Vehicle data processing method and device and vehicle

Abstract

The invention provides a vehicle data processing method and device and a vehicle, and the method comprises the steps: obtaining driving data generated when different drivers drive the vehicle, and obtaining a plurality of pieces of driving data; for each piece of driving data, identifying a target driving style of a driver corresponding to the driving data based on the driving data; associating the target driving style with the driving data to obtain associated data about the driving style and the driving data; and generating test case data for the vehicle test based on the associated data. Diversification of the test cases is realized, the diversified test cases comprise various types of driving styles, and when the diversified test cases are used for vehicle testing, the performance of the vehicle under various driving styles can be comprehensively evaluated, so that the effectiveness of the performance test result is improved.

Description

Technical Field

[0001] The present application relates to the field of vehicles, and more specifically, to a vehicle data processing method, apparatus, and vehicle in the field of vehicles. Background Art

[0002] During the vehicle R & D or iteration process, it is usually necessary to conduct a performance test on the whole vehicle; for example, when new components are added to the vehicle, intelligent functions are extended, or the power system, energy management system, etc. of the vehicle are upgraded, it is usually necessary to re - test the performance of the whole vehicle to ensure the compatibility and operation stability of the newly added or upgraded modules with the whole vehicle system.

[0003] Currently, the performance of the whole vehicle can be tested through hardware - in - the - loop testing, but the test case data is mostly constructed based on the subjective driving experience of engineers or test requirements, and the obtained test case data is relatively single. A single test case data usually only reflects the driving habits of one driver and cannot cover the diverse driving habits of all drivers. The driving habits of different drivers vary greatly in the actual driving environment, and it is difficult for a single test case data to comprehensively cover these diverse driving habits, resulting in a low effectiveness of the test results. Summary of the Invention

[0004] The present application provides a vehicle data processing method, apparatus, and vehicle. The present application realizes the diversification of test case data, and various types of driving styles are included in the diversified test case data. When the diversified test case data is used for vehicle testing, the performance of the vehicle under various driving styles can be comprehensively evaluated, thereby improving the effectiveness of the performance test results; and the test case data is generated based on the driving data generated by the driver during actual vehicle driving, and the generated test case data is more in line with the real driving conditions, making the performance test results closer to the real performance of the vehicle in the actual driving environment.

[0005] In a first aspect, a vehicle data processing method is provided, and the method includes: obtaining driving data generated by different drivers driving a vehicle to obtain a plurality of driving data; for each driving data, identifying the target driving style of the driver corresponding to the driving data based on the driving data; associating the target driving style with the driving data to obtain association data regarding the driving style and the driving data; and generating test case data for vehicle testing based on the association data.

[0006] In the embodiments of the present application, by obtaining driving data generated by different drivers driving a vehicle, a plurality of driving data are obtained. For each driving data, based on the driving data, the target driving style of the driver corresponding to the driving data is identified, and the target driving style is associated with the driving data to obtain association data regarding the driving style and the driving data. Based on the association data, a technical solution for generating test case data for vehicle testing is implemented, which realizes the diversification of the test case data. The diversified test case data includes various types of driving styles. When the diversified test case data is used for vehicle testing, the performance of the vehicle under various driving styles can be comprehensively evaluated, thereby improving the effectiveness of the performance test results. Moreover, based on the driving data generated by the driver during actual vehicle driving, the test case data is generated, and the generated test case data is more in line with the actual driving conditions, making the performance test results closer to the true performance of the vehicle in the actual driving environment.

[0007] In combination with the first aspect, in some possible implementation manners, the identifying the target driving style of the driver corresponding to the driving data based on the driving data includes: scoring the driving behavior of the driver corresponding to the driving data based on the driving data to obtain a driving behavior score; wherein, the driving behavior score is used to represent the safety degree of the driving behavior; and determining the target driving style according to the driving behavior score.

[0008] In the embodiments of the present application, by scoring the driving behavior of the driver corresponding to the driving data to obtain a driving behavior score, the safety degree of each driving behavior is respectively determined, and the driving behavior score of each driving behavior is determined, so that the driving behavior of the driver can be comprehensively and systematically evaluated.

[0009] In combination with the first aspect, in some possible implementation manners, the driving data includes at least one of acceleration data, deceleration data, and turning data;

[0010] The scoring the driving behavior of the driver corresponding to the driving data based on the driving data to obtain a driving behavior score includes: determining an initial score and a preset weight corresponding to at least one of the acceleration data, the deceleration data, and the turning data; multiplying the initial score corresponding to at least one of the acceleration data, the deceleration data, and the turning data by the preset weight to obtain at least one product; and determining the sum value of the at least one product as the driving behavior score.

[0011] In the embodiments of the present application, by determining the initial scores and preset weights corresponding to at least one of the acceleration data, deceleration data, and turning data, multiplying the initial scores corresponding to at least one of the acceleration data, deceleration data, and turning data by the preset weights to obtain at least one product, and determining the sum value of the at least one product as the driving behavior score, the relative importance of the acceleration data, deceleration data, and turning data in the driving data can be determined, making the driving behavior score more accurate and more reasonable.

[0012] In combination with the first aspect, in some possible implementation manners, the determining the target driving style according to the driving behavior score includes: correcting the driving behavior score based on at least one of the accident frequency, driving environment, and number of violations to obtain a corrected score; and obtaining the preset driving style corresponding to the corrected score to obtain the target driving style.

[0013] In the embodiments of the present application, by correcting the driving behavior score based on at least one of the accident frequency, driving environment, and number of violations to obtain a corrected score, and obtaining the preset driving style corresponding to the corrected score to obtain the target driving style, the score is dynamically corrected, avoiding the problem of score deviation in driving behavior evaluation, and making the target driving style corresponding to the driving behavior score more consistent with the actual driving style of the driver.

[0014] In combination with the first aspect, in some possible implementation manners, the correcting the driving behavior score to obtain a corrected score includes: determining a correction coefficient corresponding to the driving behavior score according to at least one of the accident frequency, driving environment, and number of violations; and multiplying the driving behavior score by the correction coefficient to obtain the corrected score.

[0015] In combination with the first aspect, in some possible implementation manners, the generating test case data for vehicle testing based on the associated data includes: obtaining the driving scenario corresponding to the driving data; and associating the driving scenario with the associated data to obtain the test case data.

