CN114677039A

CN114677039A - Vehicle running control evaluation method and device, readable storage medium and electronic equipment

Info

Publication number: CN114677039A
Application number: CN202210389882.XA
Authority: CN
Inventors: 曾路遥; 孔艺婷; 高智; 宗宁; 张晓龙
Original assignee: Shanghai Anting Horizon Intelligent Transportation Technology Co ltd
Current assignee: Shanghai Anting Horizon Intelligent Transportation Technology Co ltd
Priority date: 2022-04-14
Filing date: 2022-04-14
Publication date: 2022-06-28

Abstract

The embodiment of the disclosure discloses a vehicle running control evaluation method, a vehicle running control evaluation device, a computer readable storage medium and an electronic device, wherein the method comprises the following steps: acquiring a driving state data set which is acquired during the driving of the vehicle and aims at a target control dimension; counting at least one driving state data subset included in the driving state data set according to a corresponding counting mode to obtain counting data corresponding to each driving state data subset; determining a target driving state data subset from at least one driving state data subset, wherein the statistical data corresponding to the target driving state data subset meets the corresponding grading condition; and determining the score of the vehicle running control under the target control dimension based on the preset score corresponding to the target running state data subset. The embodiment of the disclosure can automatically, efficiently and more pertinently complete the evaluation of the running condition of the vehicle control system, and further contributes to improving the efficiency of improving the vehicle control system.

Description

Vehicle running control evaluation method and device, readable storage medium and electronic equipment

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to a method and an apparatus for evaluating vehicle driving control, a computer-readable storage medium, and an electronic device.

Background

With the continuous development of intelligent driving technology, the automobile bus technology becomes an important technical means for ensuring the safe driving of an automobile as the combination of a computer network technology and an industrial field bus control technology. In the normal use process of the vehicle, the vehicle condition continuously changes along with the increase of the driving distance, the reliability and the safety of the vehicle also change, the failure rate is improved, and great hidden danger is caused to the safe driving of the vehicle. Therefore, the function test of the vehicle can be carried out, the fault existing in the vehicle can be diagnosed accurately in time, the reason of the fault can be reasonably checked, and the development trend of the vehicle diagnosis technology is formed.

The existing intelligent driving system needs to automatically control the transverse and longitudinal movement of the vehicle, and the evaluation of the transverse and longitudinal movement control can be completed through quantitative data statistics and analysis. At present, real vehicle control variable analysis mainly depends on mature tools, such as: CANoe, CANape, and the like.

Disclosure of Invention

The embodiment of the disclosure provides a vehicle running control evaluation method and device, a computer-readable storage medium and electronic equipment.

An embodiment of the present disclosure provides a vehicle travel control evaluation method, including: acquiring a driving state data set which is acquired during the driving of the vehicle and aims at a target control dimension; counting at least one driving state data subset included in the driving state data set according to a corresponding counting mode to obtain counting data corresponding to each driving state data subset; determining a target driving state data subset from at least one driving state data subset, wherein the statistical data corresponding to the target driving state data subset meets the corresponding grading condition; and determining the score of the vehicle running control under the target control dimension based on the preset score corresponding to the target running state data subset.

According to another aspect of the embodiments of the present disclosure, there is provided a vehicle running control evaluation device including: the acquisition module is used for acquiring a driving state data set which is acquired during the driving of the vehicle and aims at the target control dimension; the statistical module is used for carrying out statistics on at least one driving state data subset included in the driving state data set according to a corresponding statistical mode to obtain statistical data corresponding to each driving state data subset; the system comprises a first determination module, a second determination module and a third determination module, wherein the first determination module is used for determining a target driving state data subset from at least one driving state data subset, and statistical data corresponding to the target driving state data subset meet corresponding grading conditions; and the second determination module is used for determining the score of the vehicle running control under the target control dimensionality based on the preset score corresponding to the target running state data subset.

According to another aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium storing a computer program for executing the above-described vehicle travel control evaluation method.

According to another aspect of the embodiments of the present disclosure, there is provided an electronic apparatus including: a processor; a memory for storing processor-executable instructions; and the processor is used for reading the executable instructions from the memory and executing the instructions to realize the vehicle running control evaluation method.

Based on the vehicle running control evaluation method, the vehicle running control evaluation device, the computer-readable storage medium and the electronic device provided by the embodiments of the present disclosure, the running state data set of the target control dimension of the vehicle is acquired, at least one running state data subset included in the running state data set is counted according to a corresponding statistical manner, statistical data corresponding to each running state data subset is obtained, then the target running state data subset is determined according to the scoring condition of each running state data subset, and finally the score of the vehicle running control is determined based on the preset score corresponding to the target running state data subset. The method and the device realize comprehensive statistics of data of all aspects under the target control dimension, accurately obtain scores for evaluating vehicle running control based on the statistical data, do not need a user to manually operate a running state data set, can automatically, efficiently and more pertinently evaluate the running state of the vehicle control system, and further contribute to improving the efficiency of the vehicle control system.

The technical solution of the present disclosure is further described in detail by the accompanying drawings and embodiments.

Drawings

The above and other objects, features and advantages of the present disclosure will become more apparent by describing in more detail embodiments of the present disclosure with reference to the attached drawings. The accompanying drawings are included to provide a further understanding of the embodiments of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the principles of the disclosure and not to limit the disclosure. In the drawings, like reference numbers generally represent like parts or steps.

