Road safety assessment method and road safety assessment device

Abstract

The invention relates to a road safety assessment method and a road safety assessment device. The method comprises the following steps: firstly, acquiring multiple groups of safety evaluation characteristic parameters of a test road; analyzing each safety evaluation characteristic parameter according to a membership model corresponding to each safety evaluation characteristic parameter to obtain a membership quantized value between each safety evaluation characteristic parameter and the overall safety of the road; and finally, determining a safety evaluation result of the test road according to the membership quantized value of the test road. By adopting the method, the accuracy of a road safety assessment result can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of road safety, and in particular to a road safety assessment method and a road safety assessment device. Background Art

[0002] In the field of transportation, assessing the safety of roads is crucial to driving safety.

[0003] In the related art, when evaluating the safety of a road, it is usually based on road experience, and the environment is photographed and evidence is collected at the road site. The road is evaluated according to the image processing method to obtain the road safety evaluation result.

[0004] However, the road safety assessment results in the related art have low accuracy. Summary of the invention

[0005] Based on this, it is necessary to provide a road safety assessment method and a road safety assessment device that can improve the accuracy of road safety assessment results in response to the above technical problems.

[0006] In a first aspect, the present application provides a road safety assessment method, the method comprising:

[0007] Acquire multiple groups of safety assessment characteristic parameters of the test road;

[0008] According to the membership model corresponding to each safety assessment characteristic parameter, each safety assessment characteristic parameter is analyzed to obtain the membership quantification value between each safety assessment characteristic parameter and the overall road safety;

[0009] According to the membership quantification value of the test road, the safety assessment result of the test road is determined.

[0010] In the technical solution provided by the embodiment of the present application, multiple groups of safety assessment characteristic parameters of the test road are obtained, and each safety assessment characteristic parameter is analyzed according to the membership model corresponding to each safety assessment characteristic parameter, and the membership quantification value between each safety assessment characteristic parameter and the overall safety of the road is obtained, and finally the safety assessment result of the test road is determined according to the membership quantification value of the test road. In this method, different types of membership models are provided to analyze different types of safety assessment characteristic parameters, quantify the correlation between the safety assessment characteristic parameters and the overall road safety, and obtain multiple membership quantification values ​​of the test road, which provides an objective, comprehensive and accurate evaluation basis for the safety evaluation of the test road, and improves the accuracy of the safety evaluation results of the test road.

[0011] In one embodiment, the membership model includes standard safety assessment parameters; and each safety assessment characteristic parameter is analyzed according to the membership model corresponding to each safety assessment characteristic parameter to obtain a membership quantified value between each safety assessment characteristic parameter and the overall road safety, including:

[0012] For any safety assessment characteristic parameter, comparing the safety assessment characteristic parameter with a standard safety assessment parameter;

[0013] According to the comparison result, determining the target membership function of the safety assessment characteristic parameter;

[0014] The safety assessment characteristic parameters are input into the target membership function to obtain the membership quantification value between the safety assessment characteristic parameters and the overall road safety.

[0015] In the technical solution provided in the embodiment of the present application, the target membership function of the security assessment characteristic parameters is determined based on the comparison results between the security assessment characteristic parameters and the standard security assessment parameters, and then the membership quantized values ​​of the security assessment characteristic parameters are calculated, which is equivalent to setting different membership functions for the security assessment characteristic parameters in different parameter intervals, and refining the calculation method of the membership quantized values ​​of different security assessment characteristic parameters of the same type, providing a targeted analysis method for the security assessment characteristic parameters, and thereby improving the accuracy of the membership quantized value calculation results.

[0016] In one embodiment, the safety assessment characteristic parameter includes a road surface parameter; the standard safety assessment parameter includes a plurality of parameters; and comparing the safety assessment characteristic parameter with the standard safety assessment parameter includes:

[0017] Divide each standard safety assessment parameter to obtain multiple pavement parameter intervals;

[0018] Determining a target pavement parameter interval that matches the pavement parameter value from among the plurality of pavement parameter intervals;

[0019] A target road surface parameter interval is determined as a comparison result.

[0020] In the technical solution provided in the embodiment of the present application, for any type of pavement parameters, multiple standard safety assessment parameters corresponding to the pavement parameters are subdivided into multiple pavement parameter intervals, and a target pavement parameter interval that better matches the actual pavement parameters of the test road is determined, providing a more reliable basis for obtaining the membership quantized value of the pavement parameters, so that the target membership function and the pavement parameters are matched, thereby improving the accuracy of the calculation results of the membership quantized value of the pavement parameters.

[0021] In one embodiment, the safety assessment characteristic parameter includes a road surface condition parameter; the standard safety assessment parameter includes a road surface condition parameter threshold; and comparing the safety assessment characteristic parameter with the standard safety assessment parameter includes:

[0022] According to the pavement condition parameter threshold, an upper limit value and a lower limit value of the pavement condition parameter are determined;

[0023] The comparison result is obtained by judging the size between the road surface condition parameter and the upper limit value of the road surface condition parameter, and the size between the road surface condition parameter and the lower limit value of the road surface condition parameter.

[0024] In the technical solution provided in the embodiment of the present application, taking into account the different characteristics of the membership function expression when the test road is uphill and downhill, the road surface condition parameter threshold is expanded to the road surface condition parameter upper limit value and the road surface condition parameter lower limit value, and the comparison result between the road surface condition parameter and the road surface condition parameter upper limit value and the road surface condition parameter lower limit value is used as the basis for obtaining the target membership function of the road surface condition parameter, so that the target membership function and the road surface condition parameter are matched, thereby improving the accuracy of the calculation result of the membership quantization value of the road surface condition parameter.

[0025] In one embodiment, the safety assessment characteristic parameters include curve parameters; and each safety assessment characteristic parameter is analyzed according to a membership model corresponding to each safety assessment characteristic parameter to obtain a membership quantified value between each safety assessment characteristic parameter and the overall safety of the road, including:

[0026] Determine the curve safety factor according to the curve parameters and the preset curve parameter threshold;

[0027] The curve safety factor is input into the membership model of the curve parameters to obtain the quantified value of the membership between the curve parameters and the overall road safety.

[0028] In the technical solution provided in the embodiment of the present application, the curve safety factor is determined according to the curve parameters and the preset curve parameter threshold, and then the curve safety factor is input into the membership model of the curve parameters to obtain the quantified value of the membership between the curve parameters and the overall safety of the road, thereby providing a complete set of quantitative evaluation schemes for curved roads and improving the scenario applicability of the road safety evaluation method.

[0029] In one embodiment, the test road includes a plurality of test roads; and determining the safety assessment result of the test road according to the membership quantization value of the test road includes:

[0030] Based on the quantized values ​​of each membership metric in each test road, obtaining the upper safety limit value and the lower safety limit value of each test road;

[0031] The safety evaluation result of each test road is determined based on the safety upper limit value and the safety lower limit value of each test road.

