A city traffic platoon cooperative control system and method based on vehicle networking

Abstract

The application provides a kind of city traffic platoon cooperative control system and method based on Internet of Vehicles, it is related to city traffic platoon cooperative control field, including adaptive platoon control unit, the adaptive platoon control unit at least includes: first control mode, when the communication quality evaluation index is higher than first threshold value, using the distributed cooperative control strategy based on full state information sharing;Second control mode, when the communication quality evaluation index is lower than the first threshold value but higher than second threshold value, using the reduced-order cooperative control strategy based on state prediction compensation;Third control mode, when the communication quality evaluation index is lower than the second threshold value, using the following control strategy based on single vehicle autonomous decision.The application can configure corresponding control mode according to actual communication bearing capacity when there is packet loss and delay variation in communication link, to reduce the queue stability risk caused by the mismatch of control mode and communication state.

Description

Technical Field

[0001] This invention relates to the field of urban traffic formation cooperative control, and more specifically, to an urban traffic formation cooperative control system and method based on the Internet of Vehicles. Background Technology

[0002] With the rapid development of the Internet of Vehicles (IoV) and intelligent connected vehicle technologies, platooning, as an advanced traffic organization method that achieves multi-vehicle coordinated following through vehicle-to-vehicle communication, has been proven to significantly improve road capacity, reduce energy consumption, and enhance driving safety. In urban traffic scenarios, the application of platooning control technology is of great value in alleviating traffic congestion and reducing stopping delays at signalized intersections.

[0003] Existing vehicle platooning control systems typically employ a distributed control architecture based on V2V (Vehicle-to-Vehicle) communication. Vehicles within the platoon coordinate control by periodically broadcasting their position, speed, acceleration, and other status information. However, V2V communication links in urban traffic environments are affected by multiple factors, including building obstruction, signal attenuation, multipath effects, and competition for communication resources. These factors commonly result in non-ideal communication characteristics such as data packet loss, random latency, and communication interruptions. Research indicates that communication latency and data packet loss significantly reduce the control accuracy of vehicle platooning, leading to speed matching deviations or uncontrolled distances, ultimately compromising platoon stability.

[0004] While existing technologies have proposed various platooning control methods, such as intelligent connected vehicle platooning control based on vehicle-road cooperation, which divides intersection segments into different functional zones and implements speed regulation control, and fixed-time convergence platooning control systems based on dynamic event triggering mechanisms to reduce communication burden, these solutions all assume that the V2V communication link is in an ideal or near-ideal state. They do not fully consider the substantial impact of non-ideal communication characteristics in complex urban environments on platooning cooperative control, and also lack adaptive adjustment mechanisms for dynamic changes in communication quality.

[0005] Therefore, how to ensure the platooning stability and collaborative control accuracy of vehicles in urban traffic environments where V2V communication suffers from non-ideal characteristics such as packet loss and latency is a technical problem that urgently needs to be solved in this field.

[0006] Therefore, we have made improvements to this by proposing a collaborative control system and method for urban traffic platooning based on the Internet of Vehicles. Summary of the Invention

[0007] The purpose of this invention is to address the problem that most existing formation control methods do not fully consider the substantial impact of non-ideal communication characteristics in complex urban environments on formation cooperative control, and also lack adaptive adjustment mechanisms for dynamic changes in communication quality. In urban traffic environments where V2V communication has non-ideal characteristics such as packet loss and latency, the stability of vehicle formations and the accuracy of cooperative control are reduced.

[0008] To achieve the above-mentioned objectives, this invention provides a vehicle-to-everything (V2X)-based urban traffic platooning cooperative control system and method to solve the aforementioned problems.

