Construction method and equipment of turning working condition characteristic distribution database and storage medium

Abstract

The invention provides a construction method and device of a turning working condition characteristic distribution database and a storage medium, and belongs to the technical field of automobile testing. The construction method comprises the following steps: collecting driving data of a target vehicle driving on a real road; detecting a vehicle turning event based on the driving data, and segmenting a corresponding turning event track fragment; calculating a turning radius representation value corresponding to each turning event track fragment; and performing statistical modeling based on the turning radius characterization values of the plurality of vehicles in the plurality of road scenes, and generating a database characterizing a turning radius distribution rule. According to the method, the database generated according to the statistical result can accurately reflect the real turning condition characteristic distribution of the automobile user, and data support is provided for determining the main distribution area and proportion of the turning radius in a test scene.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of automobile testing, and particularly relates to a construction method, equipment and storage medium of a turning condition characteristic distribution database. BACKGROUND

[0002] The performance testing and development of an automobile steering system traditionally mainly rely on standard conditions or preset simulation conditions executed on a bench or a test field. These standardized testing methods, for example, the maximum turning angle and other limit parameters are conventionally used for examination in a steering mechanical bench test, although they can evaluate the basic performance and durability of the steering system under certain conditions, there is a large difference between the conditions set and the real use conditions of the vehicle in the actual road environment.

[0003] In the actual driving process, the driving habits of different drivers, the road conditions (such as the curvature of the curve, the road conditions, the traffic density, etc.) of the area where the vehicle travels, etc. are different, which leads to the complex individual specificity and group statistical characteristics of the turning behavior of the vehicle. The existing testing data based on simplified or limit conditions are difficult to comprehensively and truly cover these characteristics, which leads to the development and verification of the steering system based on such data, different fault modes and reliability performance in the development and testing stages in the user use process, and cannot meet the user demand.

[0004] Therefore, there is an urgent need for a technical solution that can systematically collect, analyze and model real road turning data to construct a distribution database reflecting the actual turning behavior characteristics of users. SUMMARY

[0005] The purpose of the present application is to solve the problems existing in the prior art, and to provide a construction method, equipment and storage medium of a turning condition characteristic distribution database, which can systematically collect, analyze and model real road turning data, and construct a distribution database reflecting the actual turning behavior characteristics of users.

[0006] The present application is achieved by the following technical solutions:

[0007] In a first aspect, the present application provides a construction method of a turning condition characteristic distribution database of a vehicle user, which comprises:

[0008] Collecting driving data of a target vehicle in real road driving;

[0009] Based on the driving data, detecting a vehicle turning event and segmenting a corresponding turning event trajectory segment;

[0010] Calculating a turning radius representation value corresponding to each turning event trajectory segment;

[0011] Based on the turning radius representation values of multiple vehicles in multiple road scenes, statistical modeling is performed to generate a database representing the distribution law of the turning radius.

[0012] Optionally, the driving data at least includes one or more of the following: vehicle position information, time stamp, vehicle speed, yaw rate, steering wheel rotation angle, wheel speed.

[0013] Optionally, before detecting the vehicle turning event based on the driving data, the construction method further comprises:

[0014] The collected raw driving data is preprocessed, and the preprocessing includes abnormal data cleaning and data filtering and smoothing.

[0015] Optionally, detecting the vehicle turning event comprises:

[0016] Based on at least one of the yaw rate, the wheel speed difference, the steering wheel rotation angle and the driving trajectory curvature in the driving data, a comparison is made with a preset corresponding threshold value, and when the threshold value is exceeded, it is determined that the turning event starts, and when it falls below the threshold value, it is determined that the turning event ends.

[0017] Optionally, based on the Ackermann steering model, the turning radius representation value is calculated by at least one of the following calculation methods:

[0018] The turning radius is calculated based on the wheel speed difference of the left and right wheels;

[0019] The turning radius is calculated based on the steering wheel rotation angle;

[0020] The turning radius is calculated based on the yaw rate and the vehicle speed.

[0021] Optionally, based on the turning radius values calculated by at least two methods, the final turning radius representation value is determined by comparison and correction.