[0016] In the embodiments of the present application, by obtaining the driving scenario corresponding to the driving data, and associating the driving scenario with the associated data to obtain the test case data, the driving scenario of each test case data can be determined, realizing that each test case data includes the driving scenario. When the vehicle is tested with the test case data including the driving scenario, the test results in different scenarios can be obtained, making the test results more accurate.

[0017] In combination with the first aspect, in some possible implementation manners, the method further includes: in response to a vehicle test request, obtaining an item to be tested; outputting a driving style selection interface for a driver based on the item to be tested; in response to a selection operation on the driving style selection interface, obtaining a test driving style; obtaining driving data corresponding to the test driving style from the generated test case data to obtain target driving data; and testing the item to be tested according to the target driving data.

[0018] In the embodiments of the present application, by responding to a vehicle test request, obtaining an item to be tested; outputting a driving style selection interface for a driver based on the item to be tested; in response to a selection operation on the driving style selection interface, obtaining a test driving style; obtaining driving data corresponding to the test driving style from the generated test case data to obtain target driving data; and testing the item to be tested according to the target driving data, it is realized that when performing vehicle performance testing, there can be multiple test case data for users to choose from. Users can select test case data by themselves, such as selecting all test case data to make the test results more comprehensive, or selecting test case data with a stronger correlation with the item to be tested for testing to make the reliability of the test results higher, thereby improving the effectiveness of the test results.

[0019] In combination with the first aspect, in some possible implementation manners, the associating the target driving style with the driving data to obtain association data about the driving style and the driving data includes:

[0020] Taking the target driving mode as a key and the driving data as a value, establishing a mapping relationship between the key and the value to obtain a key-value pair;

[0021] Taking the key-value pair as the association data.

[0022] In a second aspect, a vehicle data processing device is provided. The device includes:

[0023] An obtaining module, configured to obtain driving data generated by different drivers driving a vehicle to obtain a plurality of pieces of driving data;

[0024] An identifying module, configured to, for each piece of driving data, identify the target driving style of the driver corresponding to the driving data based on the driving data;

[0025] An associating module, configured to associate the target driving style with the driving data to obtain association data about the driving style and the driving data;

[0026] A generating module, configured to generate test case data for vehicle testing based on the association data.

[0027] In a third aspect, a vehicle is provided, including a memory for storing executable program code; and a processor for calling and running the executable program code from the memory, so that the vehicle executes the method in the first aspect or any possible implementation manner of the first aspect as described above.

[0028] In a fourth aspect, a computer program product is provided, which includes: computer program code that, when running on a computer, causes the computer to execute the method in the first aspect or any possible implementation manner of the first aspect as described above.

[0029] In a fifth aspect, a computer-readable storage medium is provided, which stores computer program code that, when running on a computer, causes the computer to execute the method in the first aspect or any possible implementation manner of the first aspect as described above. Description of the Drawings

[0030] Figure 1 Fig. shows the system architecture diagram of a vehicle data processing system provided by an embodiment of the present application;

[0031] Figure 2 Fig. shows the schematic flowchart of a vehicle data processing method provided by an embodiment of the present application;

[0032] Figure 3 Fig. shows the schematic diagram of an interface for vehicle testing provided by an embodiment of the present application;

[0033] Figure 4 Fig. shows the schematic structural diagram of a vehicle data processing device provided by an embodiment of the present application;

[0034] Figure 5 Fig. shows the schematic structural diagram of a vehicle provided by an embodiment of the present application. Detailed Embodiments

[0035] Next, the technical solutions in the present application will be clearly and elaborately described in conjunction with the drawings. Among them, in the description of the embodiments of the present application, unless otherwise specified, " / " means "or". For example, A / B may mean A or B. The "and / or" in the text is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B may mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of the present application, "a plurality" means two or more than two.

[0036] Hereinafter, the terms "first" and "second" are for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features.

[0037] During the vehicle R & D or iteration process, it is usually necessary to conduct vehicle performance tests. When new components are added to the vehicle, intelligent functions are extended, or the vehicle's power system, energy management system, etc. are upgraded, it is usually necessary to retest the vehicle performance to ensure the compatibility and operational stability of the newly added or upgraded modules with the vehicle system.

[0038] Currently, the vehicle performance can be tested through hardware-in-the-loop testing. However, the test case data is mostly constructed based on engineers' subjective driving experience or test requirements, resulting in relatively single test case data. A single test case data usually only reflects the driving habits of one driver and cannot cover the diverse driving habits of all drivers. There are significant differences in the driving habits of different drivers in the actual driving environment, and it is difficult for single test case data to comprehensively cover these diverse driving habits, leading to a relatively low effectiveness of the test results.

[0039] Based on this, the embodiments of the present application provide a vehicle data processing method, device, and vehicle. When generating test case data for vehicle performance, different driving styles are determined based on the driving data generated by different drivers driving the vehicle, and each driving style is associated with the driving data corresponding to that driving style to obtain multiple associated data. Each associated data is used to generate a test case data, realizing the diversification of test case data. The diversified test case data includes various types of driving styles. Using the test case data including various driving styles to test the vehicle performance can comprehensively evaluate the vehicle's performance under various driving styles, thereby improving the effectiveness of the performance test results. Moreover, the test case data is generated based on the driving data generated by the driver during actual vehicle driving, and each generated test case data is more in line with the real driving conditions, making the performance test results closer to the real performance of the vehicle in the actual driving environment.

[0040] As Figure 1 shown, Figure 1 FIG. shows the system architecture diagram of a vehicle data processing system provided by the embodiments of the present application. The vehicle data processing system includes an on-vehicle diagnostic device 110, a terminal device 120, and a communication link 130.

[0041] On-board diagnostic device 110, namely On-Board Diagnostics, abbreviated as OBD, is a diagnostic system of the vehicle itself. This system can monitor various operating parameters of the vehicle in real time, including engine speed, fuel consumption, temperature, fault codes, etc. Each electronic control unit (ECU) on the vehicle can provide relevant operating data through the OBD interface. These data can be read by external diagnostic tools for fault detection, repair, or vehicle performance optimization. In the evaluation of driving behavior, the OBD device can be used to collect data on driver behavior and vehicle status, such as vehicle speed, acceleration, engine load, engine speed, etc., for evaluating the safety of driving behavior.

