Contact-non-contact multi-source decision fusion identification system and method for highway vehicle loads

Abstract

This invention discloses a contact-non-contact multi-source decision fusion identification system and method for highway vehicle loads. A non-contact tire load identification system is used to capture tire images, calculate a priori values for non-contact tire load identification, and establish a priori uncertainty quantification model. A contact tire load identification system is used to sense tire pressure, calculate a priori values for contact tire load identification, and establish a priori uncertainty quantification model. Using a contact-non-contact multi-source information decision fusion and adaptive update algorithm, the two single-source load identification results containing uncertainty are subjected to uncertainty-optimized decision fusion. Initial uncertainty is adaptively updated through inverse parameter estimation, resulting in a posteriori identification result that minimizes uncertainty while accounting for differences in vehicle type. This invention integrates visual and piezoelectric measurement information at the decision-making level, improving the accuracy and stability of the identification results.

Description

Technical Field

[0001] The present invention relates to the field of highway vehicle load identification, and in particular to a highway vehicle load contact-non-contact multi-source decision fusion identification system and method. Background Art

[0002] In recent years, vehicle-in-motion weighing (WIM) technology has been widely used in road and bridge engineering to acquire and accumulate highway traffic load data and monitor and provide early warnings for abnormal conditions such as vehicle overloading. Numerous scholars at home and abroad have conducted extensive theoretical and applied research on WIM technology. Existing WIM technologies are primarily categorized into two types: contact WIM based on piezoelectric sensing and non-contact WIM based on image recognition.

[0003] Contact-based WIM technology calculates vehicle load using tactile signals output by piezoelectric sensors. However, it's difficult to accurately measure tire width and contact area using only piezoelectric sensors, and it's also difficult to determine the relative position of each piezoelectric sensor in contact with the tire, leading to significant errors in tire load calculation. While these issues could be addressed by increasing the area and density of piezoelectric sensors, this would significantly increase the difficulty and cost of installation, data collection, commissioning, and maintenance, making practical application difficult.

[0004] Non-contact WIM technology will use machine vision methods to identify tire deformation characteristics to estimate vehicle load. Existing non-contact WIM technology generally requires obtaining the key parameter of tire pressure to establish the relationship between deformation and wheel load. Tire pressure is generally obtained through the vehicle's own tire pressure monitoring system (TPMS) or by extracting text markings on the tire sidewall. On the one hand, there are still a large number of heavy vehicles that are not equipped with TPMS at this stage, and there are technical and authority difficulties in obtaining real-time data for vehicles that have TPMS systems installed. On the other hand, extracting text markings on the tire sidewall through optical character recognition (OCR) technology can only obtain the standard tire pressure or maximum safe tire pressure of the tire. If the standard tire pressure is directly used instead of the actual tire pressure for load estimation, it will cause errors that are difficult to assess.

[0005] Therefore, the current mainstream contact / contactless WIM technologies often adopt a single data source and perception strategy, which leads to technical difficulties in accurately estimating vehicle loads and cannot meet the needs of vehicle load data collection and application. Summary of the Invention

[0006] In response to the shortcomings of the existing technology, the present invention proposes a contact-non-contact multi-source decision fusion identification system and method for highway vehicle loads. The specific technical solution is as follows:

[0007] A contact-non-contact multi-source decision fusion identification system for highway vehicle loads, comprising a contact sensor array, a data acquisition device, a video acquisition device, and a data processing device;

[0008] The contact sensor array includes a plurality of contact sensor units, each of which contains a piezoelectric sheet and a temperature sensor; the temperature sensor includes a thermistor and a measurement module thereof; the contact sensor array is embedded in the target lane and is used to collect piezoelectric signals generated by a passing vehicle and the internal temperature of each contact sensor unit in the sensor array;

[0009] The camera of the video acquisition device is installed on one side of the target lane, with the image plane of the camera perpendicular to the ground and parallel to the direction of travel of the lane, and the optical axis of the image sensor is located at the same longitudinal position of the road as the contact sensing unit embedded in the target lane; the video acquisition device is used to capture complete tire images of various vehicles;

[0010] The data acquisition device is connected to each contact sensing unit to perform signal conditioning and analog-to-digital conversion on the electrical signals collected by the contact sensing units;

[0011] The data processing device is electrically connected to the data acquisition device and the video acquisition device, and calculates the pressure measurement value acting on each contact sensing unit based on the electrical signal collected by each contact sensing unit; the video acquisition device captures the complete tire images of various vehicles to obtain tire specification information and deformation characteristics, and then calculates the non-contact tire load identification priori value and establishes a non-contact priori uncertainty quantification model; based on the contact pressure obtained by each contact sensing unit and the above-mentioned tire deformation characteristics, the contact load identification priori value is calculated and a contact priori uncertainty quantification model is established; and the contact-non-contact multi-source information decision fusion and adaptive update algorithm are used to perform uncertainty optimization decision fusion on two single-source load identification results containing uncertainty, and the initial uncertainty is adaptively updated through parameter inverse estimation to obtain a posteriori identification result that takes into account vehicle type differences and has minimized uncertainty.

[0012] Furthermore, the data processing device calculates the internal temperature of each contact sensing unit based on the electrical signal generated by the measurement module of the temperature sensor of each contact sensing unit; calculates the stress change rate acting on each unit based on the electrical signal generated by the piezoelectric piece of each contact sensing unit, and corrects the stress change rate calculation result according to the actual measured internal temperature of the unit; finally, the modified stress change rate is integrated with respect to time to obtain the pressure measurement value acting on each contact sensing unit.

