Driving stability scoring method and system, vehicle-mounted terminal and storage medium

Abstract

The invention provides a driving stability scoring method and system, a vehicle-mounted terminal and a storage medium, and the method comprises the steps: obtaining the driving data of a vehicle, the driving data comprising the real-time map data in the current time period and the real-time fluctuation amplitude and real-time fluctuation frequency of the vehicle speed; according to the current time period, the real-time map data and a preset mapping relation, the reference fluctuation amplitude and the reference fluctuation frequency of the vehicle speed are obtained through matching; calculating the real-time fluctuation amplitude and the reference fluctuation amplitude to determine a fluctuation amplitude score, and calculating the real-time fluctuation frequency and the reference fluctuation frequency to determine a fluctuation frequency score; and determining a smoothness score according to the fluctuation amplitude score and / or the fluctuation frequency score. The driving stability is evaluated through the two dimensions of the amplitude and the frequency of the speed fluctuation in combination with the two influence factors of the time period and the map data, so that the standardized and accurate quantitative evaluation of the driving stability is realized, and the accuracy of the evaluation result of the driving stability is ensured.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle driving behavior evaluation, and in particular to a driving stability scoring method, system, vehicle-mounted terminal and storage medium. Background Art

[0002] As the number of cars increases, traffic safety issues have become more prominent, among which the safety issues caused by driving behavior have become the focus of widespread attention. As an important dimension for evaluating driving safety, driving stability directly affects many aspects such as passenger comfort, vehicle wear and tear, and the incidence of traffic accidents. Generally speaking, different cars have different driving stability, and the same car has different driving stability when driven by different people. Therefore, it is very important to evaluate driving stability to improve the safety and comfort of the entire vehicle.

[0003] In related technologies, the evaluation of driving stability mainly relies on the driver's self-perception or the manual observation and experience judgment of a third party. These methods have obvious limitations, especially the low accuracy of driving stability evaluation. On the one hand, manual observation and experience judgment are easily affected by various factors, resulting in strong subjectivity and poor repeatability of the evaluation results; on the other hand, due to the lack of unified standards and quantitative indicator systems, it is difficult for different evaluators to reach a consensus, which affects the fairness and scientificity of the stability evaluation; in addition, traditional methods can often only provide qualitative analysis results and cannot achieve accurate quantification of driving stability, which to a certain extent limits the effective formulation of driving behavior improvement measures.

[0004] Therefore, it is necessary to propose a driving stability evaluation scheme to achieve accurate judgment of driving stability in a standardized and precisely quantified manner. Summary of the invention

[0005] In view of the shortcomings of the prior art described above, the present application discloses a driving smoothness scoring method, system, vehicle-mounted terminal and storage medium, which are used to solve the technical problem in the prior art that driving smoothness cannot be standardized and accurately quantified, resulting in inaccurate evaluation results.

[0006] In a first aspect, the present application provides a driving smoothness scoring method, the method comprising: acquiring driving data of a vehicle, the driving data comprising real-time map data within a current time period and a real-time fluctuation amplitude and a real-time fluctuation frequency of a vehicle speed; matching a reference fluctuation amplitude and a reference fluctuation frequency of a vehicle speed according to the current time period, the real-time map data and a preset mapping relationship, the mapping relationship being a correspondence between the two dimensions of time period and map data mapped to the fluctuation amplitude and the fluctuation frequency respectively; calculating the real-time fluctuation amplitude and the reference fluctuation amplitude to determine a fluctuation amplitude score, and calculating the real-time fluctuation frequency and the reference fluctuation frequency to determine a fluctuation frequency score; determining a smoothness score according to the fluctuation amplitude score and / or the fluctuation frequency score.

[0007] In one embodiment of the present application, the method for calculating the real-time fluctuation amplitude includes: obtaining the instantaneous speed and the average speed of the vehicle in the current time period; calculating the instantaneous speed and the average speed according to the duration corresponding to the current time period to obtain the cumulative fluctuation mileage, and calculating the instantaneous rate to obtain the cumulative mileage; calculating the ratio of the cumulative fluctuation mileage to the cumulative mileage to obtain the real-time fluctuation amplitude.

[0008] In one embodiment of the present application, the method for calculating the real-time fluctuation frequency includes: drawing an instantaneous speed curve and an average speed curve based on the instantaneous speed and the average speed in the current time period; recording the number of intersections between the instantaneous speed curve and the average speed curve; calculating the ratio of the number of intersections to the duration to obtain the real-time fluctuation frequency.

[0009] In one embodiment of the present application, the method for constructing the mapping relationship includes: collecting vehicle speed data of multiple sample vehicles, the vehicle speed data including sample instantaneous speeds and sample average speeds in different time periods and under different map data, and the map data including at least one of geographical location, road type, traffic flow and signal light status; for each of the vehicle speed data, performing speed fluctuation analysis on the corresponding time period, the sample instantaneous speed and the sample average speed to obtain sample fluctuation amplitude and sample fluctuation frequency; calculating an average value of multiple sample fluctuation amplitudes to obtain the reference fluctuation amplitude, and calculating an average value of multiple sample fluctuation frequencies to obtain the reference fluctuation frequency; constructing the mapping relationship according to the reference fluctuation amplitude and the reference fluctuation frequency in different time periods and under different map data.