[0042] The terminal device 120 is responsible for receiving driving data from the OBD, executing a vehicle data processing program, and generating test case data for vehicle testing. The terminal device 120 can be set on the vehicle or independently of the vehicle, including but not limited to: personal computers, tablets, handheld devices, in-vehicle devices, wearable devices, computing devices, or other processing devices connected to a wireless modem, etc.

[0043] The communication link 130 is used to provide data transmission between the on-board diagnostic device 110 and the terminal device 120. The communication link 130 can include various types of wired communication links or wireless communication links, such as: the wired communication link includes the Universal Serial Bus (USB), and the wireless communication link includes a Bluetooth communication link, a Wireless-Fidelity (Wi-Fi) communication link, or a microwave communication link, etc.

[0044] Next, in combination with Figure 1 the system architecture shown, the vehicle data processing method provided by the embodiments of the present application will be introduced. The vehicle data processing method provided by the embodiments of the present application is applied to the above terminal device 120, as Figure 2 shown, Figure 2 shows a schematic flowchart of a vehicle data processing method provided by the embodiments of the present application. The vehicle data processing method includes the following steps:

[0045] S201, obtain driving data generated by different drivers driving the vehicle to obtain a plurality of driving data;

[0046] In an exemplary embodiment, driving data generated by a driver driving a vehicle can be obtained from a vehicle networking or on-vehicle diagnostic device. The driving data generated by driving a vehicle can be the driving data of the driver driving the vehicle for a certain journey. The scenario of the journey can be any scenario, such as urban roads, highways, mountain curves, etc. The driving data includes at least one of acceleration data (such as acceleration, throttle pedal opening, etc.), deceleration data (such as deceleration, brake pedal travel, etc.), turning data (such as steering wheel angle, steering wheel turning speed, steering wheel torque, etc.), and driver face data (such as face images, face videos).

[0047] To improve the quality of driving data and ensure the reliability of subsequent analysis and feature extraction, the original driving data can be preprocessed; for example, duplicate data in the driving data can be removed; if there are missing values in the driving data, the driving data can be deleted; outliers in the driving data can be detected and corrected, etc.

[0048] To increase the diversity of driving data, driving prediction data can also be predicted based on the driving data generated by a driver driving a vehicle. When obtaining the driving prediction data, the driving data obtained from the vehicle networking or on-vehicle diagnostic device can be input into a trained driving data prediction model. The driving data prediction model predicts the driving data based on the input driving data and outputs the driving prediction data.

[0049] S202. For each piece of driving data, identify the target driving style of the driver corresponding to the driving data based on the driving data;

[0050] The driving style can include N (N is a positive integer) types, such as the first driving style, the second driving style,..., the Nth driving style. Among them, the safety levels of the driving behaviors corresponding to the first driving style to the Nth driving style decrease in turn. The driving style can be classified through a multi-dimensional classification system. For example, it can be gradient-divided through a safety factor threshold to form a continuous distribution of a steady type, a general type, and an aggressive type. The safety levels of the driving behaviors corresponding to the steady type, the general type, and the aggressive type decrease in turn. Among them, the safety factor threshold can be determined based on an operation behavior feature parameter group, including but not limited to the acceleration variance value, the lane-changing frequency index, the following distance coefficient, and the braking response dispersion, etc.

[0051] For each piece of driving data, identifying the driving style of the driver corresponding to the driving data can be to extract the driving behavior of the driver from the driving data, map the driving behavior included in each piece of driving data to the driving style, and then determine the primary driving style of the driver corresponding to the driving data; wherein, the driving behavior is used to represent the actions taken by the driver during the process of driving a vehicle, such as overtaking behavior, rapid acceleration behavior, rapid deceleration behavior, sharp turn behavior, etc.; extracting the driving behavior of the driver from the driving data can be to count the occurrence times of each specific driving event (such as acceleration, braking, or turning, etc.) of the vehicle during the entire journey, and compare the occurrence times with the threshold number of times corresponding to each specific driving event. For example, if the occurrence times of a specific driving event exceed the set threshold number of times, then this specific driving event represents the driving behavior of the driver during driving. Exemplarily, if the occurrence times of acceleration exceed the threshold number of times of the rapid acceleration behavior set, it is determined that the driving behavior of the driver is the rapid acceleration behavior.

[0052] When mapping the driving behavior included in each piece of driving data to different driving styles, a rule base can be pre-constructed that includes various driving styles, various driving behaviors corresponding to each driving style, and preset frequency ranges for various driving behaviors. After extracting the driving behavior corresponding to each piece of driving data, based on the driving behavior corresponding to each piece of driving data and the rule base, the primary driving style of the driver corresponding to the driving data is determined. Exemplarily, if the driving behavior extracted from a piece of driving data includes rapid acceleration behavior and rapid braking behavior, compare the occurrence times of acceleration with the preset frequency range of acceleration occurrence in each driving style that includes rapid acceleration behavior, and compare the occurrence times of braking with the preset frequency range of braking occurrence in each driving style that includes rapid braking behavior. If the occurrence times of acceleration are within the preset frequency range of acceleration occurrence in a certain driving style and the occurrence times of braking are within the preset frequency range of braking occurrence in a certain driving style, then this driving style is the primary driving style of the driver; if the occurrence times of acceleration and braking extracted from the driving data are both of low frequency, then the primary driving style of the driver is robustness.

[0053] After determining the primary driving style of the driver corresponding to each driving data, the driving state of the driver can be extracted from the driving data, and the initial driving style of the driver can be corrected through the driving state of the driver, so as to determine the final driving style of the driver corresponding to the driving data, that is, the target driving style is obtained; wherein, the driving state is used to represent the degree of concentration of the driver's attention during the process of driving a vehicle. The higher the degree of concentration, the more concentrated the driver's driving attention, and vice versa. Extracting the driving state of the driver from the driving data can be determined by comparing the proportion of the duration of a specific state (such as the distracted state, etc.) in the total duration of the entire journey with the preset time proportion interval threshold. If the proportion of the duration of the specific state in the total duration of the entire journey is within the first preset time proportion interval threshold, it is considered that the first state is the driving state of the driver; if the proportion of the duration of the specific state in the total duration of the entire journey is within the second preset time proportion interval threshold, it is considered that the second state is the driving state of the driver; if the proportion of the duration of the specific state in the total duration of the entire journey is within the third preset time proportion interval threshold, it is considered that the third state is the driving state of the driver; wherein, the degree of attention concentration of the driver corresponding to the first preset time proportion interval threshold, the second preset time proportion interval threshold, and the third preset time proportion interval threshold decreases in turn; the first state, the second state, and the third state are states in which the degree of attention concentration of the driver decreases in turn. For example, if the proportion of the sum of the time when the driver uses the mobile phone, adjusts the in-vehicle equipment, and the line of sight deviates from the front during the process of driving a vehicle in the total duration of the entire journey is within the third preset time proportion interval threshold, the driving state of the driver is the distracted driving state.

