Vehicle dynamics index design method and system, electronic equipment and storage medium

Abstract

The invention discloses a vehicle dynamics index design method and system, electronic equipment and a storage medium, and relates to the technical field of vehicle testing. The method comprises the steps that real vehicle driving big data is collected to obtain a first data set, the first data set comprises multiple pieces of driving data, elements of the driving data comprise vehicle state information and driving environment information, and the first data set is preprocessed to obtain a second data set; driving scene classification is carried out on the driving data in the second data set according to the driving environment information to obtain a third data set under multiple different driving scenes, and driving behavior feature extraction is carried out on the third data set according to the vehicle state information to obtain driving behavior features under the corresponding driving scenes; and determining dynamic indexes in the driving scene according to the driving behavior characteristics. According to the method, the vehicle dynamic index system in accordance with various driving scenes of the user can be constructed, and the comprehensiveness and accuracy of vehicle index design are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle testing, and in particular to a vehicle dynamics index design method, system, electronic equipment and storage medium. Background Art

[0002] As consumers' demand for personalized driving experience continues to increase, extracting vehicle dynamics indicators for different user groups' driving habits, preferences and needs, so that vehicles can better meet user needs in terms of handling performance, comfort, safety, etc., has become one of the important trends in modern vehicle dynamics development. At present, the design of indicators mainly adopts industry standards, that is, the indicators are designed based on typical scenarios and general needs. However, the actual driving scenarios are complex and changeable. The indicator system based on industry standards is not accurate and comprehensive enough, and is out of touch with the actual needs of users, which leads to the failure of the developed vehicles to meet user expectations, affecting user experience and satisfaction. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to propose a vehicle dynamics index design method, system, electronic device and storage medium, aiming to construct a vehicle dynamics index system that meets the needs of users in various driving scenarios and improve the comprehensiveness and accuracy of vehicle index design.

[0004] To achieve the above purpose, one aspect of an embodiment of the present application provides a vehicle dynamics index design method, comprising the following steps:

[0005] Collecting real vehicle driving big data to obtain a first data set, wherein the first data set includes a plurality of driving data, and elements of the driving data include vehicle state information and driving environment information;

[0006] Preprocessing the first data set to obtain a second data set;

[0007] Classifying the driving data in the second data set according to the driving environment information to obtain a third data set under a plurality of different driving scenarios;

[0008] Extracting driving behavior features from the third data set according to the vehicle state information to obtain driving behavior features under corresponding driving scenarios;

[0009] A dynamics index in the driving scenario is determined according to the driving behavior characteristics.

[0010] In some embodiments, preprocessing the first data set to obtain the second data set includes the following steps:

[0011] Acquire a first preset condition, wherein the first preset condition is used to indicate that the vehicle is in a stationary state;

[0012] The driving data whose vehicle status information meets the first preset condition is filtered out from the first data set to obtain a second data set.

[0013] In some embodiments, preprocessing the first data set to obtain the second data set includes the following steps:

[0014] Determining an associated second preset condition according to the evaluation requirement, wherein the second preset condition includes a plurality of requirement elements;

[0015] The elements of the driving data in the first data set are screened according to the second preset condition to obtain a second data set.

[0016] In some embodiments, the step of classifying the driving data in the second data set according to the driving environment information to obtain a third data set under a plurality of different driving scenarios includes the following steps:

[0017] Obtain environmental characteristics of different driving scenarios;

[0018] Matching the driving environment information of each piece of the driving data in the second data set with environmental features of different driving scenes respectively, and determining the environmental feature that best matches the driving environment information;

[0019] Determining a driving scene label of corresponding driving data according to an environmental feature that best matches the driving environment information;

[0020] The driving data with the same driving scene label in the second data set are divided into one category to obtain a third data set under multiple different driving scenes.

[0021] In some embodiments, extracting driving behavior features from the third data set according to the vehicle state information to obtain driving behavior features in a corresponding driving scenario includes the following steps:

[0022] Extracting the vehicle status information in the third data set to perform normal distribution analysis to obtain data distribution characteristics of the vehicle status information;

[0023] The driving behavior characteristics in the corresponding driving scenario are determined according to the data distribution characteristics.

