A method and system for intelligent driving assistance of automobile central control

Abstract

The present invention discloses a method and system for intelligent driving assistance in a vehicle center console, relating to the field of intelligent driving assistance, and is used to solve the problem that it is difficult for a driver to quickly find the required function in the center console display screen in a specific scenario. Each function block is obtained, and the current feature state of each function block is adjusted and scored, and statistical calculations are performed to obtain the sorted important features of the function blocks to be adjusted and the fixed function blocks. The sorted important features include environmental feature information and driver feature information. The sorted important feature of each function block is obtained by combining multi-source data and Kalman filtering. The function blocks to be adjusted are combined with the historical click preference in the corresponding environment of the user for matching scoring to obtain the newly adjusted scores of each function block. By comparing the newly adjusted scores of each function block with the preset function block sorting threshold, the adjustment positions of each function block are obtained, improving the operation convenience of the center console display screen and reducing the time for the user to search on the center console screen.

Description

Technical Field

[0001] The present invention relates to the field of intelligent driving assistance, and more specifically, to a method and system for intelligent driving assistance of a central control of an automobile. Background Art

[0002] With the rapid development of smart cars, driver assistance systems (ADAS) have become an indispensable part of modern cars. In existing technologies, many intelligent driver assistance systems mainly rely on various sensors (such as radar, camera, lidar, etc.) to monitor the surrounding environment in real time, and provide functional buttons such as adaptive cruise control, lane keeping assist, automatic emergency braking, blind spot monitoring, parking assist, etc. on the car's central control display screen (i.e., human-computer interaction interface).

[0003] The prior art has the following deficiencies:

[0004] At present, the central control display screen of a car can dynamically adjust the screen brightness according to the current in-car environment and the outside environment to improve the user experience. Although the size of the design function blocks and the operation path of the central control display screen have been optimized to a certain extent, the order of the various function blocks of the central control display screen has not changed significantly, which makes it difficult for the driver to quickly find the required function in a specific scenario, reducing the user experience. Therefore, an intelligent driving assistance method and system for a central control of a car are proposed.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method and system for intelligent driving assistance of a central control of an automobile, which solves the problems raised in the above-mentioned background technology by using different product inspection methods.

[0007] To achieve the above object, the present invention provides the following technical solutions: an automobile central control intelligent driving assistance method, comprising obtaining each function block, adjusting the current feature state score of each function block and performing statistical calculations, obtaining the important ranking features of the function blocks to be adjusted and the fixed function blocks, the important ranking features including environmental feature information and driver feature information, and the important ranking features of each function block are obtained by combining multi-source data and Kalman filtering;

[0008] The function blocks to be adjusted are matched and scored with the historical click preferences of the users in the corresponding environment to obtain the new adjustment scores of each function block;

[0009] The newly adjusted score of each functional block is compared with the preset functional block ranking threshold to obtain the adjustment position of each functional block.

[0010] In a preferred embodiment, the important features for sorting include environmental feature information and driver feature information, the environmental feature information includes the light intensity difference between the inside and outside of the vehicle, the current vehicle speed, and the density of vehicles on the road, and the driver feature information includes the driver's blinking frequency and the driver's night driving time;

[0011] The light intensity is collected in real time at set time intervals by the light sensors inside and outside the vehicle set at corresponding positions, and the light intensity outside the vehicle is subtracted from the light intensity inside the vehicle to obtain the light intensity difference between inside and outside the vehicle;

[0012] Furthermore, to avoid frequent changes in the arrangement order of the functional blocks in the central control display screen, a light intensity threshold is set to limit the number of changes, as follows:

[0013] Compare the light intensity difference between inside and outside the vehicle with the preset light threshold. If it is less than the light threshold, add the corresponding brightness value to the central control display screen according to the light intensity difference between inside and outside the vehicle without changing the arrangement order. If it is equal to the light threshold, no change will be made.

[0014] If it is greater than the illumination threshold, unreasonable light intensity information is generated, and the remaining environmental characteristic information and driver characteristic information at the same time point as the light intensity difference inside and outside the vehicle are collected.

