System and method for intelligent traffic guidance identification based on situation awareness

Abstract

The invention provides an intelligent traffic guidance identification system based on situation awareness. The system comprises a physical guidance identification module arranged on a road surface; the monitoring sensing modules are arranged beside the road at intervals and are used for sensing, acquiring and transmitting the road traffic flow information and monitoring the working condition information of the physical guide identification module; the edge computing control system module is used for receiving the traffic flow information and the working condition information, processing the received information based on a first algorithm and transmitting the processed information; the central computing control system module receives the processed information, iterates, optimizes and upgrades the received information based on a second algorithm and transmits an instruction, and the edge computing control system module receives the instruction and controls the physical guide identification module to generate and display guide indication information. In addition, the invention also provides a corresponding method.

Description

Technical Field

[0001] This disclosure relates to the field of intelligent transportation, and more specifically, to a system and method for intelligent traffic guidance signage based on situational awareness. Background Technology

[0002] Road traffic safety and efficiency are core concerns for the transportation industry. Traffic accidents cause significant personal injury and property damage. Therefore, improving traffic safety is of great importance to the development of the transportation industry and society. Furthermore, road traffic efficiency is a long-standing problem. Traffic congestion is a common problem plaguing large cities, not only wasting passengers' time but also causing economic losses, wasting energy, and generating unnecessary carbon emissions.

[0003] For a long time, road traffic researchers and practitioners have adopted various methods to improve road traffic safety and efficiency. While existing research and practices have yielded many positive results, traffic safety and efficiency remain two major challenges plaguing road traffic. Existing research rarely examines opportunities to improve safety and efficiency from the perspective of the road itself, the medium of road traffic. Roads, as a crucial infrastructure, currently possess almost no intelligent functions.

[0004] Currently, while some projects have installed luminescent materials or LED lights on the road surface as road marking modules, this only serves a simple directional function and cannot dynamically adjust and guide drivers to alleviate traffic congestion based on the overall road traffic flow. Furthermore, these materials and LED lights are installed in modules covered by tempered glass, thus the tempered glass also becomes part of the road surface. However, in practice, it has been found that the road surface contains many small, sharp, and hard solid objects such as pebbles and sand. These solid objects, when squeezed by vehicle tires, can cause scratches and other damage to the tempered glass. This damage significantly reduces the strength and durability of the tempered glass, eventually destroying the entire piece. This not only greatly shortens the lifespan of the road and causes inconvenience to people's travel, but also increases the safety risks for travelers.

[0005] To improve road traffic safety and efficiency, this disclosure provides a system and method for intelligent traffic guidance signage based on situational awareness, which examines and improves traffic safety and efficiency from the perspective of roads. Summary of the Invention

[0006] As per this disclosure, aspects and advantages of this disclosure will be set forth in part in the following description, or may become apparent from the description or be learned by practice of the art.

[0007] This disclosure provides a system for intelligent traffic guidance signs based on situational awareness. The system includes: a physical guidance sign module installed on the road surface; a monitoring and sensing module spaced apart along the roadside to sense and acquire road traffic flow information and monitor the operational status of the physical guidance sign module, and transmit the traffic flow information and operational status information; an edge computing control system module receiving traffic flow information and operational status information from the monitoring and sensing module, processing the received information based on a first algorithm, and transmitting the processed information; and a central computing control system module receiving the processed information from the edge computing control system module, iterating and upgrading the received information based on a second algorithm, and transmitting optimized instructions to the edge computing control system module. The edge computing control system module receives the instructions and controls the physical guidance sign module to generate and display corresponding guidance information.

[0008] In some embodiments, the physical guidance sign module includes a road surface texture plate, the surface of which has multiple downwardly embedded textures.

[0009] In some embodiments, the physical guidance identification module also includes multiple LED beads embedded in the texture, and the LED matrix of multiple LED beads is controlled by the edge computing control system module.

[0010] In some embodiments, each texture contains two to three rows of LED beads.

[0011] In some embodiments, the guidance information includes the driving direction and recommended speed.

[0012] In some embodiments, when the traffic flow on urban roads does not exceed 1,500 vehicles per hour or the traffic flow on highways does not exceed 3,000 vehicles per hour, the recommended speed and / or the arrow color of the driving direction displayed by the LED light matrix is ​​green, wherein the recommended speed is the maximum speed when the lane is designed.

[0013] In some embodiments, when the traffic flow on urban roads exceeds 1,500 vehicles per hour or the traffic flow on highways exceeds 3,000 vehicles per hour, and no traffic congestion occurs, the recommended speed and / or the arrow of the driving direction displayed by the LED light matrix is ​​yellow, wherein the recommended speed is calculated by the central computing control system module.