[0054] When correcting the initial driving style of the driver through the driving state of the driver, if the driving state characterizes that the driver's attention is highly concentrated during the process of driving a vehicle, the initial driving style of the driver can be upgraded, and the driving style with a higher level of driving behavior safety than the initial driving style is determined as the final driving style of the driver; if the driving state characterizes that the driver's attention is moderately concentrated during the process of driving a vehicle, the initial driving style of the driver remains unchanged, and the initial driving style of the driver is determined as the final driving style of the driver; if the driving state characterizes that the driver's attention is mildly distracted during the process of driving a vehicle, the initial driving style of the driver can be downgraded, and the driving style with a lower level of driving behavior safety than the initial driving style is determined as the final driving style of the driver.

[0055] S203. Associate the target driving style with the driving data to obtain the associated data about the driving style and the driving data;

[0056] After determining the driving style from the driving data, the association relationship between each driving style and all the driving data corresponding to that driving style can be constructed to obtain associated data, where the associated data includes one driving style and one piece of driving data.

[0057] S204. Generate test case data for vehicle testing based on the associated data.

[0058] For the first generation method of test case data, after obtaining multiple pieces of associated data, generate test case data through the multiple pieces of associated data. For the second generation method of test case data, after obtaining multiple pieces of associated data, if there are pieces of associated data with the same driving style among the multiple pieces of associated data, then merge the pieces of associated data with the same driving style into a new piece of associated data. The new piece of associated data includes one driving style and multiple pieces of driving data. After such processing, each finally obtained new piece of associated data includes one driving style and at least one piece of driving data, and then generate test case data through all the new pieces of associated data. Among them, the number of pieces of associated data in the test case data generated by the first generation method is greater than the number of pieces of associated data in the test case data generated by the second generation method.

[0059] After generating the test case data, the required test case data can be selected according to the actual test requirements of the vehicle for vehicle testing. Among them, the test case data can be used not only for real vehicle testing but also for simulated vehicle testing.

[0060] To facilitate the application of the test case data on the test platform, the format of the test case data can be converted into a data format recognizable by the test platform, and the test case data after format conversion is uploaded to the test platform for subsequent testing.

[0061] Through the technical solution of this application, which obtains multiple pieces of driving data by acquiring the driving data generated by different drivers driving a vehicle, for each piece of driving data, identifies the target driving style of the driver corresponding to the driving data based on the driving data, associates the target driving style with the driving data to obtain associated data regarding the driving style and the driving data, and generates test case data for vehicle testing based on the associated data, the diversification of the test case data is realized. The diversified test case data includes various types of driving styles. By using the test case data including various driving styles to conduct performance testing on the vehicle, the performance of the vehicle under various driving styles can be comprehensively evaluated, thereby improving the effectiveness of the performance test results. Moreover, the test case data is generated based on the driving data generated by the driver during actual driving of the vehicle, and each generated test case data is more in line with the real driving conditions, making the performance test results closer to the real performance of the vehicle in the actual driving environment.

[0062] In a possible implementation, identifying the target driving style of the driver corresponding to the driving data is based on the following steps:

[0063] Score the driving behavior of the driver corresponding to the driving data based on the driving data to obtain a driving behavior score; wherein, the driving behavior score is used to represent the safety level of the driving behavior;

[0064] Determine the target driving style according to the driving behavior score.

[0065] After extracting the driving behaviors from each driving data, multiple driving behaviors are obtained, and then each driving behavior is scored separately to obtain the score corresponding to each driving behavior. The sum of the scores corresponding to all driving behaviors is obtained. By matching the sum of the driving scores with the driving styles, the driving style of the driver corresponding to each driving data is obtained; wherein, when scoring each driving behavior, the score can be based on the safety level of each driving behavior of the driver during the process of driving the vehicle. If the safety level of each driving behavior of the driver during the process of driving the vehicle is high, the primary score corresponding to the driving behavior is high; if the safety level of each driving behavior of the driver during the process of driving the vehicle is low, the primary score corresponding to the driving behavior is low.

[0066] When matching the sum of the driving scores with the driving styles, score intervals of various driving styles can be pre-constructed. If the sum of the driving scores is within the score interval of a certain driving style, then this driving style is the driving style of the driver corresponding to the driving data.

[0067] When scoring the driving behavior of the driver, obtain all the driving behaviors that appear in each driving data, use all the driving behaviors as the scoring items of the driving data, evenly distribute the full score corresponding to the score to each scoring item as the full score of the scoring item. When scoring the driving behavior of the driver, the score can be based on the subjective judgment of experts or decision-makers. Or, the driving behavior can be input into a driving behavior scoring model trained based on the driving behavior and the score corresponding to the driving behavior, and the driving behavior scoring model outputs the score corresponding to the driving behavior; the higher the score, the higher the safety level of the driving behavior.

[0068] By scoring the driving behavior of the driver corresponding to the driving data to obtain a driving behavior score, the safety level of each driving behavior is determined separately, and the driving behavior score of each driving behavior is determined, which can comprehensively and systematically evaluate the driving behavior of the driver.

[0069] In a possible implementation, the driving data includes at least one of acceleration data, deceleration data, and turning data;

[0070] Identifying the driving behavior of the driver corresponding to the driving data based on the driving data and scoring it to obtain a driving behavior score, including the following steps:

[0071] Determine the initial score and preset weight corresponding to at least one of the acceleration data, deceleration data, and turning data;

[0072] Multiply the initial score corresponding to at least one of the acceleration data, deceleration data, and turning data by the preset weight to obtain at least one product;

[0073] Determine the sum value of the at least one product as the driving behavior score.

[0074] Determine whether the driving behavior of the driver during driving includes a sudden acceleration behavior through the acceleration data; determine whether the driving behavior of the driver during driving includes a sudden braking behavior through the deceleration data; determine whether the driving behavior of the driver during driving includes a sharp turning behavior through the turning data.

