Driving behavior evaluation method, computer program product, device and storage medium

Abstract

The invention relates to a driving behavior evaluation method, a computer program product, equipment and a storage medium. The method comprises the following steps: acquiring driving data with a time stamp of a target vehicle; dividing the driving data into a plurality of segments according to a preset time period, and performing statistics on road condition feature data and driving style feature data of each segment according to the driving data; according to the road condition feature data, respectively determining a road condition corresponding to each segment, a frequency weight of the road condition and an evaluation weight of the road condition; according to the driving style feature data, determining a driving style corresponding to each segment; and based on the preset basic score corresponding to each driving style, in combination with the road condition corresponding to each segment and the evaluation weight and the frequency weight of each road condition, obtaining a driving behavior evaluation result of the target vehicle. According to the driving behavior evaluation method and device, continuous, long-time and complex driving behaviors are accurately evaluated, so that the risk of the driving behaviors of the driver is measured, and the objectivity and accuracy of driving behavior evaluation are improved.

Description

Technical Field

[0001] The present application relates to the field of computer technologies, and particularly to a method for evaluating driving behavior, a computer program product, a device, and a storage medium. Background Art

[0002] The evaluation of a driver's driving behavior usually serves as an important basis for evaluating vehicle energy consumption, insurance amount, and driving safety. Especially during the driver's driving process, there may occasionally be intense driving behaviors, such as rapid acceleration, sudden braking, frequent lane changes, etc.

[0003] The evaluation indicators of existing driving behavior evaluation schemes are too single and cannot comprehensively and accurately reflect the driving behavior of users during the entire driving process. Summary of the Invention

[0004] To solve the above technical problems, the present application provides a method for evaluating driving behavior, a computer program product, a device, and a storage medium.

[0005] In a first aspect, the present application provides a method for evaluating driving behavior, including:

[0006] Obtaining driving data with time stamps of a target vehicle;

[0007] Dividing the driving data into multiple segments according to a preset time period, and respectively counting the road condition feature data and driving style feature data of each segment based on the driving data;

[0008] According to the road condition feature data, respectively determining the road condition corresponding to each segment, the frequency weight of the road condition, and the evaluation weight of the road condition;

[0009] According to the driving style feature data, respectively determining the driving style corresponding to each segment;

[0010] Based on the preset basic score corresponding to each driving style, combining the road condition corresponding to each segment, the evaluation weight of each road condition, and the frequency weight, obtaining the driving behavior evaluation result of the target vehicle.

[0011] In one embodiment, the step of respectively determining the road condition corresponding to each segment, the frequency weight of the road condition, and the evaluation weight of the road condition according to the road condition feature data includes:

[0012] Regarding the road condition feature data of each segment as a sample point for clustering, and determining a first preset number of cluster centers according to the clustering result, each cluster center corresponding to a cluster;

[0013] Based on the clustering clusters, count the total number of segments, the number of segments in each clustering cluster, and the average vehicle speed;

[0014] Determine the road condition represented by each clustering cluster according to the average vehicle speed, and use the road condition represented by the clustering cluster as the road condition corresponding to each segment in the clustering cluster;

[0015] Calculate the ratio of the number of segments of each road condition to the number of segments respectively, as the frequency weight of the corresponding road condition;

[0016] Determine the evaluation weight of each road condition according to the average vehicle speed respectively.

[0017] In one embodiment, the step of respectively determining the driving style corresponding to each segment according to the driving style characteristic data includes:

[0018] Take the driving style characteristic data of each segment as a sample point for clustering, and determine a second preset number of clustering centers according to the clustering result. Each clustering center corresponds to a clustering cluster;

[0019] Determine the driving style represented by the clustering cluster according to the driving style characteristic data of the segments in the clustering cluster, and use the driving style represented by the clustering cluster as the driving style corresponding to each segment in the clustering cluster.

[0020] In one embodiment, the step of obtaining the driving behavior evaluation result of the target vehicle based on the preset basic score corresponding to each driving style, combining the road condition corresponding to each segment, the evaluation weight and the frequency weight of each road condition includes:

[0021] Calculate the product of the preset basic score of the driving style corresponding to each segment, the evaluation weight and the frequency weight of the corresponding road condition respectively, to obtain the score of each segment;

[0022] Calculate the sum of the scores to obtain the driving behavior score of the target vehicle;

[0023] Calculate the difference between the driving behavior score and the preset basic score of each driving style respectively;

[0024] Take the driving style corresponding to the smallest difference as the target driving style of the target vehicle.

