A vehicle-road cooperative source data interactive linkage system based on public network links

Abstract

The present invention discloses a vehicle-road cooperative source data interactive linkage system based on a public network link, and specifically relates to the technical field of the Internet of Things. The system includes a roadside intelligent terminal and a vehicle-mounted receiving device, wherein the roadside intelligent terminal includes a roadside unit and a computing processing unit, and collects roadside environmental data through sensors, including video streams and sensor data, and sends them to the computing processing unit. The computing processing unit receives the data sent by the roadside unit for real-time processing and analysis, and packages the processed data and sends it to the vehicle-mounted receiving device. The vehicle-mounted receiving device is connected to the roadside intelligent terminal through a public network link, interacts with the computing processing unit, receives information from the computing processing unit for environmental analysis and behavior prediction, and sends the environmental analysis and behavior prediction results back to the computing processing unit.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and more specifically, to a vehicle-road collaborative source data interactive linkage system based on a public network link. Background Art

[0002] With the rapid development of intelligent transportation and autonomous driving technologies, vehicles need to obtain more and more accurate external environment information to achieve safe and efficient driving. Traditional vehicle perception systems mainly rely on on-board sensors, but their perception capabilities may be limited in complex environments or extreme weather. Therefore, through the information interaction between roadside intelligent terminals and vehicle terminals, the vehicle's perception of the external environment can be effectively improved, providing richer information support for assisted driving and autonomous driving.

[0003] Intelligent transportation system refers to the integration of existing transportation facilities and vehicles through modern information, communication, control and other technologies to create a safe, fast, efficient and green transportation system. ITS originated from transportation informatization and transportation engineering, and has developed rapidly around the world.

[0004] With the rapid development of information technology and communication technology, the Internet of Vehicles (IoV) has gradually emerged as an important part of intelligent transportation. IoV uses sensor devices, on-board devices and communication modules on vehicles to connect to the Internet through mobile communication technology, car navigation systems and smart terminal devices. It realizes five communication scenarios: car-to-cloud platform, car-to-car, car-to-road, car-to-people and in-car, providing environmental perception, information interaction and collaborative control capabilities for car driving and traffic management applications.

[0005] As an important part of the Internet of Vehicles and intelligent transportation, the advanced driver assistance system has made remarkable achievements in technological innovation in recent years. Breakthroughs in sensor technology, especially the fusion of high-resolution cameras, millimeter-wave radars, lidars and other sensors, have greatly improved the perception capabilities of ADAS systems. At the same time, the application of advanced algorithms such as deep learning and neural networks has enabled ADAS systems to process more complex data and achieve higher levels of driver assistance functions.

[0006] In summary, the rapid development of intelligent transportation systems, vehicle networking technology and advanced driver assistance systems has brought revolutionary changes to the transportation industry in China and even the world. Through technological innovation and policy support, we have reason to believe that future transportation will be safer, more efficient, greener and smarter. Summary of the invention

[0007] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a vehicle-road cooperative source data interactive linkage system based on a public network link, which involves the fields of intelligent transportation, vehicle networking, and autonomous driving technology to solve the problems raised in the above-mentioned background technology.

[0008] To achieve the above-mentioned purpose, the present invention provides the following technical solution: a vehicle-road cooperative source data interactive linkage system based on a public network link, the system comprising a roadside intelligent terminal and a vehicle-mounted receiving device;

[0009] Roadside intelligent terminal: It includes a roadside unit and a computing processing unit. It collects roadside environmental data, including video streams and sensor data, through sensors and sends them to the computing processing unit. The computing processing unit receives the data sent by the roadside unit for real-time processing and analysis, and packages the processed data to send to the vehicle-mounted receiving device.

[0010] On-board receiving device: connected to the roadside intelligent terminal through a public network link, interacts with the computing processing unit, receives information from the computing processing unit for environmental analysis and behavior prediction, and sends the results of environmental analysis and behavior prediction back to the computing processing unit.

