted and summed based on preset weights, and the level corresponding to the preset numerical range of the sum is used as the weather visibility level of the driving environment.

[0078] The image classification model can employ lightweight convolutional neural network architectures such as MobileNet or ShuffleNet. The environmental image sample set consists of images collected by the vehicle's CMS system under different weather visibility conditions. The environmental image sample set covers images collected in various scenarios, including sunny, cloudy, light rain, moderate rain, heavy rain, and foggy weather. Furthermore, data augmentation operations such as brightness enhancement and Gaussian blur can be applied to the samples to improve the model's generalization ability under different lighting and precipitation conditions.

[0079] Normalization is primarily used to unify the dimensional differences of various parameters, eliminating weighting biases caused by different parameter units and numerical ranges, and ensuring that each input parameter has an equivalent participation in the visibility level determination model. Weather visibility levels can be divided into 1-5 levels based on the requirements of vehicle active safety control. The preset numerical range is a range calibrated based on the distribution range of the weighted summation score (sum value) and real-vehicle road test data. Different weather visibility levels correspond to different preset numerical ranges. The weighted summation score is a comprehensive score obtained by weighting the visibility prediction value with three normalized values: wiper setting, precipitation level, and ambient light intensity, according to preset weights. Its magnitude directly reflects the quality of visibility; the higher the score, the better the visibility.

[0080] The preset weights can be determined based on correlation analysis from real-vehicle multi-scenario tests. The core basis is the Pearson correlation coefficient between each parameter and the actual visibility value, combined with the stability of the parameters under complex operating conditions for weight calibration. The sum of the weights of all parameters must be 1. For example, the weight of precipitation level is set to 0.4, as it is a core meteorological factor and the root cause of light scattering and decreased visibility. The weight of ambient light intensity is set to 0.3, which determines the basic perception threshold of the human eye and vehicle-mounted cameras. Under the same precipitation level, the actual visibility value in low-light environments at night is much lower than that in strong-light environments during the day. The weight of the visibility prediction value output by the image classification model is set to 0.15. This parameter is a direct visual representation of visibility and can intuitively reflect the recognizability of distant road views, but it is easily affected by interference factors such as camera dirt, backlight, and road surface reflection, and its stability is weaker than that of sensor parameters. The wiper operating setting weight is set to 0.15, which serves as an auxiliary verification parameter for precipitation level. The wiper operating setting is manually or automatically adjusted by the driver according to the actual precipitation intensity, which has strong scene adaptability and can effectively correct the single-point detection error of the rain sensor.

[0081] S408. Determine the scene discrimination condition that includes the driving state and weather visibility level from multiple preset scene discrimination conditions, and use it as the target scene discrimination condition.

[0082] The scene discrimination criteria are a two-dimensional scene matrix constructed based on all vehicle driving conditions. The row dimension of the matrix represents the vehicle driving state classification, and the column dimension represents the weather visibility level. Each matrix element corresponds to a unique combination of "driving state - visibility level" scene. The scene discrimination criteria are defined in the form of a triple of "driving state + visibility level + driving scene".

[0083] S410. The preset driving scenario corresponding to the target scenario discrimination condition is taken as the target driving scenario in which the vehicle is currently located.

[0084] The steps S410 and S308 are the same as those described above, and will not be repeated here.

[0085] S412. Determine the target image enhancement strategy corresponding to the target driving scenario from the preset set of image enhancement strategies.

[0086] The steps S412 and S310 are the same as those described above, and will not be repeated here.

[0087] S414. Process the environmental image data according to the target image enhancement strategy, and display the processed environmental image data.

[0088] The steps S414 and S312 are the same as those described above, and will not be repeated here.

[0089] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the steps or stages of other steps.

[0090] It should be noted that the scope of protection for the vehicle environment image display method provided in this application is not limited to the above embodiments. Any solution implemented by adding, deleting, or substituting steps from existing technologies based on the principles of this application, according to the execution order of each embodiment, is included within the scope of protection of this application. Furthermore, any reasonable substitution or combination of steps between different embodiments based on the principles of this application is included within the scope of protection of this application.