Fig. 1 is a system diagram to which the present disclosure is applicable.

Fig. 2 is a schematic flow chart of a vehicle running control evaluation method according to an exemplary embodiment of the disclosure.

Fig. 3 is a flowchart illustrating a vehicle running control evaluation method according to another exemplary embodiment of the present disclosure.

Fig. 4 is a flowchart illustrating a vehicle running control evaluation method according to another exemplary embodiment of the present disclosure.

Fig. 5 is an exemplary schematic diagram of a lateral control effect evaluation interface provided by another exemplary embodiment of the present disclosure.

Fig. 6 is a flowchart illustrating a vehicle running control evaluation method according to another exemplary embodiment of the present disclosure.

Fig. 7A is an exemplary flowchart of determining a lateral control score in a lateral control dimension provided by another exemplary embodiment of the present disclosure.

Fig. 7B is an exemplary flowchart of determining a longitudinal control score in a longitudinal control dimension provided by another exemplary embodiment of the present disclosure.

Fig. 8 is a schematic structural diagram of a vehicle running control evaluation device according to an exemplary embodiment of the present disclosure.

Fig. 9 is a schematic structural diagram of a vehicle running control evaluation device according to another exemplary embodiment of the present disclosure.

Fig. 10 is a block diagram of an electronic device provided in an exemplary embodiment of the present disclosure.

Detailed Description

Hereinafter, example embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. It is to be understood that the described embodiments are merely a subset of the embodiments of the present disclosure and not all embodiments of the present disclosure, with the understanding that the present disclosure is not limited to the example embodiments described herein.

It should be noted that: the relative arrangement of the components and steps, the numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.

It will be understood by those of skill in the art that the terms "first," "second," and the like in the embodiments of the present disclosure are used merely to distinguish one element from another, and are not intended to imply any particular technical meaning, nor is the necessary logical order between them.

It is also understood that in embodiments of the present disclosure, "a plurality" may refer to two or more and "at least one" may refer to one, two or more.

It is also to be understood that any reference to any component, data, or structure in the embodiments of the disclosure, may be generally understood as one or more, unless explicitly defined otherwise or stated otherwise.

In addition, the term "and/or" in the present disclosure is only one kind of association relationship describing an associated object, and means that three kinds of relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" in the present disclosure generally indicates that the former and latter associated objects are in an "or" relationship.

It should also be understood that the description of the various embodiments of the present disclosure emphasizes the differences between the various embodiments, and the same or similar parts may be referred to each other, so that the descriptions thereof are omitted for brevity.

Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description.

The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the disclosure, its application, or uses.

Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.

It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.

The disclosed embodiments may be applied to electronic devices such as terminal devices, computer systems, servers, etc., which are operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well known terminal devices, computing systems, environments, and/or configurations that may be suitable for use with electronic devices, such as terminal devices, computer systems, servers, and the like, include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, microprocessor-based systems, set top boxes, programmable consumer electronics, network pcs, minicomputer systems, mainframe computer systems, distributed cloud computing environments that include any of the above systems, and the like.

Electronic devices such as terminal devices, computer systems, servers, etc. may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, etc. that perform particular tasks or implement particular abstract data types. The computer system/server may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices.

Summary of the application

At present, real vehicle control variable analysis mainly depends on mature tools, such as: CANoe, CANape, and the like. Analytical tools such as CANoe and CANape are general analytical tools, and have two shortcomings when being applied to an intelligent driving system:

firstly, only the curve of each control variable can be displayed, the variation condition of the variable is compared by using the curve, and the historical statistical information of each variable cannot be output;

second, no specific evaluation conclusions can be made for the case of the target control dimension (e.g., lateral, longitudinal control) of the intelligent driving system.

Exemplary System

Fig. 1 illustrates an exemplary system architecture 100 to which a vehicle travel control evaluation method or a vehicle travel control evaluation apparatus of an embodiment of the present disclosure may be applied.

As shown in fig. 1, system architecture 100 may include terminal device 101, network 102, and server 103. Network 102 is the medium used to provide communication links between terminal devices 101 and server 103. Network 102 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

A user may use terminal device 101 to interact with server 103 over network 102 to receive or send messages and the like. Various applications, such as a photographing application, a data analysis application, a web browser application, and the like, may be installed on the terminal apparatus 101.

The terminal device 101 may be various electronic devices including, but not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle-mounted terminal (e.g., a car navigation terminal), etc., and a fixed terminal such as a digital TV, a desktop computer, etc.

The server 103 may be a server that provides various services, such as a background data processing server that processes a travel state data set uploaded by the terminal device 101. The background data processing server may process the received travel state data set to obtain a processing result (e.g., a score of vehicle travel control).

It should be noted that the vehicle running control evaluation method provided by the embodiment of the present disclosure may be executed by the server 103 or the terminal device 101, and accordingly, the vehicle running control evaluation apparatus may be provided in the server 103 or the terminal device 101.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation. In the case where the travel state data set does not need to be acquired from a remote location, the system architecture described above may include no network, only a server or a terminal device.

