A blind spot accuracy assessment method and system based on AI camera technology

Abstract

The present invention discloses a blind spot accuracy assessment method and system based on AI camera technology, which belongs to the technical field of automobile blind spot assessment. The method and system determine various influencing factors and identify the monitoring subjects corresponding to each influencing factor; set the comprehensive range of the vehicle's blind spot; set a comprehensive impact model corresponding to the comprehensive range of the blind spot; monitor the monitoring subjects of various influencing factors in real time to obtain the subject monitoring data of each influencing factor; determine the blind spot area, and generate an initial blind spot image according to the blind spot area; analyze the monitoring data of each subject through the comprehensive impact model to obtain the comprehensive impact value corresponding to each blind spot position; divide the initial blind spot image into a number of unit areas according to the comprehensive impact values, and set the unit accuracy value of each unit area; estimate the baseline risk value of each target object, identify the unit accuracy value corresponding to the unit area where the target object is located, correct the baseline risk value according to the unit accuracy value, and perform blind spot warning according to the corrected baseline risk value.

Description

Technical Field

[0001] The present invention belongs to the technical field of automobile blind spot assessment, and specifically provides a blind spot accuracy assessment method and system based on AI camera technology. Background Art

[0002] With the rapid development of the automotive industry and the continuous advancement of intelligent transportation systems, improving vehicle safety performance has become a focus of industry attention. However, during vehicle driving, blind spot issues have always been an important factor affecting driving safety due to factors such as limited driver vision and obstruction by vehicle structures. Improving its assessment accuracy is of great significance for preventing traffic accidents and ensuring driving safety. However, the blind spot assessment systems currently on the market still face many challenges in practical applications, especially in terms of the real-time and accuracy of the assessment accuracy. This has led to a lack of trust in them by many users. This is because most existing blind spot assessment systems use fixed sensors and algorithms, and lack real-time consideration of dynamic factors such as device status, environmental impact, data delay, and vehicle speed. As a result, the accuracy of blind spot assessment is often restricted by multiple factors, resulting in differences in the assessment accuracy of different areas within the blind spot, but in actual applications, they are presented to users in the same way. Based on this, in order to achieve intelligent assessment of blind spots, the present invention provides a blind spot accuracy assessment method and system based on AI camera technology. Summary of the Invention

[0003] In order to solve the problems existing in the above-mentioned solutions, the present invention provides a blind spot accuracy assessment method and system based on AI camera technology.

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] A blind spot accuracy assessment system based on AI camera technology, including an information module, a subject monitoring module, a blind spot module, and a blind spot analysis module;

[0006] The information module is used to perform information management, determine various influencing factors, and identify the monitoring subject corresponding to each influencing factor;

[0007] Set a comprehensive blind spot range for the vehicle; obtain the main material data of each monitoring subject, perform a single simulation on the main material data of each monitoring subject, and obtain the factor influence curve corresponding to each blind spot position within the comprehensive blind spot range of each influencing factor, the horizontal axis of the factor influence curve is the main monitoring data of the corresponding monitoring subject, and the vertical axis is the accuracy influence value corresponding to the corresponding subject monitoring data, and the value range of the accuracy influence value is [0, 100]; perform a comprehensive analysis based on the factor influence curves corresponding to each blind spot position to form a comprehensive influence model corresponding to the comprehensive blind spot range.

[0008] The subject monitoring module performs real-time monitoring on the monitoring subject corresponding to each influencing factor to obtain subject monitoring data corresponding to each influencing factor.

[0009] The blind area module is used to generate an initial image of the blind area in real time.

[0010] Furthermore, the method for generating the initial image of the blind area includes:

[0011] Determine the blind spot area, collect image data around the vehicle in real time through the AI ​​camera, perform feature recognition on the image data through a preset image recognition model, and obtain target information of each target object;

[0012] Determining relative data between the target object and the vehicle based on each target information, and integrating the relative data with the target information to form comprehensive target information of each target object;

[0013] A blind spot image is generated according to the blind spot area and the target comprehensive information of each target object, and is marked as a blind spot initial image.