[0022] Optionally, the turning radius representation value is the minimum turning radius value in the corresponding turning event trajectory segment.

[0023] Optionally, the statistical modeling method comprises:

[0024] The turning radius representation values are classified according to a preset classification dimension, and the classification dimension includes at least one of the following: vehicle type, road type and vehicle speed;

[0025] The classified turning radius representation values are fitted using a probability density model to obtain the probability density distribution of the turning radius.

[0026] Optionally, the generating the database representing the distribution law of the turning radius comprises: based on the statistical modeling result, generating a visual turning radius probability density curve and / or a cumulative distribution curve.

[0027] In a second aspect of the present application, an electronic device is provided, which comprises a memory and a processor, the memory storing a computer program which is run by the processor, and the computer program, when run by the processor, causes the device in which the processor is installed to perform the method for constructing a database of turning condition characteristics of a user of a vehicle according to any one of the preceding aspects.

[0028] In a third aspect of the present application, a storage medium is provided, which stores a computer program which is run on a computer, and the computer program, when run, causes the computer to perform the method for constructing a database of turning condition characteristics of a user of a vehicle according to any one of the preceding aspects.

[0029] Compared with the prior art, the present application has the advantages that: the method for constructing a database of turning condition characteristics of a user of a vehicle provided by the present application can accurately capture the complex patterns and group laws of real user turning behaviors by collecting real road driving data, detecting turning events and segmenting trajectory segments, calculating turning radius representation values, and generating a database through statistical modeling, has the advantages of being able to systematically collect, analyze and model real road turning data, and construct a distribution database reflecting the actual turning behavior characteristics of a user, thereby solving the problem that the design, development, verification and actual demand of a steering system are disconnected in the prior art, and improving the handling comfort and safety performance. BRIEF DESCRIPTION OF DRAWINGS

[0030] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description of the preferred embodiments of the present application taken in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of the present application and are incorporated in and constitute a part of the specification, illustrate embodiments of the present application and serve to explain the present application, but do not limit the present application.

[0031] Figure 1 A flowchart of a method for constructing a database of turning condition characteristics of a user of a vehicle is provided for the embodiments of the present application;

[0032] Figure 2 A schematic diagram of the principle of calculating the turning radius by the wheel speed method is provided for the embodiments of the present application;

[0033] Figure 3a A left front wheel turning angle function curve based on steering wheel angle fitting is provided for the embodiments of the present application;

[0034] Figure 3bA right front wheel rotation angle function curve diagram based on a steering wheel angle fitting is provided for an embodiment of the present application.

[0035] Figure 4 A principle diagram of calculating a turning radius by using a rotation angle method is provided for an embodiment of the present application.

[0036] Figure 5 A vehicle turning detection flow diagram is provided for an embodiment of the present application.

[0037] Figure 6 A structure diagram of an electronic device is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical scheme and advantages of the present application more obvious, the example embodiments according to the present application will be described in detail below with reference to the drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application described in the present application, all other embodiments obtained by those skilled in the art without creative labor shall fall within the protection scope of the present application.

[0039] In order to make the purpose, technical scheme and advantages of the present application more obvious, the example embodiments according to the present application will be described in detail below with reference to the drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application described in the present application, all other embodiments obtained by those skilled in the art without creative labor shall fall within the protection scope of the present application.

[0040] In order to facilitate the understanding of the present application, first, a method for constructing a turning condition characteristic distribution database of a vehicle user disclosed in an embodiment of the present application is described in detail.

[0041] Figure 1 A flow diagram of the method for constructing a turning condition characteristic distribution database of a vehicle user provided for an embodiment of the present application is shown in Figure 1 The method for constructing a turning condition characteristic distribution database of a vehicle user in the present application at least includes the following steps S100 to S500.

[0042] Step S100, collecting driving data of a target vehicle in real road driving.

[0043] The driving data refers to various types of information recorded by the vehicle during its operation in a real road environment, which can include the vehicle's position information, time stamp, vehicle speed, yaw rate, steering wheel angle, and wheel speed of each wheel, etc. These data are used to comprehensively reflect the dynamic behavior of the vehicle and the operation intention of the driver. The collection of the data can be achieved in various ways. For example, for new energy vehicles, the above data can be directly downloaded through a cloud background; for conventional fuel vehicles, the data recording device can be connected to the vehicle diagnostic system interface, and the above data can be obtained by connecting an external GPS device and an inertial measurement unit. These collected raw data form the basis for subsequent analysis.