[0013] A contact-non-contact multi-source decision fusion identification method for highway vehicle loads is implemented based on a contact-non-contact multi-source decision fusion identification system for highway vehicle loads and specifically includes the following steps:

[0014] Step 1: Using machine vision methods to identify the tire-road contact line segment, rim outer contour line, tire specification markings, and cross-sectional parameters from the tire image; simultaneously, converting the electrical signals generated by the contact sensing units into stress rate signals and temperature signals. Based on the material constitutive structure, structural dimensions, boundary conditions, and temperature sensitivity of the contact sensing units, the relationship between the stress change rate and voltage is calculated, and the contact pressure variation pattern of each contact sensing unit is obtained through integration.

[0015] Step 2: Calculate the wheel spatial position based on the rim outer contour and rim outer diameter parameters obtained in step 1, and then obtain the length L of the tire-road contact line segment;

[0016] Step 3: Calculate the prior value of non-contact tire load identification using the elliptical footprint assumption and tire pressure balance model, and perform uncertainty estimation using the uncertainty of the actual tire inflation pressure as the main source of error;

[0017] A contact sensor array and data acquisition equipment embedded in the road surface are used to acquire piezoelectric signals generated by the passing vehicle and the internal temperature of each contact sensor unit in the sensor array. The stress change rate acting on each contact sensor unit is calculated based on the electrical signal generated by the piezoelectric plate of each contact sensor unit, and the calculated stress change rate result is corrected by measuring the internal temperature of each contact sensor unit. The stress change rate is integrated with respect to time to obtain a pressure measurement value acting on each contact sensor unit. Based on the pressure obtained by the contact tire load identification system and the contact area obtained by the non-contact tire load identification system, a priori value for contact tire load identification is calculated and uncertainty estimation is performed.

[0018] Step 4: Through the contact-non-contact multi-source information decision fusion and adaptive update algorithm, the uncertainty results of non-contact load identification and contact load identification are optimized and fused to obtain a posteriori results with minimized uncertainty. The initial error distribution is adaptively and dynamically updated based on the identification results, and finally a posteriori identification result with minimized uncertainty is obtained that takes into account the differences in vehicle types.

[0019] Furthermore, in step 1, the tire-road contact line segment, rim outer contour line, tire specification mark and cross-sectional parameters are identified from the tire image using a machine vision method, specifically including:

[0020] All wheels and rims in the collected tire video are input into the pre-trained convolutional neural network, and each detected object is recorded as an initial detection box and a category probability;

[0021] The initial detection frame size, category probability, and wheel-rim detection frame position relationship in the image are used as criteria to clean the initial detection frame data. The wheel detection frame and rim detection frame in the cleaned valid image are then linearly transformed to obtain the tire-road contact ROI, rim ROI, and tire sidewall text ROI.

[0022] Finally, the tire-ground contact ROI is input into the tire deformation visual recognition framework to identify the tire-road contact area and contact line segment; the rim ROI is input into the rim outer contour visual recognition process to obtain the rim outer contour line; and the various tire parameters are obtained from the tire sidewall text ROI using a method linked to optical character recognition and standard database query.

[0023] Furthermore, in step 3, the non-contact load identification includes the following steps:

[0024] (a) Calculate the standard load value for each tire using the tire pressure balance model for non-contact load identification:

[0025] F vst,i =p st,i ·ξ G B i L i

[0026] Among them, F vst,i represents the load standard value of the i-th tire in non-contact load identification, p st,i represents the standard inflation pressure of the i-th tire, B i is the tread width of the i-th tire, L i is the length of the tire-road contact line segment of the i-th tire, ξ G is the footprint shape coefficient, defined as the footprint area and its circumscribed rectangle area B i L i The ratio of , according to the elliptical footprint area assumption, ξ G =(4+π) / 8;

[0027] (b) Taking the uncertainty of the actual tire inflation pressure as the main error body, a priori uncertainty quantification model for non-contact tire load identification is established:

[0028]

[0029]

[0030] in, represents the prior visual load recognition result of the i-th tire, w - is the uncertainty distribution of the prior visual measurement of the i-th tire; μ in and They are the ratio of the actual inflation pressure of road vehicles to the standard inflation pressure p in / p st The mean and variance of the normal distribution it follows; represents the posterior visual load recognition result of the i-th tire, which is obtained by fusion and update in subsequent steps;

[0031] For contact load identification, the following steps are involved:

[0032] (a) Calculate the contact wheel load identification standard value for each tire:

[0033] F pst,i =p mp,i ●4ξ C,i B i L i

[0034] Among them, F pst,i represents the contact wheel load identification standard value of the i-th tire, p mp,i is the average pressure measurement value of each contact sensor unit on the i-th tire, m is the number of contact sensor units with obvious piezoelectric response when the i-th tire passes through the contact sensor array; p i is the pressure measurement value of each contact sensor unit with obvious piezoelectric response; ξ C is the area reduction rate, which is defined as the ratio of the actual contact area of the tire tread to the tire footprint area;

[0035] (b) Taking the uncertainty of tire-road footprint shape as the main error component, a priori uncertainty quantification model for contact tire load identification is established:

[0036]

[0037] in, represents the priori result of the piezoresistance load of the i-th tire, v - is the uncertainty distribution of the prior piezoelectric measurement, represents the posterior result of piezoelectric load recognition, which needs to be obtained through subsequent fusion and update; μ G and are the mean and variance of the normal distribution obeyed by the imprint shape coefficient.