[0014] In some embodiments, when traffic congestion occurs, the recommended speed and / or the arrow of the driving direction displayed by the LED light matrix is ​​red, wherein the recommended speed is 0.

[0015] In some embodiments, the physical guide marking module also includes a light-guiding, wear-resistant, and waterproof sol material that fills the internal space of the texture. The light-guiding, wear-resistant, and waterproof sol material fills the entire texture cavity and forms a light-guiding structure.

[0016] In some embodiments, the light guiding structure includes an optical fiber core, an optical fiber cladding, and a structural support layer.

[0017] In some embodiments, the fiber core, fiber cladding, and structural support layer are printed in three layers by a 3D printer.

[0018] In some embodiments, the fiber core is oriented towards the oncoming vehicle and the angle with the road surface is set between 10 and 70 degrees.

[0019] In some embodiments, the fiber core is made of a mixture of silicon dioxide, silicon nitride and silicon carbide in a weight ratio of 0.5:0.25:0.25.

[0020] In some embodiments, the fiber cladding material is a mixture of polyimide and fluorinated polytetrafluoroethylene in a weight ratio of 0.5:0.5.

[0021] In some embodiments, the structural support layer is made of modified epoxy resin, which is a mixture of epoxy resin, di-tert-butyl-p-cresol, phenyl benzophenone, diphenyl methyl ketone, triphenylphosphine oxide, polyesteramide microcapsules, and boron nitride in a weight ratio of 0.7:0.05:0.05:0.05:0.05:0.05:0.05.

[0022] In some embodiments, the physical guide marking module further includes a heating guide wire disposed between adjacent textures, which is heated according to a temperature sensor or a received instruction.

[0023] In some embodiments, the inner walls of the texture are covered with a reflective material.

[0024] In some embodiments, the reflective material is composed of silica nanoparticle colloid, nano-sized glass microspheres, nano-sized reflective plastic particles, aluminum powder and chromium powder in a weight ratio of 0.4:0.15:0.15:0.15:0.15.

[0025] In some embodiments, the monitoring and sensing module includes a surveillance camera and a millimeter-wave radar.

[0026] In some embodiments, the first algorithm is synthesized based on the following AI algorithms: YOLO algorithm, Kalman filter-based vehicle tracking algorithm, convolutional neural network, radar-based vehicle speed estimation algorithm, and particle filter-based data fusion algorithm.

[0027] In some embodiments, the second algorithm is synthesized based on the following AI algorithms: genetic algorithm, simulated annealing algorithm, multi-agent collaborative optimization algorithm, deep learning fusion algorithm, and Bagging ensemble learning method.

[0028] This disclosure also provides a method for intelligent traffic guidance signs based on situational awareness, the method comprising: (a) a monitoring and sensing module sensing and acquiring road traffic flow information and monitoring the working status information of physical guidance sign modules installed on the road surface, and transmitting the traffic flow information and working status information; (b) an edge computing control system module receiving the traffic flow information and working status information from the monitoring and sensing module, processing the received information based on a first algorithm, and transmitting the processed information; (c) a central computing control system module receiving the processed information from the edge computing control system module, iterating and upgrading the received information based on a second algorithm, and transmitting optimized instructions to the edge computing control system module; (d) the edge computing control system module receiving instructions from the central computing control system module, controlling the physical guidance sign modules to generate and display corresponding guidance instruction information; and repeating steps (a)-(d) to achieve iteration and upgrading of the central computing control system module.

[0029] These and other features, aspects, and advantages of this disclosure will become more readily understood with reference to the description below and the appended claims. The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments of the technology and, together with the specification, serve to explain the principles of the technology. Attached Figure Description

[0030] The present disclosure is fully and practically available with reference to the accompanying drawings, which are intended for those skilled in the art and include the best mode of making and using the system and method, as shown in the drawings:

[0031] Figure 1 A schematic diagram of a situation-aware intelligent traffic guidance signage system according to an embodiment of the present disclosure is shown;

[0032] Figure 2 A schematic diagram of the structure of a road surface textured panel according to an embodiment of the present disclosure is shown;

[0033] Figure 3 A cross-sectional view of a light guide structure according to an embodiment of the present disclosure is shown;

[0034] Figure 4 A schematic diagram of a light-guiding optical fiber according to an embodiment of the present disclosure is shown;

[0035] Figure 5 A flowchart of a method for situation-aware intelligent traffic guidance signage according to an embodiment of the present disclosure is shown. Detailed Implementation

[0036] Embodiments of this disclosure will now be described in detail with reference to the accompanying drawings, one or more examples of which are illustrated. The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any implementation described herein as “exemplary” is not necessarily to be construed as being more preferred or advantageous than other implementations. Furthermore, each example is provided by way of explanation rather than limitation. Indeed, it will be apparent to those skilled in the art that modifications and variations can be made to this technology without departing from the scope or spirit of the claimed technology. For example, features shown or described as part of one embodiment may be used with another embodiment to produce yet another embodiment. Therefore, this disclosure is intended to cover such modifications and variations within the scope of the appended claims and their equivalents. Numerical and alphabetic designations are used in the detailed description to denote features in the drawings. Similar or analogous designations in the drawings and specification are used to refer to similar or analogous parts in this disclosure.

