Continuous distributed smart sensor for vehicle wheel mark identification and positioning method thereof

Abstract

The invention discloses a continuously distributed smart sensor for vehicle wheel mark identification and a positioning method thereof, and relates to the field of road intelligent monitoring, the smart sensor adopts a conductive nano polymer composite material as a sensing unit, and the sensing unit is composed of a polymer matrix material and a nano filler; the composite smart material is prepared by adopting a two-step method of mechanical dispersion and external electric field induced orientation, so that the good dispersion of the nanofiller in the polymer is improved, and the mechanical dispersion comprises methods of mechanical stirring, grinding, high-speed shearing, ultrasonic dispersion and the like; according to the external electric field induced orientation method, an electric field environment with certain field intensity and frequency is applied to the composite material before curing, so that the nanoparticles quickly react to an external electric field, and rotation and arrangement along the direction of the electric field are formed. And after the strip-shaped composite material is cured and molded, sticking and fixing lead electrodes at the two ends, and further performing outer coating and packaging by utilizing a polymer to obtain the smart sensor.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent road monitoring, and in particular to a continuous distributed smart sensor for vehicle wheel track recognition and a positioning method thereof. BACKGROUND

[0002] Vehicle track recognition technology, as a key link of road monitoring, traffic management and intelligent driving, has attracted widespread attention in the field of intelligent transportation systems. Among them, the accurate recognition and positioning of vehicle wheel tracks are the basic technologies for realizing lane departure detection, traffic flow monitoring, dynamic weighing and road damage prediction applications. For example, the wheel track recognition technology can accurately locate the contact position of the vehicle tire and the road surface to count the number of load actions in different areas, and further combine the road surface material characteristics and structural fatigue theory to predict the fatigue damage evolution trend of the load concentration area, providing a scientific basis for road predictive maintenance. In addition, vehicle wheel track recognition also plays an indispensable role in the development of autonomous driving technology, which can provide high-precision track positioning support for autonomous vehicles, improving their driving stability and safety. Therefore, vehicle track recognition technology not only helps to extend the service life of the road and reduce maintenance costs, but also is an important part of modern intelligent roads.

[0003] However, the current technologies for vehicle wheel track recognition mainly include three methods: camera, piezoelectric sensor and laser radar. Although the camera-based visual processing technology can provide rich image information, its accuracy is low and it is difficult to achieve cm-level wheel track recognition, which does not meet the needs of modern intelligent road construction, and is easily affected by environmental factors such as rain, fog and light. The piezoelectric sensor detects the pressure signal applied by the vehicle tire through the layout in the road surface for wheel track recognition, but its material is easily damaged by fatigue, resulting in signal attenuation and unstable data, which limits its service life. High-precision detection devices such as laser radar and millimeter wave radar are costly and difficult to implement large-scale application. These technical means are difficult to balance in terms of complex environmental adaptability, long-term stability and cost control, thereby restricting the popularization and improvement of vehicle wheel track recognition technology.

[0004] In order to solve these problems, a continuous distributed smart sensor for vehicle wheel track recognition and a positioning method thereof are urgently needed. SUMMARY

[0005] To solve the above problems, the present application proposes a continuous distributed smart sensor for vehicle wheel track recognition and a positioning method thereof, which is used for accurately recognizing the position of vehicle wheel tracks. The sensor utilizes external electric field-induced orientation technology to realize the regulation of the conductive network structure of smart sensing materials, and has high sensitivity, good durability and environmental adaptability. Combined with the wheel track transverse position positioning method proposed in the present application, the high-precision vehicle wheel track recognition goal can be achieved.

[0006] The continuous distributed smart sensor realizes directional construction of the conductive network based on external electric field induction, thereby improving the response speed and accuracy of the sensor and reducing the track line recognition error; the continuous distributed smart sensor is realized by reasonably designing the strip structure and length of the sensor and the like structural parameters.

[0007] In order to achieve the above purpose, the technical scheme of the present application is as follows:

[0008] A continuous distributed smart sensor for vehicle track recognition, the smart sensor adopts a conductive nanopolymer composite material as a sensing unit, which is composed of a polymer matrix material and a nanofiller.

[0009] The continuous distributed smart sensor comprises an encapsulation structure and a smart composite structure.