[0025] In one embodiment, the step of respectively determining the evaluation weight of each road condition according to the average vehicle speed includes:

[0026] According to the average vehicle speed, use the analytic hierarchy process to determine the evaluation weight of each road condition respectively.

[0027] In one embodiment, before respectively determining the road condition corresponding to each of the segments, the frequency weight of the road condition, and the evaluation weight of the road condition according to the road condition feature data, the method further includes: performing dimensionality reduction processing on the road condition feature data by using the principal component analysis method;

[0028] and / or,

[0029] before respectively determining the driving style corresponding to each of the segments according to the driving style feature data, the method further includes:

[0030] performing dimensionality reduction processing on the driving style feature data by using the principal component analysis method.

[0031] In one embodiment, the driving data includes vehicle speed, driving mileage, and accelerator pedal opening signal;

[0032] The step of respectively counting the road condition feature data and the driving style feature data of each of the segments according to the driving data includes:

[0033] respectively counting the road condition feature data of each of the segments according to the vehicle speed and the driving mileage;

[0034] respectively counting the driving style feature data of each of the segments according to the vehicle speed and the accelerator pedal opening signal.

[0035] In a second aspect, the present application provides a computer program product, including computer programs / instructions, and when the computer programs / instructions are executed by a processor, the steps of the method in the first aspect are implemented.

[0036] In a third aspect, the present application provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to implement the steps of the method in the first aspect.

[0037] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to implement the steps of the method in the first aspect.

[0038] On the basis of conforming to the common knowledge in the art, the above preferred conditions can be combined arbitrarily to obtain various preferred examples of the present application.

[0039] The above-mentioned driving behavior evaluation method, computer program product, device, and storage medium can achieve the following beneficial effects: By dividing driving data into multiple segments, statistically analyzing the road condition feature data and driving style feature data of each segment respectively, and combining the preset basic scores of different driving styles, the frequency weights of road conditions, and evaluation weights, it is possible to accurately evaluate continuous, long-term, and complex driving behaviors, measure the risks of drivers' driving behaviors, improve the objectivity and accuracy of driving behavior evaluation, and thus provide objective and accurate data support for subsequent analysis of corresponding vehicle energy consumption economy, insurance amount, driving comfort, driving safety, driving behavior, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic flowchart of the driving behavior evaluation method in an embodiment;

[0041] Figure 2 It is a schematic block diagram of the driving behavior evaluation system in an embodiment;

[0042] Figure 3 It is the first internal structure diagram of a computer device in an embodiment;

[0043] Figure 4 It is the second internal structure diagram of a computer device in an embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] In order to make the objectives, technical solutions, and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0045] It should be noted that the illustrations provided in this embodiment only illustrate the basic concept of the present application in a schematic manner. The figures only show the components related to the present application, rather than being drawn according to the number, shape, and size of the components in actual implementation. The form, quantity, and proportion of each component in actual implementation can be arbitrarily changed, and the layout form of its components may also be more complex. The structures, proportions, sizes, etc. shown in the drawings of this specification are only used to cooperate with the content disclosed in the specification for those skilled in this technology to understand and read, and are not used to limit the limiting conditions under which the present application can be implemented. Therefore, they do not have technical substantive significance. Any modification of the structure, change in the proportional relationship, or adjustment of the size, without affecting the efficacy that the present application can produce and the purpose that can be achieved, should still fall within the scope that the technical content disclosed in the present application can cover. At the same time, the terms such as "upper", "lower", "left", "right", "middle", and "one" cited in this specification are only for the convenience of clear narration, rather than used to limit the scope that the present application can be implemented. The change or adjustment of their relative relationship, without substantial change in the technical content, should also be regarded as the scope that the present application can be implemented.

[0046] Reference to "embodiment" in this document means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present application. The phrase appears in various places in the text does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0047] As shown herein, unless the context clearly indicates an exception, words such as "a", "an", "one", and / or "the" are not specifically singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0048] The definitions included herein, such as the terms "having", "may have", "including", or "may include" as used herein, indicate the existence of the corresponding functions, operations, elements, etc. of this article, and do not limit the existence of one or more other functions, operations, elements, etc. In addition, it should be understood that the terms "including" or "having" as used herein indicate the existence of the features, numbers, steps, operations, elements, components, or combinations thereof described in the specification, and do not exclude the existence or addition of one or more other features, numbers, steps, operations, elements, components, or combinations thereof.