[0015] In a preferred embodiment, the specific remaining environmental characteristic information includes the current vehicle speed and the density of road vehicles, and the remaining driver characteristic information includes the driver's blinking frequency and the driver's night driving time;

[0016] The speed sensor is used to monitor the motion state of the car and obtain the current speed of the car;

[0017] The road vehicle density is obtained by calculating the number of other vehicles in the neighborhood of the current driving vehicle within a set radius;

[0018] The camera in front of the driver's seat captures the driver's facial image in real time and focuses on the eye area. The facial recognition and eye tracking algorithms are used to detect the opening and closing of the eyelids. The number of blinks at the previous time point and the current time point is counted, and the driver's blinking frequency is calculated by combining the time interval between the two time points.

[0019] By setting the range of night driving time in a day, the current time and the start time are recorded in the vehicle's built-in clock system, and the difference is calculated to obtain the driver's night driving time.

[0020] In a preferred embodiment, the current vehicle speed, road vehicle density, driver blinking frequency, and driver night driving duration are fused using Kalman filtering, and the data information at a single time point is dynamically smoothed through multiple iterations;

[0021] Based on the fusion result after Kalman filtering, the importance of each functional block is scored, and the weights of each functional item corresponding to each functional block are generated. The weights are then substituted into the current parameters to calculate the current feature state adjustment score.

[0022] In a preferred embodiment, the current feature state adjustment score of each functional block is statistically calculated and compared with a preset adjustment threshold to determine the adjustment mode of the functional block. If the current feature state adjustment total score is greater than or equal to the adjustment threshold, all functional blocks of the central control display screen are marked as functional blocks to be adjusted. If the current feature state adjustment total score is less than the adjustment threshold, all functional blocks of the central control display screen are marked as fixed functional blocks.

[0023] In a preferred embodiment, the function blocks to be adjusted include current feature state adjustment scores of each function block;

[0024] By analyzing the historical usage data, we can obtain the historical usage preference values ​​of each module and obtain the historical click preference of the user in the corresponding environment;

[0025] The specific matching score acquisition logic is to establish a mapping relationship between environmental features and function block click preferences in historical preference data;

[0026] Furthermore, for each functional block, the preference matching score under the current environmental conditions is calculated according to the similarity between the current environmental characteristics and the historical preference data; specifically, the current environmental characteristics and the feature vectors of the historical data are compared, and the higher the score, the closer the preference.

[0027] In a preferred embodiment, the current feature state adjustment score is used as the basic score, combined with the user's historical click preference in the corresponding environment as the preference score, and the weights of each function item of the current feature state adjustment score corresponding to each function block are calculated based on the matching score to obtain the new adjustment score of each function block.

[0028] In a preferred embodiment, the newly adjusted score of each functional block is compared and analyzed with the functional block ranking threshold. If it is greater than or equal to the functional block ranking threshold, the functional block is marked as a priority functional block. If it is less than the functional block ranking threshold, the functional block is marked as a downgraded functional block.

[0029] The function blocks corresponding to the newly adjusted scores of the function blocks marked as priority function blocks are counted and sorted in descending order according to the score values, and the function block with the highest newly adjusted score is taken as the first function block, and so on, until all the adjusted function blocks are sorted;

[0030] Furthermore, the function blocks corresponding to the newly adjusted scores of the function blocks marked as downgraded are counted and sorted in order from small to large according to the score values. The function blocks with the lowest newly adjusted scores are taken as the first priority for downgrading, and the order is descending in sequence until all the function blocks that need to be downgraded are sorted.

[0031] An automobile central control intelligent driving assistance system includes an acquisition module, a mode module and an adjustment module, and each module is signal-connected;

[0032] The acquisition module is used to acquire each function block, adjust the current feature state score of each function block and perform statistical calculations to obtain the important features of the ranking of the function block to be adjusted and the fixed function block. The important features of the ranking include environmental feature information and driver feature information. The important features of the ranking of each function block are obtained by combining multi-source data and Kalman filtering, and the function block to be adjusted is sent to the mode module;

[0033] The mode module is used to obtain the matching score of the function block to be adjusted and the historical click preference combination of the user in the corresponding environment, obtain the new adjustment score of each function block, and send it to the adjustment module;

[0034] The adjustment module compares the newly adjusted score of each functional block with a preset functional block ranking threshold to obtain the adjustment position of each functional block.

