Highway pavement driving quality parameter determination method and related equipment

Abstract

The invention provides a highway pavement driving quality parameter determination method. The method comprises the following steps: acquiring a target driving speed; obtaining a dynamic deflection value and an international flatness index of the target road; according to a pre-established vibration characteristic value model, determining a corresponding vibration characteristic value based on the target driving speed, the dynamic deflection value of the target road and the international flatness index; and determining a driving quality parameter of the target highway pavement at the target driving speed based on the corresponding vibration characteristic value. The invention further provides a related device for determining the road surface driving quality parameters.

Description

Technical Field

[0001] The present disclosure relates to the technical field of highway pavement management and maintenance, and in particular to a method for determining highway pavement driving quality parameters and related equipment. Background Art

[0002] At present, the management and maintenance of highways are mostly carried out according to the existing "Highway Technical Condition Assessment Standards". The above standards are mainly used for highway technical condition assessment, and the assessment results can be used for highway asset management, scientific decision-making for highway maintenance, and full-life design of highway maintenance, etc.

[0003] In the above standards, the road ride quality index (RQI) is usually used as an indicator to measure the comfort and safety of highway road driving. Moreover, according to the provisions of the above standards, the above RQI is only related to the international roughness index (IRI) of the highway road. IRI is defined as the ratio of the cumulative displacement of the body suspension system within a certain driving distance when one quarter of the vehicles are traveling at a certain speed (usually between 20km / h and 120km / h) to the driving distance. It can be seen that the above RQI is mainly related to the flatness of the road surface, and has nothing to do with other characteristics of the road surface or the characteristics of the driving vehicle. However, through research, it is found that in addition to the flatness of the road surface, for vehicles traveling on the road surface, factors such as the vehicle's driving speed and the elasticity of the road surface will affect the comfort and safety of the passengers. Therefore, using the RQI defined in the above standards as an indicator to measure the comfort and safety of highway road driving cannot accurately describe the comfort and safety of the passengers. Therefore, the above indicators need to be optimized. Summary of the invention

[0004] In view of this, an embodiment of the present disclosure provides a method for determining highway pavement driving quality parameters, which can comprehensively determine the highway pavement driving quality from multiple dimensions such as the international smoothness, rebound modulus and vehicle driving speed of the highway pavement, thereby achieving the goal of optimizing indicators for measuring highway driving comfort and safety.

[0005] The method for determining the driving quality parameters of a highway pavement described in the embodiment of the present disclosure may specifically include the following steps: obtaining a target driving speed; obtaining the dynamic deflection value and the international roughness index of the target highway; and determining corresponding vibration characteristic values ​​according to the target driving speed, the dynamic deflection value and the international roughness index of the target highway based on a pre-established vibration characteristic value model; and determining the driving quality parameters of the target highway pavement at the target driving speed based on the corresponding vibration characteristic values.

[0006] In an embodiment of the present disclosure, the vibration eigenvalue model is a set of pre-established nomograms; wherein the set of nomograms includes a plurality of images reflecting the mapping relationship between the vibration eigenvalues ​​and the dynamic deflection values ​​of the road surface and the international roughness index of the road surface; and the plurality of images correspond to different vehicle driving speeds respectively.

[0007] In an embodiment of the present disclosure, determining the corresponding vibration eigenvalue according to the target driving speed, the dynamic deflection value of the target road, and the international roughness index based on a pre-established vibration eigenvalue model includes: extracting at least one target image reflecting the mapping relationship between the vibration eigenvalue of the vehicle and the dynamic deflection value of the road surface and the international roughness index of the road surface corresponding to the target driving speed from the set of nomograms; reading at least one target vibration eigenvalue corresponding to the dynamic deflection value of the target road and the international roughness index of the target road from the at least one target image; and determining the corresponding vibration eigenvalue based on the at least one target vibration eigenvalue.

[0008] In an embodiment of the present disclosure, extracting at least one target image corresponding to the target driving speed and reflecting the mapping relationship between the vibration characteristic value of the vehicle and the dynamic deflection value of the road surface and the international roughness index of the road surface from the set of nomograms includes: in response to determining that the target driving speed is the same as one of the vehicle driving speeds corresponding to the multiple images, extracting the image as the target image; or, in response to determining that the target driving speed is different from the vehicle driving speed corresponding to any image in the multiple images, extracting at least one image whose corresponding vehicle driving speed is closest to the target driving speed as the target image.

[0009] In an embodiment of the present disclosure, reading at least one target vibration characteristic value corresponding to the dynamic deflection value of the target road and the international roughness index of the target road from the at least one target image includes: for each target image, determining a target curve from the target image based on the roughness interval in which the international roughness index of the target road is located; determining a corresponding target point in the target curve using the dynamic deflection value of the target road as the value of the horizontal coordinate; and using the value of the vertical coordinate of the target point as the target vibration characteristic value.

[0010] In an embodiment of the present disclosure, determining the corresponding vibration characteristic value based on the at least one target vibration characteristic value includes: in response to determining that there is only one target vibration characteristic value, determining the target vibration characteristic value as the corresponding vibration characteristic value; or, in response to determining that there are two target vibration characteristic values, interpolating the two target vibration characteristic values ​​based on the target driving speed to obtain the corresponding vibration characteristic value.