[0044] Step S200, based on the driving data, detecting a vehicle turning event and segmenting a corresponding turning event trajectory segment.

[0045] The turning event refers to a continuous process in which the driving direction of the vehicle changes significantly during the driving process. The identification of the event is the basis for subsequent analysis of the turning characteristics of the vehicle. The turning event trajectory segment refers to a continuous data sequence corresponding to a single turning event identified from the vehicle driving data. The segment contains all the motion information required for the vehicle to complete a turn. The detection of the turning event can adopt various strategies. For example, the heading angle data of the vehicle can be continuously monitored, and when the heading angle changes continuously within a short time, it is determined as the start of a turning event, and when the heading angle returns to stable, it is determined as the end of the turning event. Alternatively, a fixed steering wheel angle threshold can be set, and when the steering wheel angle exceeds the threshold continuously, it is identified as a turning event. Once the turning event is identified, the corresponding driving data sequence is segmented into an independent turning event trajectory segment for subsequent individual analysis.

[0046] Step S300, calculating a turning radius representation value corresponding to each turning event trajectory segment.

[0047] The turning radius representation value refers to a numerical value for quantifying the turning degree of a single turning event trajectory segment. The value can reflect the curvature of the vehicle in a specific turning process. One way to calculate the turning radius representation value is to perform curve fitting on the vehicle position data in the turning event trajectory segment, for example, to fit a circular arc using the least squares method, and to take the radius of the circular arc as the turning radius representation value. Another way is to select the point with the maximum curvature in the turning trajectory segment, and calculate the instantaneous turning radius of the point as the representation value. In addition, the average lateral acceleration of the vehicle during the turning process can also be estimated, and combined with the vehicle speed, to indirectly calculate the average turning radius.

[0048] Step S400, based on the turning radius representation values of multiple vehicles in multiple road scenes, statistical modeling is performed to generate a database representing the distribution rule of the turning radius.

[0049] The statistical modeling refers to a process of using mathematical and statistical methods to analyze and induce a large amount of data to reveal the inherent law and distribution characteristics of the data. In the present application, it is used to analyze the distribution characteristics of the turning radius. The method of statistical modeling can be to directly summarize all collected turning radius representation values and calculate the basic statistical quantities such as the average, median and variance to summarize the central tendency and dispersion degree; or to construct a frequency distribution table, divide the turning radius into several intervals, and count the number of turning radius representation values in each interval to intuitively show the distribution.

[0050] The database of the turning radius distribution rule refers to a data set representing the distribution characteristics of the turning radius of the vehicle under different working conditions after statistical modeling. The database provides data support for the design and optimization of the steering system. The database can be constructed as a structured data storage form. For example, the frequency distribution data of the turning radius obtained by statistical analysis, together with the corresponding statistical parameters, can be stored in a table of a relational database, and each record contains a turning radius interval and a corresponding frequency. Alternatively, the statistical results of the turning radius of different vehicles and different road scenes can be stored in a non-relational database in the form of key-value pairs for quick retrieval.

[0051] The present application collects vehicle driving data directly from real road environment, dynamically identifies and quantifies the turning behavior, and performs statistical modeling based on diversified turning radius representation values to construct a database reflecting the distribution rule of real turning working conditions. Thus, the limitations of traditional test methods which are divorced from actual road use conditions are overcome, and typical user working conditions for steering system test can be defined according to the statistical results, providing real and comprehensive user turning working condition data support for the design and optimization of the steering system, so that the developed steering system can be more accurately related to the actual needs of users.

[0052] In addition, the present application does not rely on additional hardware devices, and uses existing user data for analysis and calculation without additional hardware investment.

[0053] Next, the calculation method of the turning radius representation value that can be used based on the Ackermann steering model in some embodiments of the present application will be described with reference to Figures 2-4

[0054] Method one, calculating the turning radius based on the wheel speed difference of the left and right wheels.

