A laser point cloud based road overhead map generation system

Abstract

The application relates to the technical field of road imaging, and particularly discloses a road bird's-eye map generation system based on laser point clouds, which comprises a data acquisition module used for acquiring surrounding environment data parameters and automobile state parameters of an automobile at different time periods during driving; the surrounding environment data parameters and the automobile state parameters at different time periods and image data of a road in front of the automobile are combined, so that the accuracy and reliability of the opening risk value of the road bird's-eye map generation system are improved, the influence of the external environment on the map generation is accurately judged, and whether the road bird's-eye map generation system is opened is decided according to the judgment result, so that the situation that the driver cannot judge the quality of the current image formation and cannot decide whether the system should be closed is avoided, and the driver makes an erroneous driving decision according to the image information with large deviation, and the driving safety of the driver is ensured.

Description

Technical Field

[0001] This invention relates to the field of road imaging technology, specifically to a road overhead map generation system based on laser point clouds. Background Technology

[0002] The generation of road top-views from laser point clouds has broad application value in autonomous driving systems. It can provide autonomous vehicles with an intuitive and comprehensive top-view of the road environment. Through the top-view, the conditions of the road and its surrounding environment can be understood more intuitively, thereby making more accurate decisions and plans.

[0003] In practical applications, the generation of road top-view images from laser point clouds involves obtaining raw laser point cloud data through scanning with a LiDAR device, analyzing the data, and processing it through point cloud denoising, point cloud filtering, and data segmentation. Finally, through coordinate transformation and projection, a two-dimensional top-view image can be obtained. This image can accurately perceive the surrounding environment, including roads, vehicles, pedestrians, and obstacles. It can not only provide data support for autonomous driving but also serve as an auxiliary capability to display information such as road conditions, obstacle locations, and vehicle distribution, helping drivers to have a more comprehensive understanding of the current road conditions and thus make safer driving decisions.

[0004] In existing technologies, when a laser point cloud road top view generation system is used in extreme environments, the generation of the road top view is affected by the external environment, resulting in a large deviation between the generated top view image and the actual situation. Since the driver cannot judge the quality of the current image formation, he cannot decide whether the system should be turned off, which leads to the driver making incorrect judgments based on the large deviation graphic information, affecting driving safety. Summary of the Invention

[0005] The purpose of this invention is to provide a road overview map generation system based on laser point clouds, solving the following technical problems:

[0006] How to determine the quality of graphic formation under extreme conditions and make decisions on switching the laser point cloud road top view generation system on and off.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] A road overview map generation system based on laser point clouds, the system comprising:

[0009] The data acquisition module is used to collect environmental data parameters and vehicle status parameters at different time periods during the vehicle's operation.

[0010] The image acquisition module includes an image capturing unit and a recognition unit, wherein the image capturing unit is used to acquire image data of the road in front of the vehicle;

[0011] The recognition unit is used to recognize the images captured by the image capturing unit and extract important information and parameters from the images;

[0012] The data processing module includes a data calculation unit and a data analysis unit. The data calculation unit is used to calculate the activation risk value of the road overhead map generation system based on the data from the environmental data acquisition module and the data from the image acquisition module.

[0013] The data analysis unit is used to compare the activation risk value of the road overview map generation system with the preset risk value, and analyze whether the current environment is qualified based on the comparison result.

[0014] The decision module is used to decide whether to activate the road overview map generation system based on the analysis results of the data analysis unit.

[0015] Furthermore, the environmental data parameters collected by the data acquisition module include temperature, humidity, light intensity, particulate matter content, and electromagnetic intensity at different time points. The vehicle status parameters collected by the data acquisition module include the vehicle's driving speed and suspension stiffness at different time points. The data collected by the image acquisition module includes the crack area, protrusion area, pothole area, and total area captured on the road in front of the vehicle at different time points.

[0016] Furthermore, the processing procedure of the data processing module includes:

[0017] S1: The vehicle driving stability risk coefficient for different time periods is calculated by combining the image data of the road in front of the vehicle and the vehicle state parameters at different time periods during the driving process through the data calculation unit.