[0010] The smart composite structure is arranged inside the encapsulation structure.

[0011] The smart composite structure is provided with conductive electrodes at both ends.

[0012] The length of the continuous distributed smart sensor is 0.2-100 meters.

[0013] The width of the continuous distributed smart sensor is 1-10 centimeters.

[0014] The present application adopts a two-step method of mechanical dispersion and external electric field induction orientation to prepare the composite smart material and improve the good dispersion of the nanofiller in the polymer. The mechanical dispersion includes mechanical stirring, grinding, high-speed shearing and ultrasonic dispersion and the like methods, and the external electric field induction orientation method is to apply an electric field environment with a certain field strength and frequency to the composite material before curing, so that the nanometer particles make a quick response to the applied electric field, forming rotation and arrangement along the direction of the electric field. Finally, after the strip-shaped composite material is cured and formed, the wire electrodes are pasted and fixed at both ends, and the polymer is further used for outer packaging to obtain the smart sensor.

[0015] Specifically, in the first aspect of the present application, a preparation method of a continuous distributed smart sensor for vehicle track recognition is provided, comprising the following steps:

[0016] S1, dispersing carbon-based nanofillers in N,N-dimethylformamide (DMF) solvent to prepare a conductive nanofiller dispersion slurry;

[0017] Preferably, the carbon-based nanofiller includes one or more of carbon nanotubes, graphene, carbon black and carbon nanofibers.

[0018] S2, the conductive nano filler dispersion slurry is treated by high shear and ultrasonic treatment to obtain a conductive nano filler slurry;

[0019] Preferably, the conductive nano filler dispersion slurry is treated by a high shear dispersion homogenizer, and then ultrasonic dispersion is performed by an ultrasonic disperser to obtain a uniformly dispersed and stable state conductive nano filler slurry;

[0020] Preferably, the high shear dispersion treatment is not less than 20 minutes, and the ultrasonic dispersion is not less than 60 minutes.

[0021] S3, the conductive nano filler slurry is mixed with the polymer matrix and stirred and ultrasonically treated to form a uniform mixture of nano filler and polymer;

[0022] Preferably, the slurry and the liquid polymer matrix are mixed in a certain proportion, and stirring and ultrasonic synchronous treatment are performed for 60 minutes to obtain a uniform mixture of nano filler and polymer;

[0023] Preferably, the polymer matrix includes one or more of epoxy resin, polyurethane, polydimethylsiloxane, and silicone rubber modified epoxy resin, polyurethane modified epoxy resin, or other thermosetting polymers.

[0024] S4, the uniform mixture is dried to obtain a pre-polymer containing nano filler;

[0025] Preferably, the mixture is placed in a vacuum environment at 80°C for drying treatment for 1 hour to promote solvent evaporation, and a pre-polymer containing nano filler is obtained.

[0026] S5, the pre-polymer is mixed with the corresponding polymer curing agent and stirred to obtain a uniform composite material;

[0027] Preferably, the pre-polymer containing nano filler and the corresponding polymer curing agent are mixed in a certain mass ratio, and a stirrer is used to stir uniformly for 3 minutes to obtain a uniformly mixed nano composite material;

[0028] The mass ratio of carbon-based nano filler to N,N-dimethylformamide solvent is 1% to 20%:

[0029] The mass ratio of the corresponding polymer curing agent to the pre-polymer is 5%-100%.

[0030] S6, an electric field is applied outside the uniform composite material to cure and form a preliminary cured nano composite material;

[0031] Preferably, the nano composite material is introduced between parallel electrodes, and an electric field is applied to make the nano filler such as carbon nanotubes arrange in the resin matrix, and the electric field is formed by connecting a power amplifier and copper parallel electrodes;

[0032] Preferably, the electric field is a sinusoidal alternating current electric field, the field strength is 2000-8000 V / cm, the electric field frequency is 2000-4000 Hz, and the electric field application time is 5-30 minutes.

[0033] During the application of the electric field, the arrangement state of the carbon-based nanofiller is observed in real time by an optical microscope, the electric field application time is controlled until the nanocomposite material enters a preliminary solidification state, the electric field application is ended, and the conductive filler is arranged in a direction along the electric field to form a three-dimensional conductive network under the action of the electric field.