[0032] In the technical solution provided in the embodiment of the present application, based on the quantified values ​​of each affiliation in each test road, the upper safety limit and the lower safety limit of each test road are obtained, and then the safety assessment results of each test road are determined, which is equivalent to comprehensively considering the differences between different quantified values ​​of affiliation in the same test road, and the differences between quantified values ​​of affiliation between different test roads in the safety assessment process, so as to objectively evaluate the weight of each safety feature parameter of the road to be tested as a whole, and improve the accuracy of the safety assessment results of the test road. In addition, the evaluation method provided in the embodiment of the present application takes multiple test roads into consideration, and can support the evaluation of multiple test roads in parallel. Compared with the method of evaluating each test road one by one, it not only improves the diversity of the evaluation reference dimensions and increases the accuracy of the evaluation results, but also improves the evaluation efficiency of the test roads.

[0033] In one embodiment, based on the quantized values ​​of each membership metric in each test road, obtaining the upper safety limit value and the lower safety limit value of each test road includes:

[0034] According to the quantified values ​​of each membership in each test road, an evaluation matrix of each test road is constructed;

[0035] According to the evaluation matrix, a weighted normalization matrix corresponding to each test road is determined;

[0036] Based on the weighted normalization matrix, the upper safety limit value and the lower safety limit value of each test road are obtained.

[0037] In the technical solution provided in the embodiment of the present application, an evaluation matrix is ​​constructed based on the quantized values ​​of each membership metric of each test road, and the evaluation matrix is ​​weighted to obtain a weighted normalized matrix, so that the weighted normalized matrix can more objectively and accurately characterize the actual road surface conditions of each test road, and the reliability of the upper and lower safety limits of each safety evaluation parameter of each test road determined in this way is also higher.

[0038] In one embodiment, determining a weighted normalized matrix corresponding to each test road according to the evaluation matrix includes:

[0039] Obtaining the importance judgment matrix of each test road;

[0040] The importance judgment matrix is ​​processed by column normalization and row normalization in turn to obtain the weight matrix of each test road;

[0041] The product result of the evaluation matrix and the weight matrix is ​​determined as a weighted normalization matrix.

[0042] In the technical solution provided by the embodiment of the present application, on the basis of obtaining the importance judgment matrix of each test road, column normalization processing is performed to facilitate the evaluation of the differences between characteristic parameters of different types of the same road, followed by row normalization processing to evaluate the differences between characteristic parameters of the same type of different roads, and to determine accurate and objective weight values ​​for each element in the importance judgment matrix. On this basis, the weighted normalization matrix determined by the product of the evaluation matrix and the weight matrix can also more accurately reflect the importance of each characteristic parameter in each test road.

[0043] In one embodiment, the safety evaluation result of each test road is determined according to the safety upper limit value and the safety lower limit value of each test road, including:

[0044] For any test road, the first Euclidean distance of the test road is determined according to each safety evaluation characteristic parameter and the safety upper limit value in the test road; the second Euclidean distance of the test road is determined according to each safety evaluation characteristic parameter and the safety lower limit value in the test road;

[0045] A safety assessment result of the test road is determined based on the first Euclidean distance and the second Euclidean distance.

[0046] In the technical solution provided in the embodiment of the present application, the distance between the test road and the optimal ideal road is quantified by calculating the first Euclidean distance between each safety assessment characteristic parameter of the test road and the upper limit of safety, and the distance between the test road and the worst road is quantified by calculating the second Euclidean distance between each safety assessment characteristic parameter of the test road and the lower limit of safety. Then, the first Euclidean distance and the second Euclidean distance are combined to determine the degree of closeness of the test road to the ideal solution, thereby improving the accuracy of the safety assessment results of the test road.

[0047] In a second aspect, the present application also provides a road safety assessment device, comprising:

[0048] A parameter acquisition module, used to obtain multiple groups of safety assessment characteristic parameters of the test road;

[0049] A membership quantization value acquisition module is used to analyze each safety assessment characteristic parameter according to the membership model corresponding to each safety assessment characteristic parameter, and obtain a membership quantization value between each safety assessment characteristic parameter and the overall road safety;

[0050] The result acquisition module is used to determine the safety assessment result of the test road according to the membership quantification value of the test road.

[0051] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method in any one of the embodiments of the first aspect are implemented.

[0052] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method in any one of the embodiments of the first aspect above.

[0053] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of the method in any one of the embodiments of the first aspect above. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0055] FIG1 is a diagram of an application environment of a road safety assessment method in one embodiment;

[0056] FIG2 is a schematic diagram of a flow chart of a road safety assessment method in one embodiment;

[0057] FIG3 is a schematic diagram of a flow chart of a step of obtaining a membership quantization value in one embodiment;

[0058] FIG4 is a schematic flow chart of a parameter comparison step in one embodiment;

[0059] FIG5 is a schematic flow chart of a parameter comparison step in another embodiment;

[0060] FIG6 is a schematic flow chart of a step of obtaining a membership quantization value in another embodiment;

[0061] FIG7 is a schematic diagram of a process flow of a safety assessment step in one embodiment;

[0062] FIG8 is a schematic flow chart of a step of obtaining upper and lower safety limits in one embodiment;

[0063] FIG9 is a schematic flow chart of a normalized matrix acquisition step in one embodiment;

[0064] FIG10 is a schematic flow chart of a safety assessment step in another embodiment;

[0065] FIG11 is a schematic flow chart of a road safety assessment method in another embodiment;

[0066] FIG12 is a structural block diagram of a road safety assessment device in one embodiment;

[0067] FIG. 13 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0069] In the field of transportation, evaluating the safety of roads is crucial to driving safety and pedestrian safety. In related technologies, when evaluating the safety of roads, it is usually based on road experience, taking environmental photos on the road site to collect evidence, and evaluating the road according to the image processing method to obtain the road safety evaluation result. However, the road safety evaluation results in related technologies are less accurate.

[0070] Based on this, an embodiment of the present application provides a road safety assessment method and a road safety assessment device, which analyzes each type of safety assessment characteristic parameter of the test road through a membership model, quantifies the relationship between each type of safety assessment characteristic parameter of the test road and road safety, obtains a membership quantified value, and then assigns a weight to each membership quantified value to obtain the safety assessment result of the test road, thereby improving the accuracy of the road safety assessment result.

[0071] The road safety assessment method provided in the embodiment of the present application can be applied in the application environment shown in Figure 1. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car devices, projection devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The head-mounted device can be a virtual reality (VR) device, an augmented reality (AR) device, smart glasses, etc. The server 104 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0072] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0073] In an exemplary embodiment, as shown in FIG2 , a road safety assessment method is provided. The method is described by taking the application of the method to a server as an example. It is understandable that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In the embodiment of the present application, the road safety assessment method includes the following steps:

[0074] S201, obtaining multiple groups of safety assessment characteristic parameters of a test road.

[0075] The test road may be an asphalt road, a cement concrete road, a block road, etc. In the embodiment of the present application, the test road may be one or more.

[0076] For each test road, the safety assessment characteristic parameters are indicators that characterize the pavement factors of the test road, including: pavement structure strength, pavement driving quality, pavement anti-skid performance, longitudinal slope gradient, longitudinal slope length, curve angle, curve length, etc.

[0077] In one scenario, the safety assessment characteristic parameters of each test road may be pre-stored in a database. In this case, the safety assessment characteristic parameters of the test road may be directly extracted from the database.