[0038] Furthermore, the step 4 specifically includes the following sub-steps:

[0039] S4.1: Calculate the fusion gain coefficient K G,i :

[0040]

[0041] S4.2: Calculate a posteriori results for contact-non-contact combined loads

[0042]

[0043]

[0044] in, are the posterior results of the i-th tire respectively The mean and variance of the normal distribution it follows;

[0045] S4.3: Use the tire pressure balance model to inversely estimate the actual tire inflation pressure and perform adaptive updates on the uncertainty quantification model for contactless load identification.

[0046] Furthermore, the S4.3 includes the following sub-steps:

[0047] S4.3.1: Using the a posteriori results obtained in step S4.2, inversely solve the actual tire inflation pressure using the tire pressure balance model described in step 3 to obtain an inverse estimate of the actual tire inflation pressure:

[0048]

[0049] S4.3.2: For tires that have completed data collection and calculation, distinguish the vehicle type to which the tires belong based on the tire specification marks obtained in step 1. When the number of collected tires for cars, light trucks, and heavy trucks exceeds the respective set thresholds M c 、M l 、M h After that, the pressure ratio The calculation results are divided into three groups: Passenger car tire pressure ratio series Light truck tire pressure ratio series Heavy truck tire pressure ratio series Solve the mean and standard deviation of the above three sequences respectively, and perform a normal distribution test to obtain the updated tire pressure ratio distribution of the three types of vehicles:

[0050]

[0051] The pressure ratio p in the prior uncertainty quantification model for non-contact tire load identification is replaced by three types of updated pressure ratio distributions. in / p stThe normal distribution obeyed by it is used to complete the classification and update of the uncertainty quantification model of non-contact tire load identification.

[0052] The beneficial effects of the present invention are as follows:

[0053] (1) The contact-non-contact multi-source decision-making fusion identification system and method for highway vehicle loads of the present invention combines contact piezoelectric sensing means and non-contact machine vision sensing means, fusing and complementing piezoelectric information and image information at the decision-making level. By establishing a priori uncertainty quantification models for the two methods and using uncertainty fusion methods to calibrate results and update errors, it not only overcomes the difficulty of accurately obtaining tire width and tire-road contact area through piezoelectric sensing alone in the prior art, but also solves the problem of the inability to obtain the actual tire pressure of the vehicle through machine vision alone in the prior art, significantly improving the reliability of the identification results compared to the prior art;

[0054] (2) The method of the present invention utilizes a parameter inverse estimation method to establish a relationship between the directly accessible contact pressure and the indirect tire pressure, thereby updating the initial pressure ratio error distribution. The updated pressure ratio error distribution takes into account the differences in inflation patterns across different vehicle types, further reducing the possibility of overestimation or underestimation of tire loads due to a significant discrepancy between the a priori uncertainty quantification model and actual conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 Flowchart of the contact-non-contact multi-source decision fusion identification method for highway vehicle loads according to an embodiment of the present invention.

[0056] Figure 2 Schematic diagram of the contact-non-contact multi-source decision fusion identification system for road vehicle loads according to an embodiment of the present invention, wherein (a) is a bird's-eye view of the system, (b) is an interaction diagram between the system and the outside world; and (c) is a schematic diagram of the data fusion foot bone.

[0057] Figure 3 Schematic diagram of the principle of the monocular mapping algorithm for tire spatial posture and tire-road contact area provided by the present invention. DETAILED DESCRIPTION

[0058] The present invention will be described in detail below with reference to the accompanying drawings and preferred embodiments, and the purpose and effects of the present invention will become more apparent. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0059] like Figure 1 As shown, the contact-non-contact multi-source decision fusion identification system for highway vehicle loads of the present invention includes a contact sensor array, a data acquisition device, a video acquisition device and a data processing device.

[0060] 1. Contact sensor array

[0061] The contact sensor array consists of multiple contact sensor units, each containing a piezoelectric element and a temperature sensor. The temperature sensor includes a thermistor and its measurement module. The contact sensor array is embedded within the target lane and collects the piezoelectric signal generated by the passing vehicle and the internal temperature of each contact sensor unit in the sensor array.

[0062] In this embodiment, the piezoelectric piece is a circular PZT-5H piezoelectric piece, and the temperature sensor is a PT1000 platinum thermal resistance temperature sensor. Figure 1 As shown, multiple contact sensor units are buried in the target lane. The burial location should be on a straight section of the road, and the burial position should be within 1 / 3 of the lane line on one side or both sides. When burying, it is necessary to groove the asphalt or concrete layer of the road surface. After installing the contact sensor unit and completing the wiring, the road surface should be leveled and the upper surface of the sensor unit should be flush with the road surface.

[0063] Before installing the contact sensor unit, it should first be temperature pre-calibrated. By measuring the piezoelectric response at different internal temperatures of the contact sensor unit, the temperature calibration coefficient λ(T) is calculated.