[0091] Based on the method described in the above embodiments, this application also provides a vehicle environment image display device for performing the steps in the above vehicle environment image display method. Please refer to... Figure 5 , Figure 5 This is a schematic diagram of the structure of a vehicle environment image display device 500 provided in an embodiment of this application. The vehicle environment image display device 500 can be a vehicle executing a vehicle environment image display method, a processor within a vehicle executing the vehicle environment image display method, or a vehicle environment image display service within a vehicle executing the vehicle environment image display method. Specifically, the vehicle environment image display device 500 includes an acquisition unit 501, a first determination unit 502, a second determination unit 503, and a processing unit 504, wherein: The acquisition unit 501 is used to acquire driving data and external environment detection data when the vehicle is in motion. The external environment detection data includes precipitation level, ambient light intensity and environmental image data. The first determining unit 502 is used to determine the target driving scenario where the vehicle is currently located based on the driving data and the external environment detection data. The second determining unit 503 is used to determine the target image enhancement strategy corresponding to the target driving scenario from a preset set of image enhancement strategies; The processing unit 504 is used to process the environmental image data according to the target image enhancement strategy and display the processed environmental image data.

[0092] In some embodiments, the driving data includes vehicle speed, driving gear, and the operating status of the electronic stability control system. The first determining unit 502 is specifically used for: The driving status of the vehicle is determined based on the vehicle speed, the driving gear, and the operating status of the electronic stability control system. From multiple preset scene discrimination conditions, a target scene discrimination condition that matches the driving state, the precipitation level, and the ambient light intensity is determined. Different scene discrimination conditions correspond to different preset driving scenarios. The preset driving scenario corresponding to the target scenario discrimination condition is taken as the target driving scenario in which the vehicle is currently located.

[0093] In some embodiments, the first determining unit 502 is specifically used for: Based on the precipitation level and the ambient light intensity, determine the weather visibility level of the driving environment; From multiple preset scene discrimination conditions, determine the scene discrimination conditions that include the driving state and the weather visibility level, and use them as the target scene discrimination conditions.

[0094] In some embodiments, the driving data also includes the wiper operating setting, and the first determining unit 502 is specifically used for: The visibility level of the driving environment is determined based on the wiper setting, the environmental image data, the precipitation level, and the ambient light intensity.

[0095] In some embodiments, the first determining unit 502 is specifically used for: The environmental image data is processed using a trained image classification model to obtain a visibility prediction value. This image classification model is trained based on a set of environmental image samples collected under different weather visibility conditions. The wiper setting, the rainfall level, and the ambient light intensity were normalized separately to obtain multiple normalized values. The visibility prediction value and the multiple normalized values ​​are weighted and summed based on preset weights, and the level corresponding to the preset numerical range of the sum is taken as the weather visibility level of the driving environment.

[0096] In some embodiments, different scene discrimination conditions include different precipitation level thresholds and / or light intensity thresholds. The display device 500 for the vehicle environment image further includes an update unit for: Monitor whether the vehicle's geographical location has changed; If changes occur, obtain historical precipitation data and / or historical sunshine data for the changed geographical location; Update the precipitation level threshold based on the historical precipitation data, and / or update the light intensity threshold based on the historical sunshine data.

[0097] In some embodiments, the updating unit is further configured to: After displaying the processed environmental image data, monitor whether the user adjusts the image parameters of the displayed environmental image data; If so, record the corresponding image parameter adjustment information; When the preset cycle time is reached, the target image enhancement strategy is updated based on all the image parameter adjustment information recorded under the target driving scenario.

[0098] As described above, the vehicle environment image display device 400 provided in this embodiment acquires driving data and external environment detection data of the vehicle during driving through the acquisition unit 501. The external environment detection data includes precipitation level, ambient light intensity, and environmental image data. The first determination unit 502 determines the target driving scenario of the vehicle based on the driving data and the external environment detection data. The second determination unit 503 determines the target image enhancement strategy corresponding to the target driving scenario from a preset set of image enhancement strategies. The processing unit 504 processes the environmental image data according to the target image enhancement strategy and displays the processed environmental image data. That is, it combines multi-source sensor signals of driving state and weather state to identify and determine the actual driving scenario, and adapts different image enhancement strategies for different driving scenarios to perform targeted processing of environmental images. This can take into account the complexity of weather and the differences in vehicle operating state to perform scene-adaptive image enhancement processing. This not only avoids the problem of over-enhancement or under-enhancement of snow and fog environment images caused by using a fixed image enhancement strategy, but also significantly improves the detail recognition and penetration enhancement effect of snow and fog environment images, which is conducive to improving driving safety.

