An intelligent road network medium for triboelectric self-driving perception of vehicle-road collaboration

Abstract

The present invention belongs to the field of vehicle-road cooperative self-driving perception, and in particular relates to an intelligent road network medium for vehicle-road cooperative triboelectric self-driving perception. It comprises: an embedded high-elastic triboelectric self-driving perception unit (1), a grid-type intermediate layer (2), an upper steel pad (301), a lower steel pad (302), a steel support and spring (303), a shock-absorbing foam (4), a high-strength flexible nano-modified caulking layer (5), and a high-strength and high-toughness waterproof packaging layer (6). The embedded high-elastic triboelectric self-driving perception unit (1) is prepared by selecting two flexible materials with significant electronegativity differences based on friction electrification and electrostatic induction effects. The present invention is suitable for sensing vehicle and road information such as average speed, vehicle weight, and road surface status of a road section, with a sensing range of 10 to 120 km / h, a vehicle weight of 2 to 20 tons, and a road friction coefficient of 0.35 to 0.8. Compared with the existing technology, the present invention has the advantages of multiple sensing information, a wide sensing range, high sensing accuracy, and high sensing network coverage.

Description

Technical Field

[0001] The present invention belongs to the field of vehicle-road collaborative self-driving perception, and in particular relates to an intelligent road network medium for vehicle-road collaborative triboelectric self-driving perception. Background Art

[0002] With the rapid development of urbanization in my country, traffic volume has increased rapidly, necessitating the development of intelligent transportation systems. However, traditional traffic management strategies are insufficient to meet the needs of today's complex traffic scenarios, and research on vehicle-road collaboration is urgent. Therefore, the invention of intelligent road network media for vehicle-road collaboration and self-driving perception provides a new solution for the development of intelligent transportation. On the one hand, intelligent road network media can achieve efficient connectivity between vehicles and road infrastructure, reduce the heterogeneity of vehicle-road information, and thus improve the ability to acquire and process traffic information. On the other hand, intelligent road network media can promote collaborative perception and unified decision-making between vehicles, improve the safety and efficiency of the transportation system, and provide functions such as intelligent driving, path planning, and obstacle avoidance, thereby reducing the occurrence of traffic accidents.

[0003] Existing intelligent traffic management technologies primarily rely on roadside sensing devices such as cameras and integrated radar and vision devices. While these devices can efficiently scan and identify vehicles and road conditions through images or video, they are unable to determine safe speeds and following distances in real time in adverse weather conditions such as rain and fog. Therefore, developing a self-propelled sensing medium that can be embedded in the surface structure of road infrastructure is a key solution to addressing the low sensing efficiency in complex environments.

[0004] At present, CN202410832062.2 discloses a method and device for providing traffic information services based on vehicle-road collaborative intelligent driving. It mainly proposes a traffic information service strategy from an architectural perspective, but never considers the design and optimization of hardware such as sensing facilities, and cannot fundamentally solve the problems of low perception efficiency and poor safety in complex environments.

[0005] Furthermore, most sensors currently used in pavement structures rely on the piezoelectric effect. By polarizing piezoelectric ceramics, these sensors exploit the positive piezoelectric effect of piezoelectric materials under external loads to sense information and collect energy. Examples include CN 111893835 A, CN 113077636 B, and CN 114293429 A. These materials must be piezoelectric, which can be inconvenient to use and brittle, making them difficult to meet durability requirements. Summary of the Invention

[0006] The purpose of this invention is to provide an intelligent road network medium for triboelectric self-driven sensing in vehicle-road collaboration. This medium, designed for complex traffic and climate scenarios in smart transportation, offers advantages such as a wide range of materials, passive sensing, diverse sensory information, a wide sensing range, high sensing accuracy, and high network coverage.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] An intelligent road network medium for triboelectric self-driving perception of vehicle-road collaboration, comprising: an embedded high-elastic triboelectric self-driving perception unit, a grid-type middle partition, an upper steel pad, a lower steel pad, steel pillars and springs, shock-absorbing foam, a high-strength flexible nano-modified caulking layer, and a high-strength and high-toughness waterproof packaging layer.