Exemplary method

Fig. 2 is a flowchart illustrating a vehicle running control evaluation method according to an exemplary embodiment of the present disclosure. The embodiment can be applied to an electronic device (such as the terminal device 101 or the server 103 shown in fig. 1), and as shown in fig. 2, the method includes the following steps:

step 201, acquiring a driving state data set which is acquired during the driving of the vehicle and aims at a target control dimension.

In this embodiment, the electronic device may acquire a set of driving state data for a target control dimension acquired during driving of the vehicle. Where the target control dimension may be a set of certain types of control modes of the vehicle. For example, the control dimension of the vehicle may include a longitudinal control dimension and a lateral control dimension, and the target control dimension may be the longitudinal control dimension or the lateral control dimension.

The driving state data set of the target control dimension may comprise at least one driving state data subset, each driving state data subset may represent a state of the vehicle. For example, when the target control dimension is a lateral control dimension, the at least one driving state data subset may include a lateral control fault monitoring data subset, a lateral acceleration monitoring data subset, a steering wheel angle monitoring data subset, a yaw rate monitoring data subset, a lateral control torque monitoring data subset, and the like.

Usually, the driving state data set CAN be collected via a CAN (Controller Area Network) bus, i.e. the data format in the driving state data set conforms to the CAN bus protocol. Each sensor on the vehicle CAN transmit the acquired data to a controller on the vehicle through a CAN bus or a remote server through the controller, so as to obtain a driving state data set.

Step 202, counting at least one driving state data subset included in the driving state data set according to a corresponding statistical mode to obtain statistical data corresponding to each driving state data subset.

In this embodiment, the electronic device may perform statistics on at least one driving state data subset included in the driving state data set according to a corresponding statistical manner, so as to obtain statistical data corresponding to each driving state data subset.

Wherein each subset of driving state data may correspond to at least one statistical mode. For example, for the lateral acceleration monitoring data subset, the corresponding statistical manner may include averaging the lateral acceleration monitoring data, and the obtained statistical data is the average lateral acceleration; or, the corresponding statistical manner may include taking a maximum value for the lateral acceleration monitoring data, and the obtained statistical data is the maximum lateral acceleration. For another example, for the steering wheel angle monitoring data subset, the corresponding statistical manner may include averaging the steering wheel angle monitoring data, and the obtained statistical data is the average steering wheel angle.

In step 203, a target driving state data subset is determined from the at least one driving state data subset.

In this embodiment, the electronic device may determine a target driving state data subset from at least one driving state data subset. And the statistical data corresponding to the target driving state data subset meet the corresponding grading conditions.

The scoring condition may be set as needed, for example, each statistical data may correspond to a threshold, and when the statistical data is greater than or equal to the threshold, it is determined that the scoring condition is satisfied. Or, each statistical data may correspond to a plurality of preset value intervals, and when the statistical data is in a certain value interval, the corresponding scoring condition is met.

As an example, for the lateral acceleration monitoring data subset, the corresponding statistical data is an average lateral acceleration, and if the average lateral acceleration is greater than or equal to a preset threshold, the lateral acceleration monitoring data subset is determined as the target driving state data subset.

And 204, determining the score of the vehicle running control under the target control dimension based on the preset score corresponding to the target running state data subset.

In this embodiment, the electronic device may determine a score of the vehicle travel control in the target control dimension based on a preset score corresponding to the target travel state data subset.

As an example, each of the driving state data subsets may correspond to a preset score, and the electronic device may determine the preset score corresponding to the target driving state data subset as the score of the vehicle driving control in the target control dimension. Or, when the number of the target driving state data subsets is at least two, the preset scores corresponding to the target driving state data subsets may be added or weighted and summed to obtain the score of the vehicle driving control in the target control dimension.

The above score may be used to evaluate the driving state of the vehicle in the target control dimension. For example, the higher the score value is, the more normal the running state of the vehicle is indicated, i.e., the better the effect of the vehicle control is; the higher the score value is, the more abnormal the running state of the vehicle is, and the higher the probability of the erroneous control of the vehicle is.

According to the method provided by the above embodiment of the present disclosure, the driving state data set of the target control dimension of the vehicle is obtained, and at least one driving state data subset included in the driving state data set is counted according to a corresponding statistical manner, so as to obtain statistical data corresponding to each driving state data subset, then the target driving state data subset is determined according to the scoring condition of each driving state data subset, and finally the score of the vehicle driving control is determined based on the preset score corresponding to the target driving state data subset. The method and the device realize comprehensive statistics of data of all aspects under the target control dimension, accurately obtain scores for evaluating vehicle running control based on the statistical data, do not need a user to manually operate a running state data set, can automatically, efficiently and more pertinently evaluate the running state of the vehicle control system, and further contribute to improving the efficiency of the vehicle control system.

In some alternative implementations, as shown in fig. 3, the step 203 includes:

step 2031, based on the preset priority order, sequentially determining whether the statistical data corresponding to each driving state data subset in the at least one driving state data subset meets the corresponding scoring condition.

The priority order is usually set according to the degree of importance of the safety of the vehicle running. For example, when the target control dimension is a lateral control dimension, the respective driving state data subsets are sorted in order of priority from high to low as: a lateral control fault monitoring data subset, a lateral acceleration monitoring data subset, a steering wheel angle monitoring data subset, a yaw rate monitoring data subset, a lateral control torque monitoring data subset. Accordingly, the statistical data of the driving state data subsets are ordered as follows: lateral control failure number, average lateral acceleration, average steering wheel angle, average yaw rate, average lateral control torque.