[0075] When determining the initial score and preset weight corresponding to at least one of the acceleration data, deceleration data, and turning data, at least one of the acceleration data, deceleration data, and turning data included in the driving data can be used as a safety dimension, and the subjective weighting method can be used to determine the initial score and preset weight corresponding to each safety dimension. Among them, the subjective weighting method refers to determining the initial score and weight of each factor according to expert experience or the subjective judgment of the decision maker. For example, if experts or decision makers believe that there are significant safety hazards in the driving behavior of the driver, then they may assign a lower initial score to the driving behavior of the driver; if experts or decision makers believe that the driving behavior of the driver has a greater impact on the safety of the vehicle, then they may assign a higher weight to the driving behavior of the driver; on the contrary, if experts or decision makers believe that there are minor safety hazards in the driving behavior of the driver, then they may assign a higher initial score to the driving behavior of the driver; if the decision maker believes that the driving behavior of the driver has a smaller impact on the safety of the vehicle, then they may assign a lower weight to the driving behavior of the driver controlled by the driver; in this embodiment, by allowing experts or decision makers to assign initial scores and preset weights to each safety dimension, the professional knowledge and experience of experts or decision makers can be fully utilized.

[0076] Among them, the higher the driving behavior score, the higher the safety of the driving behavior of the driver corresponding to the driving data, and the lower the driving behavior score, the lower the safety of the driving behavior of the driver corresponding to the driving data.

[0077] By determining the initial scores and preset weights corresponding to at least one of the acceleration data, deceleration data, and turning data, multiplying the initial scores corresponding to at least one of the acceleration data, deceleration data, and turning data by the preset weights to obtain at least one product, and determining the sum value of the at least one product as the driving behavior score, the relative importance of the acceleration data, deceleration data, and turning data in the driving data can be determined, making the driving behavior score more accurate and more reasonable.

[0078] In a possible implementation, determining the target driving style according to the driving behavior score includes the following steps:

[0079] Based on at least one of the accident frequency, driving environment, and number of violations, correcting the driving behavior score to obtain a corrected score;

[0080] Obtaining the preset driving style corresponding to the corrected score to obtain the target driving style.

[0081] The score of the driving behavior can be corrected by at least one of the accident frequency, driving environment, and number of violations; if the vehicle driven by the driver has a high accident frequency, or the driving environment is not conducive to the driver's driving (such as the light in the driving environment is dim or the road is rough), or the driver has a large number of violations when driving the vehicle, the driving behavior score corresponding to the driving data can be corrected to a lower value; on the contrary, if the vehicle driven by the driver has a low accident frequency, or the driving environment is conducive to the driver's driving (such as the light in the driving environment is bright or the road is flat), or the driver has a small number of violations when driving the vehicle, the driving behavior score corresponding to the driving data can be corrected to a higher value or remain unchanged.

[0082] Among them, when determining the number of violations of the driver when driving the vehicle, the facial data of the driver when driving the vehicle can be obtained, such as obtaining the facial image or facial video of the driver when driving the vehicle, and extracting the violation actions of the driver when driving the vehicle from the facial image or facial video, such as extracting the closing frequency of the eyes, yawning, the time of looking away from the front, using a mobile phone, smoking and other violation actions of the driver when driving the vehicle from the facial image or facial video.

[0083] Based on at least one of the accident frequency, driving environment, and number of violations, correcting the driving behavior score to obtain a corrected score; obtaining the preset driving style corresponding to the corrected score to obtain the target driving style, realizing dynamic correction of the score, avoiding the problem of score deviation in driving behavior evaluation, and making the driving style corresponding to the driving behavior score more consistent with the actual driving style of the driver.

[0084] In a possible implementation, correcting the driving behavior score to obtain a corrected score includes the following steps:

[0085] Determine a correction coefficient corresponding to the driving behavior score based on at least one of the accident frequency, driving environment, and number of violations;

[0086] Multiply the driving behavior score by the correction coefficient to obtain a corrected score.

[0087] The driving behavior score is corrected by at least one of the accident frequency, driving environment, and number of violations. It can be to determine a correction coefficient corresponding to the driving behavior score based on at least one of the accident frequency, driving environment, and number of violations, and multiply the driving behavior score by the correction coefficient to obtain a corrected score. When determining the correction coefficient corresponding to the driving behavior score based on at least one of the accident frequency, driving environment, and number of violations, a mapping relationship between at least one of the accident frequency, driving environment, and number of violations and the correction coefficient can be pre - constructed. After obtaining at least one of the accident frequency, driving environment, and number of violations, the correction coefficient is determined based on the obtained at least one of the accident frequency, driving environment, and number of violations and the mapping relationship. The larger the correction coefficient, the higher the safety level of the driving behavior.

[0088] In a possible implementation, generating test case data for vehicle testing based on associated data includes the following steps:

[0089] Obtain the driving scenario corresponding to the driving data;

[0090] Associate the driving scenario with the associated data to obtain test case data.

[0091] When obtaining the driving data, the driving scenario corresponding to the driving data can be obtained simultaneously. The driving scenario is the specific driving situation during the vehicle's driving process, including but not limited to urban roads, highways, mountain roads, desert roads, etc. Associating the driving scenario, driving data, and driving style to obtain associated data, and generating test case data through the associated data, the driving scenario of each test case data can be determined. When testing the vehicle, the test results corresponding to different scenarios can be determined.

[0092] By obtaining the driving scenario corresponding to the driving data; associating the driving scenario with the associated data to obtain test case data, the driving scenario of each test case can be determined, realizing that each test case data contains the driving scenario. When using the test case data containing the driving scenario to test the vehicle, the test results in different scenarios can be obtained, making the test results more accurate.

[0093] In a possible implementation, the method further includes the following steps:

[0094] In response to a vehicle test request, obtain the item to be tested;

[0095] Output a driving style selection interface for the driver based on the item to be tested;

[0096] In response to a selection operation on the driving style selection interface, obtain the test driving style;

[0097] Obtain the driving data corresponding to the test driving style from the generated test case data to obtain the target driving data;

[0098] Test the item to be tested according to the target driving data.