[0014] Furthermore, the method for determining the blind area includes:

[0015] Obtaining blind spot material data of the vehicle, the blind spot material data including rearview mirror parameters and corresponding material areas; performing statistics on each blind spot material data to obtain each material area corresponding to each rearview mirror parameter and a share ratio corresponding to each material area;

[0016] Establishing a three-dimensional appearance model corresponding to the vehicle, marked as a vehicle appearance model; restricting and adjusting the vehicle appearance model according to each of the material regions to form a dynamic restriction model;

[0017] Identify the rearview mirror parameters of the current vehicle, match the corresponding reference material area according to the rearview mirror parameters, and display the dynamic restriction model to the user according to the reference material area. The user determines the setting screen through the dynamic restriction model, adjusts the rearview mirror parameters according to the setting screen, and determines the blind spot area.

[0018] Furthermore, the method of matching the corresponding reference material area according to the rearview mirror parameters includes:

[0019] Identify each material area corresponding to the rearview mirror parameter, and obtain the share ratio corresponding to each material area; obtain the number of times the user uses each material area under the rearview mirror parameter;

[0020] Calculate the priority value of each material area in real time according to the formula PY=FE+YC;

[0021] Where: PY is the priority value; FE is the share value; YC is the number of applications;

[0022] The material area with the highest priority value is selected as the reference material area.

[0023] Furthermore, after the user determines the blind spot area using the vehicle appearance model, the parameters and setting screens of each rearview mirror are recorded in real time, integrated into adjustment record data, and each adjustment record data is deduplicated;

[0024] Classifying each of the adjustment record data to obtain each application classification; setting an application classification label corresponding to each application classification in the dynamic restriction model;

[0025] When the user needs to determine the blind spot area again, the determination is performed through the corresponding application classification label in the dynamic restriction model.

[0026] The blind spot analysis module is used to evaluate and analyze the initial blind spot image, obtain monitoring data of each subject, analyze the monitoring data of each subject using the comprehensive impact model, and obtain the comprehensive impact value corresponding to each blind spot position in the initial blind spot image; divide the initial blind spot image into a plurality of unit areas according to the comprehensive impact value corresponding to each blind spot position, and set a unit precision value for each unit area, wherein the unit precision value is the average value of the comprehensive impact value of each blind spot position in the unit area;

[0027] Mark the accuracy value of each unit in each unit area; estimate the baseline risk value of each target object, and perform blind spot warning based on the baseline risk value of each target object.

[0028] Furthermore, the method of dividing the initial blind spot map into a plurality of unit areas according to the comprehensive impact value of each blind spot position includes:

[0029] Step SA1: Identify the comprehensive impact value of each blind spot position; determine each initial position; mark each blind spot position adjacent to the initial position as a selected position;

[0030] The comprehensive impact value of the initial position is marked as QY1 (x，y，z) , (x, y, z) represents the corresponding position coordinates; the comprehensive influence value of the selected position is marked as QY2 (x，y，z) ;

[0031] According to the formula YM=|QY1 (x，y，z) -QY2 (x，y，z) |Calculate the first impact difference between the initial position and the selected position;

[0032] Where: YM is the first impact difference;

[0033] Merge the initial position where the first impact difference is less than the threshold X1 with the selected position to obtain the initial area;

[0034] Step SA2: Mark each blind spot position adjacent to the initial area as a selected position;

[0035] According to the formula calculating a second influence difference between the initial region and the selected location;

[0036] Where: YM´ is the second impact difference; i represents the corresponding blind spot position in the initial area, i=1, 2, ..., n, n is a positive integer; QY (x，y，z)i Indicates the comprehensive impact value of the corresponding blind spot position in the initial area; QY´ (x，y，z) Indicates the comprehensive impact value corresponding to the selected position;

[0037] Merge the initial region whose second impact difference is less than the threshold X1 with the selected position to obtain a new initial region;

[0038] Step SA3: loop step SA2 until the initial region cannot be merged, and mark the initial region as a unit region;

[0039] When there are no unmerged blind spot positions in the blind spot area, the analysis ends;

[0040] When there are unmerged blind spot locations in the blind spot area, the process returns to step SA1.