[0049] In the embodiments of the present application, prefix words such as "first" and "second" are only used to distinguish different described objects, and have no restrictive effect on the position, order, priority, quantity, content, etc. of the described objects. In the embodiments of the present application, the use of prefix words such as ordinal numbers for distinguishing described objects does not constitute a limitation on the described objects. For the statements of the described objects, refer to the descriptions in the claims or the context of the embodiments. There should be no redundant restrictions due to the use of such prefix words. In addition, in the description of this embodiment, unless otherwise specified, the meaning of "a plurality" is two or more.

[0050] In one embodiment, as Figure 1 shown, the evaluation method for driving behavior provided in this embodiment includes steps S101 to S105:

[0051] S101. Obtain the driving data with time stamps of the target vehicle.

[0052] Among them, the driving data with time stamps is actually a kind of time series data, that is, a series of relevant data with time stamps recorded in sequence according to the time during the driving process of the target vehicle. These data cover various information during the vehicle driving process, such as the vehicle speed, acceleration, braking state, steering wheel angle, engine speed, fuel consumption, driving mileage, geographical location (latitude and longitude), etc., which are the readings of various sensors of the vehicle.

[0053] S102. Divide the driving data into multiple segments according to a preset time period, and respectively count the road condition characteristic data and driving style characteristic data of each segment according to the driving data.

[0054] In one embodiment, the driving data includes vehicle speed and driving mileage. Correspondingly, step S102 includes: respectively counting the road condition characteristic data of each segment according to the vehicle speed and driving mileage.

[0055] Exemplarily, based on the vehicle speed, the maximum acceleration, maximum deceleration, acceleration time ratio, deceleration time ratio, constant speed time ratio, ratio of 0 - 20 km / h speed, ratio of 20 - 40 km / h speed, ratio of 40 - 60 km / h speed, ratio of 60 - 80 km / h speed, maximum speed, average speed, average speed excluding parking time, speed standard deviation, acceleration standard deviation, average acceleration of the acceleration section, average deceleration of the deceleration section, acceleration standard deviation during the acceleration process, deceleration standard deviation during the deceleration process, etc. of each segment can be respectively counted. Based on the driving mileage, the driving distance of each segment can be respectively counted as the road condition characteristic data.

[0056] In one embodiment, the driving data further includes accelerator pedal information, such as an accelerator pedal opening signal, and the accelerator pedal position can be determined according to the accelerator pedal opening signal. Accordingly, step S102 further includes: respectively counting the driving style characteristic data of each segment according to the vehicle speed and the accelerator pedal opening signal.

[0057] Exemplarily, based on the vehicle speed, the acceleration change rate, the standard deviation of the acceleration change rate, and the average acceleration change rate of each segment can be respectively counted. Based on the accelerator pedal opening signal, the maximum accelerator pedal position, the average accelerator pedal position, the standard deviation of the accelerator pedal position, the maximum increase rate of the accelerator pedal position, the maximum decrease rate of the accelerator pedal position, the standard deviation of the accelerator pedal position change rate, the average increase rate of the accelerator pedal, the standard deviation of the accelerator pedal position increase rate, the average decrease rate of the accelerator pedal position, the standard deviation of the accelerator pedal position decrease rate, etc. of each segment can be respectively counted as the driving style characteristic data.

[0058] Since the road condition characteristic data and the driving style characteristic data of each segment both include statistical data in multiple dimensions, in order to facilitate subsequent analysis and use, it is necessary to perform dimensionality reduction processing on them. That is, in one embodiment, before step S103, it further includes: performing dimensionality reduction processing on the road condition characteristic data by using the principal component analysis method. In another embodiment, before step S104, it further includes: performing dimensionality reduction processing on the driving style characteristic data by using the principal component analysis method.

[0059] Exemplarily, performing dimensionality reduction processing on the road condition characteristic data by using the principal component analysis method includes:

[0060] Determining an initial matrix according to the road condition characteristic data of all segments. Wherein, each row of the initial matrix respectively corresponds to a segment, and each column respectively corresponds to a data dimension. If there are m segments in total and the road condition characteristic data includes statistical data in n dimensions, an m×n initial matrix can be obtained.