[0011] In an embodiment of the present disclosure, the set of nomograms is pre-established by the following method: collecting international roughness indexes and dynamic deflection values ​​of multiple candidate roads at multiple target locations respectively; determining vibration characteristic values ​​of the multiple candidate roads at the multiple target locations at multiple preset candidate driving speeds respectively; establishing a correlation relationship between the candidate driving speed, the international roughness index, the dynamic deflection value and the vibration characteristic value for each target location on each candidate road; fitting the mapping relationship between the dynamic deflection value and the vibration characteristic value for different candidate driving speeds and different roughness intervals based on the correlation relationship, and obtaining multiple relationship curves from the dynamic deflection value to the vibration characteristic value corresponding to different candidate driving speeds and different roughness intervals; drawing the multiple relationship curves for the same candidate driving speed and different roughness intervals in one image, and obtaining an image reflecting the relationship between the vibration characteristic value and the dynamic deflection value of the road surface and the international roughness index of the road surface; and combining the multiple images for different candidate driving speeds to obtain the set of nomograms.

[0012] In an embodiment of the present disclosure, respectively determining the vibration characteristic values ​​of the multiple candidate roads at the multiple target positions at multiple pre-set candidate driving speeds includes: respectively collecting vibration characteristic data of the multiple candidate roads at the multiple target positions at the multiple pre-set candidate driving speeds; and respectively determining the vibration characteristic value corresponding to each target position on each candidate road and each candidate driving speed based on the vibration characteristic data.

[0013] In an embodiment of the present disclosure, the vibration eigenvalue model is a trained machine learning model; wherein the input of the machine learning model includes: the target driving speed, the international roughness index of the target road, and the dynamic deflection value of the target road; the output of the learning model is the corresponding vibration eigenvalue.

[0014] In an embodiment of the present disclosure, determining the driving quality parameters of the target highway pavement at the target driving speed based on the corresponding vibration characteristic values ​​includes: determining the driving quality parameters of the target highway pavement at the target driving speed according to the corresponding vibration characteristic values ​​based on a mapping relationship between pre-set vibration characteristic values ​​and driving quality parameters.

[0015] Corresponding to the above method, an embodiment of the present disclosure further provides a device for determining a road pavement ride quality parameter, comprising:

[0016] A target driving speed acquisition module is used to acquire a target driving speed;

[0017] A dynamic deflection value acquisition module is used to obtain the dynamic deflection value of the target road;

[0018] The smoothness acquisition module is used to obtain the international smoothness index of the target road;

[0019] a vibration characteristic value determination module, configured to determine a corresponding vibration characteristic value according to the target driving speed, the dynamic deflection value of the target road, and the international roughness index based on a pre-established vibration characteristic value model; and

[0020] A mapping module is used to determine a ride quality parameter of the target road surface at the target driving speed based on the corresponding vibration characteristic value.

[0021] In addition, an embodiment of the present disclosure further provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for determining road pavement driving quality parameters when executing the program.

[0022] An embodiment of the present disclosure also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the above-mentioned method for determining the highway pavement driving quality parameters.

[0023] An embodiment of the present disclosure further provides a computer program product, comprising computer program instructions, which, when executed on a computer, enable the computer to execute the above-mentioned method for determining the highway pavement ride quality parameters.

[0024] It can be seen that the above-mentioned method for determining the road pavement driving quality parameters and its related equipment can comprehensively determine the road pavement driving quality of the road from multiple dimensions such as the international smoothness of the road pavement, the rebound modulus, and the driving speed of the vehicle, thereby achieving the goal of optimizing the indicators for measuring road driving comfort and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the present disclosure or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 The implementation process of the method for determining the road pavement driving quality parameters described in the embodiment of the present disclosure is shown.

[0027] Figure 2An example of an image reflecting the correlation between the vibration characteristic value of the vehicle and the dynamic deflection value of the road surface and the international roughness index of the road surface corresponding to the vehicle driving speed V1 in a set of pre-set nomograms described in an embodiment of the present disclosure is shown.

[0028] Figure 3 The specific implementation process of determining the corresponding vibration characteristic value based on the target driving speed, the dynamic deflection value of the target road and the international roughness index of the target road according to the pre-established vibration characteristic value model described in the embodiment of the present disclosure is shown.

[0029] Figure 4 An example of the mapping relationship between the vibration characteristic values ​​and the ride quality parameters described in the embodiment of the present disclosure is shown.

[0030] Figure 5 The flowchart of the method for establishing the vibration eigenvalue model in the form of a nomogram described in the embodiment of the present disclosure is shown.

[0031] Figure 6 The internal structure of the device for determining the road pavement driving quality parameters according to the embodiment of the present disclosure is shown.

[0032] Figure 7 A more specific schematic diagram of the hardware structure of an electronic device described in an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0033] In order to make the objectives, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0034] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should be understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the embodiments of the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0035] As mentioned above, in the existing standards, the important indicator RQI used to measure the driving comfort and safety of highway pavement is mainly related to the flatness of the pavement, and has nothing to do with other characteristics of the pavement and the characteristics of the driving vehicle. However, studies have confirmed that the driving vibration caused by different types of highway pavements with the same international flatness is usually significantly different from the perception of passengers. For example, cement concrete pavement is a rigid pavement with a generally large elastic modulus, while asphalt concrete pavement is a flexible pavement with a generally small elastic modulus. The elastic modulus of the pavement will be reflected through vehicle vibration, and ultimately reflected in the riding comfort of the passengers. It can be understood that when driving on a highway pavement with the same flatness, the vehicle vibration value caused by a flexible pavement is generally smaller than that of a rigid pavement, resulting in better riding comfort. In addition, different vehicle driving speeds will generally cause different vehicle vibrations. It can be understood that on the same highway pavement, the greater the driving speed, the greater the vehicle vibration value caused, resulting in poor riding comfort. Therefore, a more comprehensive and accurate indicator is needed to measure the comfort and safety of road driving. In other words, the existing RQI indicator needs to be optimized to more accurately and comprehensively evaluate the comfort and safety of road driving.