[0041] Furthermore, before performing a blind spot warning based on the baseline risk value, the target object corresponding to the baseline risk value is identified, the unit accuracy value corresponding to the target object is identified, the baseline risk value is corrected according to the unit accuracy value, and a blind spot warning is performed using the corrected baseline risk value.

[0042] Furthermore, the revised benchmark risk value is recorded in real time, the revised benchmark risk value is marked as the assessed risk value, and the risk difference between the assessed risk value and the corresponding benchmark risk value is calculated; the abnormal optimization data is determined based on the risk difference and the benchmark risk value, and each abnormal optimization data is sent to the platform.

[0043] A blind spot accuracy assessment method based on AI camera technology, the method comprising:

[0044] Determine each influencing factor and identify the monitoring subject corresponding to each influencing factor;

[0045] Set the comprehensive range of the vehicle's blind spot; obtain the main material data of each monitoring subject, perform a single simulation on the main material data of each monitoring subject, and obtain the factor influence curve corresponding to each blind spot position within the comprehensive range of the blind spot; perform a comprehensive analysis based on the factor influence curves of each blind spot position to form a comprehensive influence model corresponding to the comprehensive range of the blind spot;

[0046] Conduct real-time monitoring of the monitoring subjects of each influencing factor and obtain the main monitoring data of each influencing factor;

[0047] Determine the blind spot area, collect image data around the vehicle in real time through the AI ​​camera, perform feature recognition on the image data using a preset image recognition model to obtain target information of each target object; determine the relative data between the target object and the vehicle based on each target information, integrate the relative data with the target information to form target comprehensive information of each target object; generate a blind spot image based on the blind spot area and the target comprehensive information of each target object, and mark it as the blind spot initial image;

[0048] The monitoring data of each subject is analyzed through the comprehensive impact model to obtain the comprehensive impact value corresponding to each blind spot position in the initial blind spot image; the initial blind spot image is divided into several unit areas according to the comprehensive impact value corresponding to each blind spot position, and the unit accuracy value of each unit area is set;

[0049] Mark the accuracy value of each unit in each unit area; estimate the baseline risk value of each target object, identify the unit accuracy value corresponding to the unit area where the target object is located, correct the baseline risk value according to the unit accuracy value, and use the corrected baseline risk value to issue a blind spot warning.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] Through the coordination of the information module, blind spot module, and blind spot analysis module, accurate blind spot assessment is achieved, reducing blind spot risks. This system addresses existing issues with blind spot assessment systems, provides strong technical support for the development of intelligent transportation and autonomous driving technologies, and continuously improves the accuracy and real-time performance of blind spot assessments. This system enables drivers to more intuitively understand the driving environment around the vehicle, reducing driving frustrations caused by blind spots. Furthermore, the system continuously optimizes blind spot monitoring and analysis algorithms based on historical accuracy assessment records, further enhancing driving comfort and convenience. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 This is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0054] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all 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.

[0055] like Figure 1 As shown, a blind spot accuracy assessment system based on AI camera technology includes an information module, a blind spot module and a blind spot analysis module;

[0056] The information module is used for information management. It determines various influencing factors applicable to blind spot assessment based on historical blind spot assessment data, such as environmental factors, equipment factors, transmission factors, vehicle speed factors, and other influencing factors. It matches the existing application blind spot assessment system with the influencing factors, determines the subject corresponding to each influencing factor, and marks it as a monitoring subject. For example, the monitoring subject of the equipment factor is each device related to the equipment factor. Subsequently, it is necessary to perform impact analysis based on the equipment performance and equipment monitoring status of the monitoring subject.

[0057] Analyze the possible blind spot positions of the vehicle based on historical blind spot assessment data to form a comprehensive blind spot range, which refers to the range relative to the vehicle's position;

[0058] Obtain possible data from each monitoring subject, integrate it into subject material data, perform a single simulation on the subject material data of each monitoring subject, understand the impact of single subject material data on blind spot images under different circumstances, and form factor influence curves corresponding to each blind spot position within the comprehensive range of the blind spot for each influencing factor; the horizontal axis of the factor influence curve is the subject monitoring data of the corresponding monitoring subject, and the vertical axis is the accuracy impact value corresponding to the corresponding subject monitoring data. The accuracy impact value is set according to the degree of impact on the true accuracy, and the value range is [0, 100]. If there is no impact, the accuracy impact value is 0. In the case of an impact equivalent to complete distortion, the accuracy impact value is 0, such as when the time delay is too large, resulting in loss of effect, or when the equipment is abnormal, resulting in inability to identify the target object, etc.; the impact ratio on the accuracy can be multiplied by 100; or it can be directly set manually during the simulation process;