[0034] S7, the preliminary solidified nanocomposite material is subjected to a stepwise temperature rising solidification treatment to obtain a smart composite material with a directional conductive network.

[0035] Preferably, the preliminary solidified nanocomposite material is placed in an oven for a stepwise temperature rising solidification treatment to obtain a smart composite material with a directional conductive network.

[0036] S8, the smart composite material is cut, conductive electrodes are arranged at both ends of the smart composite material, and a continuously distributed sensor is obtained by encapsulation.

[0037] Preferably, according to application requirements, the smart composite material is cut into a strip structure or a required shape, and silver nanowires or copper wires are fixed as electrodes at both ends of the smart material using conductive silver glue.

[0038] The smart composite material with the cut and installed electrodes is encapsulated to obtain a continuously distributed smart sensor.

[0039] The wheel trace positioning method involves a reasonable layout structure form of the continuously distributed smart sensor in the lane and a wheel trace line position solving and recognition algorithm. The typical features of the sensor layout structure form include two or more continuously distributed smart sensors perpendicular to the driving direction of the road and parallel to each other, and an oblique distributed sensor with a fixed included angle with the parallel sensors; wherein the multiple parallel sensors calculate the driving speed of the vehicle by identifying the time difference of the vehicle passing through different sensors, and the oblique distributed sensor calculates the lateral offset position of the wheel trace line by identifying the longitudinal driving distance of the vehicle in combination with the trigonometric function relationship.

[0040] In the second aspect of the present application, a positioning method of a continuously distributed smart sensor for vehicle wheel trace recognition, i.e., the layout structure form of the continuously distributed smart sensor and the wheel trace line position solving and recognition algorithm of the first aspect, is provided, which includes the following steps:

[0041] Step one, sensor layout: at least two continuous distribution of intelligent sensors are laid in the lane, which are perpendicular to the driving direction and parallel to each other, and are recorded as the first sensor and the third sensor, and an oblique distribution sensor with a fixed angle θ is laid, which is recorded as the second sensor.

[0042] Preferably, two or more continuous distribution of intelligent sensors are laid in the lane, which are perpendicular to the driving direction and parallel to each other, and are recorded as the first sensor and the third sensor, and the distance between the two sensors is L1, which satisfies L1≠0.

[0043] Preferably, the distance between the two parallel sensors is less than 2 meters, so as to avoid large calculation error of vehicle speed caused by acceleration and deceleration of the vehicle in the interval.

[0044] Preferably, an oblique distribution sensor with a fixed angle θ with the parallel sensor is laid in the lane, which is recorded as the second sensor, the second sensor is the intersection of the oblique sensor and the first sensor at the left or right edge of the lane, and the intersection of the third sensor at the right or left edge of the lane, forming an "N" shaped layout structure.

[0045] Preferably, on the basis of the existing "N" shaped layout scheme, a fourth sensor parallel to the first sensor and the third sensor is added at a position equal in distance to the first sensor and the third sensor, and the acceleration of the vehicle in the sensor range can be calculated by the response time difference of the vehicle passing through the first sensor, the fourth sensor and the third sensor, so as to eliminate the calculation error of the lateral position of the wheel trace line caused by the uniform speed assumption.

[0046] Preferably, an oblique distribution sensor with a fixed angle β with the parallel sensor and intersecting with the second sensor is laid in the lane, which is defined as the fifth sensor, and the fifth sensor intersects with the first sensor at the right or left edge of the lane, and intersects with the third sensor at the left or right edge of the lane. Under this layout form, the second sensor and the fifth sensor, two oblique sensors, can detect the lateral position of the vehicle, so as to consider the small deviation error problem of the vehicle in the sensor layout interval, and reduce the wheel trace line position recognition error caused by non-straight driving of the vehicle.

[0047] Step two, when the vehicle tires pass through the multiple sensors in turn, record the response time of each sensor.

[0048] Preferably, when the vehicle passes through the sensor layout range, the time signals triggered by the first sensor, the second sensor and the third sensor are recorded in turn as follows:

[0049] The front wheel and the rear wheel of the vehicle produce piezoelectric detection signals on the first sensor at t1 and t2 respectively.