[0018] S2: The environmental risk coefficient for different time periods is calculated by combining the surrounding environmental data parameters collected by the data calculation unit and the data acquisition module during the vehicle's driving process.

[0019] S3: The activation risk value of the road overview map generation system is calculated by combining the vehicle driving stability risk coefficient and the environmental risk coefficient at different time periods.

[0020] S4: The data analysis unit compares the activation risk value of the road overview map generation system with the preset risk value, and analyzes whether the current environment is qualified based on the comparison results.

[0021] Furthermore, the calculation process of the data calculation unit includes:

[0022] Through formula Calculate the vehicle driving stability risk coefficient at the i-th time point. ;

[0023] Where i represents data collection at fixed time intervals. Let be the area of ​​the potholes in the road in front of the car at the i-th time point. Let be the area of ​​the crack in the road in front of the car at the i-th time point. Let be the area of ​​the road bulge in front of the car at the i-th time point. Let be the total area of ​​the road in front of the car captured at the i-th time point. The road material influence coefficient is set based on empirical data fitting. Let be the speed of the car at time point i. for The standard value, For the current suspension stiffness of automobiles, The preset suspension stiffness, and 2 is the first weighting coefficient.

[0024] Furthermore, the calculation process of the computing unit also includes:

[0025] Through formula Calculate the environmental risk coefficient at time point i. ;

[0026] in, Let be the ambient light intensity at time point i. The preset light intensity, Let be the humidity in the environment at time point i. The preset humidity, Let be the particulate matter content in the environment at time point i. The preset particulate matter content, Let be the electromagnetic intensity in the environment at time point i. for The standard value, Let i be the temperature in the environment at time point i. For the preset temperature, To adjust the coefficient lookup table function, based on the data... The range of numerical values The degree of influence of the numerical value is obtained based on test data. This is the error influence function, which is set based on empirical fitting.

[0027] Furthermore, the calculation process of the data calculation unit also includes:

[0028] Through formula Calculate the activation risk value of the road overview map generation system at the i-th time point. ;

[0029] in, Let be the laser energy intensity at time point i. For the preset energy intensity, for The standard value, The influence coefficient of rainfall. and 2 is the second weighting coefficient.

[0030] Furthermore, the analysis process of the data analysis unit includes:

[0031] By assessing the risk values ​​of the road overview map generation system at various time points. Compared with the preset risk threshold value Perform a comparison;

[0032] like The system judges that the risk value of starting the road overview map at the current time point is low, which means that the external environment has little impact on map generation. This indicates that the road overview map generated by the laser point cloud at the current time point is of high quality and can play a good auxiliary role in helping drivers understand the current road conditions. The system can then be activated through the decision unit.

[0033] like The system determines that the risk value of starting the road overview map at the current time point is high, indicating that the external environment has a high impact on map generation. This means that the road overview map generated by the laser point cloud at the current time point is of low quality and cannot provide good assistance to help drivers understand the current road conditions. Therefore, the system shuts down the road overview map generation system through the decision unit.

[0034] Furthermore, the process of using the road overview map generation system includes:

[0035] S10: Collect environmental data parameters and vehicle status parameters at different time periods during the vehicle's operation through the data acquisition module;

[0036] S20: The image acquisition module acquires image data of the road in front of the vehicle and extracts important information and parameters from the image;

[0037] S30: The data calculation unit calculates the activation risk value of the road overview map generation system based on the data from the environmental data acquisition module and the image acquisition module;

[0038] S40: The data analysis unit compares the activation risk value of the road overview map generation system with the preset risk value, and analyzes whether the current environment is qualified based on the comparison results;

[0039] S50: The decision module decides whether to activate the road overview map generation system based on the analysis results of the data analysis unit.

[0040] The beneficial effects of this invention are:

[0041] (1) By combining the surrounding environment data parameters, vehicle status parameters and image data of the road in front of the vehicle at different time periods, this invention can improve the accuracy and reliability of calculating the risk value of the road overview map generation system, thereby accurately judging the impact of the external environment on map generation, and finally deciding whether to turn on the road overview map generation system based on the judgment result, thereby avoiding the situation where the driver cannot judge the quality of the current image formation and therefore cannot decide whether to turn off the system, thus avoiding the driver making wrong driving decisions based on image information with large deviations and ensuring the driver's driving safety.