[0009] The application is as follows: A vehicle-to-everything (V2X) based urban traffic platooning cooperative control system includes: The vehicle-mounted sensing and communication module is installed on each vehicle in the formation to collect the vehicle's motion status information and interact with other vehicles in the formation through the vehicle network; The edge computing and collaborative control module is communicatively connected to the vehicle-side perception and communication module. It is used to receive motion status information of each vehicle, generate formation control commands based on the information, and send the formation control commands to the vehicle-side perception and communication modules of each vehicle. The edge computing and collaborative control module includes: The communication quality assessment unit is used to monitor the packet loss rate and communication latency of the vehicle-to-everything (V2X) communication link in real time and generate communication quality assessment indicators. An adaptive formation control unit, connected to the communication quality assessment unit, is used to dynamically adjust the formation control strategy according to the communication quality assessment index. The adaptive formation control unit includes at least: In the first control mode, when the communication quality evaluation index is higher than the first threshold, a distributed collaborative control strategy based on full-state information sharing is adopted. In the second control mode, when the communication quality evaluation index is lower than the first threshold but higher than the second threshold, a reduced-order collaborative control strategy based on state prediction compensation is adopted. In the third control mode, when the communication quality assessment index is lower than the second threshold, a follow control strategy based on vehicle autonomous decision-making is adopted.

[0010] As a preferred technical solution of this application, the vehicle-side perception and communication module includes: The vehicle status acquisition unit is used to acquire the vehicle's real-time position, speed, acceleration, heading angle, and accelerator / brake pedal opening information through on-board sensors; The V2V communication unit is used to interact with other vehicles in the formation via the vehicle-to-vehicle network, receive motion status information from other vehicles, and broadcast the motion status information of its own vehicle.

[0011] As a preferred technical solution of this application, it also includes a roadside communication and computing module, which is set on the side of the urban road to obtain the status of traffic lights at intersections and communicates with the vehicle-side perception and communication module and the edge computing and collaborative control module. The roadside communication and computing module includes: Roadside sensing units are used to collect the status of traffic lights at intersections; The roadside communication unit is used to interact with the vehicle-side perception and communication modules of the platooned vehicles within the coverage area; The roadside computing unit is used to preprocess the intersection traffic light status collected by the roadside sensing unit, generate roadside traffic environment information, and send it to the edge computing and collaborative control module. The edge computing and collaborative control module is also used to assist in correcting the formation control commands based on the roadside traffic environment information.

[0012] As a preferred technical solution of this application, the communication quality assessment unit includes: The packet loss rate calculation subunit is used to calculate the packet loss rate of vehicle-to-everything (V2X) communication according to a preset time window. The delay measurement subunit is used to measure the round-trip delay and one-way delay of data packet transmission in vehicle-to-everything (V2X) communication. The comprehensive evaluation subunit is used to generate the communication quality evaluation index according to the packet loss rate and the communication delay, based on a preset weighted fusion rule.

[0013] As a preferred technical solution of this application, in the first control mode, the adaptive formation control unit implements distributed cooperative control in the following manner: Based on the motion state information of all vehicles in the formation, construct the overall dynamic model of the formation; Based on the aforementioned dynamic model, the model predictive control algorithm is used to calculate the desired acceleration of each vehicle; The desired acceleration is sent as the formation control command to each corresponding vehicle.

[0014] As a preferred technical solution of this application, in the second control mode, the adaptive formation control unit adopts the following method to achieve reduced-order cooperative control: Based on historical motion state information and the aforementioned communication quality evaluation indicators, a Kalman filter algorithm is used to predict and compensate for unreceived vehicle state information. Based on the predicted and compensated state information, the expected acceleration of each vehicle is calculated using a pre-set car-following model; The desired acceleration is sent as the formation control command to each corresponding vehicle.

[0015] As a preferred technical solution of this application, in the third control mode, the adaptive formation control unit achieves autonomous decision-making and following control for a single vehicle in the following manner: Each vehicle's onboard perception and communication module obtains the relative distance and relative speed of the vehicle ahead through onboard sensors. Each vehicle calculates its desired acceleration based on the relative distance and relative speed using a constant distance following strategy; Each vehicle performs autonomous car-following control based on the desired acceleration.

