Vehicle imaging adjusting system and method based on optical element

Abstract

The invention discloses a vehicle imaging adjustment system and method based on an optical element, and relates to the technical field of data analysis, and the method comprises the steps: obtaining vehicle real-time information through a navigation system, and obtaining road type information, weather information and illumination intensity information; an image acquisition device is arranged to acquire image information of a monitoring area, and a parameter adjustment model composed of the comprehensive environment labels and the optimal parameters under the labels is established in combination with the information. By setting prediction time, relevant information is predicted according to real-time information of a vehicle, and parameters of an image acquisition device are adjusted by using a parameter adjustment model; parameters manually adjusted by the driver are recorded, the parameters before and after adjustment and image information are output to the driver for confirmation, and therefore the parameter adjustment model is corrected, and vehicle imaging adjustment is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a vehicle imaging adjustment system and method based on optical elements. Background Art

[0002] Dashcams are essential equipment for ensuring vehicle safety. In today's complex and ever-changing traffic environment, they continuously and accurately record the driving process. In the event of a traffic accident, whether it's a collision, scrape, or rear-end collision, the stored video footage can fully reconstruct the accident scene, providing key evidence for traffic management departments to accurately determine responsibility and significantly improving accident handling efficiency.

[0003] The dashcams currently on the market find it difficult to accurately match resolution requirements during the adaptive adjustment process. When light suddenly changes or scenes switch, the parameter adjustment of the optical components in the dashcam lags, which has a significant impact on accident evidence collection and image recording quality. Summary of the Invention

[0004] The object of the present invention is to provide a vehicle imaging adjustment system and method based on optical elements to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solutions: Step S1: obtaining real-time vehicle information through a navigation system; obtaining road type information, weather information, and light intensity based on the real-time vehicle information; Step S2: Setting an image acquisition device to acquire image information of a monitoring area through the image acquisition device, where the monitoring area is the area covered by the image acquisition device; establishing a parameter adjustment model based on the image information, the road type information, the meteorological information, and the light intensity; Step S3: setting a prediction time, obtaining predicted road type information, predicted weather information, and predicted light intensity based on the real-time vehicle information; and adjusting parameters of the image acquisition device based on the predicted road type information, the predicted weather information, the predicted light intensity, and the parameter adjustment model. Step S4: Record the parameters of the image acquisition device manually adjusted by the vehicle driver, and modify the parameter adjustment model according to the parameters of the image acquisition device manually adjusted by the vehicle driver and the current road type information, the weather information, and the light intensity.

[0006] Furthermore, step S1 includes the following specific steps: The real-time vehicle information includes the vehicle's current latitude and longitude, altitude, vehicle motion state and current time; the vehicle motion state includes the vehicle's motion direction and vehicle motion speed; Obtaining the current position of the vehicle through the latitude, longitude and altitude, and obtaining road type information through the current position of the vehicle; The weather information of the current location of the vehicle is obtained through the Internet.

[0007] Furthermore, step S2 includes the following specific steps: S2-1. Establishing a comprehensive environmental label based on road type, weather information, and light intensity; the comprehensive environmental label includes all combinations of the road type, weather information, and light intensity; S2-2. Collect image information obtained by image acquisition devices with different parameters under different comprehensive environmental labels, calculate the image pixel value variance of the image information, and set the parameters of the image acquisition device corresponding to the maximum image pixel value variance under different comprehensive environmental labels as the optimal parameters of the comprehensive environmental label.

[0008] Furthermore, step S3 includes the following specific steps: S3-1. Predicting the vehicle's location after the predicted time using the real-time vehicle information to obtain predicted road type information, predicted weather information, and predicted light intensity; S3-2. Input the combination of the predicted road type information, the predicted weather information and the predicted light intensity into a parameter adjustment model to obtain the optimal parameters; and adjust the parameters of the image acquisition device according to the optimal parameters.

[0009] Furthermore, step S4 includes the following specific steps: S4-1, recording the parameters of the image acquisition device manually adjusted by the vehicle driver and the image information acquired at this time; S4-2, outputting the parameters and image information of the image acquisition device before manual adjustment by the vehicle driver and the parameters and image information of the image acquisition device after manual adjustment to the vehicle driver simultaneously; S4-3: The vehicle driver manually confirms whether to use the manually adjusted parameters of the image acquisition device as the optimal parameters under the current comprehensive environment label.