[0037] As used herein, the terms “first,” “second,” and “third” are used interchangeably to distinguish one component from another, rather than to indicate the position or importance of a single component. The singular expressions “a,” “an,” and “the” also include plural cases, unless the context explicitly specifies otherwise. The terms “coupled,” “fixed,” “connected to,” etc., refer to direct coupling, fixing, or connection, as well as indirect coupling, fixing, or connection through one or more intermediate components or features, unless otherwise specified herein. The terms “comprising,” “including,” “constituting,” “having,” or any other variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, article, or apparatus that includes a set of features is not necessarily limited to those features, but may include features not expressly listed or other features inherent to such process, method, article, or apparatus. Furthermore, unless expressly stated to the contrary, “or” is inclusive rather than exclusive. For example, any of the following satisfy conditions A or B: A is true (or exists) and B is false (or does not exist); A is false (or does not exist) and B is true (or exists); and both A and B are true (or exist).

[0038] Terms indicating approximation, such as “approximately,” “roughly,” “approximately,” or “substantially,” include values ​​that are within 10% larger or smaller than the described value. When used in the context of angles or directions, these terms include values ​​that are within 10 degrees larger or smaller than the described angle or direction. For example, “roughly vertical” includes directions that deviate from vertical by within 10 degrees in any direction (e.g., clockwise or counterclockwise).

[0039] The benefits, other advantages, and solutions to problems are described below with reference to specific embodiments. However, the benefits, advantages, solutions to problems, and any features that may cause any benefit, advantage, or solution to occur or become more apparent should not be construed as key, necessary, or essential features of any or all claims.

[0040] List of reference numerals: 1000-System; 110-5G base station; 200-Physical guidance sign module; 202-Fastener; 210-Road texture plate; 220-Texture; 230-Space; 240-LED light bead; 242-Light emission angle; 250-LED light matrix; 252-Driving direction; 254-Recommended vehicle speed; 260-Light-guiding wear-resistant and waterproof sol-gel material; 270-Light-guiding structure; 272-Fiber optic core; 274-Fiber optic cladding; 276-Structural support layer; 278-Light-guiding fiber; 280-Reflective material; 290-Heating guide wire; 300-Monitoring and sensing module; 310-Monitoring camera; 320-Millimeter-wave radar; 400-Edge computing control system module; 500-Central computing control system module; 5000-Method; 5010-5040 Steps.

[0041] Figure 1 A schematic diagram of a situation-aware intelligent traffic guidance signage system 1000 according to an embodiment of the present disclosure is shown. The system 1000 includes a physical guidance signage module 200, a monitoring and sensing module 300, an edge computing control system module 400, and a central computing control system module 500.

[0042] The physical guidance sign module 200 is installed on the road surface. For example, the integrated physical guidance sign module 200 can be installed on the road surface using several fasteners 202. The physical guidance sign module 200 includes a road texture plate 210. Figure 2 A schematic diagram of the road texture panel 210 according to an embodiment of the present disclosure is shown, wherein sub-figure (a) is a top view and sub-figure (b) is a partial cross-sectional view. The size and style of the road texture panel 210 can be customized. The road texture panel 210 does not require tempered glass coverage. In this invention, the surface of the road texture panel 210 may be formed with multiple downwardly embedded textures 220, as shown in sub-figure (b). In some embodiments, each texture 220 has a width of 2-5 mm and a depth of 3-15 mm. The spacing between the textures 220 is 5-20 mm. The intricate and complex road texture panel 210 can be realized with the help of 3D printing technology. The downwardly embedded textures 220 form a space 230 for accommodating the various components of the physical guide sign module 200, which will be discussed below.

[0043] The physical guide sign module 200 may further include a plurality of LED beads 240 embedded within the texture 220. The plurality of LED beads 240 are arranged in an LED matrix 250. The LED matrix 250 is not limited to... Figure 2 The matrix arrangement is shown. LED beads 240 are embedded within the road texture plate 210, preventing direct contact with vehicle tires. The road texture plate 210 protects the LED beads 240, enhancing its practicality. Two to three rows of LED beads 240 are arranged within each texture 220. For example, Figure 2 Sub-figure (b) shows three rows of LED beads 240. When one row of LED beads 240 fails and cannot display, the other two rows of LED beads 240 can continue to work, thus preventing the LED matrix 250 from failing to light up due to the failure of one row of LED beads 240 and improving its fault tolerance.