[0035] Technical effects and advantages of the present invention:

[0036] 1. The present invention obtains each function block, scores the current feature state adjustment of each function block and performs statistical calculations to obtain important ranking features of the function blocks to be adjusted and the fixed function blocks. The important ranking features include environmental feature information and driver feature information. The important ranking features of each function block are obtained by combining multi-source data and Kalman filtering to determine the adjustment of each function block of the current central control display screen, reduce the user's search time on the central control screen, and improve the operating convenience and driving experience of the central control display screen.

[0037] 2. The present invention matches and scores the function blocks to be adjusted with the historical click preference combinations of the users in the corresponding environment to obtain new adjustment scores for each function block, and compares the new adjustment scores for each function block with preset function block sorting thresholds to obtain adjustment positions for each function block, thereby improving the driver's operating efficiency and accuracy of the central control display screen, enhancing the user experience, and the universality of the central control display screen for specific driving environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 The present invention is a method flow chart of an automobile central control intelligent driving assistance method.

[0039] Figure 2The present invention is a schematic diagram of a module of an automobile central control intelligent driving assistance system.

[0040] Figure 3 This is a schematic diagram of a central control display screen of a central control intelligent driving assistance system for an automobile according to the present invention.

[0041] Figure 4 This is a functional implementation diagram of an automobile central control intelligent driving assistance method of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] This embodiment evaluates the specific feature overlap of the current specific environment to adjust the arrangement order of the functional buttons on the central control display screen. Specifically, there are multiple specific application environments in real application scenarios, including but not limited to the congested environment of expressways, the open environment of rural roads, the night environment of urban roads, the rainy environment of outdoor winding mountain roads, etc., and this embodiment uses the relative driving environment of multiple vehicles at night described in the background technology as the specific environment set in this embodiment 1, which will not be repeated here;

[0044] Example 1

[0045] See also Figures 1 to 4 , a method for intelligent driving assistance of automobile central control, the specific operation process is as follows:

[0046] Obtain each function block, adjust the current feature state of each function block and perform statistical calculations to obtain the important features of the function blocks to be adjusted and the fixed function blocks. The important features of the ranking include environmental feature information and driver feature information. The important features of the ranking of each function block are obtained by combining multi-source data and Kalman filtering;

[0047] Based on an automobile central control intelligent driving assistance method provided by an embodiment of the present invention, the arrangement order of each functional block of the central control display screen is improved, and the important features of sorting are extracted by combining multi-source data and Kalman filtering, thereby solving the problem that it is difficult for drivers to quickly find the required functions in specific scenarios, thereby improving the user experience;

[0048] In one implementation, each function block is scored for current feature state adjustment. Specifically, the current feature state adjustment score refers to assigning a score value to each function block by analyzing and evaluating the performance of different function blocks in a specific environment and the use needs of the driver. The score value indicates the priority and importance of the function block in the current feature state, thereby providing a basis for the dynamic adjustment of the central control display screen.

[0049] The important features for sorting include environmental feature information and driver feature information. Environmental feature information includes the light intensity difference between the inside and outside of the car, the current car speed, and the density of vehicles on the road. Driver feature information includes the driver's blinking frequency and the driver's night driving time.

[0050] The logic for obtaining the light intensity inside and outside the car is to collect the light intensity in real time at set time intervals through the light sensors inside and outside the car set at corresponding positions; and filter and average the light intensity data inside and outside the car to eliminate the fluctuations caused by instantaneous changes in light and ensure that the collected data can reflect the actual lighting level inside the car;

[0051] Subtract the light intensity outside the car from the light intensity inside the car to obtain the light intensity difference between inside and outside the car;

[0052] It should be noted that the location of the light sensor is determined by the experimenter based on the comprehensive analysis of the various parameters of the vehicle and the differences between the parameters when dealing with various environments. At the same time, the time interval is determined by the experimenter based on the frequency of vehicle energy consumption and is not limited here.

[0053] Furthermore, to avoid frequent changes in the arrangement order of the functional blocks in the central control display screen, a light intensity threshold is set to limit the number of changes, as follows:

[0054] Compare the light intensity difference between inside and outside the vehicle with the preset light threshold. If it is less than the light threshold, add the corresponding brightness value to the central control display screen according to the light intensity difference between inside and outside the vehicle without changing the arrangement order. If it is equal to the light threshold, no change is made. This is the existing technology and will not be described in detail.