[0036] In order to solve the above problems, the embodiments of the present disclosure provide a method for determining the driving quality parameters of a highway pavement, which can comprehensively determine the driving quality parameters of a highway pavement from multiple dimensions such as the international smoothness of the highway pavement, the rebound modulus, and the driving speed of the vehicle, and use the above-mentioned highway pavement driving quality parameters as indicators for measuring the comfort and safety of highway driving, and optimize the existing indicator RQI, so as to more comprehensively and accurately complete the assessment of the technical condition of the highway, and further more accurately guide the work of highway full asset management, highway maintenance decision-making, and highway maintenance full life cycle design, etc.

[0037] Specifically, the dynamic deflection value (FWD) of a highway pavement can be used to measure the rebound modulus of the overall structure of the highway. Among them, the overall structure with a larger rebound modulus has a smaller dynamic deflection value FWD; and the overall structure with a smaller rebound modulus has a larger dynamic deflection value FWD. For example, a cement concrete pavement is a rigid pavement, and its elastic modulus is usually large, so its corresponding FWD is relatively small; while an asphalt concrete pavement is a flexible pavement, and its elastic modulus is usually small, so its corresponding FWD is relatively large. Therefore, in the embodiment of the present disclosure, the dynamic deflection value of the highway pavement is used as an indicator representing the rebound modulus of the highway.

[0038] Figure 1 The implementation process of the method for determining the road pavement driving quality parameters according to some embodiments of the present disclosure is shown. Figure 1 As shown, the method for determining highway pavement driving quality parameters described in the embodiment of the present disclosure may include the following steps.

[0039] In step 110 , a target driving speed is obtained.

[0040] In step 120, the dynamic deflection value and the international roughness index of the target highway are obtained.

[0041] In step 130, based on a pre-established vibration characteristic value model, a corresponding vibration characteristic value is determined according to the target driving speed, the dynamic deflection value of the target road, and the international roughness index.

[0042] In step 140 , the driving quality parameters of the target road surface at the target driving speed are determined based on the corresponding vibration characteristic values.

[0043] Each step in the above-mentioned method for determining the road pavement ride quality parameters will be described in detail below with reference to specific examples.

[0044] With respect to the above step 110, in some embodiments of the present disclosure, when the vehicle is traveling on the above target road, the above target driving speed can be obtained by reading vehicle data (for example, reading data corresponding to the vehicle speed instrument panel). As an alternative, in some other embodiments of the present disclosure, the value of the above target driving speed can be directly set according to demand without the need for actual vehicle driving.

[0045] As for the dynamic deflection value of the target highway described in the above step 120, as mentioned above, in the embodiments of the present disclosure, the above dynamic deflection value will be used as a parameter to measure the modulus of resilience of the overall structure of the highway. In some embodiments of the present disclosure, the dynamic deflection value of the target highway can be obtained by using an existing dynamic deflection detection vehicle. Generally, the dynamic deflection values ​​at different positions on the target highway may be different. Therefore, in practical applications, a dynamic deflection detection vehicle can be used to detect the dynamic deflection values ​​at different positions on the target highway, and the average value of the dynamic deflection values ​​at the different positions can be used as the dynamic deflection value of the target highway. For example, the existing dynamic deflection detection vehicle can be used to detect and record the dynamic deflection values ​​at the above positions at every pre-set detection distance (for example, 40 meters or other arbitrary distance) on the target highway, and after the detection is completed, the average value of all the detection values ​​on the target highway can be used as the dynamic deflection value of the target highway. In addition, as an alternative to the above-mentioned solution, in other embodiments of the present disclosure, for a target highway that has completed dynamic deflection value detection within a preset relatively recent period of time (for example, within a week), the dynamic deflection value data of the target highway recorded in the database can also be directly read, thereby determining the dynamic deflection value of the above-mentioned target highway based on the read data.

[0046] With respect to the international roughness index described in the above step 120, in some embodiments of the present disclosure, an existing laser roughness vehicle can be used to obtain the international roughness index of the above target highway. Generally, the international roughness indexes at different positions on the target highway may be different. Therefore, in practical applications, the laser roughness vehicle can be used to detect the international roughness indexes at different positions on the above target highway, and the average value of the international roughness indexes at the above different positions is used as the international roughness index of the above target highway. For example, the laser roughness vehicle can be placed on a moving vehicle, and the laser roughness vehicle can continuously detect and record the international roughness indexes at different positions of the target highway during the driving process of the vehicle, and after the detection is completed, the average value of all the detected values ​​is used as the international roughness index of the above target highway. In addition, similarly, as an alternative to the above scheme, in some other embodiments of the present disclosure, for a target highway that has completed the international roughness index detection within a preset relatively recent period of time (such as within a week), the international roughness index of the target highway recorded in the database can also be directly read as the international roughness index of the target highway.

[0047] It should be noted that the above steps 110 and 120 can be executed synchronously in parallel or sequentially. For example, the above international roughness and dynamic deflection value measuring instruments can be carried on the vehicle at the same time, so that the above target driving speed, the international roughness of the target road, and the dynamic deflection value can be detected synchronously during driving. The embodiments of the present disclosure do not limit the execution order of the above steps.

[0048] With respect to the above step 130, in the embodiment of the present disclosure, the above vibration characteristic value represents the data of the driving vibration induced by the road condition affecting the psychological / physiological index of the human body. In the embodiment of the present disclosure, the above vibration characteristic value of the vehicle is used to represent the driving quality of the road surface. Generally, the larger the vibration characteristic value of the vehicle, the worse the driving quality of the road surface.