[0050] The left front wheel and the right front wheel of the vehicle generate piezoelectric detection signals on the third sensor at time t3 and t4 respectively;

[0051] The left rear wheel and the right rear wheel of the vehicle generate piezoelectric detection signals on the third sensor at time t5 and t6 respectively;

[0052] The front wheel and the rear wheel of the vehicle generate piezoelectric detection signals on the second sensor at time t7 and t8 respectively.

[0053] Step three, calculating the vehicle speed through the response time difference between adjacent sensors and the preset distance.

[0054] Preferably, the passing speed of the vehicle can be calculated according to the time difference of the left front wheel or the right front wheel of the vehicle passing through the first sensor and the second sensor, combined with the known distance L1 between the sensors, according to the following formula:

[0055]

[0056] Step four, calculating the lateral position coordinate of the vehicle wheel trace in the lane based on the vehicle speed, the sensor included angle and the trigger timing, combined with the trigonometric function relationship.

[0057] Preferably, the lateral position y of the vehicle wheel trace in the lane can be calculated through the time difference of the front wheel or the rear wheel of the vehicle passing through the first sensor and the third sensor and the second sensor, combined with the vehicle speed v, the sensor arrangement angle θ and the trigonometric function relationship. According to the piezoelectric detection signals generated when the wheels pass through the three sensors and the geometric relationship between the sensors, the lateral position y1 of the left front wheel trace in the sensor range is calculated, and correspondingly, the right front wheel y2, the left rear wheel y3 and the right rear wheel y4. The calculation formula is as follows:

[0058]

[0059]

[0060] In summary, compared with the traditional technology, the continuous distributed smart sensor for vehicle wheel trace recognition and the positioning method thereof have the following advantages:

[0061] 1. The continuous distributed smart sensor for vehicle wheel trace recognition and the positioning method thereof realize the accurate recognition of the vehicle wheel trace line through the design of the continuous distributed smart sensor, the reasonable arrangement and the trigonometric calculation. Combined with the time signal output by the sensor and the vehicle speed, the lateral deviation position of the wheel trace line can be effectively calculated, the detection requirement of centimeter-level precision is met, the technical problem of vehicle position recognition in the lane is solved, and the demand of modern intelligent road construction for high-precision traffic data is met.

[0062] 2. The selected sensor deployment method is not only simple to install and inexpensive to collect data, but also enables efficient acquisition of vehicle speed and wheel track lateral position data, significantly improving the overall detection efficiency of the system.

[0063] 3. The sensors and algorithms of this invention can adapt to various types of vehicles (such as passenger cars and heavy-duty trucks) and different complex road environments, and have a wide range of application scenarios and deployment flexibility; they can output vehicle wheel track data in real time, providing accurate data support for lane departure detection, dynamic weighing, traffic flow analysis, road damage prediction, etc., thereby significantly improving the efficiency and safety of intelligent traffic management systems.

[0064] The technical method of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0065] Figure 1 This is a flowchart illustrating the fabrication process of the continuous distributed sensitive sensor of this invention.

[0066] Figure 2 These are electron microscope (EM) images of conductive networks prepared according to the same proportions in Embodiment 1 and Comparative Example 1 of this application, wherein... Figure 2 a is a microscopic electron microscope scan of the conductive network of the sensitive material prepared in Comparative Example 1; Figure 2 b is a microscopic electron microscope scan of the oriented conductive network of the sensitive material prepared in Example 1.

[0067] Figure 3 This is a typical sensor deployment structure for vehicle wheel track recognition in this application, wherein, Figure 3 a represents the sensor deployment structure used in Comparative Example 2; Figure 3 b represents the sensor deployment structure used in Example 2;

[0068] Figure 4 This is a structural diagram of a continuous distributed smart sensor.

[0069] Figure Labels

[0070] 1. First sensor; 2. Second sensor; 3. Third sensor; 4. Fourth sensor; 5. Fifth sensor; 6. Conductive electrode; 7. Sensitive composite structure; 8. Packaging structure. Detailed Implementation

[0071] The technical method of the present invention will be further described below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of this application.

[0072] The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the application or its application or uses.