[0042] (2) The present invention accurately calculates the vehicle driving stability risk coefficient at the i-th time point by using the road surface environmental parameters and the vehicle state parameters during driving. This data can reflect the impact of vehicle stability on laser imaging at different time points, thus providing accurate and strong data support for the subsequent calculation of the risk value of the road overview map generation system, ensuring the reliability of the subsequent calculation results.

[0043] (3) This invention uses the risk values ​​of the road overview map generation system at various time points to determine the activation status. Compared with the preset risk threshold value By comparing the images, the imaging quality of the road overview map generation system under the current environment can be judged. If the quality of the road overview map generated by the laser point cloud at a certain time point is judged to be low, the decision unit can shut down the road overview map generation system in time. This can prevent the driver from making incorrect driving decisions based on image information with large deviations because the driver cannot judge the quality of the current image. This can affect the driver's driving safety.

[0044] (4) This invention collects diverse data such as surrounding environment parameters, vehicle status parameters and road image data at different time periods. Since the diverse data covers a wider range of information, it can provide more comprehensive background information. Moreover, data from different sources often have different characteristics and advantages, and they can complement and verify each other. Therefore, by calculating the risk value of starting the road overview map generation system using diverse data, it can not only improve the reliability and accuracy of the data, but also improve its sensitivity. This allows the decision-making module to decide whether to start the system based on comprehensive data analysis and prediction results, thereby reducing the risk of blind decision-making. Attached Figure Description

[0045] The invention will now be further described with reference to the accompanying drawings.

[0046] Figure 1 This is a schematic block diagram of a road overhead map generation system based on laser point clouds in this invention;

[0047] Figure 2 This is a flowchart of the data processing module's processing procedure in this invention;

[0048] Figure 3 This is a flowchart illustrating the usage process of the road overview map generation system in this invention. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] Please see Figure 1 As shown, in one embodiment, this application provides a road overview map generation system based on laser point clouds, the system comprising:

[0051] The data acquisition module is used to collect environmental data parameters and vehicle status parameters at different time periods during the vehicle's operation.

[0052] The image acquisition module includes an image capturing unit and a recognition unit, wherein the image capturing unit is used to acquire image data of the road in front of the vehicle;

[0053] The recognition unit is used to recognize the images captured by the image capturing unit and extract important information and parameters from the images;

[0054] The data processing module includes a data calculation unit and a data analysis unit. The data calculation unit is used to calculate the activation risk value of the road overhead map generation system based on the data from the environmental data acquisition module and the data from the image acquisition module.

[0055] The data analysis unit is used to compare the activation risk value of the road overview map generation system with the preset risk value, and analyze whether the current environment is qualified based on the comparison result.

[0056] The decision module is used to decide whether to activate the road overview map generation system based on the analysis results of the data analysis unit.

[0057] Through the above technical solution, this embodiment provides a data acquisition module and an image acquisition module. The two modules can acquire the surrounding environmental data parameters, vehicle status parameters, and image data of the road in front of the vehicle at different time periods during the vehicle's driving process. The recognition unit extracts important information and parameters from the images. Then, the data calculation unit calculates the activation risk value of the road overview map generation system based on the data from the environmental data acquisition module and the image acquisition module. The data analysis unit compares the activation risk value of the road overview map generation system with the preset risk value and analyzes whether the current environment is qualified based on the comparison results. Finally, the decision module decides whether to activate the road overview map generation system based on the analysis results of the data analysis unit.

[0058] By combining ambient environmental data parameters, vehicle status parameters, and image data of the road ahead of the vehicle at different time periods, this configuration improves the accuracy and reliability of calculating the risk value of the road overview map generation system. This allows for a more accurate assessment of the impact of the external environment on map generation. Finally, based on this assessment, a decision is made on whether to activate the road overview map generation system. This avoids situations where drivers cannot judge the quality of the current image and therefore cannot decide whether to shut down the system. Consequently, it prevents drivers from making incorrect driving decisions based on significantly inaccurate image information, thus ensuring driver safety.