[0078] In another scenario, the server is communicatively connected to multiple sensors of a test road, each test road corresponds to multiple sensors, and each sensor is used to monitor characteristic parameters of the corresponding test road. In this case, the server responds to an evaluation request for the test road, obtains multiple sets of characteristic parameters of the test road from multiple sensors in real time, and determines the safety evaluation characteristic parameters based on the calibrated values ​​and measured values ​​of the characteristic parameters.

[0079] For example, the characteristic parameter of the actual road deflection measured from the sensor is obtained, and the road structure strength index is determined based on the actual road deflection and the allowable road deflection; the characteristic parameter of the road lateral force coefficient measured from the sensor is obtained, and the road anti-skid performance index is determined based on the road lateral force coefficient and calibration parameters; the characteristic parameter of the road grade measured from the sensor is obtained, and the road driving quality is determined based on the road grade and the international roughness index, and so on.

[0080] S202: Analyze each safety assessment characteristic parameter according to a membership model corresponding to each safety assessment characteristic parameter to obtain a membership quantified value between each safety assessment characteristic parameter and the overall road safety.

[0081] The membership model refers to a calculation model that quantifies the close relationship between safety assessment parameters and road safety standards. Different types of safety assessment characteristic parameters correspond to different membership models, and each membership model includes multiple evaluation methods to support the evaluation of different safety assessment parameters of the same type.

[0082] For each group of safety assessment characteristic parameters, according to the values ​​of the safety assessment characteristic parameters, the evaluation method corresponding to the safety assessment characteristic parameters is determined from the membership model, and the safety assessment characteristic parameters are evaluated according to the evaluation method to obtain the membership quantification value between the safety assessment characteristic parameters and the overall safety of the road. Among them, the membership quantification value is positively correlated with the overall safety of the road, that is, the larger the membership quantification value, the higher the overall safety of the road, and the smaller the membership quantification value, the lower the overall safety of the road.

[0083] S203: Determine a safety assessment result of the test road according to the membership quantization value of the test road.

[0084] Each test road corresponds to multiple groups of safety assessment characteristic parameters, each group of safety assessment characteristic parameters corresponds to a membership quantization value, so each test road corresponds to multiple membership quantization values.

[0085] Optionally, the membership quantization values ​​of the test road are standardized and normalized, and then the processed multiple membership quantization values ​​are superimposed to obtain the safety assessment result of the test road.

[0086] Optionally, a weight value of each membership quantized value of the test road is obtained, and a weighted sum result of multiple membership quantized values ​​of the test road and each weight value is calculated to obtain a safety assessment result of the test road.

[0087] In the embodiment of the present application, multiple groups of safety assessment characteristic parameters of the test road are obtained, and each safety assessment characteristic parameter is analyzed according to the membership model corresponding to each safety assessment characteristic parameter, and the membership quantification value between each safety assessment characteristic parameter and the overall safety of the road is obtained, and finally the safety assessment result of the test road is determined according to the membership quantification value of the test road. In this method, different types of membership models are provided to analyze different types of safety assessment characteristic parameters, quantify the correlation between the safety assessment characteristic parameters and the overall road safety, and obtain multiple membership quantification values ​​of the test road, which provides an objective, comprehensive and accurate evaluation basis for the safety assessment of the test road, and improves the accuracy of the safety assessment results of the test road.

[0088] It can be seen from the above embodiments that the test road includes multiple groups of safety assessment characteristic parameters, and different safety assessment characteristic parameters correspond to different calculation methods of the historical quantization values. Next, a method for obtaining the membership quantization value is described.

[0089] In an exemplary embodiment, as shown in FIG3 , the membership model includes standard safety assessment parameters; and according to the membership model corresponding to each safety assessment characteristic parameter, each safety assessment characteristic parameter is analyzed to obtain a membership quantification value between each safety assessment characteristic parameter and the overall road safety, including the following steps:

[0090] S301: For any safety assessment characteristic parameter, compare the safety assessment characteristic parameter with a standard safety assessment parameter.

[0091] Different types of safety assessment characteristic parameters correspond to different standard safety assessment parameters.

[0092] Optionally, the standard security assessment parameter is a specific numerical value, then the comparison result of comparing the security assessment characteristic parameter with the standard security assessment parameter includes: the security assessment characteristic parameter is greater than the standard security assessment parameter, the security assessment characteristic parameter is equal to the standard security assessment parameter, and the security assessment characteristic parameter is greater than the standard security assessment parameter.

[0093] Optionally, the standard safety assessment parameter is a plurality of specific values, then a plurality of intervals are determined according to the standard safety assessment parameter, and the comparison result of comparing the safety assessment characteristic parameter with the standard safety assessment parameter is: the safety assessment characteristic parameter is within the interval of two standard safety assessment parameters.

[0094] S302: Determine a target membership function of the safety assessment characteristic parameter according to the comparison result.

[0095] The membership function is a function model for calculating the security assessment characteristic parameters, the input is the security assessment characteristic parameters, and the output is the membership quantized value corresponding to the input.

[0096] In practical applications, multiple groups of membership functions and mapping relationships between comparison results and membership functions are pre-stored in the database. Based on this, after obtaining the comparison result, the membership function matching the comparison result can be determined from the mapping relationship as the target membership function of the security assessment feature parameter.

[0097] S303: Input the safety assessment characteristic parameter into the target membership function to obtain a membership quantified value between the safety assessment characteristic parameter and the overall road safety.

[0098] Substitute the safety assessment characteristic parameters into the calculation formula of the target membership function to obtain the membership quantified value of the safety assessment characteristic parameters.

[0099] In an embodiment of the present application, based on the comparison results of the security assessment characteristic parameters and the standard security assessment parameters, the target membership function of the security assessment characteristic parameters is determined, and then the membership quantized value of the security assessment characteristic parameters is calculated, which is equivalent to setting different membership functions for the security assessment characteristic parameters in different parameter intervals, and refining the calculation method of the membership quantized value of different security assessment characteristic parameters of the same type, providing a targeted analysis method for the security assessment characteristic parameters, and thereby improving the accuracy of the membership quantized value calculation results.

[0100] Next, a comparison method of the aforementioned S301 is described.

[0101] In an exemplary embodiment, as shown in FIG4 , the safety assessment characteristic parameter includes a road surface parameter; the standard safety assessment parameter includes a plurality of parameters; and comparing the safety assessment characteristic parameter with the standard safety assessment parameter includes the following steps:

[0102] S401, dividing each standard safety assessment parameter to obtain a plurality of road surface parameter intervals.

[0103] The standard safety assessment parameters are sorted according to their numerical values ​​to obtain a standard safety assessment parameter sequence. Then, the minimum standard safety assessment parameter is used as the upper limit value of a pavement parameter interval to construct a pavement parameter interval; the maximum standard safety assessment parameter is used as the lower limit value of a pavement parameter interval to construct a pavement parameter interval; and, each two adjacent parameters in the standard safety assessment parameter sequence are used as the upper and lower limits of the interval to construct multiple pavement parameter intervals.

[0104] S402: Determine a target road surface parameter interval that matches the road surface parameter value from a plurality of road surface parameter intervals.

[0105] S403: Determine the target road surface parameter interval as the comparison result.