[0010] Furthermore, a vehicle imaging adjustment system and system based on optical elements includes an information acquisition module, a model building module, a parameter adjustment module, and a model correction module; the information acquisition module acquires real-time vehicle information and acquires road type information, weather information, and light intensity based on the real-time vehicle information; the model building module establishes a parameter adjustment model based on image information of the monitoring area acquired by the image acquisition device and the road type information, weather information, and light intensity; the parameter adjustment module obtains predicted road type information, predicted weather information, and predicted light intensity based on the real-time vehicle information, obtains optimal parameters by inputting the predicted road type information, predicted weather information, and predicted light intensity into the parameter adjustment model, and adjusts the parameters of the image acquisition device based on the optimal parameters; the model correction module records the parameters and image information of the image acquisition device manually adjusted by the vehicle driver, and compares the parameters and image information of the image acquisition device manually adjusted by the vehicle driver recorded by the correction unit with the parameters and image information before the manual adjustment, and manually selects whether to overwrite the manually adjusted parameters with the optimal parameters under the current comprehensive environment label; The output end of the information acquisition module is electrically connected to the input end of the model establishment module; the output end of the model establishment module is electrically connected to the input end of the parameter adjustment module; the output end of the prediction module is electrically connected to the input end of the model correction module.

[0011] The information acquisition module includes a vehicle information acquisition unit and an environment information acquisition unit; the vehicle information acquisition unit acquires real-time information of the vehicle through the navigation system; the environment information acquisition unit acquires road information, weather information and light intensity through the real-time information of the vehicle.

[0012] The model building module includes a label building unit and a model building unit; the label building unit builds a comprehensive environment label based on the road information, the meteorological information and the light intensity; the model building unit performs modeling based on the comprehensive environment label built by the label building unit and the image information collected by the image acquisition device under the corresponding comprehensive environment label.

[0013] The parameter adjustment module includes an information prediction unit and a parameter adjustment unit; the information prediction unit predicts the road type information, weather information and light intensity after the prediction time based on the real-time information of the vehicle to obtain the predicted road type information, predicted weather information and predicted light intensity; the parameter adjustment unit adjusts the parameters of the image acquisition device according to the predicted road type information, the predicted weather information and the predicted light intensity according to the parameter adjustment model.

[0014] The model correction module includes a data recording unit and a parameter confirmation unit; the data recording unit records the parameters of the image acquisition device manually adjusted by the vehicle driver and the image information captured by the image acquisition device after the adjustment; the parameter confirmation unit outputs the parameters and image information of the image acquisition device before the vehicle driver's manual adjustment and the parameters and image information of the image acquisition device after the manual adjustment to the vehicle driver at the same time for manual comparison to confirm whether the parameters of the manually adjusted image acquisition device are to be used as the optimal parameters under the current comprehensive environment label.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention acquires real-time vehicle information through the navigation system and constructs a parameter adjustment model based on road type, weather information, and light intensity. This model intelligently adapts to different environments and flexibly adjusts image acquisition device parameters, significantly improving imaging quality. 2. This invention predicts future environmental information using real-time vehicle information and adjusts the image acquisition device parameters in advance based on the parameter adjustment model. This ensures that the driver can always obtain clear images of the monitored area while driving, providing strong visual support for safe driving and effectively reducing the potential risk of accidents. 3. The present invention empowers the driver to manually confirm and modify the model. By recording the parameters manually adjusted by the driver and continuously optimizing the parameter settings, the imaging adjustment method and system are significantly enhanced in their ability to cope with complex and changing environments, improving the accuracy and adaptability of imaging adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic flow chart of a vehicle imaging adjustment method based on optical elements according to the present invention; Figure 2 The figure is a structural schematic diagram of a vehicle imaging adjustment method based on optical elements of the present invention. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0018] Example 1: Figure 1 As shown, the present invention provides a technical solution, a vehicle imaging adjustment method based on an optical element, and the vehicle imaging adjustment method includes the following steps: Step S1, obtaining real-time vehicle information through a navigation system; obtaining road type information, weather information, and light intensity based on the real-time vehicle information; The real-time vehicle information includes the vehicle's current latitude and longitude, altitude, vehicle motion state and current time; the vehicle motion state includes the vehicle's motion direction and vehicle motion speed; Obtaining the current position of the vehicle through the latitude, longitude and altitude, and obtaining road type information through the current position of the vehicle; The weather information of the current location of the vehicle is obtained through the Internet.