[0059] The environmental data parameters collected by the data acquisition module include temperature, humidity, light intensity, particulate matter content, and electromagnetic intensity at different time points. The vehicle status parameters collected by the data acquisition module include the vehicle's driving speed and suspension stiffness at different time points. The data collected by the image acquisition module includes the crack area, protrusion area, pothole area, and total area captured on the road in front of the vehicle at different time points.

[0060] Through the above technical solution, this embodiment provides all the data collected by the data acquisition module. The environmental data parameters include temperature, humidity, light intensity, particulate matter content, and electromagnetic intensity at different time points. The vehicle status parameters include the vehicle's driving speed and suspension stiffness at different time points. The data collected by the image acquisition module includes the crack area, protrusion area, pothole area, and total area captured on the road in front of the vehicle at different time points. By combining diverse data support, accurate data can be provided for the subsequent calculation of the risk value of the road overview map generation system, thereby improving the accuracy of the calculation results. This allows the system to make accurate assessments and judgments on whether to activate the system, ensuring that the system does not activate under strong external interference, thus avoiding the capture of images that deviate significantly from the actual situation, which could lead to incorrect driving decisions by the driver based on the image information, thereby ensuring the driver's driving safety.

[0061] It should be noted that data acquisition can be achieved through various sensors and neural network models in existing technologies, which are not described in detail here.

[0062] Please see Figure 2 As shown, the processing procedure of the data processing module includes:

[0063] S1: The vehicle driving stability risk coefficient for different time periods is calculated by combining the image data of the road in front of the vehicle and the vehicle state parameters at different time periods during the driving process through the data calculation unit.

[0064] S2: The environmental risk coefficient for different time periods is calculated by combining the surrounding environmental data parameters collected by the data calculation unit and the data acquisition module during the vehicle's driving process.

[0065] S3: The activation risk value of the road overview map generation system is calculated by combining the vehicle driving stability risk coefficient and the environmental risk coefficient at different time periods.

[0066] S4: The data analysis unit compares the activation risk value of the road overview map generation system with the preset risk value, and analyzes whether the current environment is qualified based on the comparison results;

[0067] Through the above technical solution, this embodiment provides the processing procedure of the data processing module. First, the data calculation unit combines the image data of the road in front of the car and the car state parameters at different time periods during the car's driving process to calculate the car driving stability risk coefficient at different time periods. Then, the data calculation unit combines the surrounding environment data parameters collected by the data acquisition module at different time periods during the car's driving process to calculate the environmental risk coefficient at different time periods. After that, by combining the car driving stability risk coefficient at different time periods with the environmental risk coefficient at different time periods, the activation risk value of the road overview map generation system is calculated. Finally, the data analysis unit compares the activation risk value of the road overview map generation system with the preset risk value and analyzes whether the current environment is qualified based on the comparison results.

[0068] By setting it up in this way, before calculating the risk value of activating the road overview map generation system, the system first calculates the vehicle driving stability risk coefficient for different time periods based on image data of the road in front of different vehicles and vehicle state parameters at different time periods during the vehicle's operation. Then, it calculates the environmental risk coefficient for different time periods based on the surrounding environmental data parameters at different time periods during the vehicle's operation. Finally, it combines the two sets of data to obtain the risk value of activating the road overview map generation system. With the support of diversified data, the accuracy of the calculation results can be improved, thereby ensuring the accuracy of the analysis results of the data analysis unit and providing accurate and reliable data for the subsequent decision-making module to decide whether to activate the road overview map generation system.

[0069] The calculation process of the data calculation unit includes:

[0070] Through formula Calculate the vehicle driving stability risk coefficient at the i-th time point. ;

[0071] Where i represents data collection at fixed time intervals. Let be the area of ​​the potholes in the road in front of the car at the i-th time point. Let be the area of ​​the crack in the road in front of the car at the i-th time point. Let be the area of ​​the road bulge in front of the car at the i-th time point. Let be the total area of ​​the road in front of the car captured at the i-th time point. The road material influence coefficient is set based on empirical data fitting. Let be the speed of the car at time point i. for The standard value mentioned above can be selected and set based on the allowable error in empirical data. For the current suspension stiffness of automobiles, The preset suspension stiffness, and 2 is the first weighting coefficient, which can be set based on empirical fitting;