[0073] Techniques, systems, and devices known to those of ordinary skill in the relevant art can not be discussed in detail herein. However, where appropriate, techniques, systems, and devices should be considered as being part of the specification.

[0074] In all of the compositions shown and discussed herein, any specific values should be interpreted as merely exemplary, and not a limitation. Other examples of the exemplary embodiments can have different values.

[0075] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0076] Since the sensitivity and sensing performance of the conductive composite smart material is significantly affected by the uniform dispersion and continuous distribution of the internal conductive filler, in order to realize the in-situ monitoring application of the road surface and the high-precision vehicle wheel track identification, the conductive network structure of the smart sensing material needs to be regulated and optimized. The application provides a continuous distributed smart sensor for vehicle wheel track identification and a positioning method thereof.

[0077] A continuous distributed smart sensor for vehicle wheel track identification, as shown in Figure 4 The continuous distributed smart sensor comprises an encapsulation structure 8 and a smart composite structure 7; the smart composite structure 7 is arranged inside the encapsulation structure 8; the smart composite structure 7 is a continuous strip structure; the smart composite structure 7 is provided with conductive electrodes 6 at both ends; the length of the continuous distributed smart sensor is 0.2-100 meters; and the width of the continuous distributed smart sensor is 1-10 centimeters.

[0078] Embodiment 1:

[0079] As shown in Figure 1 (a1) carbon nanotubes (CNTs) are dispersed in N,N-dimethylformamide (DMF) solvent to prepare a conductive nano-filler dispersion slurry, wherein the content of the CNTs is 5wt%.

[0080] (a2) the carbon nanotube dispersion slurry is treated by using a high-shear dispersion homogenizer for 20 minutes, and then ultrasonic dispersion is performed for 60 minutes by using an ultrasonic disperser, to obtain a uniformly dispersed and stable state conductive nano-filler slurry.

[0081] (a3) the slurry is mixed with liquid epoxy resin, and stirring and ultrasonic synchronous treatment are performed for 60 minutes, to obtain a uniform mixture of nano-filler and polymer.

[0082] (a4) The mixture is placed in a vacuum environment at 80°C for 1 hour to promote solvent evaporation, obtaining a nanofiller-containing prepolymer.

[0083] (a5) The nanofiller-containing prepolymer is mixed with a polyamide curing agent (PA) at a mass ratio of 100:30, and stirred uniformly with a stirrer for 3 minutes to obtain a uniformly mixed nanocomposite.

[0084] (a6) The nanocomposite is immediately introduced between copper parallel electrodes, and an electric field is applied to cause the carbon nanotubes to be oriented and arranged in the resin matrix; the applied electric field is a sinusoidal alternating electric field with a field strength of 4000 V / cm, an electric field frequency of 4000 Hz, and an electric field application time of 8 minutes; the structure and morphology of the conductive network are observed using a microscope as shown in Figure 2 b.

[0085] (a7) The preliminarily cured nanocomposite is placed in an oven and cured according to a stepwise temperature rising process of 60°C for 2 hours and 120°C for 4 hours.

[0086] (a8) The cured smart composite material is cut into a strip structure, and copper wires are fixed as electrodes at both ends using conductive silver glue.

[0087] (a9) According to application requirements, the smart composite material is cut into a strip structure or a desired shape, silver nanowires or copper wires are fixed as electrodes at both ends of the smart material using conductive silver glue, and the same epoxy resin as the composite material matrix is used for packaging and protection.

[0088] (a10) The packaged sensor is finally cured to complete the preparation of the smart sensor.

[0089] The method of external electric field induction used in this embodiment enhances the orderliness of the conductive filler and network structure, and this feature ensures that the network structure is more quickly disconnected and linked under external mechanical loading conditions; tests show that the mechanical response speed of the sensor under tensile and compressive deformation is less than 10 milliseconds, meeting the high-frequency application requirements; fatigue tests show that after 100,000 cycles of loading, the sensor signal attenuation is less than 5%, meeting the long-period monitoring application requirements.

[0090] Comparative Example 1 of the present embodiment:

[0091] (a1) CNTs with a content of 5wt% are dispersed in N,N-dimethylformamide (DMF) solvent, and a uniformly dispersed and stable conductive nanofiller slurry is prepared by the same 20-minute high-shear dispersion and 60-minute ultrasonic dispersion as in Example 1.