[0049] Further, in the embodiment of the present disclosure, the pre-established vibration characteristic value model described in the above step 130 represents the mapping relationship between the vehicle's driving speed, the dynamic deflection value of the road surface, and the international roughness index of the road surface and the vibration characteristic value. In practical applications, the above vibration characteristic value model can be implemented in a variety of ways.

[0050] In some embodiments of the present disclosure, the vibration eigenvalue model may be specifically in the form of a set of nomograms. Specifically, the set of nomograms may include multiple images reflecting the mapping relationship between the vibration eigenvalues ​​and the dynamic deflection value of the road surface and the international roughness index of the road surface. The multiple images correspond to different vehicle driving speeds.

[0051] Figure 2 It shows an image example corresponding to the vehicle driving speed V1 in a set of nomograms described in the embodiments of the present disclosure, reflecting the mapping relationship between the vibration characteristic values of the vehicle, the dynamic deflection values of the highway pavement, and the international roughness index of the highway pavement.

[0052] As Figure 2 shown, the abscissa of the image represents the dynamic deflection value of the highway pavement. Among them, the farther to the right, the greater the dynamic deflection value of the highway pavement, that is, 0 < F1 < F2 < F3… < F6; the ordinate of the image represents the vibration characteristic value of the vehicle. Among them, the farther down, the greater the vibration characteristic value of the vehicle, that is, 0 < a1 < a2 < a3… < a5. In addition, each continuous curve in the image corresponds to a flatness interval. For example, Figure 2 IRI 1 in Figure 2 represents the flatness interval of 0 - 1.5 m / km, IRI 2 represents the flatness interval of 1.5 - 3 m / km, IRI 3 represents the flatness interval of 3 - 5 m / km, and IRI 4 represents the flatness interval of 5 - 8 m / km. It can be seen from the Figure 2 shown image that for the same international roughness index interval, in the interval where the dynamic deflection value of the highway pavement is relatively small, the vibration characteristic value of the vehicle usually decreases as the dynamic deflection value of the highway pavement increases, that is, the greater the resilient modulus of the highway pavement, the smaller the vibration, and the higher the driving comfort usually is. When the dynamic deflection value of the highway pavement increases to a certain value, it usually indicates a greater degree of damage to the highway pavement. At this time, the vibration characteristic value of the vehicle usually further increases as the dynamic deflection value of the highway pavement increases, that is, when the degree of damage to the highway pavement is large, the vibration increases and the driving comfort deteriorates. Further, it can also be seen from the Figure 2 shown image that for the same dynamic deflection value of the highway pavement, when the value represented by the international roughness index interval is larger, that is, the smoother the road surface, the smaller the vibration characteristic value of the vehicle, that is, the higher the driving comfort.

[0053] In the case of using the nomogram as the above vibration characteristic value model, the step 130 of determining the corresponding vibration characteristic value based on the above target driving speed, the dynamic deflection value of the target highway, and the international roughness index of the target highway according to the pre-established vibration characteristic value model may specifically include multiple steps as Figure 3 shown.

[0054] In step 310, at least one target image corresponding to the target driving speed and reflecting the mapping relationship between the vibration characteristic values of the vehicle, the dynamic deflection values of the highway pavement, and the international roughness index of the highway pavement is extracted from a pre-established set of nomograms based on the above target driving speed.

[0055] Specifically, as mentioned above, the above-mentioned pre-established set of nomograms includes multiple images reflecting the mapping relationship between the vibration characteristic value of the vehicle and the dynamic deflection value of the road surface and the international roughness index of the road surface. Among them, the above-mentioned multiple images correspond to different vehicle driving speeds. Thus, in the above-mentioned step 310, if the target driving speed of the vehicle obtained in the above-mentioned step 110 is the same as one of the vehicle driving speeds corresponding to the above-mentioned multiple images, the image can be directly extracted as the above-mentioned target image. For example, it is assumed that the above-mentioned pre-established set of nomograms includes a total of four images, and the corresponding vehicle driving speeds are 70km / h, 90km / h, 110km / h and 130km / h respectively. And it is assumed that the target driving speed of the vehicle obtained in the above-mentioned step 110 is 90km / h. In this case, in the above-mentioned step 310, the image corresponding to the vehicle driving speed of 90km / h can be directly extracted from the above-mentioned multiple images as the above-mentioned target image. If the target vehicle speed obtained in step 110 is different from the vehicle speed corresponding to any of the multiple images, at least one image, usually one or two images, whose corresponding vehicle speed is closest to the target speed can be directly extracted as the target image. It is also assumed that the pre-established set of nomograms contains four images, and the corresponding vehicle speeds are 70 km / h, 90 km / h, 110 km / h and 130 km / h, respectively. It is also assumed that the target vehicle speed obtained in step 110 is 85 km / h. In this case, in step 310, the image corresponding to the vehicle speed of 90 km / h, which is closest to 85 km / h, can be directly extracted from the multiple images as the target image; or two images corresponding to the vehicle speeds of 70 km / h and 90 km / h can be directly extracted from the multiple images as the target image, so that the target speed falls within the interval of the vehicle speeds corresponding to the two target images. If it is assumed that the target vehicle speed obtained in step 110 is 60 km / h, since there is no image corresponding to a vehicle speed lower than 60 km / h in the nomogram, in step 310, an image corresponding to a vehicle speed of 70 km / h can be directly extracted from the multiple images as the target image.

[0056] In step 320, at least one target vibration characteristic value corresponding to the dynamic deflection value of the target road and the international roughness index of the target road is read from the at least one extracted image.