[0099] It should be understood that the division of the unit modules in the above-described vehicle environment image display device is only for illustrative purposes. In other embodiments, the vehicle environment image display device can be divided into different unit modules as needed to complete all or part of the functions of the above-described vehicle environment image display device.

[0100] Figure 6 This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application.

[0101] For example, such as Figure 6 As shown, the vehicle 600 includes a memory 601 and a processor 602. The memory 601 stores executable program code 6011, and the processor 602 is used to call and execute the executable program code 6011 to perform any of the above-mentioned methods for displaying vehicle environment images.

[0102] The processor 602 is the control center of the vehicle 600. It connects various parts of the vehicle 600 through various interfaces and lines. By running or loading the application program stored in the memory 601 and calling the data stored in the memory 601, it executes various functions of the vehicle 600 and processes data, thereby performing overall monitoring of the vehicle 600.

[0103] Memory 601 can be used to store application programs and data. The application programs stored in memory 601 contain instructions that can be executed in processor 602. The application programs can be composed of various functional modules. Processor 602 executes various functional applications and data processing by running the application programs stored in memory 601.

[0104] In some embodiments, vehicle 600 further includes: a radio frequency (RF) circuit, an input unit, an audio circuit, a sensor, and a power supply. The processor 602 is electrically connected to the RF circuit, the input unit, the audio circuit, the sensor, and the power supply. The RF circuit transmits and receives RF signals to establish wireless communication with network devices or other electronic devices, and to transmit and receive signals with network devices or other electronic devices. The input unit can be used to receive user-inputted numerical and character information, and to generate inputs related to user settings and function control. The audio circuit provides an audio interface between the user and vehicle 600 via speakers and microphones. The power supply powers the various components of vehicle 600. In some embodiments, the power supply can be logically connected to the processor 602 via a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system.

[0105] The sensors include wheel speed sensors, which detect wheel rotation speed in real time, providing core signals for systems such as Anti-lock Braking System (ABS), Electronic Stability Program (ESP), and Tire Pressure Monitoring System. They also include steering angle sensors, which detect the steering wheel's rotation angle, direction, and speed, working with the wheel speed sensors to provide signals for ESP and Electric Power Steering (EPS). Lateral acceleration sensors detect the vehicle's lateral acceleration (centrifugal force) during cornering. Rain sensors detect the level of precipitation in the external environment. An ambient light sensor detects ambient light intensity (e.g., day / night).

[0106] Furthermore, embodiments of this application also protect an apparatus that may include a memory and a processor, wherein the memory stores executable program code, and the processor is used to call and execute the executable program code to perform any of the above-described methods for displaying vehicle environment images.

[0107] This embodiment can divide the device into functional modules based on the above method example. For example, each module can correspond to a separate function, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0108] It should be noted that all relevant content of each step involved in the above method embodiments can be referenced from the functional description of the corresponding functional module, and will not be repeated here.

[0109] It should be understood that the apparatus provided in this embodiment is used to execute any of the above-described methods for displaying vehicle environment images, and therefore can achieve the same effect as the above-described implementation methods.

[0110] When using an integrated unit, the device may include a processing module and a storage module. When the device is applied to a vehicle, the processing module can be used to control and manage the vehicle's movements. The storage module can be used to support the vehicle in executing relevant program code.

[0111] The processing module may be a processor or a controller, which can implement or execute various exemplary logic blocks, modules, and circuits shown in conjunction with the disclosure of this application. The processor may also be a combination of functions that implement computing capabilities, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc., and the storage module may be a memory.

[0112] In addition, the device provided in the embodiments of this application may specifically be a chip, component or module. The chip may include a connected processor and a memory. The memory is used to store instructions. When the processor calls and executes the instructions, the chip can execute any of the vehicle environment image display methods provided in the above embodiments.

[0113] This embodiment also provides a computer storage medium storing computer program code. When the computer program code is run on a computer, the computer executes the above-described related method steps to implement any of the vehicle environment image display methods provided in the above embodiments.

[0114] This embodiment also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned steps to implement any of the vehicle environment image display methods provided in the above embodiments.

[0115] In this embodiment, the device, computer storage medium, computer program product or chip are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can be referred to the beneficial effects of the corresponding methods provided above, and will not be repeated here.

[0116] Through the above description of the embodiments, those skilled in the art will 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.