[0092] (a2) mixing the slurry with liquid epoxy resin, stirring and ultrasonic synchronous treatment for 60 minutes, and drying the mixture in a vacuum environment at 80°C for 1 hour to obtain a nanofiller-containing prepolymer.

[0093] (a3) mixing the nanofiller-containing prepolymer with a polyamide curing agent at a mass ratio of 100:30, stirring uniformly with a stirrer for 3 minutes to obtain a uniformly mixed nanocomposite.

[0094] (a4) omitting the electric field-induced directional arrangement step, directly curing the nanocomposite, and curing according to a stepwise heating process of 60°C for 2 hours and 120°C for 4 hours, and observing the structure and morphology of the conductive network using a microscope as shown in FIG. a. Figure 2

[0095] (a5) cutting the cured smart composite into a strip structure, and fixing copper wires as electrodes at both ends using conductive silver glue.

[0096] (a6) cutting the smart composite into a strip structure or a desired shape according to application requirements, fixing silver nanowires or copper wires as electrodes at both ends of the smart material using conductive silver glue, and encapsulating and protecting using the same epoxy resin as the composite matrix.

[0097] (a7) final curing of the encapsulated sensor to complete the preparation of the smart sensor.

[0098] The embodiment uses an external electric field-induced method to enhance the orderliness of the conductive filler and network structure, and this feature ensures that the network structure is more quickly disconnected and linked under an external mechanical loading state. Tests show that the mechanical response speed of the sensor under tensile and compressive deformation is less than 10 milliseconds, meeting the high-frequency application requirements. Fatigue tests show that after 100,000 cycles of loading, the sensor signal attenuation is less than 5%, meeting the long-period monitoring application requirements.

[0099] The absence of the electric field-induced directional arrangement step in the comparative example leads to disordered distribution of carbon nanotubes, and a stable conductive network is not formed, which further affects the conductive performance, response speed, durability, environmental adaptability, and stability of the sensor. Tests show that the mechanical response speed of the sensor prepared in Comparative Example 1 under tensile and compressive deformation is greater than 100 milliseconds, and high-frequency applications are limited. Fatigue tests show that the signal stability of the sensor after 5,000 cycles of loading cannot be guaranteed, and it is difficult to meet the long-period monitoring application requirements. These differences verify the role of electric field-induced directional arrangement in optimizing the performance of the vehicle track recognition sensor, and further highlight the technical advantages of the present application in improving the overall performance of the sensor.

[0100] ​By employing an optimized combination of polymer matrix and nanofillers, along with electric field-induced orientation technology, the sensor of this invention exhibits excellent durability and fatigue resistance, enabling stable operation in harsh environments such as high temperature, low temperature, and humid heat. Furthermore, this invention uses low-cost conductive nanocomposite materials as the core sensing unit, combined with a simple packaging process and a reasonable layout structure, reducing manufacturing and maintenance costs and laying the foundation for the large-scale application of the sensor.

[0101] Example 2:

[0102] (b1) such as Figure 3 As shown in Figure b, three sensors, labeled as sensor 1, sensor 4, and sensor 3, are installed perpendicular to the lane. These sensors are parallel to each other and perpendicular to the direction of traffic. The distance between each pair of sensors is equal, each L, and L is less than 2 meters.

[0103] (b2) A second sensor 2 is installed along the driving direction at a fixed angle θ with the parallel sensor, wherein the second sensor 2 intersects with the first sensor 1 at the left edge of the lane and intersects with the third sensor 3 at the right edge of the lane;

[0104] (b3) Taking the calculation of the lateral position of the left wheel track as an example, let the left front wheel of the vehicle pass through the first sensor 1, the second sensor 2, the fourth sensor 4, and the third sensor 3 at times t1, t2, t3, and t5 respectively. Let the vehicle's speed v1 be at the midpoint between the first sensor 1 and the fourth sensor 4, the vehicle's speed v2 be at the midpoint between the third sensor 3 and the fourth sensor 4, the vehicle's speed v3 be when the piezoelectric detection signal is generated at the fourth sensor 4, the vehicle's acceleration a be when traveling between the first sensor 1 and the third sensor 3, and the vehicle's speed v0 be when passing the first sensor 1. Then, the lateral position x2 of the left front wheel track within the sensor range can be calculated according to the following calculation process.