[0009] The lower steel plate is installed above the high-strength and high-toughness waterproof packaging layer;

[0010] Several steel pillars are fixed on the lower steel plate by internal bolts, and springs are placed on top of the steel pillars to buffer the vehicle load;

[0011] The grid-type middle partition is a fiberglass grid fixed on top of the lower steel plate, with the center and four sides aligned with the lower steel plate;

[0012] Steel struts and springs pass through the grating of the grid-type center partition;

[0013] The grid-type middle partition is provided with a number of large holes, in which embedded high-elasticity triboelectric self-driven sensing units are installed;

[0014] The high-elasticity and high-flexibility nano-modified materials are poured into the small holes of the grid-type middle partition layer to fill the original holes of the grid-type middle partition layer and form a high-strength and flexible nano-modified filling layer;

[0015] The lower surface of the upper steel plate is provided with shock-absorbing foam and is installed above the grid-type middle partition.

[0016] The embedded highly elastic triboelectric self-driven sensing unit 1 is fabricated based on the effects of triboelectric charging and electrostatic induction, preferably using two flexible materials with significantly different electronegativity. It comprises an electrode layer 101, a positive friction layer 102, and a negative friction layer 103. Wiring for the positive and negative friction layers is extended through a grid-like intermediate layer and connected to an external STM32 microcontroller for data acquisition.

[0017] Preferably, the electronegativity of the positive electrode friction layer is 2.5-3.5, and the electronegativity of the negative electrode friction layer is 1.5-2.5.

[0018] Preferably, the material of the positive electrode friction layer is polytetrafluoroethylene with an electronegativity of 3.0, and the material of the negative electrode friction layer is aluminum foil with an electronegativity of 1.5.

[0019] The steps for installing the components of the intelligent road network medium for vehicle-road cooperative self-driving perception are as follows:

[0020] The steel support is fixed to the lower steel plate by internal bolts;

[0021] Cut large holes in the fiberglass grille according to the design size of 10cm×7cm;

[0022] The fiberglass grille is fixed on top of the lower steel plate, and the center and four sides should be aligned with the corresponding positions of the lower steel plate;

[0023] The flexible encapsulated triboelectric self-driven sensing unit is fixed in the large hole cut in the fiberglass grille, and the signal transmission line is led out;

[0024] Springs are placed on top of the steel pillars to cushion vehicle loads;

[0025] Elastic foam is fixed on the inner surface of the upper steel plate to protect the triboelectric sensing unit;

[0026] Pour high elasticity and high flexibility nano-modified materials into the small holes of the FRP grille to fill the original holes of the FRP grille;

[0027] After the pouring material solidifies, cover it with the upper steel plate and make a waterproof packaging layer on top of the upper steel plate.

[0028] The height of the grid-type middle partition is 15-30 mm, the inner side length of the small hole is 25-35 mm, and the compressive strength is 30-50 MPa.

[0029] The steel support layer and upper and lower steel plates have a yield strength of 200-800 MPa. The spring is manufactured using additive manufacturing using a thermoplastic resin matrix and 0.05-0.1% nanoparticles as a 3D printing composite material. The spring modulus is 4500-8000 N / mm.

[0030] The highly flexible nano-modified material has a flexural strength of 10-15 MPa, a compressive strength of 35-60 MPa, and a rebound rate of 50-70% after curing.

[0031] The high-strength and high-toughness waterproof packaging layer is composed of high-performance silica gel and nano-SiO2, and its weight composition is: 1000-1500 parts of sodium silicate solution and 1-3 parts of sodium hydroxide particles.

[0032] The high-strength and high-toughness waterproof packaging layer has a curing time of 15-40 minutes at room temperature and a tear strength of 30-80 pli.

[0033] The intelligent road network medium is buried at a depth of 0-10 cm in the road structure, and has a longitudinal spacing of 10-60 m.

[0034] The intelligent road network medium uses open circuit voltage signals as indicators for sensing signals such as average vehicle speed, vehicle weight, and road surface status, which are specifically calibrated using parameters such as amplitude and single peak interval.