[0059] A comprehensive analysis is conducted on the factor influence curves of each influencing factor corresponding to each blind spot position to form a comprehensive influence model corresponding to the comprehensive range of the blind spot; that is, a correlation analysis is conducted on the factor influence curves of the same blind spot position to understand the comprehensive influence of different subject material data corresponding to each influencing factor on the accuracy, and form a comprehensive influence value, the value range of which is also [0, 100], which is determined in combination with the corresponding historical data and simulation method; because this module step is the preliminary preparation of the platform, it can be analyzed with the help of the platform's resources to form a sufficient training set, and then a comprehensive influence model can be developed based on the training set. Through the comprehensive influence model, each blind spot position can be analyzed according to the monitoring data of each subject to obtain the corresponding comprehensive influence value.

[0060] The subject monitoring module performs real-time monitoring on the monitoring subject corresponding to each influencing factor to obtain subject monitoring data corresponding to each influencing factor.

[0061] The blind spot module is used to generate an initial image of the blind spot in real time; determine the blind spot area, collect image data around the vehicle in real time through the AI ​​camera, perform feature recognition on the image data through a preset image recognition model, and obtain target information of each target object, such as position, size, shape, etc.; the image recognition model is established based on existing image technology to identify the image data, determine the target objects included in the image data, and then determine the target information. The target object is a vehicle, pedestrian, obstacle, etc. that needs to be monitored; based on the target information of each target object, relative data such as the distance and relative speed between the target object and the vehicle are calculated, and the obtained relative data is integrated with the target information to form comprehensive target information of each target object;

[0062] A blind spot image is generated according to the target comprehensive information of each target object and the blind spot area, and is marked as the blind spot initial image.

[0063] In one embodiment, the blind spot area is determined according to an existing blind spot determination method; for example, it is determined based on factors such as a rearview mirror, a driver's seat, and a driver.

[0064] In one embodiment, the method for determining a blind spot area includes:

[0065] Establish a three-dimensional appearance model corresponding to the vehicle and mark it as a vehicle appearance model;

[0066] Obtain a large amount of historical data on the relationship between the vehicle's blind spot area and rearview mirror angle parameters, marking it as blind spot material data. The blind spot material data consists of the rearview mirror parameters and their corresponding material areas. Statistically analyze each blind spot material data to obtain the material areas corresponding to each rearview mirror parameter and the corresponding share ratio of each material area.

[0067] The vehicle appearance model is restricted and adjusted according to each material area to form a dynamic restriction model. That is, the dynamic restriction model can only see the vehicle appearance corresponding to each material area through manual rotation and other methods. The user can adjust the dynamic restriction model according to the vehicle rotation that he can actually see, and then determine his blind spot situation;

[0068] The system identifies the current vehicle's rearview mirror parameters, matches the mirror parameters to a corresponding reference material area, and displays a dynamic restriction model to the user based on the reference material area. The user adjusts the dynamic restriction model based on the vehicle data they actually observe. The screen corresponding to the successfully adjusted dynamic restriction model is marked as the setting screen. The rearview mirror parameters are then adjusted based on the setting screen to determine the blind spot area. After the user confirms the setting screen, the user's actual viewing angle can be determined based on the setting screen. Simulated rearview mirror adjustments can also be performed to determine the accuracy of the setting screen based on changes in the user's viewing angle. Once the user's actual viewing angle is determined, the system can determine how to adjust the mirror parameters to achieve the optimal driving angle under those conditions, thereby determining the blind spot area under those conditions. Intelligent adjustment of the mirror parameters can also be achieved without collecting the user's private information. Subsequently, the system can intelligently determine the driver's seat based on the user's setting screen, allowing for rapid adjustment for previously recorded drivers. Because users often adjust the driver's seat to a predetermined position, rapid adjustment based solely on the mirror parameters can be achieved for subsequent recorded adjustments. If the rapid adjustment fails, the user can continue to adjust the setting screen as described above and re-confirm the setting screen. From a safety perspective, the blind spot area can be adaptively expanded later.