[0011] In a preferred embodiment, the roadside unit is used to collect roadside environment data and send it to the computing and processing unit. The specific steps are as follows:

[0012] Step A1, data collection: using cameras and radar sensors to collect roadside environmental data, including vehicle position, speed, and driving direction, to obtain video streams and sensor data;

[0013] Step A2, data fusion: Add a timestamp to each frame of camera image and data point collected by the radar sensor to ensure that the data from different sensors are synchronized in time, and fuse and analyze them with high-precision maps and historical data, and send them to the computing processing unit.

[0014] In a preferred embodiment, in the data collection of step A1, a camera and a radar sensor are used to collect roadside environmental data, including vehicle position, speed, and driving direction, to obtain a video stream and sensor data, further comprising the following steps:

[0015] Step A101, video stream acquisition: using a camera to capture a real-time video stream of the roadside, and recording the activities of vehicles, pedestrians, traffic lights and other road users on the road;

[0016] Step A102, sensor data acquisition: install the radar sensor at an appropriate height and angle, and configure the scanning frequency and detection range, detect the surrounding environment through radar, and capture the vehicle's position, speed and driving direction information; the vehicle speed calculation formula is: ;in, is the time interval between vehicles The distance moved within, v represents the speed of the vehicle; the calculation formula for the driving direction is: ;in, The angle representing the direction of the vehicle's travel. and They respectively represent the position coordinates of the vehicle at a certain moment.

[0017] In a preferred embodiment, the computing and processing unit receives data sent by the roadside unit and performs real-time processing and analysis, including vehicle trajectory recognition, vehicle feature recognition, and vehicle event recognition, and packages the processed data and sends it to the vehicle-mounted receiving device. The specific steps are as follows:

[0018] Step B1, vehicle trajectory recognition: perform image processing on the video stream, including image enhancement and denoising, use the target detection algorithm to mark the position and bounding box of the vehicle in the image, and determine the movement trajectory of the vehicle by tracking the vehicle target;

[0019] Step B2, vehicle feature recognition: including vehicle recognition and license plate recognition. The vehicle recognition is to recognize the brand, model, and color characteristics of the vehicle; the license plate recognition is to recognize the license plate number of the vehicle, which is used to confirm and track the identity of the vehicle;

[0020] Step B3, vehicle event recognition: monitor whether the vehicle violates traffic regulations, including running a red light, speeding, and driving in the wrong direction, and warn of collision events by analyzing the relative position and speed between vehicles.

[0021] In a preferred embodiment, in the vehicle trajectory recognition of step B1, the position and bounding box of the vehicle are marked in the image using a target detection algorithm, and the movement trajectory of the vehicle is determined by tracking the vehicle target, further comprising the following steps:

[0022] Step B101, target tracking: Use the target tracking algorithm to track the position change of the vehicle target between consecutive frames, integrate the position information of each target in different frames to form the target's motion trajectory, calculate the vehicle's speed and acceleration motion parameters based on the target's position information at different time points and combined with the time series data, and define the vehicle's position data in consecutive frames as , where t is the index of the time step, representing the moment in the time series when the vehicle is tracked, and the time interval is , use the central difference method to calculate the speed and acceleration of the vehicle. The specific calculation formula is as follows:

[0023] , , ,

[0024] in, and are the vehicle's speeds in the x and y directions, respectively. and Represents the vehicle at time step The position coordinates at the moment, that is , and are the vehicle's acceleration in the x and y directions, The velocity component at time step t, and Represents the vehicle at time step The velocity component at the moment, ;

[0025] Step B102, trajectory analysis: Analyze the determined motion trajectory, extract the vehicle's driving path and speed change, and connect all the position points The trajectory of the vehicle in space is composed to intuitively show the movement path of the vehicle and the speed at each time step. , draw a curve of speed changing with time, and analyze the speed change characteristics in the moving state.