[0105] Average velocity between sensor 1 and sensor 4:

[0106]

[0107] Average speed between the third sensor (3) and the fourth sensor (4):

[0108]

[0109] Average velocity between sensor 1 and sensor 3:

[0110]

[0111] Vehicle acceleration:

[0112]

[0113] The speed of the wheel passing through the first sensor 1:

[0114] v0 = v3 - at3(2.5)

[0115] The lateral position of the vehicle wheel trace:

[0116]

[0117] Comparative Example 2 of the present embodiment:

[0118] (b1) As shown in FIG. a, two sensors perpendicular to the driving direction of the road and parallel to each other are arranged, denoted as the first sensor 1 and the third sensor 3, and the distance between the two sensors is L1, which satisfies L1≠0. Figure 3 a

[0119] (b2) A second sensor 2 is arranged in the lane at a fixed angle θ with the parallel sensors, the second sensor 2 intersects the first sensor 1 at the left edge of the lane and intersects the third sensor 3 at the right edge of the lane, forming an "N" shaped arrangement.

[0120] (b3) Taking the lateral position of the vehicle wheel trace on the left side of the vehicle as an example, according to the aforementioned wheel trace calculation method, the vehicle wheel trace lateral position calculation formula is as formula (1.2).

[0121] In the present embodiment, a fourth sensor 4 parallel to the first sensor 1 and the third sensor 3 is added at a position equal in distance to the two parallel sensors based on the existing Z-shaped arrangement scheme. The response time difference between the vehicle passing through the first sensor 1, the third sensor 3, and the fourth sensor 4 can be used to calculate the acceleration of the vehicle within the sensor range, so that not only can the complex working conditions of non-uniform acceleration be considered in the calculation of the lateral position of the vehicle wheel trace, but also the accuracy of wheel trace recognition can be significantly improved.

[0122] The N-shaped vehicle wheel trace lateral position recognition scheme in the comparative example has a simple arrangement structure, clear geometric relationship, and intuitive calculation method. However, this scheme is based on the idealized assumption that the vehicle travels at a constant speed along the center line of the lane in a straight line, ignoring the complexity of vehicle dynamic behavior in actual traffic operation. In real road environment, the vehicle is affected by multiple factors such as driving behavior, road condition changes, traffic interference, etc., and its driving state often shows non-uniform speed and nonlinear trajectory motion deviating from the center, and there are problems such as overlapping of tire load response and difficulty in signal differentiation, so the detection accuracy cannot be guaranteed, and it is impossible to provide effective data support for road traffic safety management and intelligent road pavement structure response analysis.

[0123] It should be pointed out finally that the above embodiments are only used to illustrate the technical method of the present application but not to limit it, and although the present application is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical method of the present application can be modified or replaced equivalently, and these modifications or equivalent replacements cannot make the modified technical method deviate from the spirit and scope of the technical method of the present application.

Claims

1. A continuous distributed smart sensor for vehicle tire track recognition, characterized in that, the continuous distributed smart sensor comprises a packaging structure and a smart composite structure; the smart composite structure is arranged inside the packaging structure; the smart composite structure is provided with conductive electrodes at both ends; the length of the continuous distributed smart sensor is 0.2-100 meters; the width of the continuous distributed smart sensor is 1-10 centimeters.

2. A method for the production of a continuous distributed smart sensor for vehicle track recognition, for the production of a continuous distributed smart sensor according to claim 1, characterized in that comprising the following steps: S1, dispersing carbon-based nanofillers in N,N-dimethylformamide solvent to prepare a conductive nanofiller dispersion slurry; S2, obtaining a conductive nanofiller slurry by high shear and ultrasonic treatment of the conductive nanofiller dispersion slurry; S3, mixing and stirring the conductive nanofiller slurry with the polymer matrix to form a uniform mixture of nanofillers and polymers by ultrasonic treatment; S4, drying the uniform mixture to obtain a pre-polymer containing nanofillers; S5, mixing and stirring the pre-polymer with the corresponding polymer curing agent to obtain a uniform composite material; S6, applying an electric field to the outside of the uniform composite material to cure and form a preliminary cured nanocomposite material; S7, performing a stepwise temperature rising curing process on the preliminary cured nanocomposite material to obtain a smart composite material with a directional conductive network; S8, cutting the smart composite material, arranging conductive electrodes at both ends of the smart composite material, and obtaining a continuous distributed sensor by packaging.