[0069] In one embodiment, each rear-view mirror parameter and setting screen is recorded in real time, integrated into adjustment record data, and each adjustment record data is deduplicated; each adjustment record data is classified according to its corresponding user perspective to obtain each application classification, and the application classification is composed of its corresponding adjustment record data, that is, one application classification corresponds to one driver who has applied the vehicle; an application classification label corresponding to each application classification is set in the dynamic restriction model, and the user can clearly know which application classification it corresponds to, and can adjust each application classification, such as deleting and other adjustment operations; the user quickly adjusts the rear-view mirror parameters according to each application classification label and determines the blind spot area.

[0070] In one embodiment, a method for matching a corresponding reference material area according to rearview mirror parameters includes:

[0071] Identify the material areas corresponding to the rearview mirror parameters and the corresponding share ratios of the material areas; obtain the setting screen selected by the user under the rearview mirror parameters, identify the material areas corresponding to the setting screen, and count the number of times the user has used each material area under the rearview mirror parameters;

[0072] Calculate the priority value of each material area in real time according to the formula PY=FE+YC;

[0073] Where: PY is the priority value; FE is the share value; YC is the number of applications;

[0074] The material area with the highest priority value is selected as the base material area.

[0075] The blind spot analysis module is used to evaluate and analyze the initial blind spot image, obtain monitoring data of each subject, analyze the monitoring data of each subject through the comprehensive impact model, and obtain the comprehensive impact value corresponding to each blind spot position in the initial blind spot image; divide the initial blind spot image into a number of unit areas according to the comprehensive impact value corresponding to each blind spot position; and use the average value of the comprehensive impact value corresponding to each blind spot position in the unit area as the unit accuracy value of the unit area;

[0076] Mark the accuracy value of each unit in each unit area; estimate the risk value of each target object and mark it as the baseline risk value. The baseline risk value is estimated based on the initial image of the blind spot, that is, the risk of the current target object position is estimated using existing risk estimation technology to obtain the corresponding risk value; perform blind spot warning based on the baseline risk value of each target object.

[0077] In one embodiment, the method of dividing the initial blind spot map into a plurality of unit areas according to the comprehensive impact value of each blind spot position includes:

[0078] Step SA1: Identify the comprehensive impact value of each blind spot position; take each blind spot position close to the blind spot area boundary or unit area boundary of the vehicle as the initial position; identify each blind spot position adjacent to the initial position and mark it as a selected position, and adjacent initial positions are regarded as selected positions.

[0079] The comprehensive impact value of the initial position is marked as QY1 (x，y，z) , (x, y, z) represents the corresponding position coordinates; the comprehensive influence value of the selected position is marked as QY2 (x，y，z) ;

[0080] According to the formula YM=|QY1 (x，y，z) -QY2 (x，y，z) |Calculate the first impact difference between the initial position and the selected position;

[0081] Where: YM is the first impact difference;

[0082] Merging the initial positions whose first impact difference is less than a threshold X1 with the selected positions to obtain an initial region, wherein the threshold X1 is set according to a standard that can be considered as correspondingly equal, such as 0, 1, 2, 3, etc.;

[0083] Step SA2: Mark each blind spot position adjacent to the initial area as a selected position;

[0084] According to the formula calculating a second influence difference between the initial region and the selected location;

[0085] Where: YM´ is the second impact difference; i represents the corresponding blind spot position in the initial area, i=1, 2, ..., n, n is a positive integer; QY (x，y，z)i Indicates the comprehensive impact value of the corresponding blind spot position in the initial area; QY´ (x，y，z) Indicates the comprehensive influence value corresponding to the selected position; merge the initial area whose second influence difference is less than the threshold X1 with the selected position to obtain a new initial area;

[0086] Step SA3: looping step SA2 until the initial areas cannot be merged, marking the initial areas as unit areas; identifying whether there are any unmerged blind spots, which means that the initial areas cannot be merged anymore;

[0087] When there are no unmerged blind spots, the analysis ends;

[0088] When there are unmerged blind spot positions, return to step SA1.