[0061] Performing a de-centralization process on the initial matrix to obtain a first matrix. That is, first calculate the mean value of each column respectively, and then subtract the mean value of the column where each element in the initial matrix is located from the element.

[0062] Then calculate the covariance matrix C of the first matrix:

[0063]

[0064] where m is the number of segments, X is the first matrix, and X T is the transposed matrix of the first matrix.

[0065] Performing eigen-decomposition on the covariance matrix C to obtain the eigenvalues of the covariance matrix C and the eigenvectors corresponding to each eigenvalue.

[0066] Arrange the corresponding eigenvectors in descending order from left to right to form a matrix, and take the first k columns to form a second matrix.

[0067] According to the first matrix X and the second matrix W, calculate the third matrix Y after dimensionality reduction to m×k dimensions:

[0068] Y = XW

[0069] Among them, the eigenvectors corresponding to the eigenvalues are the principal components.

[0070] Sort the corresponding eigenvectors in descending order of eigenvalues from left to right, calculate the contribution rate of each eigenvalue respectively. The contribution rate of each eigenvalue is equal to the eigenvalue divided by the sum of all eigenvalues, and accumulate the contribution rates step by step from left to right, then the cumulative contribution rate corresponding to each principal component can be obtained.

[0071] Finally, select the first a principal components for retention, that is, obtain the target matrix after dimensionality reduction. a is determined according to a preset cumulative contribution rate threshold. For example, if the cumulative contribution rates are 40%, 70%, 90%, 95% and 100% in sequence, and if the set threshold is 90%, then select the first 3 principal components.

[0072] Similarly, an initial matrix can also be generated according to the driving style characteristic data of all segments. The process of its dimensionality reduction is the same as that of the dimensionality reduction of road condition characteristic data using the principal component analysis method, which will not be elaborated here.

[0073] S103. According to the road condition characteristic data, determine the road condition corresponding to each segment, the frequency weight of the road condition, and the evaluation weight of the road condition respectively.

[0074] In one embodiment, step S103 includes steps S1031 to S1035:

[0075] S1031. Cluster the road condition characteristic data of multiple segments, and determine the first preset number of cluster centers according to the clustering results. Each cluster center corresponds to a cluster;

[0076] S1032. Based on the clusters, count the total number of segments, the number of segments in each cluster, and the average vehicle speed;

[0077] S1033. Determine the road condition represented by each cluster according to the average vehicle speed, and use the road condition represented by the cluster as the road condition corresponding to each segment in the cluster;

[0078] S1034. Calculate the ratio of the number of segments of each road condition to the number of segments respectively, as the frequency weight of the corresponding road condition;

[0079] S1035. Determine the evaluation weight of each road condition according to the average vehicle speed respectively.

[0080] Exemplarily, step S1031 includes:

[0081] (1) Regarding the road condition feature data of each segment as a sample point;

[0082] (2) Randomly selecting 4 sample points from a number of sample points as the initial clustering centers;

[0083] (3) Calculating the distance from each sample point to the initial clustering centers, and forming a clustering cluster by combining each sample point with the nearest clustering center, that is, forming 4 clustering clusters;

[0084] (4) Calculating the position mean of the sample points in each clustering cluster, and taking the position mean as the new clustering center, that is, obtaining 4 new clustering centers;

[0085] (5) Repeating steps (3) and (4) until all the clustering centers no longer change, and forming a clustering cluster by combining each sample point with the nearest clustering center, that is, finally forming 4 clustering clusters C1, C2, C3 and C4.

[0086] In step S1032, based on the above clustering clusters, the total number of segments and the number of segments in each clustering cluster can be counted, and the average speed of the clustering cluster can be obtained according to the average vehicle speed of the segments in each clustering cluster.

[0087] In step S1033, according to the average vehicle speed, it is determined that the road conditions represented by the 4 clustering clusters are congestion, low speed, medium speed and high speed respectively, and the road condition corresponding to each segment is determined by the road condition represented by the clustering cluster to which it belongs.

[0088] In step S1034, if the number of segments in clustering clusters C1 to C4 is 937, 754, 543 and 1436 respectively, the ratio of the number of segments to the total number of segments 3670 can be calculated, and then the frequency weights of congestion, low speed, medium speed and high speed are obtained as 25.531%, 20.545%, 14.796% and 39.128% respectively.