[0010] In one embodiment of the present application, the instantaneous speed includes the instantaneous speed of the sampling points at multiple sampling points within the current time period, and the time intervals between the sampling points are the same; the instantaneous speed and the average speed are calculated according to the duration corresponding to the current time period to obtain the cumulative fluctuation mileage, including: obtaining the sampling time corresponding to each of the sampling points and the cumulative mileage of the sampling points, and calculating the cumulative mileage of the sampling points and the sampling time to obtain the average speed of the sampling points at each of the sampling points; for each of the sampling points, the corresponding instantaneous speed of the sampling point, the average speed of the sampling point and the time interval are calculated to obtain the cumulative fluctuation mileage of the sampling point; and the cumulative fluctuation mileage of multiple sampling points is summed to obtain the cumulative fluctuation mileage.

[0011] In one embodiment of the present application, the calculation method of the fluctuation amplitude score and the fluctuation frequency score includes: calculating a first relative error between the real-time fluctuation amplitude and the reference fluctuation amplitude, and calculating a second relative error between the real-time fluctuation frequency and the reference fluctuation frequency; determining the fluctuation amplitude score based on the first relative error, and determining the fluctuation frequency score based on the second relative error.

[0012] In one embodiment of the present application, determining the smoothness score based on the fluctuation amplitude score and / or the fluctuation frequency score includes: using the fluctuation amplitude score as the smoothness score; or using the fluctuation frequency score as the smoothness score; or performing weighted calculation on the fluctuation amplitude score and the fluctuation frequency score according to a preset weight coefficient to obtain the smoothness score.

[0013] In a second aspect, the present application provides an intra-process communication system, the system comprising: an acquisition module for acquiring driving data of a vehicle, the driving data comprising real-time map data within a current time period and a real-time fluctuation amplitude and a real-time fluctuation frequency of a vehicle speed; a matching module for matching a reference fluctuation amplitude and a reference fluctuation frequency of a vehicle speed according to the current time period, the real-time map data and a preset mapping relationship, the mapping relationship being a correspondence between two dimensions, namely, time period and map data, mapped to fluctuation amplitude and fluctuation frequency, respectively; a calculation module for calculating the real-time fluctuation amplitude and the reference fluctuation amplitude to obtain a fluctuation amplitude score, and calculating the real-time fluctuation frequency and the reference fluctuation frequency to obtain a fluctuation frequency score; a scoring module for determining a smoothness score according to the fluctuation amplitude score and / or the fluctuation frequency score.

[0014] In a third aspect, the present application provides a vehicle-mounted terminal, comprising: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by one or more processors, the vehicle-mounted terminal implements the driving smoothness scoring method described in the first aspect.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor of a computer, the computer executes the driving smoothness scoring method described in the first aspect.

[0016] As described above, the driving stability scoring method, system, vehicle-mounted terminal and storage medium provided by the embodiments of the present application have the following beneficial effects:

[0017] First, the driving data of the vehicle is obtained, and the driving data includes real-time map data in the current time period and the real-time fluctuation amplitude and real-time fluctuation frequency of the vehicle speed, so as to match the reference fluctuation amplitude and reference fluctuation frequency of the vehicle speed according to the current time period, the real-time map data and the preset mapping relationship, wherein the mapping relationship is the corresponding relationship between the two dimensions of the time period and the map data and the fluctuation amplitude and the fluctuation frequency respectively, then the real-time fluctuation amplitude and the reference fluctuation amplitude are calculated to determine the fluctuation amplitude score, and the real-time fluctuation frequency and the reference fluctuation frequency are calculated to determine the fluctuation frequency score, and finally the smoothness score is determined according to the fluctuation amplitude score and / or the fluctuation frequency score, and the driving smoothness is comprehensively evaluated by considering the real-time speed fluctuation degree, that is, the fluctuation amplitude and the fluctuation frequency in two dimensions, and the two influencing factors of the time period and the map data are combined in time and space, and the reference fluctuation amplitude and the reference fluctuation amplitude under the current time period and the real-time map data are sought to evaluate the real-time speed fluctuation amplitude and the real-time fluctuation frequency, so as to realize the standardized and accurate quantitative evaluation of driving smoothness and ensure the accuracy of the driving smoothness evaluation result.