[0089] In one embodiment, the problem of blind spot assessment accuracy causes a certain error in the baseline risk. Therefore, the baseline risk value can be corrected in combination with the unit accuracy value corresponding to the target object, and a blind spot warning can be performed using the corrected baseline risk value.

[0090] In one embodiment, the method for correcting the baseline risk value using the unit precision value includes:

[0091] A correction model is established based on a neural network such as a CNN network or a DNN network. A corresponding training set is manually established for training. The training set includes input data and output data. The input data is the target object position, baseline risk value, and unit accuracy value; the output data is the corrected baseline risk value. The correction model after successful training is used for analysis and correction.

[0092] In one embodiment, corrections may also be made based on other existing technologies.

[0093] In one embodiment, when a large number of corrected baseline risk values ​​are available, one will be able to intuitively understand under what circumstances the risk difference exceeds the range, determine the corresponding difference cause data, that is, the corresponding monitoring data of each subject, and integrate them into abnormal optimization data; the baseline risk value can also be combined for abnormal judgment. The larger the baseline risk value, the higher the requirement, that is, the smaller the risk difference allowed between the two; subsequently, it is convenient for the platform to optimize each monitoring subject according to the abnormal optimization data to reduce possible deviation risks.

[0094] A blind spot accuracy assessment method based on AI camera technology, the method comprising:

[0095] Determine each influencing factor and identify the monitoring subject corresponding to each influencing factor;

[0096] Set the comprehensive range of the vehicle's blind spot; obtain the main material data of each monitoring subject, perform a single simulation on the main material data of each monitoring subject, and obtain the factor influence curve corresponding to each blind spot position within the comprehensive range of the blind spot for each influencing factor, where the horizontal axis of the factor influence curve is the main monitoring data of the corresponding monitoring subject, and the vertical axis is the accuracy influence value corresponding to the corresponding subject monitoring data, and the accuracy influence value range is [0, 100]; perform a comprehensive analysis based on the influence curves of each factor corresponding to each blind spot position to form a comprehensive influence model corresponding to the comprehensive range of the blind spot;

[0097] Conduct real-time monitoring of the monitoring subjects corresponding to each influencing factor to obtain the subject monitoring data corresponding to each influencing factor;

[0098] Determine the blind spot area, collect image data around the vehicle in real time through the AI ​​camera, perform feature recognition on the image data through the preset image recognition model, and obtain target information of each target object;

[0099] Determine relative data between the target object and the vehicle based on each target information, and integrate the relative data with the target information to form comprehensive target information of each target object;

[0100] Generate a blind spot image based on the comprehensive target information of the blind spot area and each target object, and mark it as the blind spot initial image;

[0101] The monitoring data of each subject is analyzed through the comprehensive impact model to obtain the comprehensive impact value corresponding to each blind spot position in the initial blind spot image; the initial blind spot image is divided into several unit areas according to the comprehensive impact value corresponding to each blind spot position, and the unit accuracy value of each unit area is set. The unit accuracy value is the average value of the comprehensive impact value of each blind spot position in the unit area;

[0102] Mark the accuracy value of each unit in each unit area; estimate the baseline risk value of each target object, identify the target object corresponding to the baseline risk value, identify the unit accuracy value corresponding to the target object, correct the baseline risk value according to the unit accuracy value, and perform blind spot warning based on the corrected baseline risk value.

[0103] The above formulas are all calculated by removing dimensions and taking their numerical values. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and preset thresholds in the formula are set by technicians in this field according to actual conditions or obtained by simulating a large amount of data.

[0104] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, apparatus, article, or method comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, apparatus, article, or method. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, apparatus, article, or method comprising the element.

[0105] The above description is only a preferred embodiment of the present invention and does not limit the scope of the patent of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention description and drawings, or directly or indirectly applied to other related technical fields, is also included in the scope of patent protection of the present invention.

[0106] 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.