[0035] The intelligent road network medium has a sensing range of vehicle speed of 10-120 km / h, vehicle weight of 2-20 tons and road friction coefficient of 0.35-0.8.

[0036] Beneficial effects

[0037] Compared with the prior art, the present invention has the following advantages:

[0038] (1) The triboelectric self-driven sensing unit of the present invention has a wide range of functional components made of materials and can operate normally without external power supply. It can sense vehicle and road information at the same time, and can better reduce the complexity of multi-dimensional heterogeneous information in smart transportation.

[0039] (2) The present invention uses the open circuit voltage signal as the characteristic signal of the sensing information, with a response speed of <1ms and is not affected by vehicle speed, vehicle weight, etc.

[0040] (3) The intelligent road perception network adopted by the present invention can be applied to key traffic scenarios such as long tunnels, complex intersections, and viaducts, and has the advantages of good economy, over 90% perception accuracy and coverage. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 The structural diagram of the present invention includes: 1 - embedded high-elastic triboelectric self-driven sensing unit; 2 - grid-type intermediate partition; 301 - upper steel plate; 302 - lower steel plate; 303 - steel pillars and springs; 4 - shock-absorbing foam; 5 - high-strength flexible nano-modified caulking layer; and 6 - high-strength and high-toughness waterproof packaging layer.

[0042] Figure 2 This is a structural diagram of the embedded high-elastic self-driven sensing unit of the present invention; wherein 101 is the electrode layer, 102 is the positive friction layer, and 103 is the negative friction layer;

[0043] Figure 3 This is a diagram of vehicle information perception results in an embodiment of the present invention;

[0044] Figure 4 This is a schematic diagram of the layout of an intelligent road perception network in an embodiment of the present invention;

[0045] Figure 5 This is a diagram showing the perception results of vehicle motion information on a road section based on an intelligent road perception network in an embodiment of the present invention;

[0046] Figure 6 This is a diagram of a tunnel scene in which an intelligent road perception network is applied in an embodiment of the present invention;

[0047] Figure 7 A search space diagram in a tunnel according to an embodiment of the present invention;

[0048] Figure 8 is the coverage rate of the intelligent road perception network in the tunnel in the embodiment of the present invention. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0050] An intelligent road network medium for vehicle-road cooperative triboelectric self-driving perception, its structure is as follows Figure 1 As shown, it includes: an embedded high-elastic triboelectric self-driven sensing unit 1, a grid-type middle partition layer 2, an upper steel pad 301, a lower steel pad 302, steel pillars and springs 303, a shock-absorbing foam 4, a high-strength flexible nano-modified filling layer 5 and a high-strength and high-toughness waterproof packaging layer 6.

[0051] The lower steel plate 302 is installed above the high-strength and high-toughness waterproof packaging layer 6;

[0052] Several steel pillars are fixed to the lower steel plate 302 by internal bolts, and springs 303 are placed above the steel pillars to buffer the vehicle load;

[0053] The grid-type middle partition 2 is a glass fiber reinforced plastic grid fixed above the lower steel plate 302, with the center and four sides aligned with the lower steel plate 302;

[0054] The steel struts and springs 303 pass through the grid of the grid-type intermediate partition 2;

[0055] The grid-type middle partition 2 is provided with a plurality of large holes, in which the embedded high-elastic triboelectric self-driven sensing units 1 are installed;

[0056] Injecting highly elastic and flexible nano-modified materials into the small holes of the grid-type middle partition layer 2 to fill the original holes of the grid-type middle partition layer 2 to form a high-strength and flexible nano-modified filling layer 5;

[0057] A shock-absorbing foam 4 is provided on the lower surface of the upper steel backing plate and is installed above the grid-type middle partition 2 .

[0058] The embedded high elastic triboelectric self-driven sensing unit 1 is prepared based on the triboelectric and electrostatic induction effects, preferably using two flexible materials with significant electronegativity differences. Figure 2 As shown, it includes: an electrode layer 101, a positive friction layer 102 and a negative friction layer 103. The wiring of the positive friction layer and the negative friction layer is led out through a grid-type middle spacer and connected to an external STM 32 microcontroller for data acquisition.