[0064] 2. Data acquisition equipment

[0065] The data acquisition device is connected to each contact sensor unit through a shielded wire, and performs signal conditioning and analog-to-digital conversion on the electrical signals collected by the contact sensor unit.

[0066] 3. Video capture equipment

[0067] The video acquisition device is installed on one side of the target lane. During installation, it should be ensured that the image plane is perpendicular to the ground and parallel to the forward direction of the lane, and that the optical axis of the image sensor and the center of the contact sensor array embedded in the road surface are at the same position in the longitudinal direction of the road. The camera installation height can be adjusted according to the distance from the lane, with the goal of being able to capture the complete tire image of various vehicles. The camera should know the following parameters in advance: the main distance f0 (the vertical distance from the optical center to the image plane), the plane coordinates c of the intersection of the optical axis and the image plane in the image coordinate system x with c y , the pixel size of the image sensor in the horizontal / vertical direction κ x and κ y .

[0068] 4. Data processing equipment

[0069] The data processing device, typically an industrial computer, is electrically connected to the data acquisition and video acquisition equipment. It calculates the internal temperature of each contact sensing unit based on the electrical signals collected by the measurement module of the temperature sensor. It also calculates the rate of change of stress acting on each contact sensing unit based on the electrical signals generated by the piezoelectric plate of each contact sensing unit. This calculated stress rate of change is corrected by measuring the internal temperature of the unit. Finally, the stress rate of change is integrated with respect to time to obtain the pressure measurement value acting on each contact sensing unit. The data processing equipment also obtains tire specification information and deformation characteristics based on the complete tire images of various vehicles captured by the video acquisition equipment, and then calculates the non-contact tire load identification prior value and establishes a first priori uncertainty quantification model; based on the contact pressure obtained by each contact sensing unit and the above-mentioned tire deformation characteristics, the contact load identification prior value is calculated and a second priori uncertainty quantification model is established; and using the contact-non-contact multi-source information decision fusion and adaptive update algorithm, the two single-source load identification results containing uncertainty are subjected to uncertainty optimization decision fusion, and the initial uncertainty is adaptively updated through parameter inverse estimation, to obtain a posteriori identification result that takes into account vehicle type differences and has minimized uncertainty.

[0070] like Figure 2 As shown, the contact-non-contact multi-source decision fusion identification method for highway vehicle loads of the present invention includes the following steps:

[0071] Step 1: Use machine vision methods to identify the tire-road contact line segment, rim outer contour line, tire specification mark and cross-sectional parameters from the tire image; at the same time, convert the electrical signal generated by the contact sensor unit into a stress rate signal and a temperature signal. Based on the material constitutive structure, structural dimensions, boundary conditions and temperature sensitivity characteristics of the contact sensor unit, the relationship between the stress change rate and voltage is calculated, and the contact pressure change law of each contact sensor unit is obtained by integration.

[0072] (1) For contact load identification

[0073] The stress change rate acting on the piezoelectric piece is calculated from the piezoelectric signal as follows:

[0074]

[0075] Among them, d 33 (T) Reactive material constitutive property, which is the third row and third column component of the piezoelectric strain constant tensor at temperature T; T is the piezoelectric sheet temperature or the internal temperature of the contact sensor unit, and T0 is the reference temperature for piezoelectric sheet parameter calibration; A p is the load action area of the piezoelectric piece in 3-direction; λ(T) is the calibration coefficient expression obtained after the above temperature calibration; is the stress change rate in 3 directions, Um is the voltage across the piezoelectric piece, R s It is the external resistance introduced into the measurement circuit.

[0076] (2) For non-contact vehicle load identification

[0077] (2.1) The camera of the video acquisition device remains in working state and transmits the video signal to the industrial control computer. The pre-trained YOLOv8n network is deployed on the industrial control computer to detect all wheels and rims in the video captured by the camera in real time, and record each detected target as an initial detection box and a category probability.

[0078] When the initial detection frame is located in the middle of the image, multiple frames of images are continuously captured for subsequent analysis; when the initial tire detection frame appears completely in the image, data collection of the contact sensor array is triggered simultaneously.

[0079] (2.2) For the captured images, the initial detection frames are cleaned based on the size of the detection frames, the class probability, and the positional relationship between the wheel and rim detection frames. Images with wheels that are not completely within the image and with low class probability are eliminated. The wheel and rim detection frames in the cleaned valid images are linearly transformed to obtain three regions of interest (ROIs): the tire-road contact ROI, the rim ROI, and the tire sidewall text ROI.

[0080] (2.3) Use machine vision algorithms to further analyze and process the tire-road contact ROI, rim ROI, and tire sidewall text ROI. Specifically:

[0081] The tire-road contact ROI is input into a tire deformation visual recognition framework that includes grayscale value transformation, adaptive filtering, threshold segmentation, morphological operations, contact interval identification method, and geometric primitive fitting to identify the tire-road contact interval and contact line segment.

[0082] The wheel rim ROI is input into the wheel rim outer contour visual recognition process including threshold segmentation, morphological operation, regional feature screening, and fitting point sampling to obtain the wheel rim outer contour line;

[0083] Optical character recognition (OCR) and standard database query are used to obtain parameters such as rim outer diameter, tire design outer diameter, new tire design section width, and tread width from the tire sidewall text ROI.