[0072] Through the above technical solution, this embodiment provides the vehicle driving stability risk coefficient at the i-th time point. It can be done through the formula Calculations show that, obviously, the larger the area of ​​potholes, cracks, and bumps on the road in front of the car at time point i, and the higher the car's speed and suspension stiffness at time point i, the greater the vehicle's driving stability risk coefficient at time point i. This is because, under these circumstances, the car will experience strong vibrations during driving, which will affect the reflected signal received by the laser, thus causing ranging errors. Therefore, the smaller the area of ​​potholes, cracks, and bumps on the road in front of the car at time point i, and the lower the car's speed and suspension stiffness at time point i, the smaller the vehicle's driving stability risk coefficient at time point i.

[0073] This calculation method allows for the accurate calculation of the vehicle's driving stability risk coefficient at the i-th time point, based on road surface environmental parameters and vehicle state parameters during driving. This data can reflect the impact of vehicle stability on laser imaging at different time points, thus providing accurate and strong data support for the subsequent calculation of the risk value of the road overview map generation system, ensuring the reliability of the subsequent calculation results.

[0074] The calculation process of the computing unit also includes:

[0075] Through formula Calculate the environmental risk coefficient at time point i. ;

[0076] in, Let be the ambient light intensity at time point i. The preset light intensity, Let be the humidity in the environment at time point i. The preset humidity, Let be the particulate matter content in the environment at time point i. The preset particulate matter content, Let be the electromagnetic intensity in the environment at time point i. for The standard value mentioned above can be selected and set based on the allowable error in empirical data. Let i be the temperature in the environment at time point i. For the preset temperature, To adjust the coefficient lookup table function, based on the data... The range of numerical values The degree of influence of the numerical value is obtained based on test data. This is the error influence function, set based on empirical fitting.

[0077] Through the above technical solution, this embodiment provides the environmental risk coefficient at the i-th time point. It can be done through the formula Calculations show that since external environmental parameters are usually obtained through sensor acquisition, adjusting the electromagnetic intensity and temperature at the i-th time point using a coefficient lookup table function can improve the accuracy of external environmental data acquisition. Therefore, under this premise, the higher the light intensity, humidity, particulate matter content, and electromagnetic intensity at the i-th time point, the higher the environmental risk coefficient at the i-th time point, indicating that the external environment at that time point will have a significant impact on the laser imaging quality. Conversely, the lower the light intensity, humidity, particulate matter content, and electromagnetic intensity at the i-th time point, the lower the environmental risk coefficient at the i-th time point, indicating that the external environment at that time point will not have a significant impact on the laser imaging quality.

[0078] By setting it up in this way, after calculating the environmental risk coefficient at the i-th time point by combining the parameters of the external environment, since this data can reflect the impact of the external environment on laser imaging at different time points, it can provide accurate and strong data support for the subsequent calculation of the risk value of the road overview map generation system, ensuring the reliability of the subsequent calculation results.

[0079] The calculation process of the data calculation unit also includes:

[0080] Through formula Calculate the activation risk value of the road overview map generation system at the i-th time point. ;

[0081] in, Let be the laser energy intensity at time point i. For the preset energy intensity, for The standard value mentioned above can be selected and set based on the allowable error in empirical data. The rainfall influence coefficient can be set by fitting the allowable error from past test data. and 2 is the second weighting coefficient, which can be set based on empirical fitting;

[0082] Using the above technical solution, this example provides the activation risk value for the road overview map generation system at the i-th time point. It can be done through the formula The calculation shows that the vehicle driving stability risk coefficient at time point i is... Environmental risk coefficient at the i-th time point The larger the value of the laser energy and the greater the difference between the laser energy and the preset intensity, the greater the risk value at that time point, indicating that activating the road overview map generation system at that time point will result in poor imaging quality; conversely, the smaller the value of the laser energy and the greater the difference between the laser energy and the preset intensity, the greater the risk value at that time point. Environmental risk coefficient at the i-th time point The smaller the value, and the smaller the difference between the laser energy intensity and the preset intensity, the lower the risk value at that time point, indicating that the imaging effect of the road overview map generation system is good when activated at that time point.