[0044] The LED light matrix 250 is controlled by an edge computing control system module 400 connected to the physical guidance sign module 200 to display guidance information. Based on real-time traffic situation perception, the LED light matrix 250 displays guidance information such as reasonable and safe driving direction 252 and recommended speed 254 calculated by the central computing control system module 500. Figure 2 The LED light matrix displaying the driving direction 252 is shown schematically. The LED light matrix displaying the recommended speed 254 may be located within the dashed box. Alternatively, the recommended speed 254 may be displayed in conjunction with the driving direction 252.

[0045] In some embodiments, when the preceding lane is unobstructed (e.g., traffic flow of no more than 1,500 vehicles per hour on urban roads or no more than 3,000 vehicles per hour on highways), the recommended speed 254 can be the maximum speed designed for the lane. For example, 60-80 km / h for urban lanes and 100-120 km / h for highways. The arrows displaying the recommended speed 254 and / or the driving direction 252 on the LED light matrix 250 are green. The recommended speed 254 and the driving direction 252 can be displayed simultaneously, displayed at different times, or only one of them can be displayed.

[0046] In some embodiments, when there is heavy traffic in the lane ahead (more than 1,500 vehicles per hour on urban roads, and more than 3,000 vehicles per hour on highways), and traffic congestion is likely to occur but has not yet occurred, the recommended speed 254 is calculated by the central computing and control system module 500. The recommended speed 254 and / or the arrow indicating the driving direction 252 displayed on the LED light matrix 250 are yellow, reminding the driver to pay attention to their speed or change lanes to avoid exacerbating the congestion ahead.

[0047] In some embodiments, when traffic congestion occurs in the lane ahead, the recommended speed 254 is displayed as 0. The recommended speed 254 and / or the arrow for the driving direction 252 displayed on the LED light matrix 250 are red to remind the driver that traffic congestion has occurred ahead and to change lanes. In this disclosure, "traffic congestion" refers to a speed below 15-20 km / h.

[0048] Those skilled in the art will understand that the appropriate range for clear lanes, areas prone to traffic congestion but not yet congested, and areas of congestion can be set by the operator based on vehicle speed or traffic flow, depending on road type, local regulations, or actual conditions. The methods for displaying guidance information are not limited to those described above. Guidance information is primarily used to guide vehicles on the road to their destination efficiently and safely, serving as an early warning system to help drivers avoid congested sections or recommend optimal speeds and / or directions in congested areas.

[0049] The physical guidance sign module 200 also includes a light-guiding, wear-resistant, and waterproof adhesive material 260 filling the internal space 230 of the texture 220. Except for the multiple LED beads 240 embedded between the textures 220, the remaining space inside the texture 220 is filled with the light-guiding, wear-resistant, and waterproof adhesive material 260. The light-guiding, wear-resistant, and waterproof adhesive material 260 fills the entire texture cavity, forming a light-guiding structure 270. In some embodiments, the light-guiding, wear-resistant, waterproof, and heat-dissipating adhesive material 260 is printed using a high-precision 3D printer to form the light-guiding structure 270, which fills the entire texture cavity. Because the light-guiding structure 270 is realized through 3D printing technology, its external structural dimensions match the internal structure of the texture 220 that needs to be filled, allowing it to be directly installed inside the texture 220 and bonded with adhesive. Although Figure 2 Sub-figure (b) schematically marks the light guide structure 270 with a dashed box; however, the filler located between the LED beads 240 and between the inner wall of the texture 220 and the LED beads 240 also constitutes part of the light guide structure 270.

[0050] Figure 3 A cross-sectional view of a light guide structure 270 according to an embodiment of the present disclosure is shown. Figure 3As shown, the light guiding structure 270 includes an optical fiber core 272, an optical fiber cladding 274, and a structural support layer 276. The optical fiber core 272 is the core component for optical signal transmission, made of a high-refractive-index material, with a diameter ranging from a few micrometers to tens of micrometers. The optical fiber cladding 274 is a layer of material surrounding the optical fiber core 272, its function being to maintain the propagation of the optical signal within the optical fiber core 272, achieved by controlling the refractive index difference. The structural support layer 276 supports the entire light guiding wear-resistant and waterproof sol-gel material 260. Its high strength, high rigidity, chemical stability, and waterproofness can resist traffic loads and complex environmental changes, providing strong protection for the optical fiber core 272 and the optical fiber cladding 274.

[0051] In some embodiments, the light guide structure 270 can be printed in three layers by a high-precision 3D printer.