[0084] However, extracting tire specification parameters from the tire sidewall text ROI is extremely difficult due to tire contamination and wear, as well as dense printing or the use of artistic fonts. Therefore, the system prioritizes identifying the tire specification markings containing the most information. A retrieval system is then designed based on the specification information tables provided in the current national standards "Specifications, Dimensions, Pressures, and Loads for Truck Tires (GB / T 2977-2016)" and "Specifications, Dimensions, Pressures, and Loads for Passenger Car Tires (GB / T 2978-2014)" to query relevant parameters. This complementary identification and querying approach improves the effective recognition rate of tire parameters.

[0085] Due to limitations in information recognition effectiveness and database size, the tread width (B) parameter (the thickened portion of the tire's cross-section that directly contacts the road surface; the width of this portion is the tread width) may not be directly identified or retrieved. Research indicates that vehicle tire designers and manufacturers generally recommend a tread width range of 0.6-0.9 times the new tire's design cross-sectional width. Therefore, 0.75 times the new tire's design cross-sectional width is used as an approximate estimate for B.

[0086] The fields of neural networks and machine vision have developed rapidly in recent years. There are many relatively mature commercial / open source solutions for the above-mentioned algorithms and processes for geometric feature recognition and optical character recognition, which will not be discussed in detail here.

[0087] Step 2: Calculate the wheel spatial pose based on the rim outer contour and rim outer diameter parameters;

[0088] In the image processing and feature recognition process in step 1, an image coordinate system describing the pixel position is established, that is, the upper left corner of the image is the origin and the horizontal right is x. IM The positive direction of the axis, with vertical downward as y IM The positive direction is in pixels. To describe the wheel's spatial position during spatial pose calculation, a 3D camera coordinate system is established. This coordinate system has the camera's optical center as its origin, the x-axis parallel to the lane, the y-axis perpendicular to the road surface, and the z-axis pointing toward the object being measured.

[0089] The outer contour line of the wheel rim obtained in step 1 is an ellipse with the major axis parallel to the vertical direction and the minor axis parallel to the horizontal direction in the image coordinate system. The length a of the major semi-axis of the ellipse in the image coordinate system is extracted. IM , semi-minor axis length b IM And the coordinates of the ellipse center (p IM ,q IM ). Extract the actual rim radius R from the tire sidewall text ROI obtained in step S2 r . Based on the principle of photographic geometry, the coordinates of the wheel rim center N (x rc ,yrc ,z rc ) and the angle θ between the rim plane and the image plane, as Figure 3 shown.

[0090] In the camera coordinate system, any point (x, y, z) on the rim boundary satisfies:

[0091]

[0092] And the rim plane is perpendicular to the road surface, then any point (x, y, z) on the rim plane should also satisfy:

[0093] (xx rc )sinθ+(zz rc )cosθ=0

[0094] According to the perspective relationship, if the point (x IM ,y IM ) corresponds to the point (x, y, z) on the wheel rim plane in the camera coordinate system. Since the line connecting a set of corresponding points must pass through the optical center of the camera, the following relationship is satisfied:

[0095]

[0096] Combining the above three equations, we can see that the rim boundary is mapped into an ellipse in the image coordinate system, and its equation is:

[0097]

[0098] From this we can calculate:

[0099]

[0100]

[0101] In step 1, the tire-road contact line segment has been identified and the image coordinates of the two end points C have been obtained. 1,IM (x IM,1 ,y IM,1 ), C 2,IM (x IM,2 ,y IM,2 ), then the wheel rim plane equation and the straight line C1O and C2O equations can be combined, as follows Figure 3 As shown in the figure, through the monocular mapping algorithm of the tire-road contact area, the coordinates of the two end points of the tire-road contact line segment in the camera coordinate system are solved as follows:

[0102]

[0103] Where i = 1, 2. The obtained x ext,i =(x ext,i ,yext,i ,z ext,i ) is the spatial coordinates of the two end points of the tire-road contact line segment, then the actual tire-road contact length is: L = || x ext,1 -x ext,2 ||2.

[0104] Since multiple valid images are obtained for a single tire object in step 1, this step can repeatedly calculate multiple tire-road actual contact lengths with similar values and take the arithmetic average as the final result.

[0105] The monocular mapping algorithm for tire spatial pose and tire-road contact area in the present invention can solve for spatial pose using only images captured by a monocular camera, reducing the complexity of the hardware system. Furthermore, compared to the scaling factor method commonly used in existing non-contact WIM technology, the proposed algorithm is applicable to situations where the tire is parallel to the imaging plane or at a certain angle, expanding the scope of application of existing technologies.

[0106] Step 3: Calculate the prior value of non-contact tire load identification using the elliptical footprint assumption and tire pressure balance model, and perform uncertainty estimation using the uncertainty of the actual tire inflation pressure as the main source of error;

[0107] A contact sensor array and data acquisition equipment embedded in the road surface are used to acquire the piezoelectric signal generated by the passing vehicle and the internal temperature of each contact sensor unit in the sensor array. The stress change rate acting on each contact sensor unit is calculated based on the electrical signal generated by the piezoelectric plate of each contact sensor unit, and the stress change rate calculation result is corrected by measuring the internal temperature of each contact sensor unit. The stress change rate is integrated with respect to time to obtain the pressure measurement value acting on each contact sensor unit. Based on the pressure obtained by the contact tire load identification system and the contact area obtained by the non-contact tire load identification system, the contact tire load identification prior value is calculated and the uncertainty estimation is performed.