[0024] In some embodiments, determining the dynamics index in the driving scenario according to the driving behavior characteristics comprises the following steps:

[0025] Get the common indicator template;

[0026] The driving behavior characteristics are written into the general indicator template to obtain the dynamics indicator under the driving scenario.

[0027] In some embodiments, obtaining the general indicator template comprises the following steps:

[0028] Extract objective vehicle indicators related to user perception from the current indicator system;

[0029] A general indicator template is formed according to the objective indicators of the vehicle.

[0030] To achieve the above objectives, another aspect of the embodiment of the present application provides a vehicle dynamics index design system, including:

[0031] The first module is used to collect real vehicle driving big data to obtain a first data set, wherein the first data set includes multiple driving data, and the elements of the driving data include vehicle state information and driving environment information;

[0032] A second module is used to preprocess the first data set to obtain a second data set;

[0033] A third module is used to classify the driving data in the second data set according to the driving environment information to obtain a third data set under multiple different driving scenarios;

[0034] A fourth module is used to extract driving behavior features from the third data set according to the vehicle state information to obtain driving behavior features under a corresponding driving scenario;

[0035] The fifth module is used to determine the dynamic index in the driving scenario according to the driving behavior characteristics.

[0036] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application proposes an electronic device, which includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory. When the program is executed by the processor, the vehicle dynamics index design method described in the above-mentioned embodiment is realized.

[0037] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application proposes a storage medium, which is a computer-readable storage medium used for computer-readable storage, and the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the vehicle dynamics index design method described in the above-mentioned embodiment.

[0038] The vehicle dynamics index design method, system, electronic device and storage medium proposed in the present application have at least the following beneficial effects: collecting real vehicle driving big data to obtain a first data set, the first data set includes multiple driving data, and the elements of the driving data include vehicle status information and driving environment information, preprocessing the first data set to obtain a second data set, and then classifying the driving data in the second data set according to the driving environment information to obtain a third data set under multiple different driving scenarios, extracting driving behavior characteristics from the third data set according to the vehicle status information to obtain driving behavior characteristics under the corresponding driving scenario, and determining the dynamics index under the driving scenario according to the driving behavior characteristics. The present application divides the driving scenarios of real vehicle big data to obtain a variety of driving scenarios that meet the actual user scenarios, and then analyzes the driving behavior characteristics of the data under different driving scenarios, constructs a vehicle dynamics index system that meets various user driving scenarios, and improves the comprehensiveness and accuracy of vehicle index design. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is a flow chart of a vehicle dynamics index design method provided in an embodiment of the present application;

[0040] Figure 2 yes Figure 1 Flow chart of step S102 in FIG.

[0041] Figure 3 yes Figure 1 A flowchart of another embodiment of step S102;

[0042] Figure 4 yes Figure 1 Flow chart of step S103 in FIG.

[0043] Figure 5 yes Figure 1 Flow chart of step S104 in FIG.

[0044] Figure 6 yes Figure 1 Flow chart of step S105 in FIG.

[0045] Figure 7 yes Figure 6 Flow chart of step S601 in FIG.

[0046] Figure 8 is a schematic diagram of a vehicle dynamics index design system provided in an embodiment of the present application;

[0047] Fig. 9 is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application;

[0048] Fig.10It is a schematic diagram of environmental characteristics corresponding to the mountain road driving scenario provided in an embodiment of the present application;

[0049] Fig.11 It is a schematic diagram of the distribution characteristics of vehicle status information data for a mountain road driving scenario provided in an embodiment of the present application. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0051] It should be noted that, although the functional modules are divided in the system schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the system or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0053] Traditional vehicle dynamics development methods rely mainly on laboratory testing and limited user survey data, which has the following shortcomings:

[0054] Limitations of industry standards: Industry standards are based on typical scenarios and general needs and cannot fully cover the personalized needs of all users. Actual driving scenarios are complex and changeable. The indicator system based on industry standards is often not accurate and comprehensive enough and is divorced from the actual needs of users.