[0016] As a preferred technical solution of this application, it also includes: The formation disbanding management module is connected to the edge computing and collaborative control module. It is used to determine the formation disbanding conditions and initiate formation disbanding when the formation disbanding conditions are met. The formation disbandment condition is: the communication quality assessment index of any vehicle in the formation and the lead vehicle is continuously lower than the second threshold for a period of time exceeding a preset time threshold.

[0017] A method for cooperative platooning control of urban traffic based on vehicle-to-everything (V2X) communication includes the following steps: Step S1: Collect motion status information of each vehicle in the formation through the vehicle-side sensing and communication module, and share the motion status information among the vehicles in the formation through the vehicle network; Step S2: The edge computing and collaborative control module monitors the packet loss rate and communication latency of the vehicle network communication link in real time and generates communication quality assessment indicators. Step S3: The edge computing and collaborative control module dynamically selects a formation control strategy based on the communication quality evaluation index. When the communication quality evaluation index is higher than the first threshold, a distributed cooperative control strategy based on full-state information sharing is selected, and the expected acceleration of each vehicle is calculated according to the motion state information of all vehicles in the formation. When the communication quality evaluation index is lower than the first threshold but higher than the second threshold, a reduced-order cooperative control strategy based on state prediction compensation is selected. The unreceived vehicle state information is predicted and compensated according to historical motion state information. The expected acceleration of each vehicle is calculated based on the predicted and compensated state information. The prediction compensation is implemented using the Kalman filter algorithm. When the communication quality evaluation index is lower than the second threshold, a follow control strategy based on single-vehicle autonomous decision-making is selected. Each vehicle obtains information about the vehicle in front through on-board sensors and autonomously calculates its own expected acceleration. In step S4, the edge computing and collaborative control module sends the generated desired acceleration as a formation control command to the vehicle-side perception and communication module of each corresponding vehicle, and each vehicle performs the corresponding longitudinal following action according to the formation control command.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: In the scheme of this application: 1. This invention establishes a mapping relationship between communication quality assessment indicators and formation control strategies by setting up a communication quality assessment unit and an adaptive formation control unit in the edge computing and collaborative control module. Based on different scenarios—communication quality assessment indicators exceeding a first threshold, falling between the first and second thresholds, and falling below the second threshold—corresponding strategies are adopted: a full-state distributed collaborative control strategy, a state prediction compensation-based reduced-order collaborative control strategy, and a single-vehicle autonomous decision-making following control strategy. This mapping relationship ensures that the conservatism of the formation control strategy increases as the communication quality assessment indicator decreases. Therefore, when packet loss and latency changes occur in the communication link, the corresponding control mode can be configured according to the actual communication carrying capacity, reducing the risk of queue stability issues caused by mismatch between the control mode and the communication state.

[0019] 2. In the second control mode, the adaptive platooning control unit uses a Kalman filter algorithm to predict and compensate for unreceived vehicle state information. During the transition period where the communication link experiences partial packet loss but can still transmit a small amount of valid data, this prediction and compensation method enables the platoon to maintain collaborative computation among vehicles even with incomplete state information, thereby delaying the critical trigger point for the platoon to switch from collaborative control to independent vehicle control.

[0020] 3. The comprehensive evaluation subunit generates a single communication quality evaluation index from packet loss rate and communication latency according to a preset weighted fusion rule. Using a single index as the input variable for the mapping relationship avoids decision-making conflicts that may arise from overlapping criteria when setting independent criteria for packet loss and latency parameters. It also provides a single quantitative reference value for mode switching, reducing the probability of mode switching when the evaluation index fluctuates around a threshold. Attached Figure Description

[0021] Figure 1 A schematic diagram of the module structure of the vehicle-to-everything (V2X) based urban traffic platooning cooperative control system provided in this application; Figure 2 The control logic flowchart of the urban traffic formation cooperative control method based on vehicle-to-everything (V2X) provided in this application. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0023] The present invention will be further described below with reference to embodiments.