[0055] ​The turning radius is calculated based on the wheel speed difference of the left and right wheels. The principle is that the inside wheel and the outside wheel have different track lengths when the vehicle is turning, resulting in a wheel speed difference. By measuring this wheel speed difference, combined with the wheel track and other parameters of the vehicle, the turning radius of the vehicle can be calculated. This method uses the kinematic characteristics of the vehicle and directly reflects the actual motion state of the wheel, which can accurately capture the actual turning trajectory of the vehicle, especially suitable for low-speed or medium-speed turning conditions.

[0056] Specifically, the wheel speed signals of the vehicle telematics box (T-Box) are called. Figure 2 The principle diagram for calculating the turning radius using the wheel speed method is shown in Figure 2 , combined with the wheel track B R , the body length L, the wheel speed difference of the left and right wheels is calculated in real time, and the theoretical turning radius R is derived according to the Ackerman geometry as shown in the following formula:

[0057]

[0058] Where V LR is the speed of the left rear wheel (non-steering wheel), V RR is the speed of the right rear wheel (non-steering wheel), and the unit is revolutions per minute. It is worth noting that when the left and right rear wheels have a significant tire pressure loss, the rolling radius will decrease, affecting the speed, and the accuracy of this method will be reduced.

[0059] Method two, calculate the turning radius based on the steering wheel angle.

[0060] The turning radius based on the steering wheel angle refers to using the deflection angle of the front wheel (or steering wheel) of the vehicle relative to the body longitudinal axis to calculate the turning radius. This method directly uses the steering input information of the driver and reflects the driver's control intention, which can quickly and intuitively obtain the turning radius information, and has high accuracy when the steering wheel angle sensor data is reliable.

[0061] Specifically, the steering wheel angle in the electric power steering (EPS) module of the T-Box is called. The steering angle of the vehicle is calculated according to the fitting function shown in the following formula:

[0062] y l =2.0427e-08*x 3 +9.3454e-06*x 2 +6.2711e-02*x

[0063] y r =2.0426e-08*x 3+ 9.3347e-06 * x 2 + 6.2711e-02 * x

[0064] wherein y l is the output variable of the left front wheel steering angle fitting function, y r is the output variable of the right front wheel steering angle fitting function.

[0065] Figure 3a and Figure 3b show the left front wheel steering angle function curve and the right front wheel steering angle function curve based on steering wheel angle fitting, respectively. Figure 3a In the figure, the horizontal axis is the steering wheel angle, and the vertical axis is the left front wheel steering angle. Figure 3b In the figure, the horizontal axis is the steering wheel angle, and the vertical axis is the right front wheel steering angle.

[0066] Figure 4 is a schematic diagram of the principle of calculating the turning radius using the steering angle method, as shown in Figure 4 , combined with the vehicle body length L, the theoretical turning radius R shown in the following formula is derived according to the Ackermann geometric relationship:

[0067]

[0068] wherein a is the steering angle of the right front wheel, and β is the steering angle of the left front wheel.

[0069] Method three, calculating the turning radius based on the yaw rate and vehicle speed.

[0070] Calculating the turning radius based on the yaw rate and vehicle speed is to use the dynamic relationship between the yaw rate (the angular velocity of the vehicle rotating around its vertical axis) and the vehicle speed (the driving speed of the vehicle). This method uses the dynamic response characteristics of the vehicle, is suitable for high-speed or dynamic turning conditions, and can reflect the overall motion state of the vehicle. When the vehicle is driving at high speed or experiencing dynamic situations such as side slipping, it can provide more robust turning radius estimation.

[0071] Specifically, the yaw rate (also available by taking the time derivative of the heading angle) parameter of the T-Box is called, and the theoretical turning radius R shown in the following formula is derived according to the Ackermann geometric relationship:

[0072]

[0073] wherein R k is the turning radius at the kth point, with the unit of meter (m); V k is the instantaneous vehicle speed at the kth point, with the unit of meter per second (m / s); and r k is the instantaneous yaw rate at the kth point, with the unit of degree per second (° / s).