[0055] Limitations of laboratory testing: Laboratory testing is limited by cost, site and efficiency and is usually conducted in specific scenarios. The test data cannot fully reflect the actual user driving scenarios and behaviors, resulting in the vehicle testing content not meeting the actual user needs.

[0056] Limitations of user research: User research is limited by time, region, and sample size. The results cannot fully cover the needs of all user groups and it is difficult to accurately capture users’ actual testing needs.

[0057] In summary, the current design indicator system is out of touch with the testing requirements of users' actual driving scenarios and feelings, resulting in the vehicle being unable to meet user expectations in actual use, affecting user experience and satisfaction.

[0058] Based on this, the embodiments of the present application provide a vehicle dynamics index design method, system, electronic device and storage medium, which are specifically explained through the following embodiments. First, the vehicle dynamics index design method in the embodiments of the present application is described.

[0059] The vehicle dynamics index design method provided in the embodiment of the present application relates to the field of vehicle testing technology. The vehicle dynamics index design method provided in the embodiment of the present application can be applied to a terminal, can be applied to a server side, or can be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the vehicle dynamics index design method, etc., but is not limited to the above forms.

[0060] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0061] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0062] Figure 1 is an optional flow chart of the vehicle dynamics index design method provided in the embodiment of the present application, Figure 1 The method may include but is not limited to steps S101 to S105.

[0063] Step S101, collecting real vehicle driving big data to obtain a first data set, wherein the first data set includes multiple driving data, and elements of the driving data include vehicle state information and driving environment information;

[0064] Step S102, preprocessing the first data set to obtain a second data set;

[0065] Step S103, classifying the driving data in the second data set by driving scene according to the driving environment information to obtain a third data set under multiple different driving scenes;

[0066] Step S104, extracting driving behavior features from the third data set according to the vehicle state information to obtain driving behavior features under corresponding driving scenarios;

[0067] Step S105, determining the dynamics index in the driving scenario according to the driving behavior characteristics.

[0068] In some embodiments of step S101, this embodiment can collect the actual driving data (i.e., real vehicle driving big data) of each vehicle at different times in real time based on vehicle networking technology (such as vehicle-mounted sensors and vehicle-mounted intelligent devices), and store the collected real vehicle driving big data in a database to form a first data set. The first data set includes multiple driving data, and the elements of each driving data include vehicle status information and driving environment information. Among them, the vehicle status information includes information such as vehicle speed, acceleration, braking force, steering angle, vehicle position, braking force, accelerator pedal opening, and status bits of some channels, and the driving environment information includes information such as road type, traffic conditions, and weather conditions (temperature, light, rainfall, etc.). Exemplarily, the first data set formed by the real vehicle driving big data collected in real time is shown in Table 1.

[0069] Table 1 The first data set

[0070]

[0071] Each row in the table represents a piece of driving data, and each piece of driving data includes element information such as vehicle position, speed, steering wheel angle, temperature, etc. This embodiment collects various data of users in actual driving in real time through vehicle-mounted sensors and smart devices, which can fully cover different driving scenarios and user driving behaviors, and facilitate the subsequent comprehensive and accurate indicator system.

[0072] In some embodiments of step S102, the purpose of preprocessing the first data set is to remove abnormal data and invalid data in the first data set to obtain a second data set. Abnormal data is data that exceeds the normal range, for example, temperature data that is too high or too low for the area. Invalid data is data that is irrelevant to subsequent analysis requirements. By preprocessing the first data set to form a second data set with valid data and less data, the amount of data transferred in the subsequent analysis process can be reduced, thereby reducing the computational load.

[0073] See also Figure 2 In some embodiments, step S102 may include but is not limited to steps S201 to S202:

[0074] Step S201, obtaining a first preset condition, wherein the first preset condition is used to indicate that the vehicle is in a stationary state;

[0075] Step S202: filtering out driving data whose vehicle status information meets a first preset condition from the first data set to obtain a second data set.