[0106] The pavement parameter value is compared with each pavement parameter interval, and the pavement parameter interval where the pavement parameter value is located is determined as the target pavement parameter interval, that is, the comparison result of the pavement parameter value.

[0107] Taking the pavement parameter as the pavement structure strength index as an example, the calculation method of the pavement structure strength index and the method of obtaining the membership quantization value of the pavement structure strength index are described based on the embodiments of FIG. 3 and FIG. 4 :

[0108] (1) Based on the allowable deflection L of the road surface R The pavement strength structure coefficient SSR is calculated based on the measured representative deflection L0 of the pavement.

[0109]

[0110] (2) Based on the pavement strength structure coefficient SSR, calculate the weighted pavement strength structure coefficient eSSR.

[0111]

[0112] In the above formula, is the excitation coefficient, the value is 1.1, , are penalty coefficients, and their values ​​are 0.95 and 0.85 respectively.

[0113] (3) Calculate the pavement structure strength index based on the weighted pavement strength structure coefficient eSSR.

[0114]

[0115] In the above formula, and are model parameters, and their values ​​are 15.71 and -5.19 respectively.

[0116] (4) Determine the quantified value of the membership of the pavement structure strength index based on the pavement structure strength index.

[0117]

[0118] In the above formula, is the strength index of the pavement structure of the i-th section, It is the quantified value of the membership value of the pavement structure strength index of the i-th road section.

[0119] Taking the pavement parameter as the pavement anti-skid performance index as an example, the calculation method of the pavement anti-skid performance index and the method of obtaining the membership quantization value of the pavement anti-skid performance index are described in combination with the embodiments of FIG. 3 and FIG. 4 :

[0120] (1) Obtain the lateral force coefficient SFC.

[0121] The method for determining the lateral force coefficient is generally as follows: use a standard friction coefficient measuring vehicle, with its measuring wheels at 7.5°-20° to the driving direction, and when driving on a wet road at a certain speed, the ratio of the lateral friction force exerted on the test wheel to the load acting on the test wheel.

[0122] (2) Based on the lateral force coefficient SFC and calibration parameter SRI min , determine the pavement anti-skid performance index.

[0123]

[0124] In the above formula, and are model parameters, with values ​​of 28.6 and -0.105, respectively, and SRI min It is a calibration parameter and its value is 35.0.

[0125] (3) Determine the quantified value of the pavement anti-skid performance index based on the pavement anti-skid performance index.

[0126]

[0127] In the above formula, is the pavement skid resistance index of the i-th road section, It is the quantified value of the membership index of the i-th section pavement anti-skid performance index.

[0128] Taking the road parameter as the road ride quality index as an example, the calculation method of the road ride quality index and the method of obtaining the membership quantization value of the road ride quality index are described in combination with the embodiments of FIG. 3 and FIG. 4 :

[0129] (1) Obtain the road grade of the test road.

[0130] (2) Determine the road ride quality index based on the road grade and the International Roughness Index (IRI).

[0131] The calculation formula of the road ride quality index is as follows:

[0132]

[0133] Among them, RQI is the road ride quality index, and IRI is the international roughness index, which indicates the degree of change in the road surface and reflects the wear and durability of the road surface. When the road grade is expressway or highway, a0 is 0.026 and a1 is 0.65; when the road grade is other than expressway or highway, a0 is 0.0185 and a1 is 0.58.

[0134] (3) Determine the quantified value of the pavement ride quality index based on the pavement ride quality index.

[0135]

[0136] In the above formula, is the road quality index of the i-th road section, It is the quantified value of the membership value of the road ride quality index of the i-th road section.

[0137] In an embodiment of the present application, for any type of pavement parameters, multiple standard safety assessment parameters corresponding to the pavement parameters are subdivided into multiple pavement parameter intervals, and a target pavement parameter interval that better matches the actual pavement parameters of the test road is determined, providing a more reliable basis for obtaining the membership quantized value of the pavement parameters, so that the target membership function and the pavement parameters are matched, thereby improving the accuracy of the calculation results of the membership quantized value of the pavement parameters.

[0138] Next, another comparison method of the aforementioned S301 is described.

[0139] In an exemplary embodiment, as shown in FIG5 , the safety assessment characteristic parameter includes a road surface condition parameter; the standard safety assessment parameter includes a road surface condition parameter threshold; and comparing the safety assessment characteristic parameter with the standard safety assessment parameter comprises the following steps:

[0140] S501, determining an upper limit value and a lower limit value of a road surface condition parameter according to a road surface condition parameter threshold value.

[0141] The pavement condition parameter threshold, also known as the slope safety threshold, is used to characterize the degree of influence of the slope on driving safety. In the embodiment of the present application, the absolute value of the pavement condition parameter threshold is determined as the pavement condition parameter upper limit, and the opposite of the pavement condition parameter upper limit is determined as the pavement condition parameter lower limit.

[0142] S502, obtaining a comparison result by judging the difference between the road surface condition parameter and the upper limit value of the road surface condition parameter, and the difference between the road surface condition parameter and the lower limit value of the road surface condition parameter.

[0143] The road surface condition parameter is compared with the road surface condition parameter upper limit value and the road surface condition parameter lower limit value respectively to obtain comparison results. The comparison results include three types: the road surface condition parameter is greater than the road surface condition parameter upper limit value, the road surface condition parameter is less than the road surface condition parameter upper limit value, and the road surface condition parameter is less than the road surface condition parameter upper limit value and greater than the road surface condition parameter lower limit value.

[0144] Taking the longitudinal slope index as an example of the road surface condition parameter, the calculation method of the longitudinal slope index, the method of obtaining the membership quantization value of the longitudinal slope index, and the method of obtaining the membership quantization value of the longitudinal slope length index are described based on the embodiments of FIG. 3 and FIG. 5 :

[0145] (1) Obtain the longitudinal slope.

[0146] Among them, the longitudinal slope is the ratio of the height difference between two points on the same slope section of the test road route longitudinal section to their horizontal distance, expressed in the form of a percentage.

[0147] (2) The longitudinal slope is input into the membership model of the longitudinal slope. The membership quantified value of the longitudinal slope is determined based on the comparison result between the longitudinal slope and the upper limit value of the slope safety threshold and the lower limit value of the slope safety threshold.

[0148]

[0149] In the above formula, is the membership quantified value of the longitudinal slope safety index of the i-th section of road, SLP is the longitudinal slope, a0, b0, a1, b1 are the membership model fitting parameters, and the values ​​are 0.99, 0.47, 0.97, 0.52 respectively, exp() represents the exponential operation with the natural constant e as the base, is the slope safety threshold, and its value is 2%.

[0150] In the above expression, a positive slope indicates an uphill section, and a negative slope indicates a downhill section. When the slope is less than 2%, the slope safety index is not affected. When the slope is greater than 2%, the slope safety index shows an exponential decay trend with the increase of the slope, and the decay trend of the slope safety index with the increase of the slope length in the downhill section is faster than that in the uphill section.

[0151] (3) The longitudinal slope is input into the membership model of the longitudinal slope length index. The membership quantification value of the longitudinal slope length is determined based on the comparison result between the longitudinal slope and the upper limit value of the slope safety threshold and the lower limit value of the slope safety threshold.