[0083] The analysis process of the data analysis unit includes:

[0084] By assessing the risk values ​​of the road overview map generation system at various time points. Compared with the preset risk threshold value Perform a comparison;

[0085] like The system judges that the risk value of starting the road overview map at the current time point is low, which means that the external environment has little impact on map generation. This indicates that the road overview map generated by the laser point cloud at the current time point is of high quality and can play a good auxiliary role in helping drivers understand the current road conditions. The system can then be activated through the decision unit.

[0086] like The system judges that the risk value of starting the road overview map at the current time point is high, indicating that the external environment has a high impact on map generation. This means that the road overview map generated by the laser point cloud at the current time point is of low quality and cannot play a good auxiliary role in helping drivers understand the current road conditions. The system then shuts down the road overview map generation system through the decision unit.

[0087] Through the above technical solution, this embodiment uses the activation risk value of the road overview map generation system at various time points. Compared with the preset risk threshold value By comparing the images, the imaging quality of the road overview map generation system under the current environment can be judged. If the quality of the road overview map generated by the laser point cloud at a certain time point is judged to be low, the decision unit can shut down the road overview map generation system in time. This can prevent the driver from making incorrect driving decisions based on image information with large deviations because the driver cannot judge the quality of the current image. This can affect the driver's driving safety.

[0088] Please see Figure 3As shown, the process of using the road overview map generation system includes:

[0089] S10: Collect environmental data parameters and vehicle status parameters at different time periods during the vehicle's operation through the data acquisition module;

[0090] S20: The image acquisition module acquires image data of the road in front of the vehicle and extracts important information and parameters from the image;

[0091] S30: The data calculation unit calculates the activation risk value of the road overview map generation system based on the data from the environmental data acquisition module and the image acquisition module;

[0092] S40: The data analysis unit compares the activation risk value of the road overview map generation system with the preset risk value, and analyzes whether the current environment is qualified based on the comparison results;

[0093] S50: The decision module decides whether to activate the road overview map generation system based on the analysis results of the data analysis unit.

[0094] Through the above technical solution, this embodiment provides the usage process of the road overview map generation system. In this process, the system collects diverse data such as surrounding environmental parameters, vehicle status parameters, and image data of the road in front of the vehicle at different time periods. Since the diverse data covers a wider range of information and can provide more comprehensive background information, and data from different sources often have different characteristics and advantages, they can complement and verify each other. Therefore, by calculating the risk value of starting the road overview map generation system using diverse data, not only can the reliability and accuracy of the data be improved, but its sensitivity can also be enhanced. This allows the decision-making module to decide whether to start the system based on comprehensive data analysis and prediction results, reducing the risk of blind decision-making.