[0108] (1) For non-contact load identification

[0109] a. Calculate the standard load value for each tire using the tire pressure balance model for non-contact load identification:

[0110] F vst,i =p st,i ●ξ G,i B i L i

[0111] Among them, F vst,i represents the load standard value of the i-th tire in non-contact load identification, p st,i represents the standard inflation pressure of the i-th tire, B iis the tread width of the i-th tire, L i is the length L of the tire-road contact line segment of the i-th tire. Both can be directly obtained from the tire sidewall text mark ROI in step 1; ξ G is the footprint shape coefficient, which is defined as the ratio of the footprint area to its circumscribed rectangular area BL. According to the elliptical footprint area assumption, ξ G =(4+π) / 8.

[0112] b. Taking the uncertainty of the actual tire inflation pressure as the main error body, a priori uncertainty quantification model for non-contact tire load identification is established:

[0113]

[0114] in, represents the prior results of visual load recognition of the i-th tire, w - is the uncertainty distribution of the prior visual measurement of the i-th tire; μ in and They are the ratio of the actual inflation pressure of road vehicles to the standard inflation pressure p in / p st The mean and variance of the normal distribution obeyed are initially set as 1.10 and 0.05 respectively in this embodiment; represents the posterior visual load recognition result of the i-th tire, which is obtained by fusion and update in subsequent steps.

[0115] (2) For contact load identification

[0116] a. Calculate the standard value of contact wheel load identification for each tire:

[0117] F pst,i =p mp,i ·4ξ C,i B i L i

[0118] Among them, F pst,i represents the contact wheel load identification standard value of the i-th tire, p mp,i is the average pressure measurement value of each contact sensor unit on the i-th tire, m is the number of contact sensor units with obvious piezoelectric response when the i-th tire passes through the contact sensor array; p i is the pressure measurement value of each contact sensor unit with obvious piezoelectric response; ξ C is the area reduction rate, which is defined as the ratio of the actual contact area of the tire pattern to the tire footprint area. i and the length L of the tire-road contact line segment of the i-th tire iBy comparing the image frame creation time and the piezoelectric signal peak occurrence time, the visual information and piezoelectric signals from the same tire are matched.

[0119] b. Taking the uncertainty of tire-road footprint shape as the main error component, a priori uncertainty quantification model for contact tire load identification is established:

[0120]

[0121] in, represents the priori result of the piezoresistance load of the i-th tire, v - is the uncertainty distribution of the prior piezoelectric measurement, represents the posterior result of piezoelectric load recognition, which needs to be obtained through subsequent fusion and update; μ G and are the mean and variance of the normal distribution obeyed by the imprint shape coefficient.

[0122] Step 4: Through the contact-non-contact multi-source information decision fusion and adaptive update algorithm, the uncertainty results of non-contact load identification and contact load identification are optimized and fused to obtain the a posteriori results with minimized uncertainty. The initial error distribution is adaptively and dynamically updated based on the identification results.

[0123] Step 4 specifically includes the following steps:

[0124] S4.1: Calculate the fusion gain coefficient K G,i :

[0125]

[0126] S4.2: Calculate a posteriori results for contact-non-contact combined loads

[0127]

[0128] in, are the posterior results of the i-th tire respectively The mean and variance of the normal distribution that follows. Reaching the minimum value, that is, the result of the a posteriori load identification Get the best estimate.

[0129] S4.3: Use the tire pressure balance model to inversely estimate the actual tire inflation pressure and perform adaptive updates on the uncertainty quantification model for contactless load identification.

[0130] Actual tire inflation pressure p inIt is difficult to obtain under non-laboratory conditions, but pressure measurement data can be obtained through the contact tire load identification system. According to the assumption of the tire pressure balance model, theoretically, the actual tire inflation pressure and the tire-road contact pressure should be in direct proportion. Therefore, the actual tire inflation pressure p can be obtained through pressure measurement data. in It is feasible to make a reverse estimate, and the reverse estimate The pressure ratio distribution described in step 3 can be recalibrated, thereby updating the related non-contact prior uncertainty quantification model.

[0131] S4.3 specifically includes the following sub-steps:

[0132] S4.3.1: Using the a posteriori results obtained in step S4.2, inversely solve the actual tire inflation pressure using the tire pressure balance model described in step 3 to obtain an inverse estimate of the actual tire inflation pressure:

[0133]

[0134] S4.3.2: For tires that have completed data collection and calculation, distinguish the vehicle type to which the tires belong based on the tire specification marks obtained in step 1. When the number of collected tires for cars, light trucks, and heavy trucks exceeds the respective set thresholds M c 、M l 、M h After that, the pressure ratio The calculation results are divided into three groups: Passenger car tire pressure ratio series Light truck tire pressure ratio series Heavy truck tire pressure ratio series Solve the mean and standard deviation of the above three sequences respectively, and perform a normal distribution test to obtain the updated tire pressure ratio distribution of the three types of vehicles:

[0135]

[0136] Compared with the uniform pressure ratio initial distribution It is more reasonable to use different pressure ratio distributions to establish uncertainty quantification models for corresponding vehicles. In fact, heavy-duty trucks tend to use air pressures slightly higher than the standard pressure to improve carrying capacity, while family cars often use actual inflation pressures close to the standard pressure for safety reasons. Therefore, it is necessary to differentiate pressure ratio distributions by vehicle type.