[0089] When the vehicle speed is higher, the probability of traffic accidents is greater, and at the same time, a higher fatality rate is brought. Therefore, in step S1035, the risks of different road conditions can be quantified based on the analytic hierarchy process according to the average vehicle speed. That is, in one embodiment, step S105 includes: determining the evaluation weights of each road condition respectively according to the average vehicle speed by using the analytic hierarchy process.

[0090] Exemplarily, based on the above pairwise comparison of the average speeds of each road condition, their relative risks are determined. The 1-9 scale method is adopted, that is, a number between 1 and 9 is used to represent the relative risk between two factors. For details, see Table 1.

[0091] Table 11-9 Scaling Method

[0092] Data A is relative to Data B Quantization value Equally important 1 Slightly more important 3 More important 5 A bit more important 7 Extremely important 9 Intermediate value for adjacent judgment 2，4，6，8

[0093] For example, by comparing the average speeds of "congestion" and "low speed", the risk of "low speed" is greater than that of "congestion". The risk of "low speed" may be scored 3 points, and the risk of "congestion" may be scored 1 point. Then, it can be obtained that the risk of "congestion" relative to "low speed" is 1:3, and the risk of "low speed" equivalent to "congestion" is 3:1.

[0094] Based on this logic, a discriminant matrix can be generated according to the average vehicle speed and the 1-9 scaling method, as shown in Table 2.

[0095] Table 2 Discriminant Matrix

[0096]

[0097] After obtaining the discriminant matrix, in order to ensure the consistency of the judgment matrix, a consistency test is required. Given that the dimension N of the discriminant matrix is 4, calculate the consistency index CI:

[0098]

[0099] where ∈ max is the largest eigenvalue of the discriminant matrix.

[0100] Since the consistency ratio When CR is less than 0.1, it is considered that the discriminant matrix has satisfactory consistency. Otherwise, adjust the discriminant matrix until the consistency check is satisfied.

[0101] After the discriminant matrix satisfies the consistency check, calculate the eigenvector of the discriminant matrix, and then normalize the eigenvector so that the sum of its elements is 1. Then, the evaluation weights of congestion, low speed, medium speed, and high speed can be obtained, which are approximately 0.0662 + 0.1497 + 0.2399 + 0.5443.

[0102] Exemplarily, first calculate the sum of each column of the discriminant matrix, that is, add each column of the discriminant matrix to get:

[0103] Sum of congestion column = 1 + 3 + 4 + 6 = 14;

[0104] Sum of low speed column = 1 / 3 + 1 + 2 + 4 = 7.333;

[0105] Sum of medium speed column = 1 / 4 + 1 / 2 + 1 + 3 = 4.75;

[0106] Sum of high speed column = 1 / 6 + 1 / 4 + 1 / 3 + 1 = 1.75.

[0107] Then, normalize the discrimination matrix, that is, calculate each element in the discrimination matrix divided by the sum of its corresponding column. The normalization results are shown in Table 3.

[0108] Table 3 Normalization Results of the Discrimination Matrix

[0109]

[0110] Add up each row of the normalized discrimination matrix and then divide by 4 (the dimension of the discrimination matrix) to obtain:

[0111] (0.0714 + 0.0455 + 0.0526 + 0.0952) / 4 ≈ 0.0662;

[0112] (0.2143 + 0.1364 + 0.1053 + 0.1429) / 4 ≈ 0.1497;

[0113] (0.2857 + 0.2727 + 0.2105 + 0.1905) / 4 ≈ 0.2399;

[0114] (0.4286 + 0.5455 + 0.6316 + 0.5714) / 4 ≈ 0.5443.

[0115] Therefore, the final calculated evaluation weight for congestion is 0.0662, for low speed is 0.1497, for medium speed is 0.2399, and for high speed is 0.5443.

[0116] S104. Determine the driving style corresponding to each segment according to the driving style characteristic data.

[0117] In one embodiment, step S104 includes:

[0118] S1041. Use the driving style characteristic data of each segment as a sample point for clustering, and determine the second preset number of cluster centers according to the clustering results. Each cluster center corresponds to a cluster;

[0119] S1042. Determine the driving style represented by the cluster according to the driving style characteristic data of the segments in the cluster, and use the driving style represented by the cluster as the driving style corresponding to each segment in the cluster.