[0095] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A laser point cloud based road overhead map generation system, characterized by, The system comprises: a data acquisition module for acquiring surrounding environment data parameters and vehicle state parameters of the vehicle at different time periods during driving; an image acquisition module comprising an image shooting unit and an identification unit, the image shooting unit being configured to acquire image data of the road ahead of the vehicle; the identification unit being configured to identify the images shot by the image shooting unit and extract important information and parameters from the images; a data processing module comprising a data calculation unit and a data analysis unit, the data calculation unit being configured to calculate the opening risk value of the road overview map generation system according to the data of the environment data acquisition module and the data of the image acquisition module; the data analysis unit being configured to compare the opening risk value of the road overview map generation system with a preset risk value and analyze whether the current environment is qualified according to the comparison result; a decision module configured to decide whether to open the road overview map generation system according to the analysis result of the data analysis unit. The calculation process of the data calculation unit comprises: obtaining the automobile driving stability risk coefficient at the ith time point through a formula ; and ; wherein i is the data collection according to a fixed time interval, is the pothole area of the road ahead of the car at the i th time point, is the crack area of the road ahead of the car at the i th time point, is the bump area of the road ahead of the car at the i th time point, is the total area of the road ahead of the car at the i th time point, is the road material influence coefficient, which is set according to empirical data, is the driving speed of the car at the i th time point, is the standard value of , is the suspension stiffness of the current car, is the preset suspension stiffness, and 2 is the first weight coefficient; The environmental risk coefficient at the i-th time point is calculated by the formula ;​ in, Let be the ambient light intensity at time point i. The preset light intensity, Let be the humidity in the environment at time point i. The preset humidity, Let be the particulate matter content in the environment at time point i. The preset particulate matter content, Let be the electromagnetic intensity in the environment at time point i. for The standard value, Let i be the temperature in the environment at time point i. For the preset temperature, To adjust the coefficient lookup table function, based on the data... The range of numerical values The degree of influence of the numerical value is obtained based on test data. This is the error influence function, set based on empirical fitting. The opening risk value at the i-th time point of the road overhead map generation system is calculated by the formula ;​ wherein, is the laser energy intensity at the i th time point, is the preset energy intensity, is the standard value of, is the rainfall influence coefficient, and 2 is the second weight coefficient; By combining the surrounding environment data parameters and vehicle state parameters at different time periods and the image data of the road ahead of the vehicle, the accuracy and reliability of calculating the opening risk value of the road overview map generation system can be improved, so that the influence of the external environment on the map generation can be accurately judged, and then whether to open the road overview map generation system is decided according to the judgment result, thereby avoiding the situation that the driver cannot judge the quality of the current image formation, resulting in the inability to decide whether the system should be closed, and further avoiding the driver making an incorrect driving decision based on the image information with large deviation, thus ensuring the driving safety of the driver. 2.The system for generating a road overhead map based on a laser point cloud according to claim 1, wherein, The environment data parameters acquired by the data acquisition module include temperature, humidity, light intensity, particulate matter content and electromagnetic intensity at different time points, the vehicle state parameters acquired by the data acquisition module include the driving speed of the vehicle and the suspension stiffness of the vehicle at different time points, and the data acquired by the image acquisition module includes the crack area, the convex area, the pothole area and the total area of the road ahead of the vehicle at different time points. 3.The system for generating a road overhead map based on a laser point cloud according to claim 1, wherein, The processing process of the data processing module comprises: S1: calculating the vehicle driving stability risk coefficient at different time periods by combining the image data of the road ahead of the vehicle and the vehicle state parameters at different time periods during driving through the data calculation unit; S2: calculating the environment risk coefficient at different time periods by combining the surrounding environment data parameters at different time periods during driving acquired by the data acquisition module through the data calculation unit; S3: calculating the opening risk value of the road overview map generation system by combining the vehicle driving stability risk coefficient at different time periods and the environment risk coefficient at different time periods; S4: comparing the opening risk value of the road overview map generation system with a preset risk value through the data analysis unit and analyzing whether the current environment is qualified according to the comparison result. 4.The system for generating a road overhead map based on a laser point cloud according to claim 1, wherein, The analysis process of the data analysis unit comprises: By comparing the opening risk value of each time point of the road bird's eye map generation system with the preset opening risk value threshold comparison; If , the system determines that the opening risk value at the current time point is low, indicating that the influence of the external environment on the map generation is low, and that the road top view map generated by the laser point cloud at the current time point has high quality, which can play a good auxiliary role to help the driver understand the current road conditions, and the road top view map generation system is opened through the decision unit. If , the system determines that the opening risk value at the current time point is high, indicating that the external environment has a high impact on the map generation, and that the road top view map generated by the laser point cloud at the current time point has low quality, which cannot help the driver understand the current road conditions and cannot play a good auxiliary role. Through the decision unit, the road top view map generation system is closed. 5.The system for generating a road overhead map based on a laser point cloud according to claim 1, wherein, The use process of the road overview map generation system comprises: S10: acquiring surrounding environment data parameters and vehicle state parameters of the vehicle at different time periods during driving through the data acquisition module; S20: Collect image data of the road in front of the vehicle through the image acquisition module, and extract important information and parameters in the image; S30: Calculate the opening risk value of the road overview map generation system according to the data of the environmental data acquisition module and the data of the image acquisition module through the data calculation unit; S40: Compare the opening risk value of the road overview map generation system with the preset risk value through the data analysis unit, and analyze whether the current environment is qualified according to the comparison result; S50: Decide whether to open the road overview map generation system according to the analysis result of the data analysis unit through the decision module.

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