[0076] In this embodiment, the system presets a first preset condition that indicates that the vehicle is in a stationary state. For example, the first preset condition may be that the vehicle speed is zero or the vehicle is not powered on. The vehicle state information in the driving data includes the vehicle speed. The driving data that meets the first preset condition (i.e., the vehicle speed is zero) is filtered out from the first data set to obtain the second data set, thereby deleting invalid data when the vehicle is not started. Furthermore, irrelevant state information in the driving data may also be deleted.

[0077] See also Figure 3 In some embodiments, step S102 may include but is not limited to steps S301 to S302:

[0078] Step S301, determining an associated second preset condition according to the evaluation requirement, wherein the second preset condition includes a plurality of requirement elements;

[0079] Step S302: filtering the elements of the driving data in the first data set according to a second preset condition to obtain a second data set.

[0080] In this embodiment, the evaluation requirements include various vehicle evaluation levels that need to be evaluated, for example, the vehicle evaluation level may be the operability level, the stability level, or the comfort level. The system stores demand elements associated with various vehicle evaluation levels, for example, the demand elements associated with the stability level include lateral acceleration, steering wheel angle, and vehicle speed. Exemplarily, the evaluation requirements may include operability, stability, and comfort levels, and the demand elements related to vehicle maneuverability, stability, and comfort are screened from the driving data in the first data set, such as data in multiple dimensions such as vehicle speed, lateral acceleration, road conditions, and weather conditions, to obtain the second data set. Through this process, only valid information useful for analysis is retained in the data set.

[0081] In some other embodiments, after deleting the driving data of the vehicle in a stationary state in the first data set according to the first preset condition, the elements in the driving data that meet the evaluation requirements are screened according to the second preset condition to obtain the second data set.

[0082] In some embodiments of step S103, the driving data in the second data set may be classified into driving scenarios according to the road type and traffic status in the driving environment information, for example, divided into urban driving, highway driving, mountain road driving, parking lot driving, etc., and multiple third data sets under different driving scenarios are obtained by classifying the driving data in the second data set. Furthermore, the driving scenarios may be subdivided according to the weather conditions in the driving environment information, for example, sunny city driving, rainy mountain road driving, or snowy mountain road driving.

[0083] In some embodiments, a clustering algorithm (such as a K-means clustering algorithm) can be used to automatically classify the driving data in the second data set. Specifically, the number of clusters K is determined according to the number of driving scenes to be divided; the driving environment information of K driving data in the second data set is randomly selected as a sample, and the sample is used as the initial cluster center; the distance from all samples in the second data set (i.e., the driving environment information of all driving data) to each cluster center is calculated, and each sample is assigned to the cluster where the nearest cluster center is located; the cluster center of each cluster is recalculated (the cluster center can be the mean of all samples in the cluster); and the sample assignment and cluster center update steps are repeated until the cluster center no longer changes significantly or the preset number of iterations is reached, and the sample clustering result is obtained, thereby obtaining the driving data clustering result.

[0084] See also Figure 4 In some embodiments, step S103 may include but is not limited to steps S401 to S404:

[0085] Step S401, obtaining environmental characteristics of different driving scenarios;

[0086] Step S402, matching the driving environment information of each piece of driving data in the second data set with environmental features of different driving scenes respectively, and determining the environmental feature that best matches the driving environment information;

[0087] Step S403, determining a driving scene label of corresponding driving data according to the environmental features that best match the driving environment information;

[0088] Step S404: classify the driving data with the same driving scene label in the second data set into one category to obtain a third data set under multiple different driving scenes.