[0152]

[0153] in, is the membership quantified value of the safety index of the longitudinal slope length of the i-th road section, SLI is the membership value of the safety index of the longitudinal slope gradient of the road section, exp() represents the exponential operation with the natural constant e, LGL is the longitudinal slope length of the road section, is the slope safety threshold, the value is 2%, b and c are model parameters, the values ​​are 0.37 and 0.45 respectively.

[0154] In the above expression, a positive slope indicates an uphill section, and a negative slope indicates a downhill section. When the slope is less than 2%, the slope length safety index is not affected. When the slope is greater than 2%, the slope length safety index shows an exponential decay trend with the increase of the slope length. The decay trend is faster in sections with larger slopes, and at the same slope, the downhill section has a faster decay trend than the uphill section.

[0155] In the embodiment of the present application, taking into account the different characteristics of the membership function expression when the test road is uphill or downhill, the road surface condition parameter threshold is expanded to an upper limit value of the road surface condition parameter and a lower limit value of the road surface condition parameter, and the comparison result between the road surface condition parameter and the upper limit value of the road surface condition parameter and the lower limit value of the road surface condition parameter is used as the basis for obtaining the target membership function of the road surface condition parameter, so that the target membership function and the road surface condition parameter are matched, thereby improving the accuracy of the calculation result of the membership quantization value of the road surface condition parameter.

[0156] The impact of curves on road safety is mainly reflected in the impact of the curve radius and curvature change rate on the driving speed. Too high or too low driving speed may cause safety accidents. Based on this, the following is an embodiment parallel to the embodiment shown in Figure 3 to explain the method for obtaining the membership quantization value of the test road in the case of a curve.

[0157] In an exemplary embodiment, as shown in FIG6 , the safety assessment characteristic parameters include curve parameters; and according to the membership model corresponding to each safety assessment characteristic parameter, each safety assessment characteristic parameter is analyzed to obtain a membership quantization value between each safety assessment characteristic parameter and the overall road safety, including the following steps:

[0158] S601, determining a curve safety factor according to a curve parameter and a preset curve parameter threshold.

[0159] The curve parameters include the actual circular curve radius R and the actual transition curve L, the curve parameter thresholds include the calibrated minimum circular curve radius R' and the calibrated minimum transition curve L', and the calculation formula of the curve safety factor is as follows:

[0160]

[0161] Considering the influence of the geometric situation of curved roads and the radius of curved curves on driving safety, road condition signs can also be added to curved roads to remind drivers and pedestrians on the road to be safe. Please refer to Table 1, which is a corresponding table of the minimum radius of curved curves, the minimum length of transition curves, the road design speed and the road conditions.

[0162] Table 1

[0163]

[0164] In actual scenarios, signboards can be erected on the road to be tested, which include road conditions and driving speeds. In this way, when a vehicle passes through the road to be tested, the driver can adjust the vehicle speed according to the information on the signboard to improve driving safety.

[0165] S602: Input the curve safety factor into the curve parameter membership model to obtain a membership quantified value between the curve parameter and the overall road safety.

[0166] Taking the curve safety factor as w as an example, the expression of the curve parameter membership model is as follows:

[0167]

[0168] In the above formula, is the membership value of the curve safety index of the i-th test road, b is the model parameter, and its value is 0.93, and exp() represents the exponential operation with the natural constant e as the base.

[0169] In an embodiment of the present application, a curve safety factor is determined based on curve parameters and preset curve parameter thresholds, and then the curve safety factor is input into a membership model of the curve parameters to obtain a quantified value of the membership between the curve parameters and the overall road safety, thereby providing a complete set of quantitative evaluation schemes for curved roads and improving the scenario applicability of the road safety evaluation method.

[0170] It should be noted that the technical solutions provided in Figures 3 to 6 illustrate the method for obtaining the quantified values ​​of the membership of different categories of evaluation indicators of the test roads. In practical applications, the above-mentioned acquisition method can be applied to different types of test roads to obtain the quantified values ​​of the membership corresponding to multiple test roads.

[0171] Next, a possible implementation method for determining the safety evaluation result of each test road according to the membership quantization values ​​of multiple test roads is described. In an exemplary embodiment, as shown in FIG7 , the test road includes multiple test roads; determining the safety evaluation result of the test road according to the membership quantization values ​​of the test roads includes the following steps:

[0172] S701, based on the quantized values ​​of each membership metric in each test road, obtain the safety upper limit value and the safety lower limit value of each test road.

[0173] For each test road, the membership quantization values ​​of the test road are standardized according to all the membership quantization values ​​of the test road, and multiple standardized membership quantization values ​​corresponding to each test road are obtained. Then, for the standardized membership quantization value of any safety assessment characteristic parameter of the test road, the absolute value between the standardized membership quantization value and the maximum standardized membership quantization value of the same type in other test roads is taken as the upper limit of safety, and the absolute value between the standardized membership quantization value and the minimum standardized membership quantization value of the same type in other test roads is taken as the lower limit of safety, and so on, to obtain the upper limit and lower limit of safety corresponding to each safety assessment characteristic parameter in each test road.

[0174] It should be noted that each test road corresponds to a plurality of safety assessment characteristic parameters, each safety assessment characteristic parameter corresponds to a membership quantization value, and each safety assessment characteristic parameter corresponds to a safety upper limit value and a safety lower limit value.

[0175] S702: Determine the safety evaluation result of each test road according to the safety upper limit value and the safety lower limit value of each test road.

[0176] For each test road, the average of the upper and lower safety limits can be calculated based on the obtained safety upper and lower limit values ​​in the test road to obtain the safety mean, and the safety mean can be used as the safety assessment result of the test road. The distance between each safety assessment characteristic parameter and the safety mean can be further calculated, and the sum of the distances of each safety assessment characteristic parameter can be used as the safety assessment result.

[0177] In the embodiment of the present application, based on the quantified values ​​of each affiliation in each test road, the upper safety limit and the lower safety limit of each test road are obtained, and then the safety assessment results of each test road are determined, which is equivalent to comprehensively considering the differences between different quantified values ​​of affiliation in the same test road, and the differences between the quantified values ​​of affiliation between different test roads in the safety assessment process, so as to objectively evaluate the weight of each safety feature parameter of the road to be tested as a whole, and improve the accuracy of the safety assessment results of the test road. In addition, the evaluation method provided in the embodiment of the present application takes multiple test roads into consideration, and can support the evaluation of multiple test roads in parallel. Compared with the method of evaluating each test road one by one, it not only improves the diversity of the evaluation reference dimensions and increases the accuracy of the evaluation results, but also improves the evaluation efficiency of the test roads.

[0178] In actual applications, there is no unique way to obtain the upper and lower safety limits. Based on this, another implementation method of the aforementioned S701 "obtaining the upper and lower safety limits of each test road based on the quantized values ​​of each membership metric in each test road" is described below through an embodiment, as shown in FIG8 , including the following steps:

[0179] S801: construct an evaluation matrix for each test road according to the quantized value of each membership metric in each test road.

[0180] Different rows of the evaluation matrix represent different types of membership quantization values, and different columns of the evaluation matrix represent different test roads.