[0074] In an embodiment of the present application, detecting a vehicle turning event comprises:

[0075] comparing at least one of the wheel speed difference, the steering wheel angle, and / or the trajectory curvature in the driving data with a preset corresponding threshold value, determining that a turning event starts when the threshold value is exceeded, and determining that the turning event ends when the threshold value falls below.

[0076] Specifically, the wheel speed difference in the driving data refers to the speed difference between the left wheel and the right wheel of the vehicle. This difference is a physical phenomenon that inevitably occurs when the vehicle turns, and can intuitively reflect the turning trend and degree of the vehicle. Possible implementation methods include: one is to directly measure and calculate through the wheel speed sensor installed on each wheel; the other is to indirectly calculate through the vehicle dynamics model in combination with the overall speed and yaw angular velocity of the vehicle.

[0077] The steering wheel angle refers to the angle of rotation of the steering wheel relative to its center zero position (i.e. the position when the vehicle is straight driving). The steering wheel angle signal can be obtained from the electric power steering system.

[0078] The trajectory curvature refers to the degree of curvature of the vehicle's driving path, and the greater the curvature, the sharper the turn. This parameter describes the turning characteristics of the vehicle from a geometric perspective. Possible implementation methods include: one is to calculate based on the vehicle trajectory data obtained by a high-precision positioning system through mathematical methods (such as the three-point circle method or spline fitting); the other is to estimate through the Ackerman steering geometry model or more complex vehicle kinematics model in combination with the steering wheel angle and speed of the vehicle.

[0079] The preset corresponding threshold value is a critical value set for any one or more of the above turning indicators, used to distinguish between straight driving and turning of the vehicle. The setting of this threshold value is crucial for accurate identification of turning events. Possible implementation methods include: one is to determine a fixed threshold value through statistical analysis of a large amount of real driving data combined with expert experience; the other is to dynamically adjust the threshold value according to factors such as vehicle type, speed or road type, using adaptive algorithms or lookup tables to improve the robustness of detection.

[0080] When the threshold value is exceeded, it is determined that the turning event starts, and when the threshold value falls below, it is determined that the turning event ends, which defines the precise start and end boundaries of the turning event. This threshold-based judgment mechanism ensures the objectivity and consistency of the turning event detection.

[0081] By the technical solution, the application can effectively solve the problem of inaccurate or inconsistent turning event detection. By comprehensively utilizing various vehicle dynamic parameters such as wheel speed difference, steering wheel angle and / or driving trajectory curvature, and combining with a preset threshold for objective judgment, the starting and ending points of the turning event can be accurately identified, and the error caused by subjective judgment is avoided. This accurate event segmentation ensures the accuracy of the subsequent turning event trajectory segment, thereby providing high-quality input data for calculating the turning radius representation value corresponding to each turning event trajectory segment, and significantly improving the reliability and effectiveness of the construction of the automobile user turning working condition characteristic distribution database.

[0082] Next, the vehicle turning detection process of an embodiment of the application will be described with reference to Figure 5

[0083] As shown in Figure 5 , the embodiment provides a vehicle turning detection process, which comprises:

[0084] S1, data acquisition and preprocessing.

[0085] The trajectory data of the user's vehicle is continuously acquired from the T-Box. The trajectory data at least includes: GPS position information (longitude and latitude, altitude, heading angle), timestamp, vehicle speed, yaw rate, and basic signals such as steering wheel angle, wheel speed of each wheel, and EPS steering angular velocity in the Controller Area Network (CAN) data of the vehicle controller.

[0086] The original data is preliminarily cleaned and preprocessed. The cleaning is usually aimed at abnormal data points, and is used to eliminate data points obviously beyond the reasonable range (such as instantaneous speed jump, latitude and longitude coordinate drift, etc.). The preprocessing includes data filtering and smoothing, and Kalman filtering, low-pass filtering (cutoff frequency 50Hz) and other methods are used to reduce the influence of noise and measurement error.

[0087] S2, turning radius calculation.

[0088] Based on the turning radius values calculated by the above at least two methods, the final turning radius representation value is determined by comparison and correction.