[0055] If it is greater than the light threshold, unreasonable light intensity information is generated, and the remaining environmental characteristic information and driver characteristic information at the time point consistent with the light intensity difference inside and outside the car are collected;

[0056] The specific remaining environmental characteristic information includes the current vehicle speed and road vehicle density, and the remaining driver characteristic information includes the driver's blinking frequency and the driver's night driving time; and the collection time is the same as the unreasonable light intensity information;

[0057] It should be noted that the illumination threshold is obtained through comprehensive analysis of historical illumination information and human eye glare factors, which will not be elaborated here;

[0058] The current vehicle speed refers to the instantaneous speed measured by the vehicle's built-in speed sensor at the current time point. The acquisition logic is to obtain the current vehicle speed by monitoring the vehicle's motion state through the speed sensor;

[0059] The road vehicle density refers to the density of other vehicles within a certain range around the vehicle at the current time point. Its acquisition logic is to evaluate the road vehicle density by calculating the number of other vehicles in the neighborhood within the set radius of the current driving vehicle;

[0060] Specifically, the current driving vehicle is set as the core point and a radius range is defined ;

[0061] The specific radius range is set by the experimenters based on the specific road width and length, and is not limited here;

[0062] Use the driving vehicle as the starting point to calculate the radius All other vehicle positions within the neighborhood of , the specific formula is:

[0063] ;

[0064] In the formula, is the area of ​​the neighborhood;

[0065] The driver's blinking frequency refers to the number of times the driver blinks per unit time. The acquisition logic is to capture the driver's facial image in real time through a camera installed in front of the driver's seat, focus on the eye area, use facial recognition and eye tracking algorithms to detect the opening and closing of the eyelids, calculate the number of blinks, count the number of blinks at the previous time point and the current time point at the set current time point, and calculate the driver's blinking frequency by ratio with the time interval between the previous time point and the current time point;

[0066] The driver's night driving time refers to the length of time the driver drives continuously at night or in low-light conditions. This time reflects the driver's driving load in a dim environment, especially during long night driving, when encountering oncoming vehicles turning on their high beams, etc., which will seriously affect the driver's driving state. The acquisition logic is to set the night driving time range in a day, record the current time and the start time in the vehicle's built-in clock system, and calculate the difference to obtain the driver's night driving time;

[0067] It should be noted that the range of night driving time in a day is set by the experimenters according to the changes in ambient light intensity and seasonal factors, and is not limited here;

[0068] Among them, multi-source data refers to complementary data obtained from multiple sources or multiple sensors;

[0069] The current vehicle speed, road vehicle density, driver blinking frequency, and nighttime driving duration are fused using Kalman filtering. Kalman filtering is an algorithm based on state estimation. Through multiple iterations, the data information at a single time point is dynamically smoothed so that the fusion result can adapt to sudden environmental changes and obtain stable and reliable important features for sorting.

[0070] Based on the fusion results after Kalman filtering, the importance of each functional block is scored, and the weights of each functional item corresponding to each functional block are generated. The weights are substituted into the current parameters to calculate the current feature state adjustment score, which reflects the importance of each functional block in dealing with the current complex environment under the conditions of current light intensity, road complexity and driver status.

[0071] The current feature state adjustment score of each function block is statistically calculated and compared with the preset adjustment threshold to determine the adjustment mode of the function block. If the current feature state adjustment total score is greater than or equal to the adjustment threshold, all function blocks of the central control display screen are marked as function blocks to be adjusted. If the current feature state adjustment total score is less than the adjustment threshold, all function blocks of the central control display screen are marked as fixed function blocks.

[0072] The present invention obtains each function block, scores the current feature state adjustment of each function block and performs statistical calculations to obtain important ranking features of the function blocks to be adjusted and the fixed function blocks. The important ranking features include environmental feature information and driver feature information. The important ranking features of each function block are obtained by combining multi-source data and Kalman filtering to determine the adjustment of each function block of the current central control display screen, reduce the user's searching time on the central control screen, and improve the operating convenience and driving experience of the central control display screen.