[0052] First, the optical fiber core 272 is printed. In some embodiments, the material of the optical fiber core 272 can be a mixture of silicon dioxide, silicon nitride, and silicon carbide in a weight ratio of 0.5:0.25:0.25. Silicon dioxide, silicon nitride, and silicon carbide all have high refractive indices. Silicon nitride has high hardness, high melting point, and good wear resistance. Silicon carbide has excellent corrosion resistance and excellent thermal conductivity. Therefore, the optical fiber core 272 of this disclosure can adapt to the complex and ever-changing environmental conditions of roads and maintain its ability to transmit optical signals efficiently.

[0053] The optical fiber core 272 is printed using the same material as the optical fiber core 272. The light guiding angle of the optical fiber core 272 is determined by ergonomics. The optical fiber core 272 faces the direction of oncoming traffic, and the angle between the optical fiber core 272 and the road surface is controlled between 10 degrees and 70 degrees, so that the light emission angle 242 of the LED bead 240 is within the range of 10 degrees to 70 degrees.

[0054] Next, the fiber cladding 274 is printed. In some embodiments, the fiber cladding 274 is made of a mixture of polyimide and fluorinated polytetrafluoroethylene in a weight ratio of 0.5:0.5. Both polyimide and fluorinated polytetrafluoroethylene have low refractive indices, providing better fiber coupling and transmission performance, maintaining the propagation of optical signals within the fiber core 272. Polyimide has high mechanical strength, while fluorinated polytetrafluoroethylene has good corrosion resistance and thermal stability. Therefore, the fiber cladding 274 of this disclosure can be widely applied to more complex environmental conditions.

[0055] The fiber cladding 274 is printed using the same material as the fiber cladding 274, so that the fiber cladding 274 tightly wraps around the fiber core 272. The fiber core 272 and the fiber cladding 274 together form the light-guiding fiber 278. Figure 4 A schematic diagram of a light-guiding fiber 278 according to an embodiment of the present disclosure is shown. (As shown) Figure 4As shown, the angle between the optical fiber 278 and the road surface is the light emission angle 242.

[0056] Finally, the structural support layer 276 is printed. In some embodiments, the material of the structural support layer 276 can be a modified epoxy resin. In some embodiments, the modified epoxy resin can be a mixture of epoxy resin, di-tert-butyl-p-cresol, phenyl benzophenone, diphenyl methyl ketone, triphenylphosphine oxide, polyesteramide microcapsules, and boron nitride in a weight ratio of 0.7:0.05:0.05:0.05:0.05:0.05. Because epoxy resin has high strength and rigidity, it can provide excellent structural support and compressive strength, thus protecting the internal optical fiber core 272 and LED light matrix 250 from damage by traffic loads. Epoxy resin also has high transparency, chemical resistance, heat resistance, and corrosion resistance, but its antioxidant and UV resistance are poor. Therefore, di-tert-butyl-p-cresol, phenyl benzophenone, diphenyl methyl ketone, and triphenylphosphine oxide are added to improve its corresponding properties. Di-tert-butyl-p-cresol can significantly improve its antioxidant properties. Phenylacetone is a highly efficient UV absorber that can improve the UV resistance of modified epoxy resins. Diphenyl methyl ketone is a light stabilizer that can improve the stability of epoxy resins under natural light. Triphenylphosphine oxide is a secondary antioxidant that can improve the oxidative durability of epoxy resins. Polyesteramide microcapsules are abrasion-resistant enhancers that can significantly improve abrasion resistance to cope with long-term tire friction when applied to roads. Boron nitride is a material with high thermal conductivity, excellent thermal conductivity, and high transparency; boron nitride nanoparticles are used as thermally conductive fillers in epoxy resins to improve heat dissipation. None of the above additives affect the high transparency of the epoxy resin.

[0057] The structural support layer 276 is printed using the same material as the structural support layer 276. The structural support layer 276 tightly wraps around the optical fiber cladding 274, and its final external dimensions and shape are printed according to the internal spatial dimensions and shape of the texture 220 and the dimensions and shape of the LED matrix 250. The material of the structural support layer 276 is the same material that will directly contact vehicles above the road surface. The LED beads 240 and the light guide structure 270 are distributed inside the road surface texture 220, effectively avoiding direct contact with vehicle tires. Compared to traditional methods such as covering the light-emitting components with tempered glass, the structure disclosed herein effectively protects the light-emitting and light-guiding elements, improving practicality and durability.