[0120] Exemplarily, step S1041 includes:

[0121] (6) Use the driving style characteristic data of each segment as a sample point;

[0122] (7) Randomly select 3 sample points from several sample points as the initial cluster centers;

[0123] (8) Calculate the distance from each sample point to the initial cluster center, and form a cluster by combining each sample point with the nearest cluster center, that is, form 3 clusters;

[0124] (9) Calculate the position mean of the sample points in each cluster, and use the position mean as the new cluster center, that is, obtain 3 new cluster centers;

[0125] (10) Repeat steps (3) and (4) until all the cluster centers no longer change, and form a cluster by combining each sample point with the nearest cluster center, that is, finally form 3 clusters D1, D2, and D3.

[0126] Since the driving style feature data also includes statistical data in multiple dimensions, in step S1042, the statistical data of the same dimension in the segments of different clusters can be compared, and finally it is determined that the driving styles represented by the 3 clusters are conservative, traditional, and aggressive respectively.

[0127] Exemplarily, the dimension-reduced driving style feature data includes the standard deviation of the accelerator pedal position of the segment. The larger the standard deviation of the accelerator pedal position, the more frequent or unstable the pedal position change is. The smaller the standard deviation of the accelerator pedal position, the smoother the pedal position change is.

[0128] The average value of the standard deviation of the accelerator pedal position in the 3 clusters can be calculated respectively. The cluster with the smallest average value is determined to represent conservatism, the cluster with the largest average value is determined to represent aggressiveness, and the remaining cluster is determined to represent tradition.

[0129] S105. Based on the preset basic scores corresponding to each driving style, combined with the road conditions corresponding to each segment, the evaluation weights and frequency weights of each road condition, obtain the driving behavior evaluation result of the target vehicle.

[0130] In one embodiment, step S105 includes:

[0131] S1051. Calculate the product of the preset basic score of the driving style corresponding to each segment, the evaluation weight and the frequency weight of the corresponding road condition respectively, to obtain the score of each segment;

[0132] S1052. Calculate the sum of the scores to obtain the driving behavior score of the target vehicle.

[0133] Exemplarily, there are a total of J segments. Assume that the preset basic score of the road condition of the i-th segment is represented as L i , the road condition frequency weight of the i-th segment is f i , the road condition evaluation weight of the i-th segment is w i , then the driving behavior score G of the target vehicle can be calculated as follows:

[0134]

[0135] After obtaining the driving behavior score of the target vehicle, the closest driving style can also be determined according to the specific value of the driving behavior score.

[0136] In another embodiment, after step S1052, it further includes:

[0137] S1053. Calculate the differences between the driving behavior score and the preset basic scores of each driving style;

[0138] S1054. Take the driving style corresponding to the smallest difference as the target driving style of the target vehicle.

[0139] Exemplarily, assume that the preset basic score for conservative is 85 points, the preset basic score for traditional is 75 points, the preset basic score for radical is 65 points, and the calculated driving behavior score is 70 points. Since the difference from the preset basic score of traditional is the smallest, the target driving style is traditional.

[0140] It should be understood that although Figure 1 the steps in the flowchart are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise clearly stated in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, Figure 1 at least a part of the steps in

[0141] can include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these sub-steps or stages is not necessarily sequential either, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0142] To implement the above method for evaluating driving behavior, the present application also provides a computer program product, including computer programs / instructions, which when executed by a processor, implement the steps of the method in the above embodiments. Figure 2

[0142]

[0143] A data acquisition module 201, configured to acquire time-stamped driving data of the target vehicle;

[0144] A feature determination module 202, configured to divide the driving data into multiple segments according to a preset time period, and respectively count the road condition feature data and driving style feature data of each segment based on the driving data;

[0145] A road condition analysis module 203, configured to determine the road condition, frequency weight of the road condition, and evaluation weight of the road condition corresponding to each segment according to road condition feature data;

[0146] A driving style analysis module 204, configured to determine the driving style corresponding to each segment according to driving style feature data;

[0147] An evaluation module 205, configured to obtain an evaluation result of the driving behavior of the target vehicle based on a preset basic score corresponding to each driving style, in combination with the road condition corresponding to each segment, the evaluation weight and frequency weight of each road condition.

[0148] For the specific limitations of the evaluation system for driving behavior, reference can be made to the limitations of the evaluation method for driving behavior in the foregoing text, which will not be elaborated herein. Each module in the above evaluation system for driving behavior can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor in the computer device in hardware form or independent of it, or stored in the memory in the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0149] This application also provides a computer device. In one embodiment, the computer device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of the evaluation method for driving behavior in the above embodiment are implemented.