[0073] Example 2

[0074] In the first embodiment of the present invention, examples are given to illustrate how to obtain each function block, adjust the current feature state of each function block and perform statistical calculations to obtain the important ranking features of the function blocks to be adjusted and the fixed function blocks. The important ranking features include environmental feature information and driver feature information. The important ranking features of each function block are combined with the operation strategy obtained by multi-source data and Kalman filtering. However, in the first embodiment, only the function blocks of the central control display screen to be adjusted are determined, but how to adjust each function block and the corresponding position of each function block are not discussed and analyzed. Obviously, although the adjustment of the function block can meet certain user needs, in special environments, the user's favorite function block may not be in the display screen, and the display screen needs to be slid to obtain the function block, resulting in the inability to timely access the required function block in special environments, affecting the driving experience and efficiency. In view of the above problems, the second embodiment of the present invention is further refined;

[0075] The function blocks to be adjusted are matched and scored with the historical click preferences of the users in the corresponding environment to obtain the new adjustment scores of each function block;

[0076] Compare the newly adjusted scores of each functional block with the preset functional block ranking threshold to obtain the adjustment position of each functional block;

[0077] Specifically, the function blocks to be adjusted include the current feature state adjustment scores of each function block, which has been described in Embodiment 1 and will not be described in detail here;

[0078] The historical click preference of the user in the corresponding environment refers to the driver's historical usage preference for the central control function block under specific environmental characteristics (such as the environment where the difference in light intensity between the inside and outside of the car is greater than the light threshold as described in Example 1 of this embodiment). The acquisition logic is to obtain the historical click preference of the user in the corresponding environment by analyzing the historical usage data to obtain the historical usage preference value of each module;

[0079] The specific logic for obtaining the matching score is to establish a mapping relationship between the environmental features and the function block click preferences in the historical preference data (such as the mapping relationship in the environment where the difference in light intensity between the inside and outside of the vehicle is greater than the light threshold as described in Embodiment 1);

[0080] Furthermore, for each functional block, the preference matching score under the current environmental conditions is calculated according to the similarity between the current environmental characteristics and the historical preference data; specifically, the current environmental characteristics and the feature vectors of the historical data are compared, and the higher the score, the closer the preference;

[0081] Among them, calculating the preference matching score under the current environmental conditions is based on similarity calculation, such as cosine similarity, Euclidean distance, etc., which is an existing technology and will not be described in detail;

[0082] The current feature state adjustment score is used as the basic score, and the historical click preference of the user in the corresponding environment is combined as the preference score. The weights of each function item of the current feature state adjustment score corresponding to each function block in Example 1 are calculated as the calculation weights of the matching score to obtain the new adjustment score of each function block;

[0083] Compare and analyze the newly adjusted score of each function block with the function block ranking threshold. If it is greater than or equal to the function block ranking threshold, the function block is marked as a priority function block. If it is less than the function block ranking threshold, the function block is marked as a downgraded function block.

[0084] It should be noted that the function block sorting threshold is set by the experimenter based on driving safety, user convenience and the response priority of each function block, which will not be elaborated here;

[0085] The function blocks corresponding to the newly adjusted scores of the function blocks marked as priority function blocks are counted and sorted in descending order according to the score values, and the function block with the highest newly adjusted score is taken as the first function block, and so on, until all the adjusted function blocks are sorted;

[0086] Further, the function blocks corresponding to the newly adjusted scores of the function blocks marked as downgraded function blocks are counted and sorted in order from small to large according to the score values, and the function blocks with the lowest newly adjusted scores are taken as the first priority downgraded function blocks, and sorted in descending order until all the function blocks that need to be downgraded are sorted;

[0087] Specific, for example Figure 3 As shown, a schematic diagram of a central control display screen of a central control intelligent driving assistance system of a car is shown, and the central control display screen shown in the figure has six functional blocks, namely adaptive cruise control, lane keeping assist, automatic emergency braking, blind spot monitoring, parking assist and driving mode adjustment;

[0088] When it is determined that function blocks 1, 2, 3, 4, 5, and 6 are all function blocks to be adjusted, the user's historical click preference combination in the corresponding environment is obtained for matching and scoring, and the new adjustment score of each function block is obtained;

[0089] According to the comparison between the newly adjusted scores of each function block and the preset function block ranking threshold, it is specifically obtained that function blocks 1, 2, and 4 are priority function blocks, and 3, 5, and 6 are downgraded function blocks;

[0090] The newly adjusted scores of function blocks 1, 2, and 4 are counted and sorted in descending order to obtain an order of 2, 4, and 1. The newly adjusted scores of function blocks 3, 5, and 6 are counted and sorted in descending order to obtain an order of 5, 3, and 6.