[0018] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0020] Figure 1 is a schematic diagram of an implementation environment of a driving stability scoring system shown in an exemplary embodiment of the present application;

[0021] Figure 2 is a flow chart of a driving stability scoring method shown in an exemplary embodiment of the present application;

[0022] Figure 3 is a schematic diagram of a vehicle speed fluctuation amplitude shown by an exemplary embodiment of the present application;

[0023] Figure 4 is a schematic diagram of a vehicle speed fluctuation frequency shown in an exemplary embodiment of the present application;

[0024] Figure 5 is a schematic diagram of a speed change curve shown in an exemplary embodiment of the present application;

[0025] Figure 6 is a block diagram of a driving smoothness scoring system shown in an exemplary embodiment of the present application;

[0026] Figure 7 It is a structural schematic diagram of a vehicle-mounted terminal provided in one embodiment of the present application. DETAILED DESCRIPTION

[0027] The following will describe the implementation methods of the present application with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific implementation methods, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be understood that the preferred embodiments are only for illustrating the present application, not for limiting the scope of protection of the present application.

[0028] It should be noted that the illustrations provided in the following embodiments are only used to illustrate the basic concept of the present application in a schematic manner, and therefore the illustrations only show components related to the present application rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the form, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0029] In the following description, a large number of details are discussed to provide a more thorough explanation of the embodiments of the present application. However, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present application difficult to understand.

[0030] As an important dimension for evaluating driving safety, driving stability directly affects the comfort of passengers, the degree of wear and tear of the vehicle, the incidence of traffic accidents and many other aspects. Therefore, it is very important to evaluate driving stability to improve the safety and comfort of the whole vehicle. In the relevant technology, the evaluation of driving stability mainly relies on the driver's self-perception or the manual observation and experience judgment of a third party. However, the inventors of this application have found that these methods have obvious limitations, especially the low accuracy of driving stability evaluation. On the one hand, manual observation and experience judgment are easily affected by various factors, resulting in strong subjectivity and poor repeatability of the evaluation results; on the other hand, due to the lack of unified standards and quantitative indicator systems, it is difficult for different evaluators to reach a consensus, which affects the fairness and scientificity of the stability evaluation; in addition, traditional methods can often only provide qualitative analysis results, and cannot achieve accurate quantification of driving stability, which to a certain extent limits the effective formulation of driving behavior improvement measures.

[0031] Therefore, see Figure 1 , Figure 1 FIG. 1 is a schematic diagram of an implementation environment of a driving stability scoring system shown in an exemplary embodiment of the present application. Figure 1 As shown, the implementation environment includes a vehicle 110 and a driving stability scoring system 120, wherein the driving stability scoring system 120 is embedded in the vehicle 110 and is used to score the driving stability of the vehicle 110. The driving stability scoring system 120 includes but is not limited to a vehicle computer system, an on-board computer, etc., and comprehensively evaluates the driving stability by considering the real-time speed fluctuation degree, that is, the fluctuation amplitude and the fluctuation frequency in two dimensions. In addition, the two influencing factors of the time period and the map data are combined in time and space, and the reference fluctuation amplitude and the reference fluctuation amplitude under the current time period and the real-time map data are sought to evaluate the real-time speed fluctuation amplitude and the real-time fluctuation frequency, thereby realizing the standardized and accurate quantitative evaluation of driving stability and ensuring the accuracy of the driving stability evaluation results.

[0032] See also Figure 2 , Figure 2 is a flowchart of a driving stability scoring method shown in an exemplary embodiment of the present application. The method can be applied to Figure 1 The implementation environment shown is a schematic diagram. It should be understood that the method may also be applicable to other exemplary implementation environments, and this embodiment does not limit the implementation environment to which the method is applicable.

[0033] like Figure 2 As shown, in an exemplary embodiment, the driving smoothness scoring method includes at least steps S210 to S240, which are described in detail as follows:

[0034] Step S210, obtaining driving data of the vehicle, the driving data including real-time map data in the current period and real-time fluctuation amplitude and real-time fluctuation frequency of the vehicle speed.

[0035] Among them, the current time period refers to the time period of the day in which the current time is located. For example, every two hours in a day is a time period, then 8:00-10:00 is a time period, and 12:00-14:00 is a time period; real-time map data refers to the geographic information of the current location, including at least one of the real-time geographic location, road type, traffic flow and traffic light status; the real-time fluctuation amplitude of the vehicle speed refers to the range of vehicle speed changes during driving, and the real-time fluctuation frequency of the vehicle speed refers to the number of times the vehicle speed changes during driving.

[0036] The greater the speed fluctuation, the less stable the vehicle is, and the more frequent the speed fluctuation, the less stable the vehicle is. For example, see Figure 3-Figure 4 , Figure 3 is a schematic diagram of a vehicle speed fluctuation amplitude shown in an exemplary embodiment of the present application, Figure 4 is a schematic diagram of a vehicle speed fluctuation frequency shown in an exemplary embodiment of the present application, such as Figure 3 As shown in the figure, the speed curve corresponds to a relatively large fluctuation in vehicle speed, such as Figure 4 As shown, the speed curve corresponds to a relatively high speed fluctuation frequency, and the vehicle has poor stability when driving in this way.