[0059] Preferably, the electronegativity of the positive electrode friction layer is 2.5-3.5, and the electronegativity of the negative electrode friction layer is 1.5-2.5.

[0060] Preferably, the material of the positive electrode friction layer is polytetrafluoroethylene with an electronegativity of 3.0, and the material of the negative electrode friction layer is aluminum foil with an electronegativity of 1.5.

[0061] Example 1 :

[0062] In this embodiment, the embedded highly elastic triboelectric self-driven sensing unit uses an elastic, stretchable metamaterial as a spacer layer, and a carbon nanotube-coated surface layer as an electrode layer. In the absence of external force, the elastic metamaterial separates the positive and negative friction layers. Under force, the elastic metamaterial deforms to produce contact and separation motion.

[0063] Compared to traditional sensing devices, the embedded highly elastic triboelectric self-driven sensing unit utilizes polytetrafluoroethylene (PTFE) with an electronegativity of 3.0 as the negative friction layer material and aluminum foil with an electronegativity of 1.5 as the positive friction layer material. This allows for a wide range of materials and a simple preparation process, eliminating the need for polarization treatment of functional materials and achieving high flexibility. Multi-layer packaging and protection enhance the sensor's durability and ability to withstand heavy vehicle loads, extending the minimum load capacity to over 2 tons.

[0064] In this implementation, the steel support layer, upper and lower steel plates, have a yield strength of 500 MPa. The 3D-printed spring consumables consist of a thermoplastic resin: nano-SiO2 particles ratio of 1000:1, with a spring modulus of 6815.8 N / mm.

[0065] In this embodiment, the high-flexibility nano-modified material has a flexural strength of 12.8 MPa, a compressive strength of 35 MPa, and a rebound rate of 50% after curing.

[0066] The high-strength and high-toughness waterproof encapsulation layer in this embodiment is composed of high-performance silica gel and nano-SiO2, wherein the weight composition is: 1200 parts of sodium silicate solution and 1 part of sodium hydroxide particles. The tear strength after curing is 38 pli.

[0067] In this embodiment, the intelligent road network medium uses the open circuit voltage peak value and interval to represent the vehicle-road information.

[0068] The intelligent road network medium has a sensing range of vehicle speed of 10-120 km / h, vehicle weight of 2-20 tons and road friction coefficient of 0.35-0.8.

[0069] The usability test of the assembled intelligent road network medium is carried out, and the results are shown in Figure 2 A vehicle passing at 10 km / h can be observed with two distinct peaks at 4.890s and 5.786s, representing the time when the front and rear wheels passed, respectively. Based on the front and rear wheel spacing (L = 2.670m), the calculated vehicle speed is 10.95 km / h, with a perception accuracy of 90.5%. This demonstrates the feasibility of the invented intelligent road network medium for vehicle-road information perception.

[0070] Example 2 :

[0071] In this embodiment, the intelligent road network medium for vehicle-road cooperative triboelectric self-driving perception of the present invention is arranged at a longitudinal interval of 10m on the surface of a 3.5m wide road structure. Figure 4 To more accurately reflect actual operating conditions, this embodiment first fabricates a wooden frame mold of the same dimensions as the intelligent road network medium. A 100-meter tape measure is used to measure and determine the installation location of the packaging structure. Next, the wooden frame mold is secured to the gravel pavement surface, and an electric drill is used to drill the outline of the packaging structure along the mold's inner wall. Finally, the wooden frame mold is removed, and a shovel is used to excavate downward along the outline to create a 5 cm thick groove structure prototype. The assembled intelligent road sensing medium is then embedded in the pavement structure.

[0072] This embodiment mainly addresses the needs of road maintenance departments and determines traffic safety and efficiency through the changing trends of information such as the average speed of a road section, vehicle movement, and road surface roughness.

[0073] According to the embodiment, the intelligent road network media are arranged as a cross section every 10 m, that is, the longitudinal distance D between two intelligent road network media and the time difference between vehicles passing two adjacent sensing devices are used to calculate the average speed of the corresponding road section. The average speed and vehicle weight calculation process for multiple road sections is shown in Figure 5 .