[0117] In the 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 device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0118] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology 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 method for displaying vehicle environment images, characterized in that, The method includes: Acquire driving data and external environment detection data while the vehicle is in motion, including precipitation level, ambient light intensity, and environmental image data; Based on the driving data and the external environment detection data, the target driving scenario in which the vehicle is currently located is determined; Determine the target image enhancement strategy corresponding to the target driving scenario from a preset set of image enhancement strategies; The environmental image data is processed according to the target image enhancement strategy, and the processed environmental image data is displayed.

2. The method according to claim 1, characterized in that, The driving data includes vehicle speed, driving gear, and the operating status of the electronic stability control system. Determining the target driving scenario currently in which the vehicle is located based on the driving data and the external environment detection data includes: The driving state of the vehicle is determined based on the vehicle speed, the driving gear, and the operating status of the electronic stability control system. From multiple preset scene discrimination conditions, a target scene discrimination condition that matches the driving state, the precipitation level, and the ambient light intensity is determined, and different scene discrimination conditions correspond to different preset driving scenarios; The preset driving scenario corresponding to the target scenario discrimination condition is taken as the target driving scenario in which the vehicle is currently located.

3. The method according to claim 2, characterized in that, The step of determining the target scene discrimination conditions that match the driving state, the precipitation level, and the ambient light intensity from multiple preset scene discrimination conditions includes: Based on the precipitation level and the ambient light intensity, determine the weather visibility level of the driving environment; The scene discrimination condition that includes the driving state and the weather visibility level is determined from multiple preset scene discrimination conditions and used as the target scene discrimination condition.

4. The method according to claim 3, characterized in that, The driving data also includes the windshield wiper setting, and the determination of the weather visibility level based on the precipitation level and the ambient light intensity includes: The visibility level of the driving environment is determined based on the wiper setting, the environmental image data, the precipitation level, and the ambient light intensity.

5. The method according to claim 4, characterized in that, The step of determining the weather visibility level of the driving environment based on the wiper operating setting, the environmental image data, the precipitation level, and the ambient light intensity includes: The environmental image data is processed using a trained image classification model to obtain a visibility prediction value. The image classification model is trained based on a set of environmental image samples collected under different weather visibility conditions. The wiper settings, precipitation levels, and ambient light intensity are normalized to obtain multiple normalized values. The visibility prediction value and the multiple normalized values ​​are weighted and summed based on preset weights, and the level corresponding to the preset numerical range of the sum is taken as the weather visibility level of the driving environment.

6. The method according to claim 2, characterized in that, The different scene discrimination conditions include different precipitation level thresholds and / or light intensity thresholds, and the method further includes: Monitor whether the geographical location of the vehicle has changed; If a change occurs, obtain the historical precipitation data and / or historical sunshine data for the changed geographical location; The precipitation level threshold is updated based on the historical precipitation data, and / or the light intensity threshold is updated based on the historical sunshine data.

7. The method according to any one of claims 1-6, characterized in that, After displaying the processed environmental image data, the method further includes: Monitor whether the user adjusts the image parameters of the displayed environmental image data; If so, record the corresponding image parameter adjustment information; When the preset period of time is reached, the target image enhancement strategy is updated according to all the image parameter adjustment information recorded under the target driving scenario.

8. A display device for vehicle environment images, characterized in that, The device includes: The acquisition unit is used to acquire driving data and external environment detection data when the vehicle is in motion. The external environment detection data includes precipitation level, ambient light intensity and environmental image data. The first determining unit is used to determine the target driving scenario currently in which the vehicle is located based on the driving data and the external environment detection data. The second determining unit is used to determine the target image enhancement strategy corresponding to the target driving scenario from a preset set of image enhancement strategies; The processing unit is used to process the environmental image data according to the target image enhancement strategy and display the processed environmental image data.

9. A vehicle, characterized in that, include: Memory, used to store executable program code; A processor is configured to call and run the executable program code from the memory, causing the electronic device to perform the method for displaying a vehicle environment image as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The computer storage medium stores multiple instructions adapted for loading by a processor and executing the method for displaying vehicle environment images as described in any one of claims 1-7.

11. A computer program product, characterized in that, The computer program product includes computer program code that, when run on a computer, causes the computer to perform the method for displaying vehicle environment images as described in any one of claims 1 to 7.

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