[0037] In one embodiment, the method for calculating the real-time fluctuation amplitude includes: obtaining the instantaneous speed and average speed of the vehicle in the current time period; calculating the instantaneous speed and the average speed according to the duration corresponding to the current time period to obtain the cumulative fluctuation mileage, and calculating the instantaneous rate to obtain the cumulative mileage; calculating the ratio of the cumulative fluctuation mileage to the cumulative mileage to obtain the real-time fluctuation amplitude.

[0038] Among them, instantaneous speed refers to the real-time speed at each moment in the current time period, which is a plurality of speed values ​​in the current time period; average speed refers to the average speed from the start moment to the subsequent moments in the current time period, which is also a plurality of speed values ​​in the current time period; cumulative fluctuating mileage refers to the mileage in which the speed fluctuates in the current time period; and cumulative mileage refers to the mileage traveled in the current time period.

[0039] In addition, the real-time fluctuation amplitude is between [0,1]. The larger the value, the greater the vehicle speed fluctuation amplitude and the worse the driving stability.

[0040] In this embodiment, the fluctuation mileage and the total mileage are integrated according to the speed deviation between the instantaneous speed and the average speed to obtain the cumulative fluctuation mileage and the cumulative mileage in the current time period, and the real-time fluctuation amplitude is determined by the ratio of the cumulative fluctuation mileage to the cumulative mileage. In this way, the fluctuation amplitude is quantified to ensure the accuracy of the real-time fluctuation amplitude obtained.

[0041] In this embodiment, the calculation formula for the accumulated fluctuation mileage is:

[0042]

[0043] Among them, D_x(t e ) represents the cumulative fluctuation mileage in the time period, t0 represents the starting time of the time period, t e Indicates the end time of the time period, t represents t0~t e At any time between , V(t) represents the instantaneous speed at time t, and V_avg(t) represents the average speed at time t.

[0044] In this embodiment, the calculation formula for the accumulated mileage is:

[0045]

[0046] Among them, D(t e ) represents the cumulative mileage in the time period, t0 represents the starting time of the time period, t e Indicates the end time of the time period, t represents t0~t e At any time between , V(t) represents the instantaneous velocity at time t.

[0047] In one embodiment, the instantaneous speed includes the instantaneous speed of the sampling points at multiple sampling points in the current time period, and the time intervals between the sampling points are the same; according to the duration corresponding to the current time period, the instantaneous speed and the average speed are calculated to obtain the cumulative fluctuation mileage, including: obtaining the sampling time corresponding to each sampling point and the cumulative mileage of the sampling point, and calculating the cumulative mileage of the sampling point and the sampling time to obtain the average speed of the sampling point at each sampling point; for each sampling point, the corresponding instantaneous speed of the sampling point, the average speed of the sampling point and the time interval are calculated to obtain the cumulative fluctuation mileage of the sampling point; the cumulative fluctuation mileage of multiple sampling points is summed to obtain the cumulative fluctuation mileage.

[0048] Considering that it is impossible to collect continuous V(t) values, that is, instantaneous speed, when the vehicle is driving, and thus it is impossible to calculate continuous V_avg(t) values, that is, average speed, but the cumulative mileage from the start time to each moment in the current period can be accurately collected. Therefore, the instantaneous speed can be obtained by sampling in the current period and the average speed can be calculated to calculate the cumulative fluctuating mileage.

[0049] In this embodiment, the sampling time and cumulative mileage at each sampling point are obtained, the average speed at each sampling point is calculated, and then the instantaneous speed, average speed and time interval corresponding to each sampling point are calculated to obtain the cumulative fluctuation mileage corresponding to each sampling point. Finally, the cumulative fluctuation mileage of multiple sampling points is summed to obtain the total cumulative fluctuation mileage, thereby ensuring the accuracy of the obtained cumulative fluctuation mileage.

[0050] In addition, the more sampling points there are, the more accurate the accumulated fluctuation mileage is, but this application does not limit the number of sampling points.

[0051] In this embodiment, the calculation formula for the accumulated fluctuation mileage is:

[0052]

[0053] Among them, D_x(t e ) represents the cumulative fluctuation mileage in the period, N represents the number of sampling points, i represents the i sampling point, t i represents the time corresponding to the i sampling point, V(t i ) represents the instantaneous velocity at sampling point i, D(t i ) indicates that the i sampling point (t0~t i The accumulated mileage at the time represents the average speed at sampling point i, and T represents the time interval between sampling points.

[0054] In one embodiment, the calculation method of the real-time fluctuation frequency includes: drawing an instantaneous speed curve and an average speed curve according to the instantaneous speed and the average speed in the current time period; recording the number of intersections between the instantaneous speed curve and the average speed curve; calculating the ratio of the number of intersections to the duration to obtain the real-time fluctuation frequency.