[0058] In some embodiments, the physical guidance sign module 200 may further include reflective material 280 covering the inner wall of the texture 220 of the road surface texture plate 210, such as... Figure 2As shown in sub-figure (b), in some embodiments, the reflective material 280 is a light-reflecting spray material. In some embodiments, the reflective material 280 is composed of a mixture of silica nanoparticle colloid, nano-sized glass microspheres, nano-sized reflective plastic particles, aluminum powder, and chromium powder in a weight ratio of 0.4:0.15:0.15:0.15:0.15. When using the above-mentioned proportion of materials, simply aim the spray at the surface to be sprayed and spray evenly. The silica nanoparticle colloid has a large specific surface area and surface activity, which can increase the viscosity and adhesion of the solid spray. Since the latter four materials constituting the spray all have high light reflectivity and the reflective particles have a small particle size, they are easily and evenly dispersed in the spray material to achieve optical effects. In addition, aluminum powder and chromium powder have good durability and corrosion resistance, and can maintain the reflective effect under various environmental conditions, providing long-term visibility.

[0059] In some embodiments, the physical guidance marking module 200 may further include a heating guide wire 290 pre-embedded within the road texture plate 210. For example... Figure 2 As shown in sub-figure (b), the heating guide wire 290 is located between adjacent textures 220. Since the heating guide wire 290 is embedded in the road surface, it can be heated in cold or snowy weather to remove ice and snow, thus ensuring that the physical guidance sign module 200 can still function normally in harsh cold weather. The heating guide wire 290 can be automatically heated by a temperature sensor, or it can be heated by receiving instructions from the edge computing control system module 400.

[0060] The physical guidance sign module 200 is wirelessly or wiredly connected to the control terminal (including the edge computing control system module 400 and / or other existing control terminals integrated into the system 1000) of the intelligent traffic sign (including the physical guidance sign module 200 and / or existing traffic lights integrated into the system 1000). It can receive commands from the control terminal to change the sign's pattern, color, numbers, and other guidance information, guiding and influencing driver behavior, thereby improving traffic safety and efficiency. The physical guidance sign module 200 features rich information expression, easy installation, waterproofing, anti-slip properties, pressure resistance, wear resistance, durability, heat dissipation, and fault tolerance, making it highly suitable for large-scale application on highways and a key technological equipment for intelligent transportation.

[0061] The monitoring and sensing module 300 is installed beside the road and can be set up at intervals along the road. The monitoring and sensing module 300 is used to sense and acquire road traffic flow information and monitor the operational status of the physical guidance sign module 200, and transmits the acquired information to the edge computing control system module 400 via wired or wireless means. In some embodiments, the monitoring and sensing module 300 includes a monitoring camera 310 and a millimeter-wave radar 320, which are installed beside the road. In some embodiments, the monitoring and sensing module 300 may also include a light sensor, a temperature sensor, a humidity sensor, a water level sensor, a snow level sensor, and an infrared sensor for detecting the presence of people and animals.

[0062] The millimeter-wave radar 320 can perceive and acquire road traffic flow information around the clock. The monitoring camera 310 can not only acquire traffic flow information, but also monitor the working status of the physical guidance sign module 200 in real time. Together with the edge computing control system module 400, it forms a performance assurance and error correction system to prevent the physical guidance sign module 200 from displaying incorrect information and misleading drivers.

[0063] The edge computing control system module 400 receives traffic flow information and operational status information from the monitoring and sensing module 300 and the physical guidance sign module 200. In some embodiments, the edge computing control system module 400 may embed algorithms. In a preferred embodiment, it is based on the synthesis of the following algorithms: YOLO algorithm, Kalman filter-based vehicle tracking algorithm, convolutional neural network, radar-based vehicle speed estimation algorithm, and particle filter-based data fusion algorithm. The edge computing control system module 400 processes the received information based on the above algorithms and transmits the processed information to the central computing control system module 500 connected to it via a wired or wireless network (e.g., 5G base station 110). The edge computing control system module 400 can also receive and parse instructions issued by the central computing control system module 500 and control the physical guidance sign module 200 to generate corresponding guidance indication information.

[0064] The YOLO algorithm can be used to detect vehicles on the road in real time and obtain their positions and bounding boxes. A Kalman filter-based vehicle tracking algorithm tracks the motion trajectories of vehicles in video to obtain information such as vehicle speed and acceleration. Convolutional neural networks classify vehicles to count the traffic flow of different vehicle types. A radar-based vehicle speed estimation algorithm estimates vehicle speeds to count traffic flow. A particle filter-based data fusion algorithm integrates multiple data sources, such as video (from surveillance camera 310) and radar (from millimeter-wave radar 320), to improve the accuracy of traffic flow statistics.

[0065] The edge computing control system module 400 works in conjunction with the monitoring camera 310 to form a performance assurance and error correction system. When the edge computing control system module 400 detects that intelligent traffic signs (including physical guidance sign modules 200 and / or existing traffic lights integrated into the system 1000) are not functioning properly, it can control and promptly shut down the corresponding intelligent traffic signs, thereby avoiding the transmission of incorrect information to drivers and causing traffic accidents.