Step 2032, in response to determining that the statistical data corresponding to the current driving state data subset meets the corresponding scoring condition, determining the current driving state data subset as the target driving state data subset.

Specifically, whether each statistical data meets the scoring condition is sequentially judged according to the priority order, if yes, the driving state data subset corresponding to the statistical data meeting the scoring condition at present is determined as the target driving state data subset, then the scoring condition judgment is not performed on other statistical data, and then step 204 is executed; if not, continuously judging whether the next statistical data meets the corresponding grading condition.

According to the embodiment, whether each statistical data meets the corresponding scoring condition is sequentially judged according to the priority order, and the score of the vehicle running control under the target control dimension is generated as long as one statistical data meeting the scoring condition exists, so that the direct scoring according to the priority order of the running state data subset is realized, the complex scoring calculation is not needed, and the scoring efficiency is improved. Since the score is directly related to the target driving state data subset, the score can reflect the type of abnormal conditions in the vehicle control process, and the abnormal conditions can be quickly positioned.

In some optional implementations, as shown in fig. 4, after step 204, the method may further include:

step 205, displaying at least one of the following items on a display: and the score of the vehicle running control and the statistical data corresponding to the running state data subsets included in the running state data set.

As shown in fig. 5, when the target control dimension is the lateral control dimension, a lateral control effectiveness evaluation interface including a lateral control score (i.e., the score in step 204), the number of lateral control failures, the average lateral acceleration, the average steering wheel angle, the average yaw rate, and the average lateral control torque may be displayed on the display. It should be understood that when the method is executed multiple times for different target control dimensions, multiple control effect evaluation interfaces as shown in fig. 5 may be generated, for example, a longitudinal control effect evaluation interface may also be generated.

By displaying the statistical data and the scores on the display, the user can more intuitively view the running condition of the vehicle in the target control dimension, and the analysis of the control condition of the vehicle by the user is facilitated more efficiently.

and step 206, responding to the condition that the viewing operation aiming at the selected driving state data subset in the at least one driving state data subset is triggered, carrying out visualization processing on the selected driving state data subset, and displaying the visualized data on the display.

The selected driving state data subset may be selected from at least one driving state data subset in various manners, for example, the selected driving state data subset may be selected by a user by clicking an identifier of the driving state data subset, inputting a command, or the like, or may be selected by the electronic device according to a set default driving state data subset, or may be selected from at least one driving state data subset in sequence according to a preset display period.

The visualization process may include any process, and for example, a curve may be plotted using the selected traveling state data subset, the plotted curve may be displayed on a display, or a histogram may be plotted using the selected traveling state data subset.

As shown in fig. 5, the lateral control effectiveness evaluation interface includes a curve (e.g., a lateral acceleration curve) for a selected subset of the driving state data, with the horizontal axis representing time and the vertical axis representing actual collected historical data.

In the embodiment, the selected driving state data subset is subjected to visualization processing, and the driving state data subset subjected to visualization processing is displayed on the display, so that a user can more intuitively view various types of data collected currently and historically under the target control dimension, and the efficiency of analyzing the control condition of the vehicle is improved.

In some alternative implementations, the target control dimension is a lateral control dimension or a longitudinal control dimension, and the driving state data set is a lateral driving state data set or a longitudinal driving state data set.

In general, the control dimension of the vehicle may include a longitudinal control dimension and a lateral control dimension, and then the target control dimension may be the longitudinal control dimension or the lateral control dimension. The longitudinal control dimension may be a set of control manners related to longitudinal running of the vehicle, and the lateral control dimension may be a set of control manners related to lateral running of the vehicle.

The vehicle control evaluation method aiming at the transverse control dimension and the longitudinal control dimension provided by the embodiment realizes the targeted monitoring of the longitudinal control and the transverse control in the vehicle running process, and is beneficial to more efficiently analyzing and improving the related control modes of the transverse control and the longitudinal control by utilizing the acquired data.

In some alternative implementations, the lateral driving state data set includes at least one of the following lateral driving state data subsets: a lateral control fault monitoring data subset, a lateral acceleration monitoring data subset, a steering wheel angle monitoring data subset, a Yaw rate monitoring data subset, a lateral control torque monitoring data subset.

The lateral control failure monitoring data subset may be a data set fed back by an EPS (Electric Power Steering) system.

The longitudinal driving state data set comprises at least one longitudinal driving state data subset of: a longitudinal control failure monitoring data subset, a longitudinal acceleration monitoring data subset, a vehicle speed monitoring data subset, a driving safety system monitoring data subset, and a longitudinal control torque monitoring data subset.

The longitudinal control failure monitoring data subset may be a data set fed back by an ESP system (Electronic Stability Program) or a VCU (Vehicle control unit). The subset of the driving safety System monitoring data may be a data set fed back, such as an ABS (Antilock Brake System), an ESP System, etc.

As shown in fig. 6, the step 202 may include any one of the following sub-steps:

step 2021, in response to determining that the driving state data set of the target control dimension is a lateral driving state data set, performing statistics on at least one lateral driving state data subset included in the lateral driving state data set according to a corresponding statistical manner to obtain at least one of the following statistical data: lateral control failure number, average lateral acceleration, average steering wheel angle, average yaw rate, average lateral control torque.