[0089] Preferably, the above three turning radius calculation methods can respectively obtain the turning radius value of the vehicle trajectory point, and the difference between the three radius values is compared and integrated to finally determine an actual turning radius value after correction (a dynamic error tolerance can be considered), so that whether the vehicle is in a stable steering state can be more accurately identified, and the effective turning trajectory segment can be more accurately segmented in a high dynamic scene (such as emergency lane changing and high-speed turning), and the detection accuracy is significantly improved.

[0090] ​S3, filtering of abnormal data and invalid state.

[0091] Abnormal yaw rate filtering: judge if the yaw rate is less than 0.01. If true, it is possible that the sensor is abnormal or in a specific invalid state. The system will set the turning radius of this point to a maximum value (e.g. 10000 meters) and mark it as a non-turning state.

[0092] Parking / low speed state exclusion: judge if the vehicle speed is lower than a preset parking threshold (e.g. 2 km / h). If the vehicle is in a parking or extremely low speed creeping state, even if there is a steering wheel angle, its behavior does not belong to the typical driving turning condition. At this time, the system will also set the turning radius of this data point to a maximum value (10000 meters), so as to exclude it from the valid driving turning event.

[0093] Valid turning radius range limitation: set the physical valid range of the turning radius. The system presets an "upper limit value of 10000" and a "lower limit value of 5". The radius values exceeding this range (i.e. R > 10000 meters or R < 5 meters) are respectively regarded as approximate straight line state and untrusted or extreme state, and are processed accordingly to ensure that the analyzed turning event has practical engineering significance.

[0094] S4, curvature conversion and turning event determination

[0095] After the above filtering is completed, the inverse operation is performed to convert the turning radius R that has passed the verification and range limitation into the trajectory curvature K (K = 1 / R). This conversion makes the subsequent determination logic more concise and intuitive. The converted curvature value is compared with a preset curvature threshold (i.e. the inverse of the turning radius threshold) in real time. When the curvature value of the trajectory point continuously exceeds the threshold, it is determined that the turning event starts; when the curvature value falls below the threshold, it is determined that the turning event ends. Thus, the system can accurately segment individual and valid turning event trajectory segments from the continuous driving data.

[0096] Alternatively, the turning event can also be detected by comparing the turning radius, yaw rate, wheel speed difference, steering wheel angle with the preset corresponding threshold. When the value continuously exceeds the preset threshold, it is determined that the turning starts; when the value falls below the threshold, it is determined that the turning ends.

[0097] For example, in a car with a wheelbase of 2800 mm, in the turning event detection stage, the wheel speed difference threshold is set to 5 km / h, the direction angle threshold is set to 15°, and the curvature radius threshold is set to 2000 meters. When it is detected that the speed difference between the left and right wheels exceeds 5 km / h, the direction angle exceeds 15°, and the calculated trajectory curvature radius is less than 2000 meters, it is determined that a turning event occurs. According to the trajectory points at the start and end of the turning event, the corresponding trajectory segment is segmented.

[0098] The final output of this flow is a series of turn event trajectory segments with clear time boundaries. Each segment contains a series of trajectory points and their corresponding, accurately calculated and validity verified turn radius values.

[0099] The turn event detection and trajectory segment segmentation method based on wheel speed, direction angle, and curvature radius threshold in the above scheme first dynamically couples the wheel speed difference and yaw rate based on the Ackerman model, improving the accuracy in high dynamic scenarios.

[0100] In an embodiment of the present application, the method of statistical modeling comprises:

[0101] Classifying the turn radius representation values according to a preset classification dimension, the classification dimension including at least one of vehicle type, road type, and vehicle speed;

[0102] Fitting the classified turn radius representation values using a probability density model to obtain the probability density distribution of the turn radius.

[0103] Here, fitting the classified turn radius representation values means matching the discrete turn radius representation value data classified according to the preset classification dimension (such as vehicle type, road type, and vehicle speed) with the selected probability density model to find the model parameters that best represent the distribution of these data. Obtaining the probability density distribution of the turn radius means that after fitting, a continuous mathematical function is obtained, which can describe the probability density of the turn radius at different values under a specific classification dimension, thereby accurately representing the statistical characteristics of the turn radius.