[0066] The central computing control system module 500 receives information processed by the edge computing control system module 400 from the edge computing control system module 400. The central computing system module 500 is equipped with an intelligent traffic optimization program, which can continuously iterate and upgrade the program through information collection and feedback to optimize issued instructions, thereby improving road traffic safety and efficiency. In some embodiments, the intelligent traffic optimization program may be based on the synthesis of the following algorithms: genetic algorithm, simulated annealing algorithm, multi-agent collaborative optimization algorithm, deep learning fusion algorithm, and Bagging ensemble learning method.

[0067] Genetic algorithms optimize traffic signal control (e.g., the guidance information displayed by the physical guidance sign module 200) by statistically analyzing traffic flow. Simulated annealing algorithms can perform local searches within the search space to further optimize the solution. Multi-agent cooperative optimization algorithms decompose the traffic network into multiple agents, each representing a traffic node or region, and then use cooperative optimization algorithms, such as cooperative Q-learning and particle swarm optimization, to enable the agents to optimize traffic flow through communication and cooperation. This method can achieve overall traffic flow cooperative optimization, considering the mutual influence between nodes. Deep learning fusion algorithms model traffic flow using a reinforcement learning framework, train agents, provide representations of states and actions, and iteratively upgrade based on feedback data to optimize signal control decisions (e.g., the control of guidance information displayed by the physical guidance sign module 200 and traffic lights), such as dynamically adjusting recommended vehicle speeds and dynamically adjusting traffic light timing to maximize traffic flow efficiency and safety. Bagging ensemble learning can integrate the above algorithms.

[0068] In some embodiments, the physical guidance sign module 200, the monitoring and sensing module 300, and the edge computing system module 400 can be arranged and installed continuously or intermittently on the road and / or beside the road according to a certain pattern, and powered by the municipal power grid. The central computing system module 500 manages all intelligent traffic signs within a certain area of ​​roads.

[0069] Figure 5A flowchart of a situational awareness-based intelligent traffic guidance signage method 5000 according to an embodiment of this disclosure is shown. At 5010, a monitoring and sensing module 300 senses and acquires road traffic flow information and monitors the operational status information of physical guidance signage modules 200 installed on the road surface, and transmits the traffic flow information and operational status information. At 5020, an edge computing control system module 400 receives the traffic flow information and operational status information from the monitoring and sensing module 300, processes the received information based on a first algorithm, and transmits the processed information. At 5030, a central computing control system module 500 receives the processed information from the edge computing control system module 400, iterates and upgrades the received information based on a second algorithm, and transmits optimized instructions to the edge computing control system module 400. At 5040, the edge computing control system module 400 receives instructions from the central computing control system module 500 to control the physical guidance signage modules 200 to generate and display corresponding guidance indication information. Steps 5010-5040 are repeated to achieve iteration and upgrading of the central computing control system module 500.

[0070] The situational awareness-based intelligent traffic guidance signage system 1000 and method 5000 disclosed herein intelligentize roads by collecting and feeding back road information, enabling coordinated operation of roads, vehicles, and pedestrians, thereby improving road traffic safety and operational efficiency. The system 1000 can dynamically adjust based on overall road traffic flow to guide drivers in predicting speed and direction, thus alleviating traffic congestion. The complete set of road-based intelligent distributed traffic signs disclosed herein features high practicality, wide applicability, low carbon footprint and energy saving, convenient installation and disassembly, high safety, and good durability. This disclosure can be applied to highway design and can provide guidance to road planners, highway designers and construction personnel, automobile manufacturers, and autonomous driving solution providers.

[0071] This specification uses examples to disclose this disclosure, including best practices, and to enable any person skilled in the art to practice this disclosure, including making and using any device or system and methods of performing any combination. The patent scope of this disclosure is defined by the claims, but may include other examples that would occur to a person skilled in the art. The scope of the claims covers such other examples if they include structural elements that are not distinct from the literal expression of the claims, or if they include equivalent structural elements that are not substantially different from the literal expression of the claims.

Claims

1. A system for intelligent traffic guidance signage based on situational awareness, the system comprising: A physical guidance sign module, wherein the physical guidance sign module is installed on the road surface; A monitoring and sensing module is installed at intervals along the roadside to sense and acquire road traffic flow information and monitor the working status information of the physical guidance sign module, and transmit the traffic flow information and the working status information. An edge computing control system module receives traffic flow information and working status information from the monitoring and sensing module, processes the received information based on a first algorithm, and transmits the processed information. The central computing control system module receives the processed information from the edge computing control system module, iterates and upgrades the received information based on a second algorithm, and sends optimized instructions to the edge computing control system module. The edge computing control system module receives the instruction and controls the physical guidance identifier module to generate and display corresponding guidance instruction information.