The number of the transverse control faults can be obtained according to data statistics fed back by the EPS system.

Step 2022, in response to determining that the driving state data set of the target control dimension is a longitudinal driving state data set, performing statistics on at least one longitudinal driving state data subset included in the longitudinal driving state data set according to a corresponding statistical manner, to obtain at least one of the following statistical data: longitudinal control failure times, average longitudinal acceleration, average vehicle speed, driving safety system triggering times, average longitudinal control torque, average longitudinal impact degree and longitudinal acceleration standard deviation.

The number of longitudinal control faults can be obtained according to data statistics fed back by an ESP system or a VCU. The triggering times of the driving safety system can be counted. The average longitudinal jerk may be a differential calculation performed at each point on the curve corresponding to the longitudinal acceleration, and an average of the results of the differential calculations.

The statistical data related to the average value may be obtained by averaging all the data included in the traveling state data subset, or may be obtained by averaging some of the data. For example, the user may select a time period over which to average the data collected.

The type of the driving state data subsets and the method for obtaining the corresponding statistical data provided by the embodiment can realize that the driving states of the vehicle in the transverse control dimension or the longitudinal control dimension can be comprehensively reflected by fewer types of driving state data subsets, thereby being beneficial to improving the efficiency of the vehicle driving control analysis.

In some optional implementations, the scoring condition of the statistical data corresponding to each of the at least one subset of the lateral driving state data includes at least one of:

the number of lateral control failures is greater than or equal to a preset number of lateral control failures, the average lateral acceleration is greater than or equal to a preset lateral acceleration, the average steering wheel angle is greater than or equal to a preset angle, the average yaw rate is greater than or equal to a preset yaw rate, and the average lateral control torque is greater than or equal to a preset lateral control torque.

The scoring condition of the statistical data corresponding to each of the at least one longitudinal driving state data subset includes at least one of the following:

the longitudinal control failure frequency is greater than or equal to a preset longitudinal control failure frequency, the average longitudinal acceleration is greater than or equal to a preset longitudinal acceleration, the average vehicle speed is greater than or equal to a preset vehicle speed, the triggering frequency of the driving safety system is greater than or equal to a preset triggering frequency, the average longitudinal control torque is greater than or equal to a preset longitudinal control torque, the average longitudinal impact is greater than or equal to a preset impact, and the longitudinal acceleration standard deviation is greater than or equal to a preset standard deviation.

When the statistical data corresponding to a certain driving state data subset meets the scoring condition, the fact that the vehicle control mode corresponding to the driving state data subset has a problem is shown, and then the corresponding vehicle driving control score can be determined.

The embodiment provides the scoring conditions corresponding to various driving state data subsets, and can accurately judge different driving state data subsets, so that the accuracy of generating the driving control score of the vehicle is improved.

Referring now to fig. 7A, an exemplary flow chart for determining a lateral control score in the lateral control dimension provided by embodiments of the present disclosure is shown. As shown in fig. 7A, according to the priority order of the statistical data corresponding to each lateral driving state data subset, first, it is determined whether the number of lateral control failures is greater than or equal to a preset number of lateral control failures, if so, a lateral control score of 0% is output, the process is ended, otherwise, the determination of the next priority is continued. And then, judging whether the average transverse acceleration is greater than or equal to the preset transverse acceleration, if so, outputting a transverse control score of 30%, ending the process, and otherwise, continuing to judge the next priority. And then, judging whether the average steering wheel turning angle is larger than or equal to a preset turning angle, if so, outputting a transverse control score of 45%, ending the process, and otherwise, continuing to judge the next priority. And then, judging whether the average yaw angular velocity is greater than or equal to a preset yaw angular velocity, if so, outputting a lateral control score of 55%, ending the process, and otherwise, continuing to judge the next priority. And finally, judging whether the average transverse control torque is larger than or equal to the preset transverse control torque, if so, outputting a transverse control score of 75 percent, and ending the process, otherwise, outputting a transverse control score of 85 percent, and ending the process.

The lateral control failure times shown in fig. 7A have the highest priority, that is, if the lateral control failure times are greater than or equal to the preset lateral control failure times, which indicates that the failure occurring in the lateral control of the vehicle is the most serious, the lateral control score is directly set to 0%.

Referring to FIG. 7B, an exemplary flow chart for determining a longitudinal control score in the longitudinal control dimension is shown. As shown in fig. 7B, according to the priority order of the statistical data corresponding to each longitudinal driving state data subset, first, it is determined whether the longitudinal control failure frequency is greater than or equal to a preset longitudinal control failure frequency, if so, a longitudinal control score of 0% is output, the process is ended, otherwise, the next priority determination is continued. And then, judging whether the average longitudinal impact degree is greater than or equal to a preset longitudinal impact degree, if so, outputting a longitudinal control score of 50%, ending the process, and otherwise, continuing to judge the next priority. And then, judging whether the triggering frequency of the driving safety system is greater than or equal to the preset triggering frequency, if so, outputting a longitudinal control score of 65%, ending the process, and otherwise, continuing to judge the next priority. And finally, judging whether the longitudinal acceleration standard deviation is greater than or equal to a preset standard deviation, if so, outputting a longitudinal control score of 75 percent, ending the process, otherwise, outputting a longitudinal control score of 85 percent, and ending the process.