[0104] Specifically, first, for each segmented turn event segment, the theoretically calculated turn radius is a continuous curve including the minimum value if the frequency is high enough, and in this embodiment, the minimum turn radius represents this turn event segment. Defining the turn radius representation value as the minimum turn radius value in the corresponding turn event trajectory segment ensures that when building the vehicle user turn working condition characteristic distribution database, the most severe or extreme turn conditions experienced by the vehicle during the turning process can be focused on and accurately captured. This way makes the database more truly and comprehensively reflect the potential needs and challenges of users to the vehicle's steering performance in actual driving, especially in critical scenarios such as emergency avoidance or passing through narrow curves.

[0105] Then the turning radius data of the target user in all road scenes is collected, the representative turning radius (associated with parameters such as vehicle speed and EPS steering angular velocity as needed) of each turning event segment is statistically distributed and modeled for visualization, and the turning radius data in each type of road scene can be statistically subdivided according to vehicle type, road type (highway, urban, rural), vehicle speed, etc. For example, 10,000 turning radius data of turning events are collected in an urban road scene. A Gaussian mixture model is used to fit these data to obtain the probability density function of the turning radius.

[0106] According to the technical solution described above, when performing statistical modeling, instead of treating all turning radius characteristic values as a whole, the turning radius characteristic values are classified in detail according to at least one of the preset classification dimensions, such as vehicle type, road type, and vehicle speed. This classification processing enables the statistical modeling to generate independent turning radius distribution rules for different driving scenarios and vehicle characteristics, thereby avoiding distortion of the statistical results due to data generalization. Fitting the classified turning radius characteristic values using a probability density model can convert the discrete, classified turning radius data into continuous, statistically meaningful probability density distribution. This solves the problem in traditional methods that data is only simply classified and cannot accurately quantify the distribution rule, so that the database can more accurately and finely reflect the real turning working condition characteristics of automobile users in different classification dimensions (such as vehicle type, road type, and vehicle speed), thereby optimizing the parameter settings and adjusting the distribution proportion of the steering angle and steering angular velocity in the development and verification test scheme of the whole vehicle durability test / steering system bench test, increasing the test weight of high-frequency turning working conditions, reducing the test working condition proportion of low-frequency turning working conditions, and realizing user working condition-oriented whole vehicle / component bench test development.

[0107] In an embodiment of the present application, generating a database representing the turning radius distribution rule includes: based on the statistical modeling result, generating a visual turning radius probability density curve and / or a cumulative distribution curve.

[0108] Specifically, the MATLAB software can be used to plot the probability density curve and / or the cumulative distribution curve of the turning radius in each type of road scene to form a visual turning working condition characteristic distribution database to describe the distribution rule. The database constructed by this embodiment can clearly show the distribution of the user turning radius in different road scenes, providing specific data reference for performance debugging of the automobile steering system in different scenarios, such as in the urban arterial road scenario, the steering assist characteristic of the steering system can be optimized according to the turning radius distribution to improve the steering feel of the user in this scenario.

[0109] The embodiment of the present application further provides an electronic device, which comprises a memory and a processor, and the memory stores a computer program which is run by the processor, and the computer program, when being run by the processor, causes the device provided with the processor to execute the method for constructing the database of the turning working condition characteristics of the vehicle user according to any one of the above-mentioned embodiments.

[0110] Next, the electronic device 100 for implementing the method for constructing the database of the turning working condition characteristics of the vehicle user according to the embodiment of the present application is described below with reference to Figure 6

[0111] As shown in Figure 6 , the electronic device 100 comprises a processor 110, a memory 120 and a communication interface 130. The processor 110, the memory 120 and the communication interface 130 can be interconnected through a communication bus 140 and / or other forms of connection mechanism (not shown) for communication.

[0112] It should be noted that Figure 6 the components and structures of the electronic device 100 shown are only exemplary and are not restrictive, and the electronic device can also have other components and structures according to needs.

[0113] Optionally, the communication interface 130 can further comprise a transmitter and / or a receiver.

[0114] The processor 110 can be a microcontroller unit (MCU), a central processing unit (CPU), a digital signal processor (DSP), a single-chip microcomputer and an embedded device, or other forms of processing units with data processing capability and / or instruction execution capability.