2. The system according to claim 1, wherein, The physical guidance sign module includes a road surface texture board, the surface of which has multiple downwardly embedded textures.

3. The system according to claim 2, wherein, The physical guidance identification module also includes multiple LED beads embedded in the texture, and the LED matrix of the multiple LED beads is controlled by the edge computing control system module.

4. The system according to claim 3, wherein, Each texture contains 2 to 3 rows of LED beads.

5. The system according to claim 3, wherein, The guidance information includes the driving direction and recommended speed.

6. The system according to claim 5, wherein, When the traffic flow on urban roads does not exceed 1,500 vehicles per hour or the traffic flow on highways does not exceed 3,000 vehicles per hour, the recommended speed and / or the arrow color of the driving direction displayed by the LED light matrix is ​​green, wherein the recommended speed is the maximum speed designed for the lane.

7. The system according to claim 5, wherein, When the traffic flow on urban roads exceeds 1,500 vehicles per hour or on highways exceeds 3,000 vehicles per hour, and there is no traffic congestion, the recommended speed and / or the arrow indicating the direction of travel displayed by the LED light matrix will be yellow, wherein the recommended speed is calculated by the central computing and control system module.

8. The system according to claim 5, wherein, When traffic congestion occurs, the recommended speed and / or the arrow indicating the direction of travel displayed by the LED light matrix will be red, wherein the recommended speed is 0.

9. The system according to claim 3, wherein, The physical guide marking module also includes a light-guiding, wear-resistant, and waterproof sol material that fills the internal space of the texture. The light-guiding, wear-resistant, and waterproof sol material fills the entire texture cavity and forms a light-guiding structure.

10. The system according to claim 9, wherein, The light guiding structure includes an optical fiber core, an optical fiber cladding, and a structural support layer.

11. The system according to claim 10, wherein, The optical fiber core, the optical fiber cladding, and the structural support layer are printed in three layers by a 3D printer.

12. The system according to claim 10, wherein, The fiber core is oriented towards the oncoming vehicle and the angle with the road surface is set between 10 and 70 degrees.

13. The system according to claim 10, wherein, The optical fiber core is made of a mixture of silicon dioxide, silicon nitride and silicon carbide in a weight ratio of 0.5:0.25:0.

25.

14. The system according to claim 10, wherein, The cladding material of the optical fiber is a mixture of polyimide and fluorinated polytetrafluoroethylene in a weight ratio of 0.5:0.

5.

15. The system according to claim 10, wherein, The structural support layer is made of modified epoxy resin, which is a mixture of epoxy resin, di-tert-butyl-p-cresol, phenyl benzophenone, diphenyl methyl ketone, triphenylphosphine oxide, polyesteramide microcapsules, and boron nitride in a weight ratio of 0.7:0.05:0.05:0.05:0.05:0.05:0.

05.

16. The system according to claim 2, wherein, The physical guidance identification module also includes heating guide wires disposed between adjacent textures, which are heated according to a temperature sensor or the received command.

17. The system according to claim 2, wherein, The inner walls of the texture are covered with a reflective material.

18. The system according to claim 17, wherein, The reflective material is composed of silica nanoparticle colloid, nano-sized glass microspheres, nano-sized reflective plastic particles, aluminum powder and chromium powder in a weight ratio of 0.4:0.15:0.15:0.15:0.

15.

19. The system according to claim 2, wherein, The monitoring and sensing module includes a surveillance camera and a millimeter-wave radar.

20. The system according to claim 1, wherein, The first algorithm is synthesized based on the following AI algorithms: YOLO algorithm, Kalman filter-based vehicle tracking algorithm, convolutional neural network, radar-based vehicle speed estimation algorithm, and particle filter-based data fusion algorithm.

21. The system according to claim 1, wherein, The second algorithm is based on the following AI algorithms: genetic algorithm, simulated annealing algorithm, multi-agent collaborative optimization algorithm, deep learning fusion algorithm, and Bagging ensemble learning method.

22. A method for intelligent traffic guidance signage based on situational awareness, the method comprising: (a) The monitoring and sensing module senses and acquires road traffic flow information and monitors the working status information of the physical guidance sign modules set on the road surface, and transmits the traffic flow information and the working status information. (b) The edge computing control system module receives the traffic flow information and the working status information from the monitoring and sensing module, processes the received information based on the first algorithm, and transmits the processed information. (c) The central computing control system module receives the processed information from the edge computing control system module, iterates and upgrades the received information based on the second algorithm, and sends optimized instructions to the edge computing control system module. (d) The edge computing control system module receives the instruction from the central computing control system module and controls the physical guidance identification module to generate and display the corresponding guidance instruction information; and Repeat steps (a)-(d) to iterate and upgrade the central computing control system module.

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