[0019] Step S2: Setting an image acquisition device to acquire image information of a monitoring area through the image acquisition device, where the monitoring area is the area covered by the image acquisition device; establishing a parameter adjustment model based on the image information, the road type information, the meteorological information, and the light intensity; S2-1. Establishing a comprehensive environmental label based on road type, weather information, and light intensity; the comprehensive environmental label includes all combinations of the road type, weather information, and light intensity; S2-2. Collect image information obtained by image acquisition devices with different parameters under different comprehensive environmental labels, calculate the image pixel value variance of the image information, and set the parameters of the image acquisition device corresponding to the maximum image pixel value variance under different comprehensive environmental labels as the optimal parameters of the comprehensive environmental label.

[0020] Step S3: setting a prediction time, obtaining predicted road type information, predicted weather information, and predicted light intensity based on the real-time vehicle information; and adjusting parameters of the image acquisition device based on the predicted road type information, the predicted weather information, the predicted light intensity, and the parameter adjustment model. S3-1. Predicting the vehicle's location after the predicted time using the real-time vehicle information to obtain predicted road type information, predicted weather information, and predicted light intensity; S3-2. Input the combination of the predicted road type information, the predicted weather information and the predicted light intensity into a parameter adjustment model to obtain the optimal parameters; and adjust the parameters of the image acquisition device according to the optimal parameters.

[0021] Step S4: Record the parameters of the image acquisition device manually adjusted by the vehicle driver, and modify the parameter adjustment model according to the parameters of the image acquisition device manually adjusted by the vehicle driver and the current road type information, the weather information, and the light intensity.

[0022] S4-1, recording the parameters of the image acquisition device manually adjusted by the vehicle driver and the image information acquired at this time; S4-2, outputting the parameters and image information of the image acquisition device before manual adjustment by the vehicle driver and the parameters and image information of the image acquisition device after manual adjustment to the vehicle driver simultaneously; S4-3: The vehicle driver manually confirms whether to use the manually adjusted parameters of the image acquisition device as the optimal parameters under the current comprehensive environment label.

[0023] For example: The navigation system indicates that the vehicle is located at 30.67°N, 104.06°E, with an altitude of 500 meters, a moving direction of due east, a speed of 60 km / h, and the current time is 10:00 on April 11, 2025.

[0024] The vehicle's location was determined using latitude, longitude, and altitude. Map data revealed that the vehicle was located on a two-way, four-lane urban secondary road with a speed limit of 60 km / h and a few commercial shops on both sides of the road. The road type was determined to be an urban secondary road.

[0025] Get the weather information for the vehicle's current location, which shows sunny weather, temperature 25°C, humidity 50%, and wind speed 2. Get the light intensity at the vehicle's current location, which is 5000 lux.

[0026] The comprehensive environmental label combination is urban secondary road-sunny day-5000lux.

[0027] Collect image data from image acquisition devices with different parameters, such as aperture, shutter speed, and sensitivity, under the comprehensive environmental label "Urban Secondary Road - Sunny Day - 5000 Lux." Images are acquired at a certain frame rate per second, for example, 30 frames per second.

[0028] For a set of images with image acquisition device parameters of aperture F5.6, shutter speed 1 / 200s, and sensitivity ISO100, 100 consecutive frames of images are randomly selected, the variance of the image pixel values ​​of each frame of these images is calculated, and then the average value is taken, assuming the average value is 300.

[0029] After testing multiple parameters, we found that when the parameters were aperture F8.0, shutter speed 1 / 160s, and ISO 100, we also sampled 100 frames and calculated the average pixel value variance, resulting in a maximum value of 450. Therefore, these parameters were set as the optimal parameters for the "Urban Secondary Road - Sunny Day - 5000 Lux" comprehensive environment label.