[0057] In some embodiments of the present disclosure, for each of the above target images, the target vibration characteristic value corresponding to the dynamic deflection value of the target road and the international roughness index of the target road can be directly read from the target image.

[0058] Specifically, in the process of reading the vibration characteristic values of the vehicle corresponding to the dynamic deflection value of the target road and the international roughness index of the target road from the target image, first, based on the roughness interval where the international roughness index of the target road is located, the target curve is determined from the target image; then, the target point corresponding to the target curve is determined with the dynamic deflection value of the target road as the abscissa value; finally, the ordinate value of the above target point is used as the determined target vibration characteristic value.

[0059] In step 330, the corresponding vibration characteristic value is determined based on at least one target vibration characteristic value.

[0060] In some embodiments of the present disclosure, if there is only one target vibration characteristic value, the target vibration characteristic value can be directly determined as the corresponding vibration characteristic value. If there are two target vibration characteristic values, interpolation operations need to be performed on the two determined target vibration characteristic values based on the target driving speed, so as to obtain the corresponding vibration characteristic value.

[0061] Specifically, the above interpolation operation is usually performed based on the target driving speed. Assume that the target driving speed is Vt. At the same time, assume that the driving speeds of the vehicle corresponding to the two target images extracted in the above step 310 are V1 and V2 respectively, where V1 < Vt < V2 should be satisfied. In addition, further assume that the target vibration characteristic values read from the two target images by the above method are A1 and A2 respectively. Then the corresponding vibration characteristic value A finally obtained through interpolation operation can be expressed as:

[0062] It can be seen from this that through the above pre-set nomogram form, the corresponding vibration characteristic value can be determined based on the target driving speed, the dynamic deflection value of the target road, and the international roughness index of the target road by reading the graph and performing simple operations. The establishment method of the above nomogram will be described in detail later and will be skipped here for the time being.

[0063] In addition to the above nomogram form, in some other embodiments of the present disclosure, the above vibration characteristic value model can also be implemented by a trained machine learning model. For example, it can be implemented by a trained long short-term memory (LSTM) neural network. Specifically, the input of the above machine learning model can include: the target driving speed, the international roughness index of the target road, and the dynamic deflection value of the target road. The output of the above learning model can be the corresponding vibration characteristic value. The establishment method of the above machine learning model will also be described in detail later and will be skipped here for the time being.

[0064] Regarding the above step 140, as previously mentioned, generally, the larger the vibration eigenvalue of the vehicle, the worse the driving quality of the road surface. Therefore, there is an inverse relationship between the vibration eigenvalue of the vehicle and the driving quality parameter of the road surface. That is, the larger the vibration eigenvalue of the vehicle, the smaller the corresponding driving quality parameter of the road surface.

[0065] Specifically, in some embodiments of the present disclosure, based on the pre-set mapping relationship between the vibration eigenvalue and the driving quality parameter, the driving quality parameter of the target road surface at the target driving speed can be determined according to the corresponding vibration eigenvalue. For example, multiple vibration eigenvalue intervals can be pre-set based on the value range of the vibration eigenvalue, and multiple driving quality parameter value intervals can be pre-set at the same time. Then, establish the mapping relationship between the above multiple vibration eigenvalue intervals and multiple driving quality parameter value intervals, so that the larger the vibration eigenvalue of the vehicle, the smaller the corresponding driving quality parameter value interval of the road surface. In this way, after determining the corresponding vibration eigenvalue through the above step 130, the interval where the driving quality parameter is located can be determined according to the above mapping relationship.

[0066] Figure 4 shows an example of the mapping relationship between the vibration eigenvalue and the driving quality parameter described in the embodiments of the present disclosure. As Figure 4 shown, three vibration eigenvalue intervals [A1, +∞), [A2, A1), and [A3, A2) can be set. Among them, A1 > A2 > A3. Further, three driving quality parameter value intervals [RQI1, RQI2), [RQI2, RQI3), and [RQI3, 100] can also be set. Among them, RQI1 < RQI2 < RQI3. In Figure 4 it, [A1, +∞) corresponds to [RQI1, RQI2), [A2, A1) corresponds to [RQI2, RQI3), and [A3, A2) corresponds to [RQI3, 100]. In this way, after determining the corresponding vibration eigenvalue through the above step 130, the vibration eigenvalue interval where the corresponding vibration eigenvalue is located can be determined first, and the corresponding driving quality parameter value interval can be further determined, so as to determine the driving quality parameter of the target road surface at the target driving speed. It can be understood that the setting of the above vibration eigenvalue interval and driving quality parameter value interval, as well as the setting of the mapping relationship between the vibration eigenvalue interval and the driving quality parameter value interval, can all be realized based on practice and experience. The present application does not limit its specific setting method.

[0067] As an alternative to the above implementation, in some other embodiments of the present disclosure, the functional relationship between the vibration characteristic value and the ride quality parameter value can also be obtained directly based on the data collected when the road is tested for technical conditions (the larger the vibration characteristic value of the vehicle, the smaller the ride quality parameter value of the corresponding road surface). In this way, in the above step 140, the ride quality parameter of the target road surface at the target driving speed can be directly determined based on the above functional relationship and the vibration characteristic value determined in the above step 130.

[0068] It can be seen that the road driving quality parameters of the target highway can be determined by the above method. Furthermore, road maintenance decisions and highway management decisions can be made based on the determined road driving quality parameters of the target highway. For example, it is determined whether the road needs to be repaired or the maximum speed limit of the highway is determined based on the road driving quality parameters. Since the road driving quality parameters determined by the above method not only consider the international roughness index of the highway, but also consider other factors such as the dynamic bending value of the rebound modulus representing the overall structure of the highway and the driving speed of vehicles traveling on the highway, the existing RQI index is optimized. Therefore, the road driving quality parameters determined by the above method are more comprehensive and accurate, so that the management decisions and maintenance decisions of the highway are more accurate and efficient.