[0055] In this embodiment, speed fluctuation is defined as the number of times the instantaneous speed changes from higher than the average speed to lower than the average speed, or from lower than the average speed to higher than the average speed; the real-time fluctuation frequency is defined as the number of times the instantaneous speed fluctuates above and below the average speed, that is, each time the speed changes from higher than the average speed to lower than the average speed is recorded as one time, and each time the speed changes from lower than the average speed to higher than the average speed is recorded as one time. The number of times the instantaneous speed fluctuates above and below the average speed is the number of corner points of the instantaneous speed curve and the average speed curve after the start time, and the corner points of the two at the start time are not counted.

[0056] See also Figure 5 , Figure 5 FIG. 1 is a schematic diagram of a speed change curve shown in an exemplary embodiment of the present application. Figure 5As shown in the curve diagram, an instantaneous speed curve V(t) is included, and an average speed curve V_avg(t) is included. In this exemplary embodiment, from the start time t0 until t e , the number of corner points between the instantaneous speed curve and the average speed curve is 4.

[0057] In this embodiment, the real-time fluctuation frequency is evaluated by using the number of times the instantaneous speed of the vehicle fluctuates above and below the average speed in the current period and the duration of the current period, thereby ensuring the reliability of the obtained real-time fluctuation frequency.

[0058] In this embodiment, the calculation formula of the real-time fluctuation frequency is:

[0059]

[0060] Among them, H(t e ) represents the real-time fluctuation frequency in the time period, N represents the number of intersections between the instantaneous speed curve and the average speed curve in the time period, t0 represents the starting time of the time period, t e Indicates the end time of the period, t e -t0 indicates the duration of the time period.

[0061] Step S220, according to the current time period, real-time map data and the preset mapping relationship, the reference fluctuation amplitude and reference fluctuation frequency of the vehicle speed are matched, and the mapping relationship is the corresponding relationship between the two dimensions of time period and map data mapped to the fluctuation amplitude and the fluctuation frequency respectively.

[0062] In this embodiment, considering that the driving speed will be affected to different degrees in different time periods and different map data, the reference fluctuation amplitude and reference fluctuation frequency are determined according to the real-time time and space of the current time period and real-time map data. In this way, the reliability of the obtained reference fluctuation amplitude and reference fluctuation frequency is guaranteed, and the smoothness evaluation is performed in a flexible manner of reference fluctuation amplitude and reference fluctuation frequency, thereby ensuring the objectivity and reliability of the evaluation result.

[0063] Among them, the mapping relationship is the correspondence between the two dimensions of time period and map data and the fluctuation amplitude and fluctuation frequency respectively, specifically including: the fluctuation amplitude and fluctuation frequency in different time periods and the same map data; the fluctuation amplitude and fluctuation frequency in the same time period and different map data; the fluctuation amplitude and fluctuation frequency in different time periods and different map data.

[0064] In addition, because the real-time map data includes at least one of the geographical location, road type, traffic flow and signal light status, there are multiple influencing factors in the dimension of map data.

[0065] In one embodiment, a mapping relationship is constructed by: collecting vehicle speed data of multiple sample vehicles, the vehicle speed data including sample instantaneous speeds and sample average speeds at different time periods and under different map data, and the map data including at least one of geographical location, road type, traffic flow, and signal light status; for each vehicle speed data, performing speed fluctuation analysis on the corresponding time period, sample instantaneous speed, and sample average speed to obtain a sample fluctuation amplitude and a sample fluctuation frequency; calculating an average value of multiple sample fluctuation amplitudes to obtain a reference fluctuation amplitude, and calculating an average value of multiple sample fluctuation frequencies to obtain a reference fluctuation frequency; and constructing a mapping relationship based on the reference fluctuation amplitudes and reference fluctuation frequencies at different time periods and under different map data.

[0066] Among them, the method of obtaining the sample instantaneous speed and the sample average speed is the same as mentioned above, which will not be repeated here; the method of calculating the sample fluctuation amplitude and the sample fluctuation frequency is also the same as mentioned above, which will not be repeated here.

[0067] In this embodiment, sample data is collected to obtain reference fluctuation amplitudes and reference fluctuation frequencies in various time periods and various map data, thereby constructing a mapping relationship between the fluctuation amplitude and the fluctuation frequency mapped in the two dimensions of time period and map data. In this way, the flexibility of the reference fluctuation amplitude and the reference fluctuation frequency based on different time periods and different map data is guaranteed, so as to ensure the objectivity and reliability of the smoothness evaluation.

[0068] As a possible embodiment, before calculating the average value of multiple sample fluctuation amplitudes to obtain a reference fluctuation amplitude, the multiple sample fluctuation amplitudes are arranged in order of size, and the multiple sample fluctuation amplitudes are optimized by combining the extreme value method and the quantity method, that is, the larger and smaller values ​​in the multiple data are eliminated according to the distribution characteristics of the multiple sample fluctuation amplitudes to exclude the sample fluctuation amplitudes caused by extreme situations, and the sample fluctuation amplitudes whose number does not reach a preset quantity threshold are eliminated to exclude the sample fluctuation amplitudes caused by non-repeatable situations. Before calculating the average value of multiple sample fluctuation frequencies to obtain a reference fluctuation frequency, the same processing is also performed to ensure the reliability of the sample data.