[0181] According to the rule that each test road corresponds to a column of membership quantization values, the membership quantization values ​​of each test road are combined to obtain the membership quantization value sequence of each test road. Then, according to the rule that the same type of membership quantization values ​​of different test roads correspond to a row of membership quantization values, the membership quantization value sequence of each test road is combined to obtain the evaluation matrix of each test road.

[0182] Taking M test roads and each test road corresponding to N types of membership quantization values ​​as an example, the size of the membership quantization value sequence of each test road is N×1, and the size of the evaluation matrix of the M test roads is N×M.

[0183] S802: Determine a weighted normalization matrix corresponding to each test road according to the evaluation matrix.

[0184] According to the preset scoring rules or empirical values, a weight value can be assigned to each element in the evaluation matrix to obtain the weight matrix of the evaluation matrix, and then the evaluation matrix and the weight matrix are multiplied element by element to obtain the weighted normalized matrix. That is, the size of the evaluation matrix, the weight matrix and the weighted normalized matrix are consistent.

[0185] In an exemplary embodiment, as shown in FIG9 , determining a weighted normalized matrix corresponding to each test road according to the evaluation matrix includes the following steps:

[0186] S901, obtaining an importance judgment matrix of each test road.

[0187] For each test road, the relative importance of each pair of safety assessment characteristic parameters of the test road is compared, and the initial evaluation values ​​of multiple safety assessment characteristic parameters corresponding to the test road are determined to obtain an importance judgment matrix.

[0188] Use A ij Represents the importance judgment matrix. Each element a in the importance judgment matrix ij The scaling method is shown in Table 2:

[0189] Table 2

[0190]

[0191] S902, performing column normalization and row normalization processing on the importance judgment matrix in sequence to obtain a weight matrix for each test road.

[0192] For each matrix element a in the importance judgment matrix ij , first perform column normalization to obtain a column normalized matrix.

[0193]

[0194] In the formula, is a normalized matrix, n is the number of characteristic parameters for security assessment, and is also the number of rows in the importance judgment matrix.

[0195] Then, each matrix element in the column-normalized matrix is ​​row-normalized to obtain the indicator weight of each row.

[0196]

[0197] in, is the weight of the i-th safety assessment feature parameter in the test road, n is the number of safety assessment feature parameters, and is also the number of rows in the importance judgment matrix.

[0198] It should be noted that each column element in the weight matrix represents the weight of different safety assessment feature parameters of a test road. If the weight of the same safety assessment feature parameter of different test roads is the same, then the elements in each row of the weight matrix are the same.

[0199] S903: Determine the product of the evaluation matrix and the weight matrix as a weighted normalization matrix.

[0200] The evaluation matrix and the weight matrix are multiplied element by element, the size of the evaluation matrix is ​​kept unchanged, and a weight is assigned to each initial evaluation value in the evaluation matrix to obtain a weighted normalized matrix.

[0201] In the technical solution provided in FIG9 , after obtaining the importance judgment matrix of each test road, column normalization is performed to evaluate the differences between characteristic parameters of different types of the same road, and then row normalization is performed to evaluate the differences between characteristic parameters of the same type of different roads, and to determine accurate and objective weight values ​​for each element in the importance judgment matrix. On this basis, the weighted normalization matrix determined by the product of the evaluation matrix and the weight matrix can also more accurately reflect the importance of each characteristic parameter in each test road.

[0202] S803: Obtain the upper safety limit value and the lower safety limit value of each test road based on the weighted normalization matrix.

[0203] In the weighted normalized matrix, the elements of each column represent the weights corresponding to multiple membership quantization values ​​of a test road, and the elements of each row represent the weights corresponding to multiple test roads under the same type of membership quantization values.

[0204] According to the preset correlation coefficient calculation formula, the correlation coefficient of each element in the weighted normalized matrix in the global matrix is ​​calculated to obtain the correlation coefficient matrix. Then, the maximum value of each column element in the correlation coefficient matrix is ​​determined as the upper safety limit value, and the maximum value of each column element in the correlation coefficient matrix is ​​determined as the lower safety limit value.

[0205] Z ij Taking the representation weighted normalized matrix as an example, the correlation coefficient calculation formula for each matrix element is:

[0206]

[0207] In the above formula, represents the correlation coefficient of the element in the i-th row and j-th column (that is, the i-th membership quantization value in the j-th test road), represents the maximum value of the elements in the i-th row, Represents a resolution coefficient of 0.5. Repeat the above calculation formula for each matrix element to obtain a correlation coefficient matrix.

[0208] Next, in the correlation coefficient matrix, the maximum correlation coefficient among the i correlation coefficients in each column element is determined as the upper limit of safety, and the minimum correlation coefficient is determined as the lower limit of safety. In other words, one column element corresponds to one upper limit of safety and one lower limit of safety.

[0209] In an embodiment of the present application, an evaluation matrix is ​​constructed based on the quantized values ​​of each membership measure of each test road, and the evaluation matrix is ​​weighted to obtain a weighted normalized matrix, so that the weighted normalized matrix can more objectively and accurately characterize the actual road surface conditions of each test road, and the reliability of the upper and lower safety limits of each safety assessment parameter of each test road determined in this way is also higher.

[0210] It should be noted that a test road corresponds to multiple types of safety assessment parameters, and multiple types of safety assessment parameters correspond to a set of safety upper limit values ​​and safety lower limit values. After obtaining the safety upper limit value and safety lower limit value of each test road, it is necessary to further summarize the multiple types of safety assessment parameters according to the safety upper limit value and safety lower limit value to obtain the overall safety assessment index of the test road.

[0211] In an exemplary embodiment, as shown in FIG10 , determining the safety evaluation result of each test road according to the safety upper limit value and the safety lower limit value of each test road includes the following steps:

[0212] S1001, for any test road, determine the first Euclidean distance of the test road according to each safety assessment characteristic parameter and the safety upper limit value in the test road; determine the second Euclidean distance of the test road according to each safety assessment characteristic parameter and the safety lower limit value in the test road.

[0213] The first Euclidean distance indicates the distance between the test road and the optimal road. The larger the first Euclidean distance is, the farther the test road is from the optimal road, and the lower the safety of the test road is.

[0214] Among them, the expression of the first Euclidean distance is as follows:

[0215]

[0216] In the formula, is the first Euclidean distance, is the jth safety evaluation characteristic parameter of the i-th test road, n is the number of safety evaluation characteristic parameters, is the upper limit of safety of the i-th test road.

[0217] The second Euclidean distance indicates the distance between the test road and the worst road. The larger the second Euclidean distance is, the farther the test road is from the worst road, and the higher the safety of the test road is.

[0218] Among them, the expression of the second Euclidean distance is as follows:

[0219]

[0220] In the formula, is the second Euclidean distance, is the jth safety evaluation characteristic parameter of the i-th test road, n is the number of safety evaluation characteristic parameters, is the lower limit of safety of the i-th test road.

[0221] S1002: Determine a safety assessment result of the test road according to the first Euclidean distance and the second Euclidean distance.