[0089] In this embodiment, the system presets environmental features under different driving scenarios, for example, sunny city driving can be less than zero rainfall, the vehicle position is in a preset area (urban area), the road type is a flat highway, etc. The driving environment information of each driving data in the second data set is matched with the environmental features of different driving scenarios, and the environmental features that best match the driving environment information are determined. Then, the driving scene label of the corresponding driving data is determined according to the environmental features that best match the driving environment information. For example, the driving scene label can be sunny, the temperature is about 20°C, mountain road driving, there is a ramp, etc. For example, the environmental features corresponding to the driving scene are as follows: Fig.10 The driving data with the same driving scene label in the second data set are divided into one category to obtain a third data set under multiple different driving scenes.

[0090] In some embodiments of step S104, after the driving data in the second data set is divided based on the driving scene to obtain a third data set under multiple different driving scenes, the driving behavior feature can be extracted based on all vehicle state information in the third data set to obtain the driving behavior feature under the corresponding driving scene of the third data set. The driving behavior feature represents the most likely behavior of the vehicle in the corresponding scene, for example, in a sunny mountain road driving scene, the vehicle speed is 70 kph.

[0091] See also Figure 5 In some embodiments, step S104 may include but is not limited to steps S501 to S502:

[0092] Step S501, extracting the vehicle status information in the third data set to perform normal distribution analysis to obtain data distribution characteristics of the vehicle status information;

[0093] Step S502: determining driving behavior characteristics in a corresponding driving scenario according to data distribution characteristics.

[0094] In this embodiment, the normal distribution (also called Gaussian distribution) is a continuous probability distribution whose shape is determined by two parameters: mean (μ) and standard deviation (σ). In a normal distribution, most of the data is concentrated near the mean and gradually decreases on both sides, forming a symmetrical bell-shaped curve. The vehicle state information in the third data set is extracted, such as vehicle speed, longitudinal acceleration, lateral acceleration, etc., and a normal distribution analysis is performed on each vehicle state information in the third data set to obtain the corresponding data distribution characteristics. The driving behavior characteristics under the corresponding driving scenario are determined based on the data distribution characteristics (such as data mean, variance, etc.). Fig.11 As shown in the figure, in the driving scenario of mountain road driving (including ramps) on a sunny day and around 20°C, the driving behavior characteristics are that the vehicle speed is concentrated around 70kph, the lateral acceleration range is within 0.6g, the longitudinal acceleration range is within 0.4g, and the braking range is within 0.5g. The driving behavior characteristics will be used to further extract vehicle dynamics indicators related to the user's driving experience.

[0095] In some embodiments of step S105, after obtaining the driving behavior characteristics in different driving scenarios, the dynamics indicators in different driving scenarios can be determined based on the indicator system of existing typical scenarios and the driving behavior characteristics of the driving scenarios analyzed above, thereby improving the comprehensiveness of the construction of the vehicle dynamics indicator system.

[0096] See also Figure 6 In some embodiments, step S105 may include but is not limited to steps S601 to S602:

[0097] Step S601, obtaining a general indicator template;

[0098] Step S602, writing the driving behavior characteristics into a general index template to obtain the dynamics index in the driving scenario.

[0099] In this embodiment, the general indicator template includes a custom driving scenario part and objective indicators. The objective indicators are fixedly written into the general indicator template. For example, the objective indicators may be steering sensitivity, yaw lag time, and steering hand force, etc. The custom driving scenario part is used to write the driving behavior characteristics of the required driving scenario.

[0100] For example, a generic indicator template could be:

[0101] [Custom Driving Scenario] Steering sensitivity: used to describe whether the steering response at medium speed is flexible;

[0102] [Custom Driving Scenario] Yaw lag time: used to describe whether the steering response at medium speed is fast;

[0103] [Custom Driving Scenario] Steering force: used to describe whether the steering force at medium speed is light.

[0104] The driving behavior characteristics of the driving scenario of driving on a mountain road (including a slope) on a sunny day at about 20°C, that is, the vehicle speed is 70 kph, are written into the above general indicator template to obtain the dynamic indicators under this driving scenario, as follows:

[0105] 70kph steering sensitivity: used to describe whether the steering response at medium speed is flexible;

[0106] 70kph yaw lag time: used to describe whether the steering response at medium speed is quick;

[0107] Steering effort at 70kph: used to describe whether the steering effort at medium speed is light.