[0091] The order of reordering the function blocks is function block 2 (lane keeping assist), function block 4 (blind spot monitoring), function block 1 (adaptive cruise control), function block 5 (parking assist), function block 3 (automatic emergency braking) and function block 6 (driving mode adjustment);

[0092] It should be noted that Figure 3 The function blocks are only schematic diagrams. It can be understood by those skilled in the art that the setting of the function blocks is not limited to that described in this embodiment. Under different users and scenarios, there are multiple different function block adjustments to meet the user's usage needs, which will not be described in detail here.

[0093] The present invention matches and scores the function blocks to be adjusted with the historical click preference combinations of the users in the corresponding environment to obtain new adjustment scores for each function block, and compares the new adjustment scores for each function block with preset function block sorting thresholds to obtain adjustment positions for each function block, thereby improving the driver's central control operation efficiency and accuracy, enhancing the user experience, and the universality of the central control display screen for specific driving environments.

[0094] Example 3

[0095] See also Figure 2 , an automobile central control intelligent driving assistance system, including an acquisition module, a mode module and an adjustment module, and each module is signal-connected;

[0096] The acquisition module is used to acquire each function block, adjust the current feature state score of each function block and perform statistical calculations to obtain the important features of the ranking of the function block to be adjusted and the fixed function block. The important features of the ranking include environmental feature information and driver feature information. The important features of the ranking of each function block are obtained by combining multi-source data and Kalman filtering, and the function block to be adjusted is sent to the mode module;

[0097] The mode module is used to obtain the matching score of the function block to be adjusted and the historical click preference combination of the user in the corresponding environment, obtain the new adjustment score of each function block, and send it to the adjustment module;

[0098] The adjustment module compares the newly adjusted score of each functional block with a preset functional block ranking threshold to obtain the adjustment position of each functional block.

[0099] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0100] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0101] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0102] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0103] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0104] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0105] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0106] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0107] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0108] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An intelligent driving assistance method for a car central control, characterized in that: It includes obtaining each function block, adjusting the current feature state score of each function block and performing statistical calculations to obtain the important features of the function blocks to be adjusted and the fixed function blocks, the important features of the ranking include environmental feature information and driver feature information, and the important features of the ranking of each function block are obtained by combining multi-source data and Kalman filtering; The function blocks to be adjusted are matched and scored with the historical click preferences of the users in the corresponding environment to obtain the new adjustment scores of each function block; Compare the newly adjusted scores of each functional block with the preset functional block ranking threshold to obtain the adjustment position of each functional block; The important features for sorting include environmental feature information and driver feature information. Environmental feature information includes the light intensity difference between the inside and outside of the car, the current car speed, and the density of vehicles on the road. Driver feature information includes the driver's blinking frequency and the driver's night driving time. The light intensity is collected in real time at set time intervals by the light sensors inside and outside the vehicle set at corresponding positions, and the light intensity outside the vehicle is subtracted from the light intensity inside the vehicle to obtain the light intensity difference between inside and outside the vehicle; The function blocks to be adjusted include the current feature state adjustment scores of each function block; By analyzing the historical usage data, we can obtain the historical usage preference values ​​of each module and obtain the historical click preference of the user in the corresponding environment; The specific matching score acquisition logic is to establish a mapping relationship between environmental features and function block click preferences in historical preference data; Furthermore, for each functional block, the preference matching score under the current environmental conditions is calculated according to the similarity between the current environmental characteristics and the historical preference data; specifically, the current environmental characteristics and the feature vectors of the historical data are compared, and the higher the score, the closer the preference.

2. The method for intelligent driving assistance of a central control system of an automobile according to claim 1, characterized in that: Furthermore, to avoid frequent changes in the arrangement order of the functional blocks in the central control display screen, a light intensity threshold is set to limit the number of changes, as follows: Compare the light intensity difference between inside and outside the vehicle with the preset light threshold. If it is less than the light threshold, add the corresponding brightness value to the central control display screen according to the light intensity difference between inside and outside the vehicle without changing the arrangement order. If it is equal to the light threshold, no change will be made. If it is greater than the illumination threshold, unreasonable light intensity information is generated, and the remaining environmental characteristic information and driver characteristic information at the same time point as the light intensity difference inside and outside the vehicle are collected.