[0030] Set the prediction time to 10 minutes. Based on the vehicle's real-time information, predict the vehicle's location 10 minutes from now. Map matching predicts the road type to be a main urban road. The forecasted weather is still sunny, and the predicted light intensity is 5500 lux.

[0031] The predicted comprehensive environmental label combination is: urban main road, sunny day, 5500 lux. This combination is input into the parameter adjustment model to obtain the optimal parameters for this comprehensive environmental label: aperture F9.0, shutter speed 1 / 125s, and ISO 100. The vehicle system automatically adjusts the parameters of the image acquisition device based on the optimal parameters to ensure the optimal quality of subsequent images.

[0032] The driver found the image quality unsatisfactory and manually adjusted the image acquisition device parameters to aperture F11.0, shutter speed 1 / 100s, and sensitivity ISO 200. The system recorded the adjusted parameters and the image information collected within a period of time after the adjustment.

[0033] The system displays the image information collected before and after the manual adjustment in a split-screen display for the driver to compare. The driver confirms that the manually adjusted image quality is better and selects the adjusted parameters as the optimal settings for the current combined environment of Urban Main Road, Sunny Day, and 5500 lux.

[0034] The system modifies the parameter adjustment model based on the driver's confirmation and updates the optimal parameters corresponding to the comprehensive environmental label.

[0035] Example 2: The navigation system obtains that the vehicle is located at 28.5° north latitude and 113.2° east longitude, with an altitude of 800 meters, a moving direction of due east, a speed of 25 km / h, and the current time is 15:00 on January 18, 2025.

[0036] The vehicle's location is determined by latitude, longitude and altitude, and map data is queried to determine that the vehicle is located in a mountainous area.

[0037] Get the weather information for the vehicle's current location, which shows cloudy, 15°C temperature, 65% humidity, and possible short gusts in mountainous areas. Get the light intensity at the vehicle's current location, which is 3000 lux.

[0038] The comprehensive environmental label combination is mountainous area-cloudy-3000lux.

[0039] Image data was collected using image acquisition devices with different parameters under the "mountainous area, cloudy, 3000 lux" comprehensive environment label. Images were acquired at a certain frame rate per second, for example, 25 frames per second.

[0040] For a set of image acquisition device parameters with aperture F4.0, shutter speed 1 / 125s, and sensitivity ISO200, 200 consecutive frames of video images are randomly selected, the variance of the image pixel values ​​of each frame of these images is calculated, and then the average value is calculated, resulting in a value of 280.

[0041] After testing a large number of different parameter combinations, we found that when the parameters were aperture F5.6, shutter speed 1 / 100s, and sensitivity ISO 160, the average variance of the pixel values ​​calculated from the same number of video frames reached a maximum value of 350. Therefore, aperture F5.6, shutter speed 1 / 100s, and sensitivity ISO 160 were set as the optimal parameters for this comprehensive environment tag.

[0042] The prediction time is set to 15 minutes. Based on the vehicle's real-time information, the vehicle's location 15 minutes from now is predicted. Map matching predicts the road type to be a multi-lane parking lot approach road on a flat hilltop. The predicted weather is sunny, and the predicted light intensity is 4500 lux.

[0043] The predicted comprehensive environmental label combination is "approach road to a multi-lane parking lot on a hilltop, sunny day, 4500 lux." This combination is input into the parameter adjustment model to obtain the optimal parameters for this comprehensive environmental label: aperture F8.0, shutter speed 1 / 200s, and ISO 100. The vehicle system automatically adjusts the parameters of the image acquisition device based on the optimal parameters to ensure optimal image quality for subsequent capture.

[0044] The driver finds the image quality unsatisfactory and manually adjusts the image acquisition device parameters to aperture F9.0, shutter speed 1 / 160s, and ISO 200. The system records the adjusted parameters and the image information collected within a period of time after the adjustment.