[0108] See also Figure 7 In some embodiments, step S601 may include but is not limited to steps S701 to S702:

[0109] Step S701, extracting vehicle objective indicators related to user perception from the current indicator system;

[0110] Step S702, forming a general indicator template according to the objective indicators of the vehicle.

[0111] In this embodiment, the system stores a currently used dynamics index system. For example, the dynamics index system includes evaluation index contents at different levels, such as power performance, braking performance, handling performance, smoothness, economy, and safety performance. The embodiment of the present application can extract vehicle objective indicators related to user perception from the current index system. For example, the extracted objective indicators may be steering sensitivity, yaw lag time, steering hand force, steering control accuracy, etc. A general index template is formed based on the extracted vehicle objective indicators, thereby constructing a dynamics index system with higher user perception efficiency under different driving scenarios.

[0112] In some embodiments, the objective index test of the related technology usually adopts the driving behavior characteristics at a speed of 100 kph, and obtains the steering sensitivity, yaw lag time, steering hand force and other indicators at 100 kph. For a purely mechanical system, the test data at a speed of 100 kph can be used to predict the situation of each objective index at 70 kph.

[0113] In the embodiments of the present application, with the development of electronic control systems, many parameter calibrations vary with speed. For example, the power curve of the power steering (EPS) and the proportional curve of the rear wheel steering (RWS) are mostly calibrated at intervals of 10kph, thereby constructing a corresponding index system, but this index system cannot effectively cover all actual driving scenarios. Therefore, the embodiments of the present application analyze the actual vehicle driving big data, determine a variety of actual driving scenarios, and the driving behaviors adopted in different driving scenarios, thereby constructing a comprehensive and accurate vehicle dynamics index system, and guide vehicle testing and development based on the index system, reduce unnecessary development costs, and enable the vehicle to meet user needs and experience.

[0114] See also Figure 8 The embodiment of the present application also provides a vehicle dynamics index design system, including:

[0115] The first module is used to collect real vehicle driving big data to obtain a first data set, wherein the first data set includes multiple driving data, and the elements of the driving data include vehicle state information and driving environment information;

[0116] The second module is used to preprocess the first data set to obtain a second data set;

[0117] A third module is used to classify the driving scenes of the driving data in the second data set according to the driving environment information to obtain third data sets under multiple different driving scenes;

[0118] A fourth module is used to extract driving behavior features from the third data set according to the vehicle state information to obtain driving behavior features under corresponding driving scenarios;

[0119] The fifth module is used to determine the dynamic indicators in the driving scenario according to the driving behavior characteristics.

[0120] It can be understood that the contents of the above-mentioned vehicle dynamics index design method embodiment are all applicable to the present system embodiment, the functions specifically implemented by the present system embodiment are the same as those in the above-mentioned vehicle dynamics index design method embodiment, and the beneficial effects achieved are also the same as those achieved by the above-mentioned vehicle dynamics index design method embodiment.

[0121] The embodiment of the present application also provides an electronic device, the electronic device comprising: a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory, and the program is executed by the processor to realize the above-mentioned vehicle dynamics index design method. The electronic device can be any intelligent terminal including a tablet computer, a vehicle-mounted computer, etc.

[0122] See also Fig. 9 , Fig. 9The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0123] The processor 901 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0124] The memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 902, and the processor 901 calls and executes the vehicle dynamics index design method of the embodiment of this application;

[0125] Input / output interface 903, used to implement information input and output;

[0126] Communication interface 904, used to realize communication interaction between the device and other devices, which can be realized by wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0127] A bus 905 that transmits information between various components of the device (e.g., the processor 901, the memory 902, the input / output interface 903, and the communication interface 904);

[0128] The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .

[0129] An embodiment of the present application also provides a storage medium, which is a computer-readable storage medium used for computer-readable storage. The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the above-mentioned vehicle dynamics index design method.