[0099] As Figure 3 shown, Figure 3 As shown, a schematic diagram of an interface for vehicle testing provided by an embodiment of the present application is shown. As Figure 3 (a) shown, on the graphical user interface 301 of the test platform, a test item selection interface 302 is displayed. The user can select the test item in the test item selection interface 302. After the detection platform detects the selected test item, the selected test item is determined as the item to be tested. For example, if the user selects thermal management, then the item to be tested is thermal management, and a driving style selection interface 303 is displayed on the graphical user interface 301. As Figure 3 (b) shown, Figure 3 (b) shows the driving style selection interface 303. Multiple driving styles are displayed on the driving style selection interface 303, such as driving style 1, driving style 2,..., driving style m (m is a positive integer greater than 1). The user can select a driving style as the test driving style on the driving style selection interface 303. For example, if the user selects driving style m, then the test driving style is driving style m; after obtaining driving style m, based on the association relationship between the driving style and the driving data, obtain the driving data corresponding to driving style m from the generated test case data to obtain the target driving data. The vehicle can be controlled for testing through the target driving data, or the vehicle operation can be simulated for testing using the target driving data.

[0100] As Figure 3 shown, as Figure 3 (a) shown, on the graphical user interface 301 of the test platform, a test item selection interface 302 is displayed. The user can select the test item in the test item selection interface 302. After the detection platform detects the selected test item, the selected test item is determined as the item to be tested. For example, if the user selects thermal management, then the item to be tested is thermal management, and a driving style selection interface 303 is displayed on the graphical user interface 301. As Figure 3 (b) shown, Figure 3(b) shows a driving style selection interface 303, on which a plurality of driving styles are displayed, such as driving style 1, driving style 2, ..., driving style m (m is a positive integer greater than 1). The user can select a driving style as a test driving style in the driving style selection interface 303. For example, if the user selects driving style m, the test driving style is driving style m. After obtaining driving style m, a driving scene selection interface 304 is displayed on the image user interface 301. A plurality of driving scenes are displayed on the driving scene selection interface 304, such as driving scene 1, driving scene 2, ..., driving scene n (n is a positive integer). The user can select a driving scene as a test driving scene in the driving scene selection interface 304. For example, if the user selects driving scene n, the test driving scene is driving scene n. After obtaining driving scene n, based on the association between driving scenes, driving styles and driving data, driving data corresponding to driving scene n is obtained from the generated test case data to obtain target driving data. The vehicle can be controlled by the target driving data for testing, or the target driving data can be used to simulate vehicle operation for testing.

[0101] Among them, when selecting the test style, all driving styles can be selected, and the test platform can sequentially obtain the driving data corresponding to each driving style, test each driving data sequentially, and output the test results corresponding to each driving data sequentially. When selecting the test style, you can also match the driving style that is more closely related to the test item for testing, obtain the driving data corresponding to the driving style that is more closely related, use the driving data corresponding to the driving style that is more closely related for testing, and output the test results corresponding to the driving data corresponding to the driving style that is more closely related. For example, you can select a driving style according to different driving modes (such as sports mode), road types, etc.; for example, on a highway, you can select the driving data corresponding to a robust driving style to test the stability of the cruising torque in the sports mode; and on a city road, you can use the driving data corresponding to an aggressive driving style to test the response speed of the vehicle's sports mode. In the economic mode, select the driving data corresponding to the general driving style to verify the energy-saving effect of the vehicle; in the sports mode, use the driving data corresponding to the aggressive driving style to test the power output of the vehicle. In a high temperature environment, select the driving data corresponding to the aggressive driving style to test the heat dissipation capacity of the vehicle; in a low temperature environment, use the driving data corresponding to the robust driving style to test the thermal insulation effect of the vehicle.

[0102] By responding to a vehicle test request, obtain the items to be tested; based on the items to be tested, output a driving style selection interface for the driver; in response to a selection operation on the driving style selection interface, obtain the test driving style; obtain the driving data corresponding to the test driving style from the generated test case data to obtain the target driving data; test the items to be tested according to the target driving data, it is realized that when testing the vehicle performance, there can be multiple test case data for the user to choose. The user can choose the test case data by himself, such as choosing all the test case data to make the test result more comprehensive, or choosing the test case data with stronger relevance to the items to be tested for testing to make the reliability of the test result higher, thereby improving the effectiveness of the test result.

[0103] In a possible implementation manner, associate the target driving style with the driving data to obtain association data about the driving style and the driving data, including:

[0104] Use the target driving mode as the key and the driving data as the value to establish a mapping relationship between the key and the value to obtain a key-value pair;

[0105] Use the key-value pair as the association data.

[0106] After identifying the target driving style corresponding to each driving data, use the target driving mode as the key and the driving data as the value to establish a mapping relationship between the key and the value to obtain a key-value pair; use one key-value pair as an association data, and then obtain multiple association data about the driving style and the driving data.

[0107] Exemplarily, as shown in Table 1, Table 1 shows some driving data examples provided by the embodiments of the present application;

[0108] Table 1

[0109]

[0110] The acceleration data, deceleration data, turning data, and driver's facial data in Table 1 above are the data actually generated by the driver during the process of driving a vehicle. The number of driving data between different drivers can be the same or different. Suppose there are 15 accelerations in the driving data corresponding to Driver 1. Assuming that the acceleration frequency threshold is 12 and the number of occurrences of acceleration is greater than the acceleration frequency threshold, then the driving behavior of this driver during the driving process includes a sudden acceleration behavior; there are 16 decelerations in the driving data corresponding to Driver 1. Assuming that the deceleration frequency threshold is 18 and the number of occurrences of deceleration is less than the deceleration frequency threshold, then the driving behavior of this driver during the driving process does not include a sudden deceleration behavior; there are 13 steering wheel angles in the driving data corresponding to Driver 1. Assuming that the steering wheel angle frequency threshold is 10 and the number of occurrences of the steering wheel angle is greater than the steering wheel angle frequency threshold, then the driving behavior of this driver during the driving process includes a sharp turning behavior.

[0111] Suppose there are 11 accelerations in the driving data corresponding to Driver 2. Assuming that the acceleration frequency threshold is 12 and the number of occurrences of acceleration is less than the acceleration frequency threshold, then the driving behavior of this driver during the driving process does not include a sudden acceleration behavior; there are 20 decelerations in the driving data corresponding to Driver 2. Assuming that the deceleration frequency threshold is 18 and the number of occurrences of deceleration is greater than the deceleration frequency threshold, then the driving behavior of this driver during the driving process includes a sudden deceleration behavior; there are 15 steering wheel angles in the driving data corresponding to Driver 2. Assuming that the steering wheel angle frequency threshold is 10 and the number of occurrences of the steering wheel angle is greater than the steering wheel angle frequency threshold, then the driving behavior of this driver during the driving process includes a sharp turning behavior.