The priority of the longitudinal control failure number shown in fig. 7B is highest, that is, if the longitudinal control failure number is greater than or equal to the preset longitudinal control failure number, which indicates that the failure occurring in the longitudinal control of the vehicle is the most serious, the longitudinal control score is directly set to 0%.

It should be noted that, in the example shown in fig. 7A and 7B, the maximum score is 85%, and according to actual needs, more judgment conditions may be set and corresponding scores may be set arbitrarily.

Exemplary devices

Fig. 8 is a schematic structural diagram of a vehicle running control evaluation device according to an exemplary embodiment of the present disclosure. The present embodiment can be applied to an electronic device, and as shown in fig. 8, a vehicle travel control evaluation apparatus includes: an obtaining module 801, configured to obtain a driving state data set for a target control dimension acquired during driving of a vehicle; a statistical module 802, configured to perform statistics on at least one driving state data subset included in the driving state data set according to a corresponding statistical manner, to obtain statistical data corresponding to each driving state data subset; a first determining module 803, configured to determine a target driving state data subset from at least one driving state data subset, where statistical data corresponding to the target driving state data subset meets a corresponding scoring condition; the second determining module 804 is configured to determine a score of vehicle driving control in the target control dimension based on the preset score corresponding to the target driving state data subset.

In this embodiment, the acquisition module 801 may acquire a driving state data set for a target control dimension acquired during driving of the vehicle. Where the target control dimension may be a set of certain types of control modes of the vehicle. For example, the control dimension of the vehicle may include a longitudinal control dimension and a lateral control dimension, and the target control dimension may be the longitudinal control dimension or the lateral control dimension.

In this embodiment, the statistical module 802 may perform statistics on at least one driving state data subset included in the driving state data set according to a corresponding statistical manner, so as to obtain statistical data corresponding to each driving state data subset.

In this embodiment, the first determination module 803 may determine a target driving state data subset from at least one driving state data subset. And the statistical data corresponding to the target driving state data subset meet the corresponding grading conditions.

In this embodiment, the second determining module 804 may determine the score of the vehicle running control in the target control dimension based on a preset score corresponding to the target running state data subset.

As an example, each of the subsets of driving state data may correspond to a preset score, and the second determination module 804 may determine the preset score corresponding to the target subset of driving state data as the score of the vehicle driving control in the target control dimension. Or, when the number of the target driving state data subsets is at least two, the preset scores corresponding to the target driving state data subsets may be added or weighted and summed to obtain the score of the vehicle driving control in the target control dimension.

The above-described score may be used to evaluate the driving state of the vehicle in the target control dimension. For example, the higher the score value is, the more normal the running state of the vehicle is indicated, i.e., the better the effect of the vehicle control is; the higher the score value is, the more abnormal the running state of the vehicle is, and the higher the probability of the erroneous control of the vehicle is.

Referring to fig. 9, fig. 9 is a schematic structural diagram of a vehicle running control evaluation device according to another exemplary embodiment of the present disclosure.

In some optional implementations, the first determining module 803 includes: a first determining unit 8031, configured to sequentially determine, based on a preset priority order, whether statistical data corresponding to each driving state data subset of the at least one driving state data subset meets a corresponding scoring condition; a second determining unit 8032, configured to determine the current driving state data subset as the target driving state data subset in response to determining that the statistical data corresponding to the current driving state data subset satisfies the corresponding scoring condition.

In some optional implementations, the apparatus further comprises: a first display module 805 for displaying on a display at least one of: and the score of the vehicle running control and the statistical data corresponding to the running state data subsets included in the running state data set.

In some optional implementations, the apparatus further comprises: a second display module 806, configured to perform visualization processing on a selected driving status data subset of the at least one driving status data subset in response to triggering the viewing operation for the selected driving status data subset, and display the visualized data on the display.

In some alternative implementations, the lateral driving state data set includes at least one of the following lateral driving state data subsets: a lateral control fault monitoring data subset, a lateral acceleration monitoring data subset, a steering wheel angle monitoring data subset, a yaw rate monitoring data subset, a lateral control torque monitoring data subset; the longitudinal driving state data set comprises at least one longitudinal driving state data subset of: a longitudinal control fault monitoring data subset, a longitudinal acceleration monitoring data subset, a vehicle speed monitoring data subset, a driving safety system monitoring data subset and a longitudinal control torque monitoring data subset; the statistics module 802 includes: a first statistical unit 8021, configured to, in response to determining that the driving state data set of the target control dimension is a lateral driving state data set, perform statistics on at least one lateral driving state data subset included in the lateral driving state data set in a corresponding statistical manner, so as to obtain at least one of the following statistical data: the number of lateral control failures, the average lateral acceleration, the average steering wheel angle, the average yaw rate, and the average lateral control torque; or, the second statistical unit 8022 is configured to, in response to determining that the driving state data set of the target control dimension is the longitudinal driving state data set, perform statistics on at least one longitudinal driving state data subset included in the longitudinal driving state data set according to a corresponding statistical manner, so as to obtain at least one of the following statistical data: longitudinal control failure times, average longitudinal acceleration, average vehicle speed, driving safety system triggering times, average longitudinal control torque, average longitudinal impact degree and longitudinal acceleration standard deviation.