[0074] Figure 5 The figure shows the output voltage information of three adjacent intelligent road network media A2, B2 and C2 when a car is driving on the anti-skid road surface at a speed of 5km / h in an actual field test. The relative time when the peak signal of each intelligent road network medium appears is extracted as T. A , T B and T CThe time difference between the peak values ​​of adjacent sensing devices represents the time required for the vehicle to pass between the two corresponding sensing devices. A2-B2 =T B -T A It represents the time required for the car to pass A2 and B2. Figure 4 The calculation results show that the average speed of cars traveling along the A2-B2 section is 4.74 km / h, and the average speed along the B2-C2 section is 5.194 km / h. The above steps complete the calculation and evaluation of the average speed for each section. The calculation of other road surface and vehicle information, such as average anti-skid performance and vehicle count, can be completed using the same principles as above.

[0075] Example 3 :

[0076] According to the "Technical Standards for Highway Engineering" (JTGB01-2023), taking a two-lane one-way highway tunnel as an example, the present invention deploys an intelligent road network medium for vehicle-road collaborative triboelectric self-driving perception to improve the light and dark effects of tunnel entrances and exits, traffic bottlenecks, slippery roads, and blind spots.

[0077] This embodiment takes into account the significant safety risks at tunnel entrances and exits, such as visual adjustment, traffic congestion, weather and environmental influences, and poor road design. Furthermore, issues such as light differences inside and outside the tunnel, slippery roads, and air quality can also increase driver difficulty and the risk of accidents.

[0078] like Figure 6 For a single-lane road with a width of 3.75 m, the width for a dual-lane road is 7.5 m. Based on the normal distribution of wheel tracks during driving, the initial sensing devices are placed within the center of the wheel track distribution in the middle of the lane. Considering that the 95% confidence interval of the normal distribution of wheel tracks lies between [0.4880, 1.5120] and [2.2380, 3.2620], a grid layout is implemented with a width of 2 m horizontally (along the lane width) and 10 m vertically (along the driving direction), with an initial number of 8 sensors.

[0079] In this embodiment, the particle swarm optimization algorithm is used to evaluate the coverage accuracy of the sensing network, and its search space is as follows: Figure 6 .It can be found that the search space is distributed in a step-like manner.

[0080] In this example, the intelligent road network medium has a sensing range of 1.0m, and the network optimization is performed on a 1500m tunnel space. As shown in Table 1, with the increase in the number of iterations, the coverage rate increases from 82.07% to 90.67%, and the calculation time is only 8.28s. After 10,000 iterations, the intelligent road network coverage rate in the tunnel is Figure 8, which proves the advantages of the self-driving intelligent road network medium proposed in this invention in vehicle-road collaborative perception.

[0081] Table 1 Intelligent road network medium coverage and time consumption

[0082]

[0083] The above describes in detail the preferred embodiments of the present invention. It should be understood that those skilled in the art can make numerous modifications and variations based on the concepts of the present invention without inventive effort. Therefore, any technical solutions that can be derived by those skilled in the art through logical analysis, reasoning, or limited experimentation based on the concepts of the present invention and the prior art should be within the scope of protection defined by the claims.

Claims

1. An intelligent road network medium for vehicle-road cooperative triboelectric self-driving perception, characterized by: include: Embedded high-elastic triboelectric self-driven sensing unit (1), grid-type intermediate spacer (2), upper steel pad (301), lower steel pad (302), steel pillars and springs (303), shock-absorbing foam (4), high-strength flexible nano-modified caulking layer (5) and high-strength and high-toughness waterproof packaging layer (6); The lower steel pad (302) is installed above the high-strength and high-toughness waterproof packaging layer (6); A plurality of steel pillars are fixed on the lower steel pad (302) by means of internal bolts, and springs (303) are sleeved on the steel pillars to buffer the vehicle load; The grid-type middle partition (2) is a glass fiber reinforced plastic grid fixed above the lower steel plate (302), with the center and four sides aligned with the lower steel plate (302); The steel struts and springs (303) pass through the lattice of the lattice-type intermediate partition (2); The grid-type middle partition (2) is provided with a plurality of large holes, and embedded high-elasticity triboelectric self-driven sensing units (1) are installed in the large holes; Injecting a highly elastic and highly flexible nano-modified material into the small holes of the grid-type intermediate spacer (2) to fill the original holes of the grid-type intermediate spacer (2) and form a highly strong and flexible nano-modified gap filling layer (5); A shock-absorbing foam (4) is provided on the lower surface of the upper steel backing plate (301), and is installed above the grid-type middle partition (2); The embedded high-elastic triboelectric self-driven sensing unit (1) is prepared by selecting two flexible materials with significant electronegativity differences based on triboelectric charging and electrostatic induction effects, and comprises an electrode layer (101), a positive electrode friction layer (102) and a negative electrode friction layer (103). The wiring of the positive electrode friction layer and the negative electrode friction layer is led out through a grid-type intermediate spacer and connected to an external STM 32 single-chip computer for data acquisition.