[0150] In one embodiment, the computer device may be a server, and its internal structure diagram may be as Figure 3 shown. The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store relevant data for evaluating driving behavior. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an evaluation method for driving behavior is implemented.

[0151] In one embodiment, the computer device may be a terminal, and its internal structure diagram may be as Figure 4As shown. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a method for evaluating driving behavior. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the outer shell of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0152] Those skilled in the art can understand that Figure 3 and Figure 4 the structure shown in

[0153] is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements. This application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the method for evaluating driving behavior in the above embodiments.

[0154] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above various methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0155] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0156] The above embodiments only represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. An evaluation method for driving behavior, characterized in that, The evaluation method includes: Obtaining the driving data of the target vehicle with time stamps; Dividing the driving data into multiple segments according to a preset time period, and respectively counting the road condition feature data and driving style feature data of each segment based on the driving data; According to the road condition feature data, respectively determining the road condition corresponding to each segment, the frequency weight of the road condition, and the evaluation weight of the road condition; According to the driving style feature data, respectively determining the driving style corresponding to each segment; Based on the preset basic scores corresponding to each driving style, combining the road condition corresponding to each segment, the evaluation weight of each road condition, and the frequency weight, obtaining the driving behavior evaluation result of the target vehicle.

2. The evaluation method according to claim 1, wherein, The step of respectively determining the road condition corresponding to each segment, the frequency weight of the road condition, and the evaluation weight of the road condition according to the road condition feature data includes: Taking the road condition feature data of each segment as a sample point for clustering, and determining a first preset number of cluster centers according to the clustering result, with each cluster center corresponding to a cluster; Based on the clusters, counting the total number of segments, the number of segments in each cluster, and the average vehicle speed; Determining the road condition represented by each cluster according to the average vehicle speed, and taking the road condition represented by the cluster as the road condition corresponding to each segment in the cluster; Respectively calculating the ratio of the number of segments of each road condition to the total number of segments as the frequency weight of the corresponding road condition; Determining the evaluation weight of each road condition according to the average vehicle speed respectively.

3. The evaluation method according to claim 1, wherein The step of respectively determining the driving style corresponding to each segment according to the driving style feature data includes: Taking the driving style feature data of each segment as a sample point for clustering, and determining a second preset number of cluster centers according to the clustering result, with each cluster center corresponding to a cluster; Determining the driving style represented by the cluster according to the driving style feature data of the segments in the cluster, and taking the driving style represented by the cluster as the driving style corresponding to each segment in the cluster.

4. The evaluation method according to claim 1, wherein, The step of obtaining the driving behavior evaluation result of the target vehicle based on the preset basic scores corresponding to each driving style, combining the road condition corresponding to each segment, the evaluation weight of each road condition, and the frequency weight includes: Respectively calculating the product of the preset basic score of the driving style corresponding to each segment, the evaluation weight of the corresponding road condition, and the frequency weight to obtain the score of each segment; Calculating the sum of the scores to obtain the driving behavior score of the target vehicle; Calculating the difference between the driving behavior score and the preset basic score of each driving style respectively; Taking the driving style corresponding to the smallest difference as the target driving style of the target vehicle.

5. The evaluation method according to claim 2, wherein The step of respectively determining the evaluation weight of each road condition according to the average vehicle speed includes: According to the average vehicle speed, respectively determining the evaluation weight of each road condition by using the analytic hierarchy process.

6. The evaluation method according to claim 1, characterized in that, Before respectively determining the road conditions corresponding to each of the segments, the frequency weights of the road conditions, and the evaluation weights of the road conditions according to the road condition feature data, the method further includes: performing dimensionality reduction processing on the road condition feature data by using the principal component analysis method; and / or Before respectively determining the driving styles corresponding to each of the segments according to the driving style feature data, the method further includes: performing dimensionality reduction processing on the driving style feature data by using the principal component analysis method.

7. The evaluation method according to claim 1, characterized in that The driving data includes vehicle speed, driving mileage, and an accelerator pedal opening signal; The step of respectively counting the road condition feature data and the driving style feature data of each of the segments according to the driving data includes: respectively counting the road condition feature data of each of the segments according to the vehicle speed and the driving mileage; respectively counting the driving style feature data of each of the segments according to the vehicle speed and the accelerator pedal opening signal.

8. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1-7 are implemented.

9. A computer device, comprising a memory, a processor, and a computer program stored on the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1-7 are implemented.

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