[0045] The system displays the image information collected before and after the manual adjustment in a split-screen display for the driver to compare. The driver confirms that the image before the manual adjustment is better and selects the parameters before the manual adjustment as the optimal parameters for the current environment label: multi-lane parking lot approach road, sunny day, 4500 lux.

[0046] The system modifies the parameter adjustment model based on the driver's confirmation and updates the optimal parameters corresponding to the comprehensive environmental label.

[0047] Example 3: Figure 2 As shown, the present invention provides a vehicle imaging adjustment system based on optical elements, which includes an information acquisition module, a model building module, a parameter adjustment module, and a model correction module; The information acquisition module includes a vehicle information acquisition unit and an environment information acquisition unit; the vehicle information acquisition unit acquires real-time information of the vehicle through the navigation system; the environment information acquisition unit acquires road information, weather information and light intensity through the real-time information of the vehicle; The model building module includes a label building unit and a model building unit; the label building unit builds a comprehensive environment label based on the road information, the weather information and the light intensity; the model building unit builds a model based on the comprehensive environment label built by the label building unit and the image information collected by the image acquisition device under the corresponding comprehensive environment label; The parameter adjustment module includes an information prediction unit and a parameter adjustment unit; the information prediction unit predicts road type information, weather information and light intensity after the prediction time based on the real-time vehicle information to obtain predicted road type information, predicted weather information and predicted light intensity; the parameter adjustment unit adjusts the parameters of the image acquisition device according to the predicted road type information, the predicted weather information and the predicted light intensity according to the parameter adjustment model; The model correction module includes a data recording unit and a parameter confirmation unit; the data recording unit records the parameters of the image acquisition device manually adjusted by the vehicle driver and the image information captured by the image acquisition device after the adjustment; the parameter confirmation unit outputs the parameters and image information of the image acquisition device before the vehicle driver's manual adjustment and the parameters and image information of the image acquisition device after the manual adjustment to the vehicle driver at the same time for manual comparison to confirm whether the parameters of the manually adjusted image acquisition device are to be used as the optimal parameters under the current comprehensive environment label.

[0048] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. A vehicle imaging adjustment method based on optical elements, characterized by: The vehicle imaging adjustment method comprises: Step S1, obtaining real-time vehicle information through a navigation system; obtaining road type information, weather information, and light intensity based on the real-time vehicle information; Step S2: Setting an image acquisition device to acquire image information of a monitoring area through the image acquisition device, where the monitoring area is the area covered by the image acquisition device; establishing a parameter adjustment model based on the image information, the road type information, the meteorological information, and the light intensity; Step S3: setting a prediction time, obtaining predicted road type information, predicted weather information, and predicted light intensity based on the real-time vehicle information; and adjusting parameters of the image acquisition device based on the predicted road type information, the predicted weather information, the predicted light intensity, and the parameter adjustment model. Step S4: Record the parameters of the image acquisition device manually adjusted by the vehicle driver, and modify the parameter adjustment model according to the parameters of the image acquisition device manually adjusted by the vehicle driver and the current road type information, the weather information, and the light intensity.

2. The vehicle imaging adjustment method based on optical elements according to claim 1, characterized in that: The step S1 further includes: The real-time vehicle information includes the vehicle's current latitude and longitude, altitude, vehicle motion state and current time; the vehicle motion state includes the vehicle's motion direction and vehicle motion speed; Obtaining the current position of the vehicle through the latitude, longitude and altitude, and obtaining road type information through the current position of the vehicle; The weather information of the current location of the vehicle is obtained through the Internet.

3. The vehicle imaging adjustment method based on optical elements according to claim 1, characterized in that: The step S2 further includes: the parameter adjustment model includes: S2-1. Establishing a comprehensive environmental label based on road type, weather information, and light intensity; the comprehensive environmental label includes all combinations of the road type, weather information, and light intensity; S2-2. Collect image information obtained by image acquisition devices with different parameters under different comprehensive environmental labels, calculate the image pixel value variance of the image information, and set the parameters of the image acquisition device corresponding to the maximum image pixel value variance under different comprehensive environmental labels as the optimal parameters of the comprehensive environmental label.