[0112] After extracting the driving behaviors from each driving data, each driving behavior is scored, as shown in Table 2. Table 2 shows the scoring examples corresponding to some driving behaviors in Table 1;

[0113] Table 2

[0114]

[0115] Add up the scores of all driving behaviors of each driver in Table 2 to obtain the sum of the scores of the driving behaviors of each driver. By matching the sum of the driving scores with the driving style, if the sum of the driving scores is within the score range of a certain driving style, then this driving style is the driving style of the driver corresponding to the driving data; for example, the sum of the scores of all driving behaviors of Driver 1 is 86 points. Match the sum of the driving scores corresponding to Driver 1 with the driving style. If the sum of the driving scores corresponding to Driver 1 is within the score range of the first type of driving style, then the first type of driving style is the driving style of the driver corresponding to the driving data.

[0116] After obtaining the scores corresponding to some driving behaviors shown in Table 3, at least one of the accident frequency, driving environment, and number of violations when the driver drives the vehicle can be obtained, and the driving behavior score is corrected by at least one of the accident frequency, driving environment, and number of violations. If the frequency of accidents of the vehicle driven by the driver is relatively high, or the driving environment is not conducive to the driver's driving, or the driver has a relatively large number of violations during driving, the driving behavior score is reduced by a correction coefficient; if the frequency of accidents of the vehicle driven by the driver is relatively low, or the driving environment is conducive to the driver's driving, or the driver has a relatively small number of violations during driving, the driving behavior score is increased by a correction coefficient.

[0117] After correcting the driving score, the corrected driving score is matched with the driving style. If the corrected driving score is within the score range of a certain driving style, then this driving style is the driving style of the driver corresponding to the driving data.

[0118] After determining the driving style of the driver corresponding to the driving data, the driving style is associated with the driving data to obtain associated data. For example, the driving data corresponding to driver 1 in Table 2 is associated with the first type of driving style of the driver corresponding to this driving data to obtain associated data 1, and the driving data corresponding to driver 2 in Table 2 is associated with the second type of driving style of the driver corresponding to this driving data to obtain associated data 2, and so on. As shown in Table 3, Table 3 shows some examples of the association between driving style and driving data;

[0119] Table 3

[0120] Driving data corresponding to the driver Driving style of the driver corresponding to the driving data Associated data Driving data corresponding to Driver 1 The first type of driving style Associated data 1 Driving data corresponding to Driver 2 The second type of driving style Associated data 2 …… …… ……

[0121] After obtaining multiple associated data, test case data is generated through the multiple associated data. When generating test case data, it can be generated through the associated data. For example, diverse test case data is generated through the associated data 1, associated data 2, etc. in Table 3. Since the associated data is generated based on different driving styles, the generated test case data contains different driving styles. Using the test case data including various driving styles to perform performance tests on the vehicle can comprehensively evaluate the performance of the vehicle under various driving styles.

[0122] The following is an apparatus embodiment of the present application, which can be used to execute the method embodiment of the present application.

[0123] As Figure 4 shown, Figure 4 shows a schematic structural diagram of a vehicle data processing device provided by an embodiment of the present application.

[0124] Exemplarily, as Figure 4 shown, the device 400 includes:

[0125] An acquisition module 401, configured to acquire driving data generated by different drivers driving a vehicle, and obtain a plurality of driving data;

[0126] An identification module 402, configured to, for each driving data, identify the target driving style of the driver corresponding to the driving data based on the driving data;

[0127] An association module 403, configured to associate the target driving style with the driving data to obtain association data regarding the driving style and the driving data;

[0128] A generation module 404, configured to generate test case data for vehicle testing based on the association data.

[0129] In a possible implementation, the identification module 402 is further configured to:

[0130] Score the driving behavior of the driver corresponding to the driving data based on the driving data to obtain a driving behavior score; wherein, the driving behavior score is used to represent the safety level of the driving behavior;

[0131] Determine the target driving style according to the driving behavior score.

[0132] In a possible implementation, the driving data includes at least one of acceleration data, deceleration data, and turning data;

[0133] The identification module 402 is further configured to:

[0134] Determine the initial score and the preset weight corresponding to at least one of the acceleration data, the deceleration data, and the turning data;

[0135] Multiply the initial score corresponding to at least one of the acceleration data, the deceleration data, and the turning data by the preset weight to obtain at least one product;

[0136] Determine the sum value of the at least one product as the driving behavior score.

[0137] In a possible implementation, the identification module 402 is further configured to:

[0138] Correct the driving behavior score based on at least one of the accident frequency, the driving environment, and the number of violations to obtain a corrected score;

[0139] Obtain the preset driving style corresponding to the corrected score to obtain the target driving style.

[0140] In a possible implementation, the identification module 402 is further configured to:

[0141] Determine the correction coefficient corresponding to the driving behavior score according to at least one of the accident frequency, the driving environment, and the number of violations;

[0142] Multiply the driving behavior score by the correction coefficient to obtain a corrected score.

[0143] In one possible implementation, the generation module 404 is further configured to:

[0144] Obtain the driving scenario corresponding to the driving data;

[0145] Associate the driving scenario with the associated data to obtain test case data.

[0146] In one possible implementation, the apparatus 400 further includes:

[0147] A test module, configured to obtain an item to be tested in response to a vehicle test request;

[0148] Output a driving style selection interface for the driver based on the item to be tested;

[0149] In response to a selection operation on the driving style selection interface, obtain a test driving style;

[0150] Obtain the driving data corresponding to the test driving style from the generated test case data to obtain target driving data;

[0151] Test the item to be tested according to the target driving data.

[0152] In one possible implementation, the association module 403 is further configured to:

[0153] Use the target driving mode as the key and the driving data as the value to establish a mapping relationship between the key and the value, obtaining a key-value pair;

[0154] Use the key-value pair as the associated data.

[0155] It should be noted that for the vehicle data processing apparatus provided in the above embodiments, when executing the vehicle data processing method, only the above-mentioned division of each functional module is used for illustration. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above.