[0130] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0131] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0132] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0133] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0134] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0135] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the above modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, systems or units, which can be electrical, mechanical or other forms.

[0136] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0137] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0138] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0139] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A vehicle dynamics index design method, characterized in that: The following steps are involved: Collecting real vehicle driving big data to obtain a first data set, wherein the first data set includes a plurality of driving data, and elements of the driving data include vehicle state information and driving environment information; Preprocessing the first data set to obtain a second data set; Classifying the driving data in the second data set according to the driving environment information to obtain a third data set under a plurality of different driving scenarios; Extracting driving behavior features from the third data set according to the vehicle state information to obtain driving behavior features under corresponding driving scenarios; A dynamics index in the driving scenario is determined according to the driving behavior characteristics.

2. The vehicle dynamics index design method according to claim 1, characterized in that: The preprocessing of the first data set to obtain the second data set comprises the following steps: Acquire a first preset condition, wherein the first preset condition is used to indicate that the vehicle is in a stationary state; The driving data whose vehicle status information meets the first preset condition is filtered out from the first data set to obtain a second data set.

3. The vehicle dynamics index design method according to claim 1, characterized in that: The preprocessing of the first data set to obtain the second data set comprises the following steps: Determining an associated second preset condition according to the evaluation requirement, wherein the second preset condition includes a plurality of requirement elements; The elements of the driving data in the first data set are screened according to the second preset condition to obtain a second data set.

4. The vehicle dynamics index design method according to claim 1, characterized in that: The step of classifying the driving scene of the driving data in the second data set according to the driving environment information to obtain a third data set under a plurality of different driving scenes comprises the following steps: Obtain environmental characteristics of different driving scenarios; Matching the driving environment information of each piece of the driving data in the second data set with environmental features of different driving scenes respectively, and determining the environmental feature that best matches the driving environment information; Determining a driving scene label of corresponding driving data according to an environmental feature that best matches the driving environment information; The driving data with the same driving scene label in the second data set are divided into one category to obtain a third data set under multiple different driving scenes.

5. The vehicle dynamics index design method according to claim 1, characterized in that: The step of extracting driving behavior features from the third data set according to the vehicle state information to obtain driving behavior features under corresponding driving scenarios comprises the following steps: Extracting the vehicle status information in the third data set to perform normal distribution analysis to obtain data distribution characteristics of the vehicle status information; The driving behavior characteristics in the corresponding driving scenario are determined according to the data distribution characteristics.

6. The vehicle dynamics index design method according to claim 1, characterized in that: Determining the dynamics index in the driving scenario according to the driving behavior characteristics comprises the following steps: Get the common indicator template; The driving behavior characteristics are written into the general indicator template to obtain the dynamics indicator under the driving scenario.

7. The vehicle dynamics index design method according to claim 6, characterized in that: The obtaining of the general indicator template comprises the following steps: Extract objective vehicle indicators related to user perception from the current indicator system; A general indicator template is formed according to the objective indicators of the vehicle.

8. A vehicle dynamics index design system, characterized in that: include: The first module is used to collect real vehicle driving big data to obtain a first data set, wherein the first data set includes multiple driving data, and the elements of the driving data include vehicle state information and driving environment information; A second module is used to preprocess the first data set to obtain a second data set; A third module is used to classify the driving data in the second data set according to the driving environment information to obtain a third data set under multiple different driving scenarios; A fourth module is used to extract driving behavior features from the third data set according to the vehicle state information to obtain driving behavior features under a corresponding driving scenario; The fifth module is used to determine the dynamic index in the driving scenario according to the driving behavior characteristics.

9. An electronic device, characterized in that: The electronic device includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory. When the program is executed by the processor, the steps of the vehicle dynamics index design method as described in any one of claims 1 to 7 are realized.

10. A computer storage medium, the storage medium being a computer-readable storage medium, used for computer-readable storage, characterized in that: The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the vehicle dynamics index design method according to any one of claims 1 to 7.

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