[0137] S4.3.3: Replace the pressure ratio p in the prior uncertainty quantification model for non-contact tire load identification using three updated pressure ratio distributions in / p st Normal distribution This completes the classification and update of the uncertainty quantification model for non-contact tire load identification. Subsequent tire load identification will determine the vehicle type based on tire specifications and select the corresponding uncertainty quantification model for non-contact load identification system for decision fusion.

[0138] The inverse estimation, data accumulation, and distribution update methods described in this step can be executed repeatedly. That is, after the system completes data collection and accumulation for kM tires, it should also perform k rounds of updates on the initial distribution, so that the uncertainty quantification model of the non-contact load identification system gradually approaches the actual situation and reaches a stable state.

[0139] Those skilled in the art will understand that the foregoing descriptions are merely preferred embodiments of the invention and are not intended to limit the invention. Although the invention has been described in detail with reference to the foregoing examples, those skilled in the art will still be able to modify the technical solutions described in the foregoing examples or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, etc. made within the spirit and principles of the invention shall be included within the scope of protection of the invention.

Claims

1. A contact-non-contact multi-source decision fusion identification system for highway vehicle loads, characterized by: including contact sensor array, data acquisition equipment, video acquisition equipment and data processing equipment; The contact sensor array includes a plurality of contact sensor units, each of which contains a piezoelectric sheet and a temperature sensor; the temperature sensor includes a thermistor and a measurement module thereof; the contact sensor array is embedded in the target lane and is used to collect piezoelectric signals generated by a passing vehicle and the internal temperature of each contact sensor unit in the sensor array; The camera of the video acquisition device is installed on one side of the target lane, with the image plane of the camera perpendicular to the ground and parallel to the direction of travel of the lane, and the optical axis of the image sensor is located at the same longitudinal position of the road as the contact sensing unit embedded in the target lane; the video acquisition device is used to capture complete tire images of various vehicles; The data acquisition device is connected to each contact sensing unit to perform signal conditioning and analog-to-digital conversion on the electrical signals collected by the contact sensing units; The data processing device is electrically connected to the data acquisition device and the video acquisition device, and calculates the pressure measurement value acting on each contact sensing unit based on the electrical signal collected by each contact sensing unit; the video acquisition device captures the complete tire images of various vehicles to obtain tire specification information and deformation characteristics, and then calculates the non-contact tire load identification priori value and establishes a non-contact priori uncertainty quantification model; based on the contact pressure obtained by each contact sensing unit and the above-mentioned tire deformation characteristics, the contact load identification priori value is calculated and a contact priori uncertainty quantification model is established; and the contact-non-contact multi-source information decision fusion and adaptive update algorithm are used to perform uncertainty optimization decision fusion on two single-source load identification results containing uncertainty, and the initial uncertainty is adaptively updated through parameter inverse estimation to obtain a posteriori identification result that takes into account vehicle type differences and has minimized uncertainty.

2. The contact-non-contact multi-source decision fusion identification system for highway vehicle loads according to claim 1 is characterized in that: The data processing device calculates the internal temperature of each contact sensing unit based on the electrical signal generated by the measurement module of the temperature sensor of each contact sensing unit; calculates the stress change rate acting on each unit based on the electrical signal generated by the piezoelectric plate of each contact sensing unit, and corrects the stress change rate calculation result using the actual measured internal temperature of the unit; finally, the modified stress change rate is integrated with respect to time to obtain the pressure measurement value acting on each contact sensing unit.

3. A contact-non-contact multi-source decision fusion identification method for highway vehicle loads, characterized by: The method is implemented based on the road vehicle load contact-non-contact multi-source decision fusion identification system according to claim 1 or 2, and specifically comprises the following steps: Step 1: Using machine vision methods to identify the tire-road contact line segment, rim outer contour line, tire specification markings, and cross-sectional parameters from the tire image; simultaneously, converting the electrical signals generated by the contact sensing units into stress rate signals and temperature signals. Based on the material constitutive structure, structural dimensions, boundary conditions, and temperature sensitivity of the contact sensing units, the relationship between the stress change rate and voltage is calculated, and the contact pressure variation pattern of each contact sensing unit is obtained through integration. Step 2: Calculate the wheel spatial position based on the rim outer contour and rim outer diameter parameters obtained in step 1, and then obtain the length L of the tire-road contact line segment; Step 3: Calculate the prior value of non-contact tire load identification using the elliptical footprint assumption and tire pressure balance model, and perform uncertainty estimation using the uncertainty of the actual tire inflation pressure as the main source of error; Using a contact sensor array embedded in the road surface and data acquisition equipment, the system acquires piezoelectric signals generated by vehicles passing by and the internal temperature of each contact sensor unit in the sensor array. Based on the electrical signals generated by the piezoelectric plates of each contact sensor unit, the system calculates the stress change rate acting on each contact sensor unit. The calculated stress change rate is then corrected by measuring the internal temperature of each contact sensor unit. The stress change rate is integrated with respect to time to obtain the pressure measurement value acting on each contact sensing unit. Based on the pressure obtained by the contact tire load identification system and the contact area obtained by the non-contact tire load identification system, the contact tire load identification prior value is calculated and the uncertainty is estimated. Step 4: Through the contact-non-contact multi-source information decision fusion and adaptive update algorithm, the uncertainty results of non-contact load identification and contact load identification are optimized and fused to obtain a posteriori results with minimized uncertainty. The initial error distribution is adaptively and dynamically updated based on the identification results, and finally a posteriori identification result with minimized uncertainty is obtained that takes into account the differences in vehicle types.