[0222] The proportion of the second Euclidean distance in the total distance corresponding to the first Euclidean distance and the second Euclidean distance is used to represent the closeness of the test road to the ideal road. The expression of the safety evaluation result is as follows:

[0223]

[0224] In the formula, is the first Euclidean distance, is the second Euclidean distance, is the safety assessment result of the jth test road.

[0225] It can be seen from the above expression that the smaller the first Euclidean distance is and the larger the second Euclidean distance is, the larger the value of the safety assessment result is, the closer the test road is to the ideal road, and the higher the safety of the test road is.

[0226] In an embodiment of the present application, the distance between the test road and the optimal ideal road is quantified by calculating the first Euclidean distance between each safety assessment characteristic parameter of the test road and the upper limit of safety. The distance between the test road and the worst road is quantified by calculating the second Euclidean distance between each safety assessment characteristic parameter of the test road and the lower limit of safety. Then, the first Euclidean distance and the second Euclidean distance are combined to determine the degree of closeness of the test road to the ideal solution, thereby improving the accuracy of the safety assessment results of the test road.

[0227] In an exemplary embodiment, taking the test road as an asphalt road as an example, as shown in FIG11 , a road safety assessment method is provided, comprising the following steps:

[0228] S1101, based on the asphalt pavement road construction standards, construct an asphalt pavement safety assessment index system.

[0229] The safety assessment index system includes two characteristic aspects: pavement parameters and road geometry. Among them, pavement parameters include pavement structure strength, pavement driving quality and pavement anti-skid performance, and road geometry includes: longitudinal slope gradient, longitudinal slope length, curve angle and curve length.

[0230] S1102, for each asphalt pavement, according to the characteristic parameter values ​​and business attributes of each evaluation index of the asphalt pavement, the membership quantization value of each evaluation index is calculated to obtain an evaluation index matrix.

[0231] Each section of asphalt pavement is processed by the above steps to obtain the membership quantified values ​​of each evaluation index corresponding to the multiple sections of asphalt pavement.

[0232] S1103, obtaining the weight of each evaluation index, performing weighted calculation on the quantified value of the membership of each evaluation index and the corresponding weight, and obtaining a standard decision matrix corresponding to the multiple sections of asphalt pavement.

[0233] Optionally, based on the analytic hierarchy process (AHP), the weights of various evaluation indicators are evaluated to obtain a weighted matrix, and then the weighted matrix and the evaluation indicator matrix are multiplied to obtain a standard decision matrix.

[0234] S1104: For any asphalt pavement, based on the standard decision matrix, calculate the maximum correlation distance and the minimum correlation distance between the asphalt pavement and other asphalt pavements.

[0235] Optionally, based on a grey relational analysis (GRA), a grey relational coefficient in a standard decision matrix is ​​calculated, and based on a maximum grey relational coefficient and a minimum grey relational coefficient, a maximum relational distance and a minimum relational distance between an asphalt pavement and other asphalt pavements are calculated.

[0236] S1105: For any asphalt pavement, the Euclidean distance between the asphalt pavement and the ideal pavement is calculated according to the maximum correlation distance and the minimum correlation distance, so as to obtain a safety assessment result of the asphalt pavement.

[0237] The asphalt road safety assessment method provided in the embodiment of the present application is based on the constructed road safety assessment system, which effectively quantifies the safety performance of asphalt roads, provides a certain basis for road improvement, expansion, renovation and maintenance, and provides a solution for building a road safety assessment system. Among them, the construction of the asphalt road safety assessment index system includes indicators of multiple dimensions such as pavement structure strength, pavement driving quality, pavement anti-skid performance, longitudinal slope gradient, longitudinal slope length, curve angle, curve length, etc., which can comprehensively characterize the actual pavement state of the asphalt road. In addition, by calculating the characteristic membership values ​​corresponding to different pavement evaluation indicators, the association between the evaluation indicators of each dimension and the overall safety of the road is further objectively and accurately characterized, providing a reliable basis for calculating the safety assessment results. Furthermore, based on the subjective and objective weighting of the indicator system, the influence of each evaluation index item on the overall safety of the road is weighted again to improve the accuracy of the safety assessment results.

[0238] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0239] Based on the same inventive concept, the embodiment of the present application also provides a road safety assessment device for implementing the road safety assessment device method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more road safety assessment device embodiments provided below can refer to the limitations of the road safety assessment device method above, and will not be repeated here.

[0240] In an exemplary embodiment, as shown in FIG. 12 , a road safety assessment device is provided, including: a parameter acquisition module 1201, a membership quantization value acquisition module 1202 and a result acquisition module 1203, wherein:

[0241] A parameter acquisition module 1201 is used to acquire multiple groups of safety assessment characteristic parameters of the test road;

[0242] A membership quantization value acquisition module 1202 is used to analyze each safety assessment characteristic parameter according to the membership model corresponding to each safety assessment characteristic parameter, and obtain a membership quantization value between each safety assessment characteristic parameter and the overall safety of the road;

[0243] The result acquisition module 1203 is used to determine the safety assessment result of the test road according to the membership quantization value of the test road.

[0244] In an exemplary embodiment, the membership model includes standard security assessment parameters; the membership quantization value acquisition module 1202 includes: a parameter comparison unit, a function determination unit and a membership quantization value calculation unit, wherein:

[0245] A parameter comparison unit, for comparing any safety assessment characteristic parameter with a standard safety assessment parameter;

[0246] A function determination unit, used to determine a target membership function of a safety assessment characteristic parameter according to the comparison result;

[0247] The membership quantization value calculation unit is used to input the safety assessment characteristic parameter into the target membership function to obtain the membership quantization value between the safety assessment characteristic parameter and the overall road safety.

[0248] In an exemplary embodiment, the safety assessment characteristic parameter includes a road surface parameter; the standard safety assessment parameter includes a plurality of parameters; the parameter comparison unit includes: an interval division subunit, an interval determination subunit and a comparison result determination subunit, wherein:

[0249] An interval division subunit is used to divide each standard safety assessment parameter to obtain multiple pavement parameter intervals;

[0250] An interval determination subunit, for determining a target pavement parameter interval that matches the pavement parameter value from a plurality of pavement parameter intervals;

[0251] The comparison result determination subunit is used to determine the target road surface parameter interval as the comparison result.

[0252] In an exemplary embodiment, the safety assessment characteristic parameter includes a road surface condition parameter; the standard safety assessment parameter includes a road surface condition parameter threshold; the parameter comparison unit includes: an upper and lower limit determination subunit and an upper and lower limit comparison subunit, wherein:

[0253] The upper and lower limit determination subunit is used to determine the upper limit value of the road surface condition parameter and the lower limit value of the road surface condition parameter according to the road surface condition parameter threshold value;

[0254] The upper and lower limit comparison subunit is used to obtain a comparison result by judging the size between the road surface condition parameter and the upper limit value of the road surface condition parameter, and the size between the road surface condition parameter and the lower limit value of the road surface condition parameter.

[0255] In an exemplary embodiment, the safety assessment characteristic parameters include curve parameters; the membership quantization value acquisition module 1202 includes: a coefficient determination unit and a curve quantization value determination unit, wherein:

[0256] A coefficient determination unit, used for determining a curve safety factor according to curve parameters and a preset curve parameter threshold;

[0257] The curve quantization value determination unit is used to input the curve safety factor into the membership model of the curve parameter to obtain the membership quantization value between the curve parameter and the overall road safety.