[0069] The specific method for establishing the vibration eigenvalue model will be described in detail below with reference to the accompanying drawings and examples.

[0070] In some embodiments of the present disclosure, for the vibration eigenvalue model in the form of the above-mentioned nomogram, the following can be used: Figure 5 The multiple steps shown establish the above-mentioned vibration eigenvalue model.

[0071] In step 510, the international roughness index and dynamic deflection values ​​of multiple candidate roads at multiple target locations are collected respectively.

[0072] In the embodiment of the present disclosure, in order to ensure the diversity of the collected samples, the types and flatness of the above-mentioned multiple candidate roads are preferably different, so that the international flatness index and dynamic deflection value of the collected multiple candidate roads at different target positions can also be different, thereby ensuring that the collected international flatness index can fall into different flatness intervals and the collected dynamic deflection value can also be basically evenly distributed within the effective range. The setting of the target position can also be set according to needs and actual conditions. For example, a target position can be set every 40 meters from the starting point to the end point on the candidate road.

[0073] In step 520 , vibration characteristic values ​​of the plurality of candidate roads at the plurality of target locations at a plurality of preset candidate driving speeds are respectively determined.

[0074] Specifically, the above step 520 may include the following two steps:

[0075] First, for each candidate road, vibration characteristic data at each target position on the candidate road at a plurality of preset candidate driving speeds are collected respectively.

[0076] In some embodiments of the present disclosure, a strapdown inertial navigation system may be used to collect the above-mentioned vibration characteristic data. Specifically, the above-mentioned vibration characteristic data may include twelve vibration indicators: X, Y, Z three-axis acceleration, X, Y, Z three-axis gyroscope angular velocity, pitch angle, yaw angle, roll angle, and X, Y, Z three-axis magnetic field.

[0077] The candidate driving speeds may be set as required, for example, may be set to the four driving speeds of 70 km / h, 90 km / h, 110 km / h and 130 km / h in the aforementioned example.

[0078] It should be noted that the data collection for the international flatness index, dynamic deflection value and vibration characteristic data can be completed simultaneously or separately. The present application does not limit the execution order of the above steps.

[0079] Secondly, based on the collected vibration characteristic data, the vibration characteristic value corresponding to each target position on each candidate road and each candidate driving speed is determined respectively.

[0080] In some embodiments of the present disclosure, the above-mentioned vibration characteristic value can be obtained based on one or more of the above-mentioned vibration characteristic data. For example, in some examples, the above-mentioned vibration characteristic value can be calculated based on the acceleration of the three axes of X, Y, and Z. For example, the vibration characteristic value A can be calculated based on the following calculation formula: Among them, a x represents the X-axis acceleration; a y represents the Y-axis acceleration; a z Represents the Z-axis acceleration.

[0081] In step 530, for each target position on each candidate road, a correlation relationship between the candidate travel speed, the international roughness index, the dynamic deflection value, and the vibration characteristic value is established.

[0082] In some embodiments of the present disclosure, since the collected data are all associated with the target position, in actual application, the target position can be numbered first, and then the collected data can be sorted based on a table, so as to obtain the candidate driving speed, international roughness index, dynamic deflection value and vibration characteristic value corresponding to each target position, so as to directly obtain the correlation between the data. In some examples, for example, the candidate driving speed, international roughness index, dynamic deflection value and vibration characteristic value corresponding to each target position can be recorded in Table 1 below.

[0083]

[0084]

[0085] Table 1

[0086] In step 540, based on the above association relationship, the mapping relationship between the dynamic deflection value and the vibration characteristic value is fitted for different candidate driving speeds and different flatness intervals, and multiple relationship curves from the dynamic deflection value to the vibration characteristic value corresponding to different candidate driving speeds and different flatness intervals are obtained.

[0087] In the embodiments of the present disclosure, any fitting algorithm can be used to obtain the above-mentioned multiple relationship curves reflecting the mapping relationship between the dynamic deflection value and the vibration characteristic value corresponding to different candidate driving speeds and different flatness intervals. The embodiments of the present disclosure do not limit the specific fitting algorithm. It should be noted that when fitting, the dynamic deflection value is used as the independent variable of the mapping relationship, and the vibration characteristic value is used as the dependent variable of the mapping relationship. In this way, after fitting, a similar relationship curve can be obtained. Figure 2 A continuous curve is shown in .

[0088] In step 550, multiple relationship curves for the same candidate driving speed and different roughness intervals are plotted in an image to obtain an image reflecting the relationship between the vibration characteristic value of the vehicle and the dynamic deflection value of the road surface and the international roughness index of the road surface.

[0089] In the embodiment of the present disclosure, after multiple relationship curves for the same candidate driving speed and different flatness intervals are drawn in one image, the following can be obtained: Figure 2 Image shown.

[0090] In step 560, a plurality of images for different candidate driving speeds are combined to obtain the nomogram.

[0091] It can be seen from this that through the above Figure 4The method shown can obtain the candidate driving speed, international roughness index, dynamic deflection value and vibration characteristic value corresponding to each target position on different candidate roads through data collection. Then, a plurality of relationship curves reflecting the mapping relationship between dynamic deflection value and vibration characteristic value corresponding to different candidate driving speeds and different roughness intervals are obtained through fitting algorithm. Finally, after combining the above-mentioned multiple relationship curves, a nomogram that can reflect the relationship between multiple dimensional features of candidate driving speed, international roughness index, dynamic deflection value and vibration characteristic value is obtained. In this way, in the subsequent application of highway pavement driving quality parameter assessment, the highway pavement driving quality parameters can be determined directly by reading the map.