[0069] Step S230, calculating the real-time fluctuation amplitude and the reference fluctuation amplitude to determine the fluctuation amplitude score, and calculating the real-time fluctuation frequency and the reference fluctuation frequency to determine the fluctuation frequency score.

[0070] In this embodiment, the real-time fluctuation amplitude and the reference fluctuation amplitude are calculated, that is, the fluctuation amplitude score is determined according to the deviation between the real-time fluctuation amplitude and the reference fluctuation amplitude; the real-time fluctuation frequency and the reference fluctuation frequency are calculated, that is, the fluctuation frequency score is determined according to the deviation between the real-time fluctuation frequency and the reference fluctuation frequency.

[0071] In one embodiment, the calculation method of the fluctuation amplitude score and the fluctuation frequency score includes: calculating the first relative error between the real-time fluctuation amplitude and the reference fluctuation amplitude, and calculating the second relative error between the real-time fluctuation frequency and the reference fluctuation frequency; determining the fluctuation amplitude score based on the first relative error, and determining the fluctuation frequency score based on the second relative error.

[0072] Among them, the higher the volatility score and the volatility frequency score, the smaller the volatility and the volatility frequency.

[0073] In this embodiment, the fluctuation amplitude score and the fluctuation frequency score are determined by means of relative error, which can more accurately measure the difference between real-time data and reference data. Not only the change in absolute value but also the proportion of change is considered, making the scoring more accurate. The relative error is converted into a standardized form of score, which can realize direct comparison of fluctuation amplitude and fluctuation frequency between different data.

[0074] In a possible embodiment, the score can be determined by the reciprocal of the relative error, that is, the reciprocal of the first relative error is used as the fluctuation amplitude score, and the reciprocal of the second relative error is used as the fluctuation frequency score. At this time, the higher the score, the smaller the fluctuation amplitude and fluctuation frequency.

[0075] In a possible embodiment, the score can be determined by standardizing the relative errors, that is, 1 minus the first relative error to obtain the fluctuation amplitude score, and 1 minus the second relative error to obtain the fluctuation frequency score. In this case, the higher the score, the smaller the fluctuation amplitude and fluctuation frequency.

[0076] Step S240, determining a stability score according to the fluctuation amplitude score and / or the fluctuation frequency score.

[0077] Among them, the higher the score, the better the stability.

[0078] In this embodiment, the stability score may be determined according to either the fluctuation amplitude score or the fluctuation frequency score, or may be determined in a combination of the fluctuation amplitude score and the fluctuation frequency score.

[0079] In one embodiment, a smoothness score is determined based on the fluctuation amplitude score and / or the fluctuation frequency score, including: using the fluctuation amplitude score as the smoothness score; or using the fluctuation frequency score as the smoothness score; or performing weighted calculation on the fluctuation amplitude score and the fluctuation frequency score according to a preset weight coefficient to obtain the smoothness score.

[0080] In this embodiment, one of the fluctuation amplitude score and the fluctuation frequency score can be selected for smoothness assessment, which is flexible. At the same time, the fluctuation amplitude score and the fluctuation frequency score can also be combined to perform smoothness assessment, which can more comprehensively evaluate the smoothness of vehicle driving.

[0081] In a possible embodiment, the scoring results are displayed and reported.

[0082] In a possible embodiment, the smoothness score is compared with a preset score threshold, and when the smoothness score is less than the score threshold, an early warning is triggered to ensure driving safety.

[0083] In a possible embodiment, when creating a mapping relationship, after optimizing multiple sample fluctuation amplitudes and multiple sample fluctuation frequencies, when obtaining the real-time fluctuation amplitude and the real-time fluctuation frequency, the real-time fluctuation amplitude will be compared with the optimized multiple sample fluctuation amplitudes to determine the user's fluctuation amplitude ranking, and the real-time fluctuation frequency will be compared with the optimized multiple sample fluctuation frequencies to determine the user's fluctuation frequency ranking.

[0084] For example, the real-time fluctuation frequency of a user under a certain map data in a certain period of time is 100 times / hour, and there are 1,000 sample fluctuation frequencies, of which 90% of the samples, or 900, have a fluctuation frequency higher than 100 times / hour, and the remaining 10%, or 100, are equal to or lower than 100 times / hour. In this case, it can be displayed and announced that "your real-time fluctuation frequency under a certain map data in a certain period of time is 100 times / hour, which beats 90% of users."

[0085] In a possible embodiment, the ranking of the real-time fluctuation amplitude and the real-time fluctuation frequency, as well as the ranking of the stability score can be performed through networking.