[0107] For ease of description, the above devices are described separately based on their functions. Of course, when implementing this application, the functions of each unit can be implemented in the same or multiple software and / or hardware. Those skilled in the art will appreciate that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0108] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 The functionality specified in one or more boxes.

[0109] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or a function specified in multiple boxes.

[0110] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

Claims

1. A blind spot accuracy assessment system based on AI camera technology, characterized in that: Including information module, subject monitoring module, blind spot module and blind spot analysis module; The information module is used to perform information management, determine various influencing factors, and identify the monitoring subject corresponding to each influencing factor; Setting a comprehensive blind spot range for the vehicle; obtaining subject material data of each monitoring subject, performing a single simulation on the subject material data of each monitoring subject, and obtaining a factor influence curve corresponding to each blind spot position within the comprehensive blind spot range for each influencing factor, wherein the horizontal axis of the factor influence curve is the subject monitoring data of the corresponding monitoring subject, and the vertical axis is the accuracy influence value corresponding to the corresponding subject monitoring data, and the accuracy influence value has a value range of [0, 100]; performing a comprehensive analysis based on the factor influence curves corresponding to each blind spot position to form a comprehensive influence model corresponding to the comprehensive blind spot range; The subject monitoring module performs real-time monitoring on the monitoring subject corresponding to each influencing factor to obtain subject monitoring data corresponding to each influencing factor; The blind area module is used to generate an initial image of the blind area in real time; The blind spot analysis module is used to evaluate and analyze the initial blind spot image, obtain monitoring data of each subject, analyze the monitoring data of each subject using the comprehensive impact model, and obtain the comprehensive impact value corresponding to each blind spot position in the initial blind spot image; divide the initial blind spot image into a plurality of unit areas according to the comprehensive impact value corresponding to each blind spot position, and set a unit precision value for each unit area, wherein the unit precision value is the average value of the comprehensive impact value of each blind spot position in the unit area; Mark the accuracy value of each unit in each unit area; Estimate the baseline risk value of each target object and issue blind spot warnings based on the baseline risk value of each target object; Methods for dividing the initial blind spot map into several unit areas according to the comprehensive impact value of each blind spot position include: Step SA1: Identify the comprehensive impact value of each blind spot position; determine each initial position; mark each blind spot position adjacent to the initial position as a selected position; The comprehensive impact value of the initial position is marked as QY1 (x，y，z) , (x, y, z) represents the corresponding position coordinates; the comprehensive influence value of the selected position is marked as QY2 (x，y，z) ; According to the formula YM=|QY1 (x，y，z) -QY2 (x，y，z) |Calculate the first impact difference between the initial position and the selected position; Where: YM is the first impact difference; Merge the initial position where the first impact difference is less than the threshold X1 with the selected position to obtain the initial area; Step SA2: Mark each blind spot position adjacent to the initial area as a selected position; According to the formula calculating a second influence difference between the initial region and the selected location; Where: YM´ is the second impact difference; i represents the corresponding blind spot position in the initial area, i=1, 2, ..., n, n is a positive integer; QY (x，y，z)i Indicates the comprehensive impact value of the corresponding blind spot position in the initial area; QY´ (x，y，z) Indicates the comprehensive impact value corresponding to the selected position; Merge the initial region whose second impact difference is less than the threshold X1 with the selected position to obtain a new initial region; Step SA3: loop step SA2 until the initial region cannot be merged, and mark the initial region as a unit region; When there are no unmerged blind spot positions in the blind spot area, the analysis ends; When there are unmerged blind spot locations in the blind spot area, the process returns to step SA1.

2. The blind spot accuracy assessment system based on AI camera technology according to claim 1, characterized in that: The method for generating the initial image of the blind area includes: Determine the blind spot area, collect image data around the vehicle in real time through the AI ​​camera, perform feature recognition on the image data through a preset image recognition model, and obtain target information of each target object; Determining relative data between the target object and the vehicle based on each target information, and integrating the relative data with the target information to form comprehensive target information of each target object; A blind spot image is generated according to the blind spot area and the target comprehensive information of each target object, and is marked as a blind spot initial image.