3. The method for fabricating a continuous distributed sensor for vehicle wheel track recognition according to claim 2, characterized in that, The carbon-based nanofillers are one or more of carbon nanotubes, graphene, carbon black, or carbon nanofibers; The polymer matrix includes one or more of epoxy resin, polyurethane, polydimethylsiloxane, and silicone rubber modified epoxy resin, polyurethane modified epoxy resin, or other thermosetting polymers.

4. The method for fabricating a continuous distributed sensor for vehicle wheel track recognition according to claim 2, characterized in that, The electric field is a sinusoidal alternating current field with a field strength of 2000-8000 V / cm, a frequency of 2000 Hz-4000 Hz, and an application time of 5-30 minutes.

5. The method for fabricating a continuous distributed sensor for vehicle wheel track recognition according to claim 2, characterized in that, The mass ratio of carbon-based nanofillers to N,N-dimethylformamide solvent is 1% to 20%; The mass ratio of the corresponding polymer curing agent to the pre-polymer is 5%-100%.

6. A method for positioning a continuous distributed smart sensor for vehicle track recognition, based on the continuous distributed smart sensor prepared according to any one of claims 2 to 5, characterized in that, comprising the following steps: Step one, laying out the sensor: laying out at least two continuous distributed smart sensors perpendicular to the driving direction and parallel to each other in the lane, denoted as the first sensor and the third sensor, and one oblique distributed sensor at a fixed angle θ, denoted as the second sensor; Step two, recording the response time of each sensor when the vehicle tires pass over the sensors in turn; Step three, calculating the vehicle speed by the response time difference between adjacent sensors and the preset distance; Step four, calculating the lateral position coordinates of the vehicle tire track in the lane based on the vehicle speed, the sensor angle, and the triggering time sequence, combined with the trigonometric function relationship.

7. The method of positioning a continuously distributed smart sensor for vehicle track recognition according to claim 5, wherein, Laying out the sensor also includes adding a parallel sensor between the first sensor and the third sensor, denoted as the fourth sensor; The sensor arrangement further comprises a fifth sensor arranged in the lane at a fixed angle β with the first or third sensor and intersecting the second sensor, the fifth sensor intersecting the first sensor at the right or left edge of the lane and intersecting the third sensor at the left or right edge of the lane.

8. The method of claim 6, wherein, When the vehicle tires pass through the sensors in turn, the specific content of recording the response time of each sensor is: The front and rear wheels of the vehicle generate piezoelectric detection signals on the first sensor at times t1 and t2, respectively; The front left and right wheels of the vehicle generate piezoelectric detection signals on the third sensor at times t3 and t4, respectively; The rear left and right wheels of the vehicle generate piezoelectric detection signals on the third sensor at times t5 and t6, respectively; The front and rear wheels of the vehicle generate piezoelectric detection signals on the second sensor at times t7 and t8, respectively.

9. The method of claim 6, wherein, The specific content of calculating the vehicle speed through the response time difference between adjacent sensors and the preset distance is: Wherein, v is the vehicle speed, L1 is the distance between the first sensor and the third sensor.

10. The method of claim 6, wherein, The specific content of calculating the lateral position coordinate of the vehicle wheel track in the lane based on the vehicle speed, the sensor angle and the trigger timing, combined with the trigonometric function relationship is: Through the time difference of the front or rear wheel passing through the first and third sensors, combined with the vehicle speed v, the sensor arrangement angle θ and the trigonometric function relationship, the lateral position y of the vehicle wheel track in the lane is calculated; According to the piezoelectric detection signals generated when the wheels pass through the three sensors and the geometric relationship between the sensors, the lateral position y1 of the front left wheel track in the sensor range is calculated, and correspondingly, the lateral positions y2 of the front right wheel, y3 of the rear left wheel and y4 of the rear right wheel are calculated.

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