[0258] In an exemplary embodiment, the test road includes multiple roads; the result acquisition module 1203 includes: a membership quantization value calculation unit and a result determination unit, wherein:

[0259] A membership quantization value calculation unit, used for obtaining a safety upper limit value and a safety lower limit value of each test road based on each membership quantization value in each test road;

[0260] The result determination unit is used to determine the safety evaluation result of each test road according to the safety upper limit value and the safety lower limit value of each test road.

[0261] In an exemplary embodiment, the membership quantization value calculation unit includes: a matrix construction subunit, a matrix determination subunit and a matrix calculation subunit, wherein:

[0262] A matrix construction subunit, used to construct an evaluation matrix for each test road according to the quantified values ​​of each membership metric in each test road;

[0263] A matrix determination subunit, used to determine a weighted normalization matrix corresponding to each test road according to the evaluation matrix;

[0264] The matrix calculation subunit is used to obtain the upper safety limit value and the lower safety limit value of each test road based on the weighted normalization matrix.

[0265] In an exemplary embodiment, the matrix determination subunit is also used to obtain the importance judgment matrix of each test road; the importance judgment matrix is ​​sequentially column normalized and row normalized to obtain the weight matrix of each test road; and the product result of the evaluation matrix and the weight matrix is ​​determined as a weighted normalized matrix.

[0266] In an exemplary embodiment, the matrix calculation subunit is also used to determine, for any test road, a first Euclidean distance of the test road based on various safety assessment characteristic parameters and an upper safety limit value in the test road; determine a second Euclidean distance of the test road based on various safety assessment characteristic parameters and a lower safety limit value in the test road; and determine a safety assessment result of the test road based on the first Euclidean distance and the second Euclidean distance.

[0267] Each module in the above-mentioned road safety assessment device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each module.

[0268] In an exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be shown in FIG13. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. 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 the computer program in the non-volatile storage medium. The database of the computer device is used to store data related to data set enhancement, including but not limited to constraints, text generation models, etc. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication 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, a road safety assessment method is implemented. The server may be an independent physical server, or a server cluster or distributed system consisting of multiple physical servers, or a cloud server providing cloud computing services.

[0269] Those skilled in the art will understand that the structure shown in FIG. 13 is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0270] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0271] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0272] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0273] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0274] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and 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-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0275] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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, they should be considered to be within the scope of this application.

[0276] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A road safety assessment method, characterized in that: The method comprises: Acquire multiple groups of safety assessment characteristic parameters of the test road; Analyze each of the safety assessment characteristic parameters according to the membership model corresponding to each of the safety assessment characteristic parameters to obtain a membership quantified value between each of the safety assessment characteristic parameters and the overall road safety; A safety assessment result of the test road is determined according to the membership quantization value of the test road.

2. The method according to claim 1, characterized in that The membership model includes standard safety assessment parameters; the safety assessment characteristic parameters are analyzed according to the membership model corresponding to each safety assessment characteristic parameter to obtain a membership quantified value between each safety assessment characteristic parameter and the overall road safety, including: For any safety assessment characteristic parameter, comparing the safety assessment characteristic parameter with the standard safety assessment parameter; Determining a target membership function of the safety assessment characteristic parameter according to the comparison result; The safety assessment characteristic parameter is input into the target membership function to obtain a membership quantified value between the safety assessment characteristic parameter and the overall road safety.

3. The method according to claim 2, characterized in that The safety assessment characteristic parameter includes a road surface parameter; the standard safety assessment parameter includes a plurality of parameters; and comparing the safety assessment characteristic parameter with the standard safety assessment parameter includes: Dividing each of the standard safety assessment parameters to obtain a plurality of road surface parameter intervals; Determining a target road surface parameter interval that matches the road surface parameter value from the plurality of road surface parameter intervals; The target road surface parameter interval is determined as the comparison result.

4. The method according to claim 2, characterized in that: The safety assessment characteristic parameters include road surface condition parameters; The standard safety assessment parameter includes a road surface condition parameter threshold; and comparing the safety assessment characteristic parameter with the standard safety assessment parameter includes: Determining an upper limit value and a lower limit value of a road surface condition parameter according to the road surface condition parameter threshold value; The comparison result is obtained by judging the difference between the road surface condition parameter and the upper limit value of the road surface condition parameter, and the difference between the road surface condition parameter and the lower limit value of the road surface condition parameter.

5. The method according to claim 1, characterized in that The safety assessment characteristic parameters include curve parameters; the safety assessment characteristic parameters are analyzed according to the membership model corresponding to each safety assessment characteristic parameter to obtain a membership quantization value between each safety assessment characteristic parameter and the overall road safety, including: Determining a curve safety factor according to the curve parameter and a preset curve parameter threshold; The curve safety factor is input into the membership model of the curve parameter to obtain a quantified value of the membership between the curve parameter and the overall safety of the road.

6. The method according to any one of claims 1 to 5, characterized in that: The test roads include a plurality of roads; and determining the safety assessment result of the test roads according to the membership quantization values ​​of the test roads includes: Based on the quantized values ​​of each membership metric in each of the test roads, obtaining the upper safety limit value and the lower safety limit value of each of the test roads; The safety evaluation result of each test road is determined according to the safety upper limit value and the safety lower limit value of each test road.

7. The method according to claim 6, characterized in that The step of obtaining the upper safety limit value and the lower safety limit value of each test road based on the quantized value of each membership metric in each test road comprises: Constructing an evaluation matrix for each test road according to the quantified values ​​of each membership metric in each test road; Determining a weighted normalized matrix corresponding to each of the test roads according to the evaluation matrix; Based on the weighted normalization matrix, an upper safety limit value and a lower safety limit value of each test road are obtained.

8. The method according to claim 7, characterized in that Determining a weighted normalized matrix corresponding to each of the test roads according to the evaluation matrix includes: Obtaining an importance judgment matrix for each of the test roads; Performing column normalization and row normalization processing on the importance judgment matrix in sequence to obtain a weight matrix of each test road; The product result of the evaluation matrix and the weight matrix is ​​determined as the weighted normalization matrix.

9. The method according to claim 6, characterized in that Determining the safety evaluation result of each test road according to the safety upper limit value and the safety lower limit value of each test road includes: For any test road, determining a first Euclidean distance of the test road according to each of the safety assessment characteristic parameters and the safety upper limit value in the test road; determining a second Euclidean distance of the test road according to each of the safety assessment characteristic parameters and the safety lower limit value in the test road; A safety assessment result of the test road is determined according to the first Euclidean distance and the second Euclidean distance.

10. A road safety assessment device, characterized in that: The device comprises: A parameter acquisition module, used to obtain multiple groups of safety assessment characteristic parameters of the test road; A membership quantization value acquisition module is used to analyze each of the safety assessment characteristic parameters according to the membership model corresponding to each of the safety assessment characteristic parameters, and obtain a membership quantization value between each of the safety assessment characteristic parameters and the overall road safety; The result acquisition module is used to determine the safety assessment result of the test road according to the membership quantization value of the test road.

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