[0026] In a preferred embodiment, in the vehicle event identification of step B3, monitoring whether the vehicle violates traffic rules, including running a red light, speeding, and driving against traffic, and warning of a collision event by analyzing the relative position and speed between the vehicles, further includes the following steps:

[0027] Step B301, red light running detection: analyzing the vehicle position and the state of the traffic light, if the vehicle continues to move under the red light state, it is determined to be a violation of traffic regulations;

[0028] Step B302, speeding detection: judging whether the vehicle exceeds the speed limit based on the vehicle speed and the speed limit information of the road section;

[0029] Step B303, wrong-way detection: analyzing the driving direction of the vehicle and the prescribed direction of the road. When the vehicle is in the opposite direction to the prescribed driving direction of the road, it is determined that there is wrong-way behavior;

[0030] Step B304, collision warning: the positions of vehicles A and B are represented as and , and the speeds are expressed as and , then the relative positions of vehicles A and B are: , the relative speed is: ; Based on the relative position and speed information between the vehicles, the shortest distance between the two vehicles is calculated. When the shortest distance is less than half the length of the vehicle, a collision is determined and an early warning is issued. The specific calculation formula is as follows:

[0031]

[0032] in, is the shortest distance, is the position vector of vehicle A relative to vehicle B, is the velocity vector of vehicle A relative to vehicle B, is the magnitude of the velocity vector.

[0033] In a preferred embodiment, the vehicle-mounted receiving device is initialized when the vehicle is started, connected to the roadside intelligent terminal through a public network link, interacts with the computing processing unit, receives information from the computing processing unit for environmental analysis and behavior prediction, and sends the environmental analysis and behavior prediction results back to the computing processing unit. The specific steps are as follows:

[0034] Step C1, establishing a connection: When the vehicle is started, the on-board receiving device starts to initialize and establishes a connection with the public network link through the vehicle's on-board network;

[0035] Step C2, information interaction: Using the established public network link, the vehicle-mounted receiving device actively connects to the nearby roadside intelligent terminal, and interacts with the computing processing unit, receives information from the computing processing unit, and performs behavior prediction and environmental analysis.

[0036] In a preferred embodiment, in the step C1 of establishing a connection, establishing a connection with a public network link through the vehicle's onboard network further includes the following steps:

[0037] Step C101, identity verification: The vehicle-mounted receiving device uses RSA signature to verify the authenticity and integrity of the digital certificate provided by the public network link server. The specific formula is: ; where m is the message and s is the signature. is the server's public key;

[0038] Step C102, key exchange: After the identity authentication is successful, a shared key is generated through the Diffie-Hellman key exchange protocol, which is used to securely exchange symmetric keys on the public network link. The specific formula is as follows: ; Where g is the public basis, p is the prime modulus, and a and b are the private parameters of both parties;

[0039] Step C103, encryption: After establishing a shared key, the communicating parties use the AES symmetric encryption algorithm to protect the privacy and integrity of the data. The specific formula is: ; Where k is the shared key, M is the plaintext message, and C is the encrypted ciphertext.

[0040] In a preferred embodiment, in the information interaction of step C2, the vehicle-mounted receiving device actively connects to the nearby roadside intelligent terminal by using the established public network link, and interacts with the computing processing unit to receive information from the computing processing unit, perform environmental analysis and behavior prediction, and further includes the following steps:

[0041] Step C201, road condition detection: according to the data collected by the roadside intelligent terminal, including the images captured by the camera, the road condition is analyzed, the slippery road surface and obstacles are detected, the color image is converted into a grayscale image, and the change of the pixel grayscale value is analyzed, and a grayscale threshold is set. The area below the threshold indicates a slippery road surface:

[0042] Step C202, traffic flow prediction: Combine the vehicle position and speed data collected by the roadside intelligent terminal to predict the traffic flow in the future time period. The specific calculation formula is as follows:

[0043]

[0044] in, is the position x and time The traffic density, is the flow function, describing the density The traffic flow under yes About Time The partial derivative of represents the rate of change of traffic density over time, yes The partial derivative with respect to position x represents the rate of change of the flow function with position.

[0045] The beneficial effects of the present invention are: by real-time collection of multi-dimensional information such as video information, vehicle trajectory recognition, vehicle feature recognition, and event recognition, and high-precision fusion of this information, a more comprehensive and accurate environmental perception capability is provided for the vehicle. During data transmission, encryption technology is used to ensure the security of information, prevent data from being illegally acquired and tampered with, and accurately send the collected information to the on-board receiving device at the vehicle end through a public network link, thereby avoiding information flooding and redundancy and improving the efficiency and security of data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a system flow chart of the present invention;

[0047] Figure 2 It is a structural block diagram of the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0049] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "plurality" means two or more, unless otherwise clearly and specifically defined.