[0092] In other embodiments of the present disclosure, for the above-mentioned method of using a machine learning model to implement a vibration eigenvalue model, it is also possible to first use the following method: Figure 5 Steps 510-530 shown establish a training data set. It can be understood that the vibration characteristic values ​​in the acquired data can be used as labels for the training data set. Further, the machine learning model is trained based on the established data set, so as to establish a mapping relationship between multiple dimensional features of driving speed, international smoothness index, dynamic deflection value and vibration characteristic value within the machine learning model. In this way, for the trained machine learning model, the corresponding vibration characteristic value, that is, the road surface driving quality parameter, can be directly output based on the input driving speed, international smoothness index and dynamic deflection value.

[0093] It can be seen from this that no matter whether the form of a nomogram or a machine learning model is used, a mapping relationship between multiple dimensional features such as driving speed, international smoothness index, dynamic deflection value, and vibration characteristic value can be established based on the collected data, and the driving quality parameters of the road surface can be quickly determined based on the established mapping relationship in the subsequent application of road surface driving quality parameter assessment. Moreover, since the embodiment of the present disclosure comprehensively determines the driving quality parameters of the road surface based on multiple dimensions such as the international smoothness, rebound modulus, and driving speed of the vehicle, and uses the above-mentioned road surface driving quality parameters as an indicator for measuring the comfort and safety of road driving, the existing method of only considering the single indicator of international smoothness is optimized, so that the technical condition assessment of the road can be completed more comprehensively and accurately, and then more accurately guide the work of road full asset management, road maintenance decision-making, and road maintenance full life design, etc.

[0094] Based on the above-mentioned method for determining road pavement driving quality parameters, an embodiment of the present disclosure further provides a device for determining road pavement driving quality parameters. Figure 6 The internal structure of the device for determining the road pavement driving quality parameters according to the embodiment of the present disclosure is shown. Figure 6As shown, the above-mentioned highway pavement driving quality parameter determination device may include the following modules.

[0095] The target driving speed acquisition module 610 is used to acquire the target driving speed.

[0096] The dynamic deflection value acquisition module 620 is used to acquire the dynamic deflection value of the target highway.

[0097] The roughness acquisition module 630 is used to obtain the international roughness index of the target road.

[0098] The vibration characteristic value determination module 640 is used to determine the corresponding vibration characteristic value according to the target driving speed, the dynamic deflection value of the target road and the international roughness index based on the pre-established vibration characteristic value model.

[0099] The mapping module 650 is used to determine the driving quality parameters of the target road surface at the target driving speed based on the corresponding vibration characteristic values.

[0100] It should be noted that the specific implementation methods of the various modules included in the apparatus for determining the driving quality parameters of a highway pavement described in the embodiments of the present disclosure can refer to the aforementioned embodiments, and will not be repeated here. Furthermore, the apparatus for determining the driving quality parameters of a highway pavement described in the aforementioned embodiments has the same beneficial effects as the corresponding method embodiments, and will not be repeated here.

[0101] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments, the present disclosure also provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for determining the road pavement driving quality parameters described in any of the above-mentioned embodiments is implemented.

[0102] Figure 7 A more specific hardware structure diagram of an electronic device provided in this embodiment is shown, and the device may include: a processor 2010, a memory 2020, an input / output interface 2030, a communication interface 2040, and a bus 2050. The processor 2010, the memory 2020, the input / output interface 2030, and the communication interface 2040 are connected to each other in communication within the device through the bus 2050.

[0103] The processor 2010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0104] The memory 2020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 2020 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 2020 and are called and executed by the processor 2010.

[0105] The input / output interface 2030 is used to connect input / output devices to realize information input and output. The input / output devices can be configured in the device as components, or can be externally connected to the device to provide corresponding functions. The input devices can include microphones, various sensors, etc., and the output devices can include displays, speakers, vibrators, indicator lights, etc.

[0106] The communication interface 2040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0107] The bus 2050 includes a path that transmits information between the various components of the device (eg, the processor 2010, the memory 2020, the input / output interface 2030, and the communication interface 2040).

[0108] It should be noted that, although the above device only shows the processor 2010, the memory 2020, the input / output interface 2030, the communication interface 2040, and the bus 2050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0109] The electronic device of the above embodiment is used to implement the corresponding highway pavement driving quality parameter determination method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0110] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the method for determining the highway pavement driving quality parameters as described in any of the above embodiments.

[0111] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0112] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the method for determining the road pavement driving quality parameters as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0113] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples. Based on the concept of the present disclosure, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present disclosure as described above, which are not provided in detail for the sake of simplicity.

[0114] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present disclosure difficult to understand, the known power / ground connections to the integrated circuit (IC) chips and other components may or may not be shown in the provided figures. In addition, the device can be shown in the form of a block diagram to avoid making the embodiments of the present disclosure difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present disclosure will be implemented (that is, these details should be fully within the scope of understanding of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present disclosure, it is apparent to those skilled in the art that the embodiments of the present disclosure can be implemented without these specific details or with changes in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0115] Although the present disclosure has been described in conjunction with specific embodiments of the present disclosure, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0116] The embodiments of the present disclosure are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A method for determining road pavement ride quality parameters, comprising: Get the target driving speed; Obtaining the dynamic deflection value and international roughness index of the target highway; Based on a pre-established vibration characteristic value model, a corresponding vibration characteristic value is determined according to the target driving speed, the dynamic deflection value of the target road, and the international roughness index; as well as A ride quality parameter of the target road surface at the target travel speed is determined based on the corresponding vibration characteristic value.