4. The vehicle imaging adjustment method based on optical elements according to claim 1, characterized in that: The step S3 further comprises: S3-1. Predicting the vehicle's location after the predicted time using the real-time vehicle information to obtain predicted road type information, predicted weather information, and predicted light intensity; S3-2. Input the combination of the predicted road type information, the predicted weather information and the predicted light intensity into a parameter adjustment model to obtain the optimal parameters; and adjust the parameters of the image acquisition device according to the optimal parameters.

5. The vehicle imaging adjustment method based on optical elements according to claim 1, characterized in that: The step S4 further includes: S4-1, recording the parameters of the image acquisition device manually adjusted by the vehicle driver and the image information acquired at this time; S4-2, outputting the parameters and image information of the image acquisition device before manual adjustment by the vehicle driver and the parameters and image information of the image acquisition device after manual adjustment to the vehicle driver simultaneously; S4-3: The vehicle driver manually confirms whether to use the manually adjusted parameters of the image acquisition device as the optimal parameters under the current comprehensive environment label.

6. A vehicle imaging adjustment system based on an optical element, applied to the vehicle imaging adjustment method based on an optical element according to any one of claims 1 to 5, characterized in that: The system includes an information acquisition module, a model building module, a parameter adjustment module, and a model correction module; the information acquisition module acquires real-time vehicle information and, based on the real-time vehicle information, acquires road type information, weather information, and light intensity; the model building module establishes a parameter adjustment model based on image information of the monitoring area acquired by an image acquisition device and the road type information, weather information, and light intensity; the parameter adjustment module acquires predicted road type information, predicted weather information, and predicted light intensity based on the real-time vehicle information, inputs the predicted road type information, predicted weather information, and predicted light intensity into the parameter adjustment model to obtain optimal parameters, and adjusts the parameters of the image acquisition device based on the optimal parameters; the model correction module records the parameters and image information of the image acquisition device manually adjusted by the vehicle driver, compares the parameters and image information of the image acquisition device manually adjusted by the vehicle driver recorded by the correction unit with the parameters and image information before the manual adjustment, and manually selects whether to overwrite the manually adjusted parameters with the optimal parameters under the current comprehensive environment label; The output end of the information acquisition module is electrically connected to the input end of the model establishment module; the output end of the model establishment module is electrically connected to the input end of the parameter adjustment module; the output end of the prediction module is electrically connected to the input end of the model correction module.

7. The vehicle imaging adjustment system based on optical elements according to claim 6, characterized in that: The information acquisition module includes a vehicle information acquisition unit and an environment information acquisition unit; the vehicle information acquisition unit acquires real-time information of the vehicle through the navigation system; the environment information acquisition unit acquires road information, weather information and light intensity through the real-time information of the vehicle.

8. The vehicle imaging adjustment system based on optical elements according to claim 6, characterized in that: The model building module includes a label building unit and a model building unit; the label building unit builds a comprehensive environment label based on the road information, the meteorological information and the light intensity; the model building unit performs modeling based on the comprehensive environment label built by the label building unit and the image information collected by the image acquisition device under the corresponding comprehensive environment label.

9. The vehicle imaging adjustment system based on optical elements according to claim 6, characterized in that: The parameter adjustment module includes an information prediction unit and a parameter adjustment unit; the information prediction unit predicts the road type information, weather information and light intensity after the prediction time based on the real-time information of the vehicle to obtain the predicted road type information, predicted weather information and predicted light intensity; the parameter adjustment unit adjusts the parameters of the image acquisition device according to the predicted road type information, the predicted weather information and the predicted light intensity according to the parameter adjustment model.

10. The vehicle imaging adjustment system based on optical elements according to claim 6, characterized in that: The model correction module includes a data recording unit and a parameter confirmation unit; the data recording unit records the parameters of the image acquisition device manually adjusted by the vehicle driver and the image information captured by the image acquisition device after the adjustment; the parameter confirmation unit outputs the parameters and image information of the image acquisition device before the vehicle driver's manual adjustment and the parameters and image information of the image acquisition device after the manual adjustment to the vehicle driver at the same time for manual comparison to confirm whether the parameters of the manually adjusted image acquisition device are to be used as the optimal parameters under the current comprehensive environment label.

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