In some optional implementations, the scoring condition of the statistical data corresponding to each of the at least one subset of the lateral driving state data includes at least one of: the number of lateral control failures is greater than or equal to a preset number of lateral control failures, the average lateral acceleration is greater than or equal to a preset lateral acceleration, the average steering wheel angle is greater than or equal to a preset steering angle, the average yaw rate is greater than or equal to a preset yaw rate, and the average lateral control torque is greater than or equal to a preset lateral control torque; the scoring condition of the statistical data corresponding to each of the at least one longitudinal driving state data subset includes at least one of the following: the longitudinal control failure frequency is greater than or equal to the preset longitudinal control failure frequency, the average longitudinal acceleration is greater than or equal to the preset longitudinal acceleration, the average vehicle speed is greater than or equal to the preset vehicle speed, the triggering frequency of the driving safety system is greater than or equal to the preset triggering frequency, and the average longitudinal control torque is greater than or equal to the preset longitudinal control torque.

The vehicle driving control evaluation device provided by the above embodiment of the present disclosure obtains the driving state data set of the target control dimension of the vehicle, and performs statistics on at least one driving state data subset included in the driving state data set according to a corresponding statistical manner to obtain statistical data corresponding to each driving state data subset, then determines the target driving state data subset according to the scoring condition of each driving state data subset, and finally determines the score of the vehicle driving control based on the preset score corresponding to the target driving state data subset. The method and the device realize comprehensive statistics of data of all aspects under the target control dimension, accurately obtain scores for evaluating vehicle running control based on the statistical data, do not need a user to manually operate a running state data set, can automatically, efficiently and more pertinently evaluate the running state of the vehicle control system, and further contribute to improving the efficiency of the vehicle control system.

Exemplary electronic device

Next, an electronic apparatus according to an embodiment of the present disclosure is described with reference to fig. 10. The electronic device may be either or both of the terminal device 101 and the server 103 as shown in fig. 1, or a stand-alone device separate from them, which may communicate with the terminal device 101 and the server 103 to receive the collected input signals therefrom.

FIG. 10 shows a block diagram of an electronic device in accordance with an embodiment of the disclosure.

As shown in fig. 10, the electronic device 1000 includes one or more processors 1001 and memory 1002.

The processor 1001 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 1000 to perform desired functions.

Memory 1002 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. Volatile memory can include, for example, Random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, Read Only Memory (ROM), a hard disk, flash memory, and the like. One or more computer program instructions may be stored on a computer readable storage medium and executed by the processor 1001 to implement the vehicle travel control evaluation methods of the various embodiments of the present disclosure described above and/or other desired functions. Various contents such as a travel state data set may also be stored in the computer-readable storage medium.

In one example, the electronic device 1000 may further include: an input device 1003 and an output device 1004, which are interconnected by a bus system and/or other form of connection mechanism (not shown).

For example, when the electronic device is the terminal device 101 or the server 103, the input device 1003 may be a device such as a mouse, a keyboard, and various sensors, and inputs a travel state data set, various commands, and the like. When the electronic device is a stand-alone device, the input device 1003 may be a communication network connector for receiving the input travel state data set, various commands, and the like from the terminal device 101 and the server 103.

The output device 1004 may output various information including the score of the determined vehicle running control to the outside. The output devices 1004 may include, for example, a display, speakers, printer, and the like, as well as a communication network and its connected remote output devices.

Of course, for simplicity, only some of the components of the electronic device 1000 relevant to the present disclosure are shown in fig. 10, and components such as buses, input/output interfaces, and the like are omitted. In addition, electronic device 1000 may include any other suitable components depending on the particular application.

Exemplary computer program product and computer-readable storage Medium

In addition to the above-described methods and apparatus, embodiments of the present disclosure may also be a computer program product comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in the vehicle travel control assessment method according to various embodiments of the present disclosure described in the "exemplary methods" section of this specification, supra.

The computer program product may write program code for carrying out operations for embodiments of the present disclosure in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.

Furthermore, embodiments of the present disclosure may also be a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, cause the processor to perform steps in a vehicle travel control evaluation method according to various embodiments of the present disclosure described in the "exemplary methods" section above in this specification.

The computer-readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The foregoing describes the general principles of the present disclosure in conjunction with specific embodiments, however, it is noted that the advantages, effects, etc. mentioned in the present disclosure are merely examples and are not limiting, and they should not be considered essential to the various embodiments of the present disclosure. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the disclosure is not intended to be limited to the specific details so described.

In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts in the embodiments are referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

The block diagrams of devices, apparatuses, systems referred to in this disclosure are only given as illustrative examples and are not intended to require or imply that the connections, arrangements, configurations, etc. must be made in the manner shown in the block diagrams. These devices, apparatuses, devices, systems may be connected, arranged, configured in any manner, as will be appreciated by those skilled in the art. Words such as "including," "comprising," "having," and the like are open-ended words that mean "including, but not limited to," and are used interchangeably therewith. The words "or" and "as used herein mean, and are used interchangeably with, the word" and/or, "unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".

The methods and apparatus of the present disclosure may be implemented in a number of ways. For example, the methods and apparatus of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above unless specifically stated otherwise. Further, in some embodiments, the present disclosure may also be embodied as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.