[0050] In the description of the present application, the term "for example" is used to mean "used as an example, illustration or description". Any embodiment described as "for example" in the present application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid unnecessary details to obscure the description of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present application.

[0051] Example 1

[0052] This embodiment provides Figure 1 and Figure 2 A vehicle-road cooperative source data interactive linkage system based on a public network link is shown, the system comprising a roadside intelligent terminal and a vehicle-mounted receiving device;

[0053] Roadside intelligent terminal: It includes a roadside unit and a computing processing unit. It collects roadside environmental data, including video streams and sensor data, through sensors and sends them to the computing processing unit. The computing processing unit receives the data sent by the roadside unit for real-time processing and analysis, and packages the processed data to send to the vehicle-mounted receiving device.

[0054] On-board receiving device: connected to the roadside intelligent terminal through a public network link, interacts with the computing processing unit, receives information from the computing processing unit for environmental analysis and behavior prediction, and sends the results of environmental analysis and behavior prediction back to the computing processing unit.

[0055] In this embodiment, it is specifically necessary to explain that the roadside intelligent terminal includes a roadside unit and a computing processing unit, which collects roadside environmental data, including video streams and sensor data, through sensors and sends them to the computing processing unit. The computing processing unit receives the data sent by the roadside unit and performs real-time processing and analysis, and packages and sends them to the vehicle-mounted receiving device;

[0056] Furthermore, the roadside unit is used to collect roadside environmental data and send it to the computing and processing unit, and the specific steps are as follows:

[0057] Step A1, data collection: using cameras and radar sensors to collect roadside environmental data, including vehicle position, speed, and driving direction, to obtain video streams and sensor data;

[0058] Step A2, data fusion: add a timestamp to each frame of camera image and data point collected by radar sensor to ensure that the data of different sensors are synchronized in time, and fuse and analyze with high-precision map and historical data, and send it to the computing processing unit;

[0059] Among them, in the data collection of step A1, a camera and a radar sensor are used to collect roadside environmental data, including vehicle position, speed, and driving direction, to obtain a video stream and sensor data, which further includes the following steps:

[0060] Step A101, video stream acquisition: correctly install the camera at the designated location, and configure the resolution, frame rate and viewing angle parameters, use the camera to capture the real-time video stream of the roadside, and record the activities of vehicles, pedestrians, traffic lights and other road users on the road;

[0061] Step A102, sensor data acquisition: install the radar sensor at an appropriate height and angle, and configure the scanning frequency and detection range, detect the surrounding environment through radar, and capture the vehicle's position, speed and driving direction information;

[0062] The vehicle speed calculation formula is: ;in, is the time interval between vehicles The distance moved within, v represents the speed of the vehicle;

[0063] The driving direction calculation formula is: ;in, The angle representing the direction of the vehicle's travel. and Respectively represent the position coordinates of the vehicle at a certain moment;

[0064] Furthermore, the computing and processing unit receives data sent by the roadside unit and the vehicle-mounted receiving device and performs real-time processing and analysis, including vehicle trajectory recognition, vehicle feature recognition, and vehicle event recognition, and packages the processed data and sends it to the vehicle-mounted receiving device. The specific steps are as follows:

[0065] Step B1, vehicle trajectory recognition: perform image processing on the video stream, including image enhancement and denoising, to improve image quality, use the target detection algorithm to mark the position and bounding box of the vehicle in the image, and determine the movement trajectory of the vehicle by tracking the vehicle target;

[0066] Step B2, vehicle feature recognition: including vehicle recognition and license plate recognition. The vehicle recognition is to recognize the brand, model, and color characteristics of the vehicle; the license plate recognition is to recognize the license plate number of the vehicle, which is used to confirm and track the identity of the vehicle;