2. The intelligent road network medium for vehicle-road cooperative triboelectric self-driving perception according to claim 1 is characterized in that: The electronegativity of the negative electrode friction layer is 2.5 - 3.5, and the electronegativity of the positive electrode friction layer is 1.5-2.

5.

3. The intelligent road network medium for vehicle-road cooperative triboelectric self-driving perception according to claim 2 is characterized in that: The negative electrode friction layer material is polytetrafluoroethylene with an electronegativity of 3.0, and the positive electrode friction layer material is aluminum foil with an electronegativity of 1.

5.

4. The intelligent road network medium for vehicle-road cooperative triboelectric self-driving perception according to claim 1 is characterized in that: The height of the grid-type middle partition is 15-30 mm, the inner side length of the small hole is 25-35 mm, and the compressive strength is 30-50 MPa.

5. The intelligent road network medium for vehicle-road cooperative triboelectric self-driving perception according to claim 1 is characterized in that: The upper and lower steel plates have a yield strength of 200-800 MPa. The spring is made of a thermoplastic resin matrix and is manufactured by additive manufacturing by adding 0.05%-0.1% nanoparticles as a 3D printing composite material. The elastic coefficient is 4500-8000 N / mm.

6. The intelligent road network medium for vehicle-road cooperative triboelectric self-driving perception according to claim 1 is characterized in that: The highly flexible nano-modified material has a flexural strength of 10-15 MPa, a compressive strength of 35-60 MPa, and a rebound rate of 50-70% after curing.

7. The intelligent road network medium for vehicle-road cooperative triboelectric self-driving perception according to claim 1 is characterized in that: The high-strength and high-toughness waterproof packaging layer is composed of high-performance silica gel and nano-SiO2, wherein the weight composition is: 1000-1500 parts of sodium silicate solution and 1-3 parts of sodium hydroxide particles; The high-strength and high-toughness waterproof packaging layer has a curing time of 15-40 minutes at room temperature and a tear strength of 30-80 pli.

8. The intelligent road network medium for vehicle-road cooperative triboelectric self-driving perception according to claim 1 is characterized in that: The intelligent road network medium is buried at a depth of 0-10 cm in the road structure, and has a longitudinal spacing of 10-60 m.

9. The intelligent road network medium for vehicle-road cooperative triboelectric self-driving perception according to claim 1 is characterized in that: The installation steps are: (1) The steel support is fixed on the lower steel plate by internal bolts; (2) Cut large holes in the FRP grille according to the designed size; (3) The FRP grille is fixed on the top of the lower steel plate, and the center and four sides should be aligned with the corresponding positions of the lower steel plate; (4) Fix the flexible encapsulated triboelectric self-driven sensing unit in the large hole cut in the fiberglass grille, and lead out the signal transmission line; (5) Put a spring on top of the rigid support to cushion the vehicle load; (6) Fix elastic foam on the inner surface of the upper steel plate to protect the triboelectric self-driven sensing unit; (7) Pour highly elastic and flexible nano-modified materials into the small holes of the FRP grille to fill the original holes of the FRP grille; (8) After the pouring material solidifies, cover it with the upper steel plate and make a waterproof packaging layer on top of the upper steel plate.

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