2. The method according to claim 1, wherein: The vibration characteristic value model is a set of pre-established nomograms; wherein the set of nomograms includes multiple images reflecting the mapping relationship between the vibration characteristic value and the dynamic deflection value of the highway pavement and the international roughness index of the highway pavement; and the multiple images correspond to different driving speeds respectively.

3. The method according to claim 2, wherein: Based on the pre-established vibration characteristic value model, the corresponding vibration characteristic value is determined according to the target driving speed, the dynamic deflection value of the target road and the international roughness index, including: Extracting at least one target image reflecting the mapping relationship between the vibration characteristic value and the dynamic deflection value of the road surface and the international roughness index of the road surface corresponding to the target driving speed from the set of nomograms; reading at least one target vibration characteristic value corresponding to the dynamic deflection value of the target road and the international roughness index of the target road from the at least one target image; and The corresponding vibration eigenvalue is determined based on the at least one target vibration eigenvalue.

4. The method according to claim 3, wherein: Extracting at least one target image corresponding to the target driving speed and reflecting the mapping relationship between the vibration characteristic value of the vehicle and the dynamic deflection value of the road surface and the international roughness index of the road surface from the set of nomograms includes: In response to determining that the target driving speed is the same as one of the driving speeds corresponding to the multiple images, extracting the image as the target image; or, In response to determining that the target driving speed is different from the driving speed corresponding to any image in the multiple images, at least one image whose corresponding driving speed is closest to the target driving speed is extracted as the target image.

5. The method according to claim 3, wherein: Reading at least one target vibration characteristic value corresponding to the dynamic deflection value of the target road and the international roughness index of the target road from the at least one target image comprises: For each target image, determining a target curve from the target image based on a roughness interval of an international roughness index of the target highway; Determine the corresponding target point on the target curve using the dynamic deflection value of the target road as the value of the abscissa; and The value of the ordinate of the target point is used as the target vibration characteristic value.

6. The method according to claim 3, wherein: Determining the corresponding vibration characteristic value based on the at least one target vibration characteristic value comprises: In response to determining that there is only one target vibration characteristic value, determining the target vibration characteristic value as the corresponding vibration characteristic value; and In response to determining that there are two target vibration characteristic values, an interpolation operation is performed on the two target vibration characteristic values ​​based on the target driving speed to obtain the corresponding vibration characteristic values.

7. The method according to claim 2, wherein: The set of nomograms is pre-established by the following method: Collect the international roughness index and dynamic deflection value of multiple candidate roads at multiple target locations respectively; respectively determining vibration characteristic values ​​of the plurality of candidate roads at the plurality of target locations at a plurality of preset candidate driving speeds; For each target position on each candidate road, establishing a correlation relationship between the candidate travel speed, international roughness index, dynamic deflection value and vibration characteristic value; Based on the association relationship, for different candidate driving speeds and different flatness intervals, the mapping relationship between the dynamic deflection value and the vibration characteristic value is fitted to obtain multiple relationship curves from the dynamic deflection value to the vibration characteristic value corresponding to different candidate driving speeds and different flatness intervals; A plurality of relationship curves for the same candidate driving speed and different smoothness intervals are plotted in one image, so as to obtain an image reflecting the relationship between the vibration characteristic value and the dynamic deflection value of the road surface and the international smoothness index of the road surface; as well as The plurality of images for different candidate driving speeds are combined to obtain the set of nomograms.

8. The method according to claim 7, wherein: Respectively determining the vibration characteristic values ​​of the plurality of candidate roads at the plurality of target locations at a plurality of preset candidate driving speeds includes: respectively collecting vibration characteristic data of the plurality of candidate roads at the plurality of target locations at the plurality of preset candidate driving speeds; and The vibration characteristic value corresponding to each target position on each candidate road and each candidate driving speed is determined based on the vibration characteristic data.

9. The method according to claim 1, wherein: The vibration eigenvalue model is a trained machine learning model; wherein the input of the machine learning model includes: the target driving speed, the international roughness index of the target road, and the dynamic deflection value of the target road; the output of the learning model is the corresponding vibration eigenvalue.

10. The method according to claim 1, wherein: Determining the driving quality parameters of the target road surface at the target driving speed based on the corresponding vibration characteristic values ​​includes: determining the driving quality parameters of the target road surface at the target driving speed according to the corresponding vibration characteristic values ​​based on a predetermined mapping relationship between the vibration characteristic values ​​and the driving quality parameters.

11. A device for determining road pavement ride quality parameters, comprising: A target driving speed acquisition module is used to acquire a target driving speed; A dynamic deflection value acquisition module is used to obtain the dynamic deflection value of the target road; The smoothness acquisition module is used to obtain the international smoothness index of the target road; A vibration characteristic value determination module, used to determine the corresponding vibration characteristic value according to the target driving speed, the dynamic deflection value of the target road and the international roughness index based on a pre-established vibration characteristic value model; as well as A mapping module is used to determine a ride quality parameter of the target road surface at the target driving speed based on the corresponding vibration characteristic value.

12. An electronic device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for determining the road pavement driving quality parameters as described in any one of claims 1 to 10 is implemented.

13. A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute the method for determining the highway pavement ride quality parameters according to any one of claims 1 to 10.

14. A computer program product, comprising computer program instructions, which, when executed on a computer, enable the computer to execute the method for determining a highway pavement ride quality parameter according to any one of claims 1 to 10.

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