[0067] Step B3, vehicle event recognition: monitor whether the vehicle violates traffic rules, including running a red light, speeding, and driving against traffic, and warn of collision events by analyzing the relative position and speed between vehicles;

[0068] Among them, in the vehicle trajectory recognition of step B1, the position and boundary box of the vehicle are marked in the image using the target detection algorithm, and the movement trajectory of the vehicle is determined by tracking the vehicle target, which further includes the following steps:

[0069] Step B101, target tracking: Use the target tracking algorithm to track the position change of the vehicle target between consecutive frames, integrate the position information of each target in different frames to form the target's motion trajectory, calculate the vehicle's speed and acceleration motion parameters based on the target's position information at different time points and combined with the time series data, and define the vehicle's position data in consecutive frames as , where t is the index of the time step, representing the moment in the time series when the vehicle is tracked, and the time interval is , use the central difference method to calculate the speed and acceleration of the vehicle. The specific calculation formula is as follows:

[0070] , , ,

[0071] in, and are the vehicle's speeds in the x and y directions, respectively. and Represents the vehicle at time step The position coordinates at the moment, that is , and are the vehicle's acceleration in the x and y directions, The velocity component at time step t, and Represents the vehicle at time step The velocity component at the moment, ;

[0072] Step B102, trajectory analysis: Analyze the determined motion trajectory, extract the vehicle's driving path and speed change, and connect all the position points The trajectory of the vehicle in space is composed to intuitively show the movement path of the vehicle and the speed at each time step. , draw a curve of speed changing with time, and analyze the speed changing characteristics in motion;

[0073] In the vehicle event identification step B3, monitoring whether the vehicle violates traffic rules, including running a red light, speeding, and driving against traffic, and warning of a collision event by analyzing the relative position and speed between vehicles, further includes the following steps:

[0074] Step B301, red light running detection: analyzing the vehicle position and the state of the traffic light, if the vehicle continues to move under the red light state, it is determined to be a violation of traffic regulations;

[0075] Step B302, speeding detection: judging whether the vehicle exceeds the speed limit based on the vehicle speed and the speed limit information of the road section;

[0076] Step B303, wrong-way detection: analyzing the driving direction of the vehicle and the prescribed direction of the road. When the vehicle is in the opposite direction to the prescribed driving direction of the road, it is determined that there is wrong-way behavior;

[0077] Step B304, collision warning: the positions of vehicles A and B are represented as and , and the speeds are expressed as and , then the relative positions of vehicles A and B are: , the relative speed is: ; Based on the relative position and speed information between the vehicles, the shortest distance between the two vehicles is calculated. When the shortest distance is less than half the length of the vehicle, a collision is determined and an early warning is issued. The specific calculation formula is as follows:

[0078]

[0079] in, is the shortest distance, is the position vector of vehicle A relative to vehicle B, is the velocity vector of vehicle A relative to vehicle B, is the magnitude of the velocity vector.

[0080] In this embodiment, it is specifically necessary to explain the vehicle-mounted receiving device, which is initialized when the vehicle is started, connected to the roadside intelligent terminal through a public network link, interacts with the computing processing unit, receives information from the computing processing unit for environmental analysis and behavior prediction, and sends the environmental analysis and behavior prediction results back to the computing processing unit. The specific steps are as follows:

[0081] Step C1, establishing a connection: When the vehicle is started, the on-board receiving device starts to initialize and establishes a connection with the public network link through the vehicle's on-board network;

[0082] Step C2, information interaction: Using the established public network link, the vehicle-mounted receiving device actively connects to the nearby roadside intelligent terminal, and interacts with the computing processing unit, receives information from the computing processing unit, and performs behavior prediction and environmental analysis;

[0083] Wherein, in the step C1 of establishing a connection, establishing a connection with a public network link through the vehicle's onboard network further includes the following steps:

[0084] Step C101, identity verification: The vehicle-mounted receiving device uses RSA signature to verify the authenticity and integrity of the digital certificate provided by the public network link server. The specific formula is: ; where m is the message and s is the signature. is the server's public key;