[0156] In addition, the vehicle data processing apparatus provided in the above embodiments and the embodiments of the vehicle data processing method belong to the same concept. Therefore, for the details not disclosed in the device embodiments of this specification, please refer to the embodiments of the vehicle data processing method in the above of this specification, and details will not be repeated here.

[0157] As Figure 5 shown, Figure 5 Fig. shows a schematic structural diagram of a vehicle provided in an embodiment of the present application.

[0158] Exemplarily, as Figure 5 shown, the vehicle 500 includes: a memory 501 and a processor 502. Among them, an executable program code 5011 is stored in the memory 501, and the processor 502 is configured to call and execute the executable program code 5011 to execute a vehicle data processing method.

[0159] In addition, an embodiment of the present application also protects a device, which may include a memory and a processor. Among them, an executable program code is stored in the memory, and the processor is configured to call and execute the executable program code to execute a vehicle data processing method provided by an embodiment of the present application.

[0160] This embodiment can divide the functions of the device according to the above method examples. For example, it can correspond to each functional module, or integrate two or more functions into one processing module. The above integrated module can be implemented in the form of hardware. It should be noted that the division of modules in this embodiment is illustrative, only a logical function division, and there may be other division methods in actual implementation.

[0161] It should be noted that all relevant contents of each step involved in the above method embodiment can be cited in the function description of the corresponding functional module, and will not be repeated here.

[0162] It should be understood that the device provided in this embodiment is used to execute the above vehicle data processing method, so the same effect as the above implementation method can be achieved.

[0163] In the case of adopting an integrated unit, the device may include a processing module and a storage module. Among them, when the device is applied to a vehicle, the processing module can be used to control and manage the actions of the vehicle. The storage module can be used to support the vehicle to execute relevant program codes, etc.

[0164] Among them, the processing module can be a processor or a controller, which can implement or execute various exemplary logical blocks, modules, and circuits shown in combination with the disclosure content of the present application. The processor can also be a combination of computing functions, such as including a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc. The storage module can be a memory.

[0165] In addition, the device provided by the embodiment of the present application may specifically be a chip, a component, or a module. The chip may include a connected processor and a memory; among them, the memory is used to store instructions, and when the processor calls and executes the instructions, the chip can execute a vehicle data processing method provided by the above embodiment.

[0166] This embodiment also provides a computer-readable storage medium, in which computer program code is stored. When the computer program code runs on a computer, the computer is caused to execute the above-related method steps to implement a vehicle data processing method provided in the above embodiment.

[0167] This embodiment also provides a computer program product. When the computer program product runs on a computer, the computer is caused to execute the above-related steps to implement a vehicle data processing method provided in the above embodiment.

[0168] Among them, the device, computer-readable storage medium, computer program product or chip provided in this embodiment are all used to execute the corresponding method provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method provided above, and will not be elaborated here.

[0169] Through the description of the above embodiments, those skilled in the art can understand that for the convenience and conciseness of description, only the above division of each functional module is used as an example. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above.

[0170] In the embodiments provided in this application, it should be understood that the disclosed device and method can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point, the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be electrical, mechanical or other forms.

[0171] The above content is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A vehicle data processing method, characterized in that: The method comprises: Acquire driving data generated by different drivers driving vehicles to obtain multiple driving data; for each driving data, identifying a target driving style of the driver corresponding to the driving data based on the driving data; Associating the target driving style with the driving data to obtain associated data about the driving style and the driving data; Test case data for vehicle testing is generated based on the associated data.

2. The method according to claim 1, characterized in that: The identifying, based on the driving data, a target driving style of the driver corresponding to the driving data, comprises: Scoring the driving behavior of the driver corresponding to the driving data based on the driving data to obtain a driving behavior score; wherein the driving behavior score is used to indicate the safety level of the driving behavior; The target driving style is determined according to the driving behavior score.

3. The method according to claim 2, characterized in that The driving data includes at least one of acceleration data, deceleration data, and turning data; The step of identifying the driving behavior of the driver corresponding to the driving data based on the driving data and scoring the driving behavior score to obtain the driving behavior score includes: Determine an initial score and a preset weight corresponding to at least one of the acceleration data, the deceleration data, and the turning data; multiplying an initial score corresponding to at least one of the acceleration data, the deceleration data, and the turning data by a preset weight to obtain at least one product; A sum value of the at least one product is determined as the driving behavior score.

4. The method according to claim 2, characterized in that: The determining the target driving style according to the driving behavior score comprises: Modifying the driving behavior score based on at least one of the accident frequency, the driving environment, and the number of violations to obtain a modified score; A preset driving style corresponding to the modified score is obtained to obtain the target driving style.

5. The method according to claim 3, characterized in that: The step of correcting the driving behavior score to obtain a corrected score includes: Determine a correction coefficient corresponding to the driving behavior score according to at least one of the accident frequency, the driving environment, and the number of violations; The driving behavior score is multiplied by the correction coefficient to obtain the corrected score.

6. The method according to claim 1, characterized in that The generating test case data for vehicle testing based on the associated data includes: Acquire a driving scene corresponding to the driving data; The driving scenario is associated with the associated data to obtain the test case data.

7. The method according to claim 1, characterized in that The method further comprises: Responding to a vehicle test request, obtaining items to be tested; Outputting a driving style selection interface for the driver based on the items to be tested; In response to a selection operation on the driving style selection interface, obtaining a test driving style; Acquire driving data corresponding to the test driving style from the generated test case data to obtain target driving data; The item to be tested is tested according to the target driving data.

8. The method according to claim 1, characterized in that The step of associating the target driving style with the driving data to obtain associated data about the driving style and the driving data includes: Taking the target driving mode as a key and the driving data as a value, establishing a mapping relationship between the key and the value to obtain a key-value pair; The key-value pair is used as the associated data.

9. A vehicle data processing device, characterized in that: The device comprises: An acquisition module, used to acquire driving data generated by different drivers driving vehicles, to obtain a plurality of driving data; an identification module, configured to identify, for each driving data, a target driving style of a driver corresponding to the driving data based on the driving data; an associating module, configured to associate the target driving style with the driving data to obtain associated data about the driving style and the driving data; A generating module is used to generate test case data for vehicle testing based on the associated data.

10. A vehicle, characterized in that: The vehicle comprises: A memory for storing executable program codes; A processor, configured to call and run the executable program code from the memory, so that the vehicle executes the method according to any one of claims 1 to 8.

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