[0086] The above-mentioned driving smoothness scoring method first obtains the driving data of the vehicle, which includes the real-time map data in the current time period and the real-time fluctuation amplitude and real-time fluctuation frequency of the vehicle speed, so as to match the reference fluctuation amplitude and reference fluctuation frequency of the vehicle speed according to the current time period, the real-time map data and the preset mapping relationship, wherein the mapping relationship is the corresponding relationship between the two dimensions of the time period and the map data and the fluctuation amplitude and the fluctuation frequency respectively, then calculates the real-time fluctuation amplitude and the reference fluctuation amplitude to determine the fluctuation amplitude score, and calculates the real-time fluctuation frequency and the reference fluctuation frequency to determine the fluctuation frequency score, and finally determines the smoothness score according to the fluctuation amplitude score and / or the fluctuation frequency score, and comprehensively evaluates the driving smoothness by considering the real-time speed fluctuation degree, that is, the fluctuation amplitude and the fluctuation frequency in two dimensions, and combines the two influencing factors of the time period and the map data in time and space, seeks the reference fluctuation amplitude and the reference fluctuation amplitude under the current time period and the real-time map data to evaluate the real-time speed fluctuation amplitude and the real-time fluctuation frequency, thereby realizing the standardized and accurate quantitative evaluation of driving smoothness and ensuring the accuracy of the driving smoothness evaluation result.

[0087] Moreover, it helps the driver understand his driving habits and improve driving safety. In addition, the method is based on the commonly used speed data and map data of the car, which is simple and easy to implement and can be promoted in practical applications.

[0088] See also Figure 6 , Figure 6 is a block diagram of a driving smoothness scoring system shown in an exemplary embodiment of the present application. The system can be applied to Figure 1 The implementation environment shown is a schematic diagram. It should be understood that the system may also be applicable to other exemplary implementation environments, and this embodiment does not limit the implementation environment to which the system is applicable.

[0089] like Figure 6 As shown, in an exemplary embodiment, the driving stability scoring system 600 includes at least an acquisition module 610, a matching module 620, a calculation module 630 and a scoring module 640, which are described in detail as follows:

[0090] The acquisition module 610 is used to acquire the driving data of the vehicle, the driving data including the real-time map data in the current period and the real-time fluctuation amplitude and real-time fluctuation frequency of the vehicle speed;

[0091] The matching module 620 is used to match the reference fluctuation amplitude and reference fluctuation frequency of the vehicle speed according to the current time period, the real-time map data and the preset mapping relationship, wherein the mapping relationship is the corresponding relationship between the two dimensions of time period and map data and the fluctuation amplitude and the fluctuation frequency respectively;

[0092] The calculation module 630 is used to calculate the real-time fluctuation amplitude and the reference fluctuation amplitude to obtain the fluctuation amplitude score, and to calculate the real-time fluctuation frequency and the reference fluctuation frequency to obtain the fluctuation frequency score;

[0093] The scoring module 640 is used to determine a stability score according to the fluctuation amplitude score and / or the fluctuation frequency score.

[0094] It should be noted that the driving smoothness scoring system provided in the above embodiment and the driving smoothness scoring method provided in the above embodiment belong to the same concept, wherein the contents of the operations performed by each module have been described in detail in the method embodiment and will not be repeated here.

[0095] See also Figure 7 , Figure 7 It is a structural schematic diagram of a vehicle-mounted terminal provided in one embodiment of the present application. Figure 7 The structure diagram of the computer system of the vehicle terminal suitable for implementing the embodiment of the present application is shown. It should be noted that: Figure 7 The computer system 700 of the vehicle-mounted terminal shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0096] like Figure 7 As shown, computer system 700 includes a central processing unit (CPU) 701, which can perform various appropriate actions and processes according to the program stored in read-only memory (ROM) 702 or the program loaded from storage part 708 to random access memory (RAM) 703, such as executing the method in the above embodiment. In RAM 703, various programs and data required for system operation are also stored. CPU 701, ROM 702 and RAM 703 are connected to each other through bus 704. Input / output (I / O) interface 705 is also connected to bus 704.

[0097] The following components are connected to the I / O interface 705: an input section 706 including a keyboard, a mouse, etc.; an output section 707 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the I / O interface 705 as needed. A removable medium 711, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 710 as needed so that a computer program read therefrom is installed into the storage section 708 as needed.

[0098] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 709, and / or installed from a removable medium 711. When the computer program is executed by a central processing unit (CPU) 701, various functions defined in the system of the present application are executed.

[0099] The present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor of a computer, the computer executes the driving smoothness scoring method as described above. The computer-readable storage medium may be included in the electronic device described in the above embodiment, or may exist independently without being assembled into the electronic device.

[0100] It should be noted that the computer-readable medium shown in the embodiment of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable computer program is carried. This propagated data signal can take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. A computer program contained on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0101] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0102] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0103] The above embodiments are merely illustrative of the principles and effects of the present application, and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A driving stability scoring method, characterized in that: The method comprises: Acquiring driving data of the vehicle, the driving data including real-time map data within a current period and real-time fluctuation amplitude and real-time fluctuation frequency of the vehicle speed; According to the current time period, the real-time map data and a preset mapping relationship, a reference fluctuation amplitude and a reference fluctuation frequency of the vehicle speed are matched, wherein the mapping relationship is a corresponding relationship between the two dimensions of time period and map data and the fluctuation amplitude and the fluctuation frequency respectively; Calculating the real-time fluctuation amplitude and the reference fluctuation amplitude to determine a fluctuation amplitude score, and calculating the real-time fluctuation frequency and the reference fluctuation frequency to determine a fluctuation frequency score; A stability score is determined according to the fluctuation amplitude score and / or the fluctuation frequency score.