3. The method for intelligent driving assistance of a central control system of an automobile according to claim 2, characterized in that: The specific remaining environmental characteristic information includes the current vehicle speed and the density of vehicles on the road, and the remaining driver characteristic information includes the driver's blinking frequency and the driver's night driving time; The speed sensor is used to monitor the motion state of the car and obtain the current speed of the car; The road vehicle density is obtained by calculating the number of other vehicles in the neighborhood of the current driving vehicle within a set radius; The camera in front of the driver's seat captures the driver's facial image in real time and focuses on the eye area. The facial recognition and eye tracking algorithms are used to detect the opening and closing of the eyelids. The number of blinks at the previous time point and the current time point is counted, and the driver's blinking frequency is calculated by combining the time interval between the two time points. By setting the range of night driving time in a day, the current time and the start time are recorded in the vehicle's built-in clock system, and the difference is calculated to obtain the driver's night driving time.

4. The method for intelligent driving assistance of a central control system of an automobile according to claim 3, characterized in that: The current vehicle speed, road vehicle density, driver blinking frequency, and nighttime driving duration are integrated using Kalman filtering, and the data information at a single time point is dynamically smoothed through multiple iterations; Based on the fusion result after Kalman filtering, the importance of each functional block is scored, and the weights of each functional item corresponding to each functional block are generated. The current parameters are then used to calculate the current feature state adjustment score.

5. The method for intelligent driving assistance of a central control system of an automobile according to claim 4, characterized in that: The current feature state adjustment score of each function block is statistically calculated and compared with the preset adjustment threshold to determine the adjustment mode of the function block. If the current feature state adjustment total score is greater than or equal to the adjustment threshold, all function blocks of the central control display screen are marked as function blocks to be adjusted. If the current feature state adjustment total score is less than the adjustment threshold, all function blocks of the central control display screen are marked as fixed function blocks.

6. The method for intelligent driving assistance of a central control system of an automobile according to claim 1, characterized in that: The current feature state adjustment score is taken as the basic score, and combined with the user's historical click preference in the corresponding environment as the preference score. Based on each function block, the weight of each function item of its corresponding current feature state adjustment score is calculated as the calculation weight of the matching score to obtain the new adjustment score of each function block.

7. The method for intelligent driving assistance of a central control system of an automobile according to claim 6, characterized in that: Compare and analyze the newly adjusted score of each function block with the function block ranking threshold. If it is greater than or equal to the function block ranking threshold, the function block is marked as a priority function block. If it is less than the function block ranking threshold, the function block is marked as a downgraded function block. The function blocks corresponding to the newly adjusted scores of the function blocks marked as priority function blocks are counted and sorted in descending order according to the score values, and the function block with the highest newly adjusted score is taken as the first function block, and so on, until all the adjusted function blocks are sorted; Furthermore, the function blocks corresponding to the newly adjusted scores of the function blocks marked as downgraded are counted and sorted in order from small to large according to the score values. The function blocks with the lowest newly adjusted scores are taken as the first priority for downgrading, and the order is descending in sequence until all the function blocks that need to be downgraded are sorted.

8. An automobile central control intelligent driving assistance system, used to implement an automobile central control intelligent driving assistance method as claimed in any one of claims 1 to 7, characterized in that: It includes an acquisition module, a mode module and an adjustment module, and the signals of each module are connected; The acquisition module is used to acquire each function block, adjust the current feature state score of each function block and perform statistical calculations to obtain the important features of the ranking of the function block to be adjusted and the fixed function block. The important features of the ranking include environmental feature information and driver feature information. The important features of the ranking of each function block are obtained by combining multi-source data and Kalman filtering, and the function block to be adjusted is sent to the mode module; The mode module is used to obtain the matching score of the function block to be adjusted and the historical click preference combination of the user in the corresponding environment, obtain the new adjustment score of each function block, and send it to the adjustment module; The adjustment module compares the newly adjusted score of each functional block with a preset functional block ranking threshold to obtain the adjustment position of each functional block.

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