[0085] Step C102, key exchange: After the identity authentication is successful, a shared key is generated through the Diffie-Hellman key exchange protocol, which is used to securely exchange symmetric keys on the public network link. The specific formula is as follows: ; Where g is the public basis, p is the prime modulus, and a and b are the private parameters of both parties;

[0086] Step C103, encryption: After establishing a shared key, the communicating parties use the AES symmetric encryption algorithm to protect the privacy and integrity of the data. The specific formula is: ; Where k is the shared key, M is the plaintext message, and C is the encrypted ciphertext;

[0087] In the information interaction of step C2, the vehicle-mounted receiving device actively connects to the nearby roadside intelligent terminal by using the established public network link, and interacts with the computing processing unit to receive information from the computing processing unit, perform environmental analysis and behavior prediction, and further includes the following steps:

[0088] Step C201, road condition detection: according to the data collected by the roadside intelligent terminal, including the images captured by the camera, the road condition is analyzed, the slippery road surface and obstacles are detected, the color image is converted into a grayscale image, and the change of the pixel grayscale value is analyzed, and a grayscale threshold is set. The area below the threshold indicates a slippery road surface:

[0089] Step C202, traffic flow prediction: Combine the vehicle position and speed data collected by the roadside intelligent terminal to predict the traffic flow in the future time period. The specific calculation formula is as follows:

[0090]

[0091] in, is the position x and time The traffic density, is the flow function, describing the density The traffic flow under yes About Time The partial derivative of represents the rate of change of traffic density over time, yes The partial derivative with respect to position x represents the rate of change of the flow function with position.

[0092] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0093] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0094] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0095] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0096] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0097] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0098] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A vehicle-road cooperative source data interactive linkage system based on a public network link, characterized by: Including roadside intelligent terminals and vehicle-mounted receiving equipment; Roadside intelligent terminal: It includes a roadside unit and a computing processing unit. It collects roadside environmental data, including video streams and sensor data, through sensors and sends them to the computing processing unit. The computing processing unit receives the data sent by the roadside unit for real-time processing and analysis, and packages the processed data to send to the vehicle-mounted receiving device. The roadside unit is used to collect roadside environment data and send it to the computing and processing unit. The specific steps are as follows: Step A1, data collection: using cameras and radar sensors to collect roadside environmental data, including vehicle position, speed, and driving direction, to obtain video streams and sensor data, further comprising the following steps: Step A101, video stream acquisition: using a camera to capture a real-time video stream of the roadside, and recording the activities of vehicles, pedestrians, traffic lights and other road users on the road; Step A102, sensor data collection: detect the surrounding environment through radar to capture the vehicle's position, speed and driving direction information; the vehicle speed calculation formula is: ;in, is the time interval between vehicles The distance moved within, v represents the speed of the vehicle; the calculation formula for the driving direction is: ;in, The angle representing the direction of the vehicle's travel. and Respectively represent the position coordinates of the vehicle at a certain moment; Step A2, data fusion: add a timestamp to each frame of camera image and data point collected by radar sensor to ensure that the data of different sensors are synchronized in time, and fuse and analyze with high-precision map and historical data, and send it to the computing processing unit; The computing and processing unit receives the data sent by the roadside unit and performs real-time processing and analysis, including vehicle trajectory recognition, vehicle feature recognition, and vehicle event recognition, and packages the processed data and sends it to the vehicle-mounted receiving device. The specific steps are as follows: Step B1, vehicle trajectory recognition: perform image processing on the video stream, including image enhancement and denoising, use the target detection algorithm to mark the position and bounding box of the vehicle in the image, and determine the movement trajectory of the vehicle by tracking the vehicle target; Step B2, vehicle feature recognition: including vehicle recognition and license plate recognition. The vehicle recognition is to recognize the brand, model, and color characteristics of the vehicle; the license plate recognition is to recognize the license plate number of the vehicle, which is used to confirm and track the identity of the vehicle; Step B3, vehicle event recognition: monitoring whether vehicles violate traffic regulations, and warning of collision events by analyzing the relative positions and speeds between vehicles; On-board receiving device: connected to the roadside intelligent terminal through a public network link, interacts with the computing processing unit, receives information from the computing processing unit for environmental analysis and behavior prediction, and sends the results of environmental analysis and behavior prediction back to the computing processing unit. The specific steps are as follows: Step C1, establishing a connection: When the vehicle is started, the vehicle-mounted receiving device starts to initialize and establishes a connection with the public network link through the vehicle-mounted network, further comprising the following steps: Step C101, identity verification: The vehicle-mounted receiving device uses RSA signature to verify the authenticity and integrity of the digital certificate provided by the public network link server. The specific formula is: ; where m is the message and s is the signature. is the server's public key; Step C102, key exchange: After the identity authentication is successful, a shared key is generated through a key exchange protocol, which is used to securely exchange symmetric keys on the public network link. The specific formula is as follows: ; Where g is the public basis, p is the prime modulus, and a and b are the private parameters of both parties; Step C103, encryption: After establishing a shared key, the communicating parties use a symmetric encryption algorithm to protect the privacy and integrity of the data. The specific formula is: ; Where k is the shared key, M is the plaintext message, and C is the encrypted ciphertext; Step C2, information interaction: Using the established public network link, the vehicle-mounted receiving device actively connects to the roadside intelligent terminal, and interacts with the computing processing unit, receives information from the computing processing unit, and performs environmental analysis and behavior prediction, further including the following steps: Step C201, road condition detection: according to the data collected by the roadside intelligent terminal, including the images captured by the camera, the road condition is analyzed, the slippery road surface and obstacles are detected, the color image is converted into a grayscale image, and the change of the pixel grayscale value is analyzed, and a grayscale threshold is set. The area below the threshold indicates a slippery road surface: Step C202, traffic flow prediction: Combine the vehicle position and speed data collected by the roadside intelligent terminal to predict the traffic flow in the future time period. The specific calculation formula is as follows: in, is the position x and time The traffic density, is the flow function, describing the density The traffic flow under yes About Time The partial derivative of yes The partial derivative with respect to the position x.