2. The driving stability scoring method according to claim 1, characterized in that: The calculation method of the real-time fluctuation range includes: Obtaining the instantaneous speed and average speed of the vehicle during the current period; According to the duration corresponding to the current period, the instantaneous speed and the average speed are calculated to obtain the cumulative fluctuation mileage, and the instantaneous rate is calculated to obtain the cumulative mileage; The ratio of the accumulated fluctuation mileage to the accumulated driving mileage is calculated to obtain the real-time fluctuation amplitude.

3. The driving stability scoring method according to claim 2, characterized in that: The calculation method of the real-time fluctuation frequency includes: Draw an instantaneous speed curve and an average speed curve according to the instantaneous speed and the average speed in the current time period; Recording the number of intersections between the instantaneous speed curve and the average speed curve; The ratio of the number of intersections to the duration is calculated to obtain the real-time fluctuation frequency.

4. The driving stability scoring method according to claim 1, characterized in that: The mapping relationship is constructed in the following manner: Collecting vehicle speed data of a plurality of sample vehicles, the vehicle speed data including sample instantaneous speeds and sample average speeds at different time periods and under different map data, the map data including at least one of geographical location, road type, traffic flow, and signal light status; For each of the vehicle speed data, a speed fluctuation analysis is performed on the corresponding time period, the sample instantaneous speed, and the sample average speed to obtain a sample fluctuation amplitude and a sample fluctuation frequency; Calculating an average value of a plurality of the sample fluctuation amplitudes to obtain the reference fluctuation amplitude, and calculating an average value of a plurality of the sample fluctuation frequencies to obtain the reference fluctuation frequency; The mapping relationship is constructed according to the reference fluctuation amplitude and the reference fluctuation frequency in different time periods and different map data.

5. The driving stability scoring method according to claim 2 or 3, characterized in that: The instantaneous speed includes the instantaneous speed of the sampling points at a plurality of sampling points in the current time period, and the time intervals between the sampling points are the same; The step of calculating the instantaneous speed and the average speed according to the duration corresponding to the current time period to obtain the accumulated fluctuation mileage includes: Obtaining the sampling time and the cumulative mileage of each sampling point, and calculating the cumulative mileage of the sampling point and the sampling time to obtain the average speed of each sampling point; For each of the sampling points, the corresponding instantaneous speed of the sampling point, the average speed of the sampling point and the time interval are calculated to obtain the cumulative fluctuation mileage of the sampling point; The accumulated fluctuation mileage of the plurality of sampling points is summed to obtain the accumulated fluctuation mileage.

6. The driving stability scoring method according to any one of claims 1 to 4, characterized in that: The calculation method of the fluctuation amplitude score and the fluctuation frequency score includes: Calculating a first relative error between the real-time fluctuation amplitude and the reference fluctuation amplitude, and calculating a second relative error between the real-time fluctuation frequency and the reference fluctuation frequency; The fluctuation amplitude score is determined based on the first relative error, and the fluctuation frequency score is determined based on the second relative error.

7. The driving stability scoring method according to claim 6, characterized in that: Determining the stability score according to the fluctuation amplitude score and / or the fluctuation frequency score includes: Using the fluctuation amplitude score as the stability score; or Using the fluctuation frequency score as the stability score; or The fluctuation amplitude score and the fluctuation frequency score are weighted according to a preset weight coefficient to obtain the stability score.

8. A driving stability scoring method system, characterized in that: The system comprises: An acquisition module, used to acquire driving data of the vehicle, the driving data including real-time map data in a current period and real-time fluctuation amplitude and real-time fluctuation frequency of the vehicle speed; A matching module, used for matching a reference fluctuation amplitude and a reference fluctuation frequency of the vehicle speed according to the current time period, the real-time map data and a preset mapping relationship, wherein the mapping relationship is a corresponding relationship between the two dimensions of time period and map data and the fluctuation amplitude and the fluctuation frequency respectively; a calculation module, configured to calculate the real-time fluctuation amplitude and the reference fluctuation amplitude to obtain a fluctuation amplitude score, and to calculate the real-time fluctuation frequency and the reference fluctuation frequency to obtain a fluctuation frequency score; The scoring module is used to determine a stability score according to the fluctuation amplitude score and / or the fluctuation frequency score.

9. A vehicle-mounted terminal, characterized in that: include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, enables the vehicle terminal to implement the driving smoothness scoring method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor of a computer, the computer is caused to execute the driving smoothness scoring method as claimed in any one of claims 1 to 7.

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