4. The contact-non-contact multi-source decision fusion identification method for highway vehicle loads according to claim 3 is characterized in that: In the first step, a machine vision method is used to identify the tire-road contact line segment, the rim outer contour line, the tire specification mark and the cross-sectional parameters from the tire image, specifically including: All wheels and rims in the collected tire video are input into the pre-trained convolutional neural network, and each detected object is recorded as an initial detection box and a category probability; The initial detection frame size, category probability, and wheel-rim detection frame position relationship in the image are used as criteria to clean the initial detection frame data. The wheel detection frame and rim detection frame in the cleaned valid image are then linearly transformed to obtain the tire-road contact ROI, rim ROI, and tire sidewall text ROI. Finally, the tire-ground contact ROI is input into the tire deformation visual recognition framework to identify the tire-road contact area and contact line segment; the rim ROI is input into the rim outer contour visual recognition process to obtain the rim outer contour line; and the various tire parameters are obtained from the tire sidewall text ROI using a method linked to optical character recognition and standard database query.

5. The contact-non-contact multi-source decision fusion identification method for highway vehicle loads according to claim 4 is characterized in that: In the step 3, for non-contact load identification, the following steps are included: (a) Calculate the standard load value for each tire using the tire pressure balance model for non-contact load identification: F vst,i =p st,i ·x G B i L i Among them, F vst,i represents the load standard value of the i-th tire in non-contact load identification, p st,i represents the standard inflation pressure of the i-th tire, B i is the tread width of the i-th tire, L i is the length of the tire-road contact line segment of the i-th tire, ξ G is the footprint shape coefficient, defined as the footprint area and its circumscribed rectangle area B i L i The ratio of , according to the elliptical footprint area assumption, ξ G =(4+π) / 8; (b) Taking the uncertainty of the actual tire inflation pressure as the main error body, a priori uncertainty quantification model for non-contact tire load identification is established: in, represents the prior visual load recognition result of the i-th tire, w i - is the uncertainty distribution of the prior visual measurement of the i-th tire; μ in and They are the ratio of the actual inflation pressure of road vehicles to the standard inflation pressure p in / p st The mean and variance of the normal distribution it follows; represents the posterior visual load recognition result of the i-th tire, which is obtained by fusion and update in subsequent steps; For contact load identification, the following steps are involved: (a) Calculate the contact wheel load identification standard value for each tire: F pst,i =p mp,i ·4ξ C,i B i THE i Among them, F pst,i represents the contact wheel load identification standard value of the i-th tire, p mp,i is the average pressure measurement value of each contact sensor unit on the i-th tire, m is the number of contact sensor units with obvious piezoelectric response when the i-th tire passes through the contact sensor array; p i is the pressure measurement value of each contact sensor unit with obvious piezoelectric response; ξ C is the area reduction rate, which is defined as the ratio of the actual contact area of the tire tread to the tire footprint area; (b) Taking the uncertainty of tire-road footprint shape as the main error component, a priori uncertainty quantification model for contact tire load identification is established: in, represents the priori result of the piezoresistance load of the i-th tire, v - is the uncertainty distribution of the prior piezoelectric measurement, represents the posterior result of piezoelectric load recognition, which needs to be obtained through subsequent fusion and update; μ G and are the mean and variance of the normal distribution obeyed by the imprint shape coefficient.

6. The contact-non-contact multi-source decision fusion identification method for highway vehicle loads according to claim 5 is characterized in that: The step 4 specifically includes the following sub-steps: S4.1: Calculate the fusion gain coefficient K G,i : S4.2: Calculate a posteriori results for contact-non-contact combined loads in, are the posterior results of the i-th tire respectively The mean and variance of the normal distribution it follows; S4.3: Use the tire pressure balance model to inversely estimate the actual tire inflation pressure and perform adaptive updates on the uncertainty quantification model for contactless load identification.

7. The contact-non-contact multi-source decision fusion identification method for highway vehicle loads according to claim 6 is characterized in that: The S4.3 includes the following sub-steps: S4.3.1: Using the a posteriori results obtained in step S4.2, inversely solve the actual tire inflation pressure using the tire pressure balance model described in step 3 to obtain an inverse estimate of the actual tire inflation pressure: S4.3.2: For tires that have completed data collection and calculation, distinguish the vehicle type to which the tires belong based on the tire specification marks obtained in step 1. When the number of collected tires for cars, light trucks, and heavy trucks exceeds the respective set thresholds M c 、M l 、M h After that, the pressure ratio / p st,i The calculation results are divided into three groups: Passenger car tire pressure ratio series Light truck tire pressure ratio series Heavy truck tire pressure ratio series Solve the mean and standard deviation of the above three sequences respectively, and perform a normal distribution test to obtain the updated tire pressure ratio distribution of the three types of vehicles: The pressure ratio p in the prior uncertainty quantification model for non-contact tire load identification is replaced by three types of updated pressure ratio distributions. in / p st The normal distribution obeyed by it is used to complete the classification and update of the uncertainty quantification model of non-contact tire load identification.

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