3. The blind spot accuracy assessment system based on AI camera technology according to claim 2, characterized in that: Methods for determining blind spot areas include: Obtaining blind spot material data of the vehicle, the blind spot material data including rearview mirror parameters and corresponding material areas; performing statistics on each blind spot material data to obtain each material area corresponding to each rearview mirror parameter and a share ratio corresponding to each material area; Establishing a three-dimensional appearance model corresponding to the vehicle, marked as a vehicle appearance model; restricting and adjusting the vehicle appearance model according to each of the material regions to form a dynamic restriction model; Identify the rearview mirror parameters of the current vehicle, match the corresponding reference material area according to the rearview mirror parameters, and display the dynamic restriction model to the user according to the reference material area. The user determines the setting screen through the dynamic restriction model, adjusts the rearview mirror parameters according to the setting screen, and determines the blind spot area.

4. The blind spot accuracy assessment system based on AI camera technology according to claim 3 is characterized in that: Methods for matching corresponding reference material areas based on rearview mirror parameters include: Identify each material area corresponding to the rearview mirror parameter, and obtain the share ratio corresponding to each material area; obtain the number of times the user uses each material area under the rearview mirror parameter; Calculate the priority value of each material area in real time according to the formula PY=FE+YC; Where: PY is the priority value; FE is the share value; YC is the number of applications; The material area with the highest priority value is selected as the reference material area.

5. The blind spot accuracy assessment system based on AI camera technology according to claim 3 is characterized in that: After the user determines the blind spot area using the vehicle appearance model, the system records the parameters and setting screens of each rearview mirror in real time, integrates them into adjustment record data, and removes duplicates from each adjustment record data. Classifying each of the adjustment record data to obtain each application classification; setting an application classification label corresponding to each application classification in the dynamic restriction model; When the user needs to determine the blind spot area again, the determination is performed through the corresponding application classification label in the dynamic restriction model.

6. The blind spot accuracy assessment system based on AI camera technology according to claim 1, characterized in that: Before performing a blind spot warning based on a baseline risk value, identify the target object corresponding to the baseline risk value, identify the unit accuracy value corresponding to the target object, correct the baseline risk value based on the unit accuracy value, and perform a blind spot warning using the corrected baseline risk value.

7. The blind spot accuracy assessment system based on AI camera technology according to claim 6, characterized in that: Record the revised benchmark risk value in real time, mark the revised benchmark risk value as the assessed risk value, and calculate the risk difference between the assessed risk value and the corresponding benchmark risk value; Determine abnormal optimization data based on the risk difference and benchmark risk value, and send each abnormal optimization data to the platform.

8. A blind spot accuracy assessment method based on AI camera technology, characterized in that: A blind spot accuracy assessment system based on AI camera technology, as applied to any one of claims 1 to 7, comprising: Determine each influencing factor and identify the monitoring subject corresponding to each influencing factor; Set the comprehensive range of the vehicle's blind spot; obtain the main material data of each monitoring subject, perform a single simulation on the main material data of each monitoring subject, and obtain the factor influence curve corresponding to each blind spot position within the comprehensive range of the blind spot; perform a comprehensive analysis based on the factor influence curves of each blind spot position to form a comprehensive influence model corresponding to the comprehensive range of the blind spot; Conduct real-time monitoring of the monitoring subjects of each influencing factor and obtain the main monitoring data of each influencing factor; Determine the blind spot area, collect image data around the vehicle in real time through the AI ​​camera, perform feature recognition on the image data using a preset image recognition model to obtain target information of each target object; determine the relative data between the target object and the vehicle based on each target information, integrate the relative data with the target information to form target comprehensive information of each target object; generate a blind spot image based on the blind spot area and the target comprehensive information of each target object, and mark it as the blind spot initial image; The monitoring data of each subject is analyzed through the comprehensive impact model to obtain the comprehensive impact value corresponding to each blind spot position in the initial blind spot image; the initial blind spot image is divided into several unit areas according to the comprehensive impact value corresponding to each blind spot position, and the unit accuracy value of each unit area is set; Mark the accuracy value of each unit in each unit area; estimate the baseline risk value of each target object, identify the unit accuracy value corresponding to the unit area where the target object is located, correct the baseline risk value according to the unit accuracy value, and use the corrected baseline risk value to issue a blind spot warning.

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