[0115] The memory 120 can be various forms of computer readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory can include, for example, random access memory (RAM), cache, synchronous dynamic random access memory (SDRAM) and the like. The non-volatile memory can include, for example, read-only memory (ROM), hard disk, flash memory (Flash) and the like. One or more computer program instructions can also be stored on the computer readable storage medium, and the memory 120 can run the program instructions to implement the method for constructing the database of the turning working condition characteristics of the vehicle user according to the above-mentioned embodiment of the present application.

[0116] ​The embodiment of the present application further provides a storage medium, and the storage medium stores a computer program.

[0117] Among them, the electronic device and the storage medium provided by the embodiment of the present application are used to execute the corresponding method provided above, so the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method provided above, which will not be described here.

[0118] Finally, it should be noted that the above technical solutions are only one embodiment of the present application. For those skilled in the art, on the basis of the application of the method and principle disclosed in the present application, various types of improvements or modifications can be easily made, and are not limited to the method described in the above embodiment of the present application. Therefore, the above-described method is only preferred, and does not have a limiting meaning.

Claims

1. A method for constructing a database of automotive user turning condition characteristics, characterized in that: The construction method includes: Collect driving data of the target vehicle on real roads; Based on the driving data, vehicle turning events are detected, and the corresponding turning event trajectory segments are segmented. Calculate the turning radius representation value corresponding to each of the turning event trajectory segments; Based on the turning radius representation values ​​of multiple vehicles under various road scenarios, statistical modeling is performed to generate a database representing the distribution pattern of turning radii.

2. The method for constructing a database of automotive user turning condition characteristics according to claim 1, characterized in that: The driving data includes at least one or more of the following: vehicle location information, timestamp, vehicle speed, yaw rate, steering wheel angle, and wheel speed of each wheel.

3. The method for constructing a database of automotive user turning condition characteristics according to claim 1 or 2, characterized in that: Before detecting vehicle turning events based on the driving data, the construction method further includes: The collected raw driving data is preprocessed, including anomaly cleaning and data filtering and smoothing.

4. The method for constructing a database of automotive user turning condition characteristics according to claim 1, characterized in that: Detection of vehicle turning events includes: Based on at least one of the yaw rate, wheel speed difference, steering wheel angle, and driving trajectory curvature in the driving data, a corresponding preset threshold is compared. When the value exceeds the threshold, a turning event is determined to begin. When the value falls back below the threshold, a turning event is determined to end.

5. The method for constructing a database of automotive user turning condition characteristics according to claim 1, characterized in that: Based on the Ackermann steering model, the turning radius characterization value is calculated using at least one of the following methods: Calculate the turning radius based on the wheel speed difference between the left and right wheels; Calculate the turning radius based on the steering wheel angle; The turning radius is calculated based on the yaw rate and vehicle speed.

6. The method for constructing a database of automotive user turning condition characteristics according to claim 5, characterized in that: The final turning radius characterization value is determined by comparison and correction based on the turning radius values ​​calculated using at least two methods.

7. The method for constructing a database of automotive user turning condition characteristics according to claim 1, characterized in that: The turning radius representation value is the minimum turning radius value in the corresponding turning event trajectory segment.

8. The method for constructing a database of automotive user turning condition characteristics according to claim 7, characterized in that: Methods for statistical modeling include: The turning radius characterization value is classified according to a preset classification dimension, which includes at least one of vehicle type, road type, and vehicle speed. The probability density model is used to fit the classified turning radius characterization values ​​to obtain the probability density distribution of the turning radius.

9. The method for constructing a database of automotive user turning condition characteristics according to claim 1, characterized in that: Generating a database characterizing the distribution patterns of turning radii includes: generating visualized turning radius probability density curves and / or cumulative distribution curves based on the statistical modeling results.

10. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program executed by the processor, the computer program, when executed by the processor, causing the device equipped with the processor to perform the method for constructing a database of automotive user turning condition characteristics as described in any one of claims 1-9.

11. A storage medium, characterized in that, The storage medium stores a computer program that runs on a computer. When the computer program runs, it causes the computer to execute the method for constructing a database of automotive user turning condition characteristics as described in any one of claims 1-9.

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