2. According to the public network link-based vehicle-road cooperative source data interactive linkage system of claim 1, it is characterized by: In the vehicle trajectory recognition of step B1, the position and boundary box of the vehicle are marked in the image using a target detection algorithm, and the movement trajectory of the vehicle is determined by tracking the vehicle target, further comprising the following steps: Step B101, target tracking: Use the target tracking algorithm to track the position change of the vehicle target between consecutive frames, integrate the position information of each target in different frames to form the target's motion trajectory, calculate the vehicle's speed and acceleration motion parameters based on the target's position information at different time points and combined with the time series data, and express the vehicle's position data in consecutive frames as , where t is the index of the time step, representing the moment in the time series when the vehicle is tracked, and the time interval is , use the central difference method to calculate the velocity and acceleration. The specific calculation formula is as follows: ， ， ， in, and are the vehicle's speeds in the x and y directions, Represents the vehicle at time step The position coordinates at the time, and are the vehicle's acceleration in the x and y directions, The velocity component at time step t, Represents the vehicle at time step The velocity component of the moment; Step B102, trajectory analysis: Analyze the determined motion trajectory, extract the vehicle's driving path and speed change, and connect all the position points The motion trajectory of the vehicle is composed of the velocity at each time step , draw a curve of speed changing with time, and analyze the speed change characteristics in the moving state.

3. According to the public network link-based vehicle-road cooperative source data interactive linkage system of claim 1, it is characterized by: In the vehicle event identification step B3, whether the vehicle violates the traffic rules is monitored, and the relative position and speed between the vehicles are analyzed to warn of the collision event. The positions of vehicles A and B are represented as and , and the speeds are expressed as and , then the relative positions of vehicles A and B are: , the relative speed is: ; Based on the relative position and speed information between the vehicles, the shortest distance between the two vehicles is calculated. When the shortest distance is less than half the length of the vehicle, a collision is determined and an early warning is issued. The specific calculation formula is as follows: in, is the shortest distance, is the position vector of vehicle A relative to vehicle B, is the velocity vector of vehicle A relative to vehicle B, is the magnitude of the velocity vector.

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