It is also noted that in the devices, apparatuses, and methods of the present disclosure, each component or step can be decomposed and/or recombined. These decompositions and/or recombinations are to be considered equivalents of the present disclosure.

The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the disclosure. Thus, the present disclosure is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit embodiments of the disclosure to the form disclosed herein. While a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, alterations, additions and sub-combinations thereof.

Claims

1. A vehicle running control evaluation method comprising:

acquiring a driving state data set which is acquired during the driving of the vehicle and aims at a target control dimension;

counting at least one driving state data subset included in the driving state data set according to a corresponding counting mode to obtain counting data corresponding to each driving state data subset;

determining a target driving state data subset from the at least one driving state data subset, wherein the statistical data corresponding to the target driving state data subset meets the corresponding grading condition;

and determining the score of the vehicle running control under the target control dimension based on the preset score corresponding to the target running state data subset.

2. The method of claim 1, wherein said determining a target driving state data subset from said at least one driving state data subset comprises:

whether the statistical data corresponding to each driving state data subset in the at least one driving state data subset meets the corresponding grading condition or not is sequentially determined based on a preset priority sequence;

in response to determining that the statistical data corresponding to the current driving state data subset meets the corresponding scoring condition, determining the current driving state data subset as a target driving state data subset.

3. The method of claim 1, wherein after the determining the score for vehicle travel control in the target control dimension, the method further comprises:

displaying on the display at least one of: and the score of the vehicle running control and the running state data subset included in the running state data set respectively correspond to the statistical data.

4. The method of claim 1, wherein after the determining the score for vehicle travel control in the target control dimension, the method further comprises:

in response to triggering a viewing operation for a selected one of the at least one subset of driving state data, performing visualization processing on the selected subset of driving state data and displaying the visualized data on a display.

5. A method according to any one of claims 1-4, wherein the target control dimension is a lateral control dimension or a longitudinal control dimension and the driving status data set is a lateral driving status data set or a longitudinal driving status data set.

6. The method according to claim 5, wherein the set of lateral driving state data comprises at least one of the following subsets of lateral driving state data: a lateral control fault monitoring data subset, a lateral acceleration monitoring data subset, a steering wheel angle monitoring data subset, a yaw rate monitoring data subset, a lateral control torque monitoring data subset;

the longitudinal driving state data set comprises at least one longitudinal driving state data subset of: a longitudinal control fault monitoring data subset, a longitudinal acceleration monitoring data subset, a vehicle speed monitoring data subset, a driving safety system monitoring data subset and a longitudinal control torque monitoring data subset;

the step of counting at least one driving state data subset included in the driving state data set according to a corresponding statistical manner to obtain statistical data corresponding to each driving state data subset includes:

in response to determining that the driving state data set of the target control dimension is a transverse driving state data set, performing statistics on at least one transverse driving state data subset included in the transverse driving state data set according to a corresponding statistical mode to obtain at least one of the following statistical data: the number of lateral control failures, the average lateral acceleration, the average steering wheel angle, the average yaw rate, and the average lateral control torque; alternatively, the first and second electrodes may be,

in response to the fact that the running state data set of the target control dimension is determined to be a longitudinal running state data set, counting at least one longitudinal running state data subset included in the longitudinal running state data set according to a corresponding statistical mode, and obtaining at least one piece of statistical data as follows: longitudinal control failure times, average longitudinal acceleration, average vehicle speed, driving safety system triggering times, average longitudinal control torque, average longitudinal impact degree and longitudinal acceleration standard deviation.

7. The method according to claim 6, wherein the scoring condition of the statistical data respectively corresponding to the at least one subset of lateral driving state data comprises at least one of: the number of lateral control failures is greater than or equal to a preset number of lateral control failures, the average lateral acceleration is greater than or equal to a preset lateral acceleration, the average steering wheel steering angle is greater than or equal to a preset steering angle, the average yaw angular velocity is greater than or equal to a preset yaw angular velocity, and the average lateral control torque is greater than or equal to a preset lateral control torque;

the scoring condition of the statistical data corresponding to each of the at least one longitudinal driving state data subset includes at least one of the following: the longitudinal control failure frequency is greater than or equal to a preset longitudinal control failure frequency, the average longitudinal acceleration is greater than or equal to a preset longitudinal acceleration, the average vehicle speed is greater than or equal to a preset vehicle speed, the driving safety system triggering frequency is greater than or equal to a preset triggering frequency, and the average longitudinal control torque is greater than or equal to a preset longitudinal control torque.

8. A vehicle running control evaluation device comprising:

the acquisition module is used for acquiring a driving state data set which is acquired during the driving of the vehicle and aims at the target control dimension;

the statistical module is used for carrying out statistics on at least one driving state data subset included in the driving state data set according to a corresponding statistical mode to obtain statistical data corresponding to each driving state data subset;

the first determination module is used for determining a target driving state data subset from the at least one driving state data subset, wherein the statistical data corresponding to the target driving state data subset meets the corresponding scoring condition;

and the second determination module is used for determining the score of the vehicle running control under the target control dimension based on the preset score corresponding to the target running state data subset.

9. A computer-readable storage medium, the storage medium storing a computer program for performing the method of any of the preceding claims 1-7.

10. An electronic device, the electronic device comprising:

a processor;

a memory for storing executable instructions of the processor;

the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method of any one of claims 1 to 7.