[0024] Example: Please refer to Figures 1 to 2 As shown, the present invention provides a collaborative control system and method for urban traffic platooning based on vehicle-to-everything (V2X) networks, which is applicable to collaborative control scenarios for intelligent connected vehicle platooning in urban road environments. By establishing a mapping relationship between communication quality evaluation indicators and control strategies at the edge computing layer, the conservatism of the platooning control mode can be automatically adapted to changes in communication quality, thereby maintaining the platooning stability when there are packet loss and latency changes in the communication link. Overall, the system comprises three layers: a vehicle-side perception and communication module, a roadside communication and computing module, and an edge computing and collaborative control module. The vehicle-side perception and communication module is installed on each vehicle in the platoon, responsible for collecting its own motion status information and interacting with other vehicles in the platoon via V2V links. The roadside communication and computing module is deployed along urban roads, responsible for acquiring roadside information such as intersection traffic light status. The edge computing and collaborative control module, as the core decision-making layer, communicates with the above two modules, receives motion status information from each vehicle and roadside traffic environment information, generates platoon control commands based on this information, and issues them to each vehicle. In its implementation, the vehicle-side perception and communication module is implemented through an onboard unit. It collects information on the vehicle's position, speed, acceleration, heading angle, and throttle / brake pedal opening at a frequency of 10Hz to 50Hz, and broadcasts data packets via a V2V link at fixed intervals (e.g., 100ms). This module further includes a vehicle status acquisition unit and a V2V communication unit: the vehicle status acquisition unit fuses GPS, IMU, wheel speed sensor, and CAN bus data using a unified timestamp, outputting a synchronized vehicle status vector, where the throttle / brake pedal opening information is obtained by reading the vehicle's CAN bus; the V2V communication unit encapsulates the vehicle's status information into broadcast data packets at fixed intervals and sends them within the communication range, while simultaneously continuously listening for broadcast data packets from surrounding vehicles, receiving, parsing, and caching them in local memory for use by the upper layer. The roadside communication and computing module connects to the traffic signal controller via wired or wireless means to acquire real-time traffic light phases, remaining time, and timing schemes. This module includes a roadside sensing unit, a roadside communication unit, and a roadside computing unit: the roadside sensing unit directly reads the light color status and countdown information from the signal controller via a hard-wired interface or communication protocol; the roadside communication unit uses the same communication standard and broadcast cycle as V2V to broadcast the processed traffic light status to vehicles entering the coverage area; the roadside computing unit converts the light color status into standard codes, calculates the remaining time of the current phase, predicts the next phase switching time, and generates roadside traffic environment information, which is then sent to the edge computing and collaborative control module. The edge computing and collaborative control module is deployed on roadside edge computing nodes, employing a centralized architecture to uniformly process data from all vehicles within the platoon. Platoon control commands are transmitted to each vehicle via V2I downlink in the form of desired acceleration. This module includes a communication quality assessment unit and an adaptive platoon control unit, which is the core of this invention. The communication quality assessment unit is used to monitor the packet loss rate and communication latency of the vehicle-to-everything (V2X) communication link in real time and generate communication quality assessment indicators. The packet loss rate is obtained by dividing the number of lost data packets within a preset time window (e.g., 200ms) by the total number of packets to be received. Communication latency is measured by the difference between the sending timestamp embedded in the data packet and the local timestamp at the receiving end, determining round-trip latency and one-way latency. This unit further includes a packet loss rate calculation subunit, a latency measurement subunit, and a comprehensive assessment subunit. The comprehensive assessment subunit generates the communication quality assessment indicator Q according to the following weighted fusion rules: in, For packet loss rate, To normalize the ratio of the measured latency to the preset maximum tolerable latency (e.g., 200ms) to the [0,1] interval, and For preset weighting coefficients and + =1; the weighting coefficient is preset according to the application scenario—packet loss rate weighting in dense urban communication scenarios. =0.6, Delay Weight =0.4, in sparse communication scenarios in suburban areas =0.4、 =0.6; once set, it remains constant during system operation; The adaptive formation control unit is connected to the communication quality assessment unit and dynamically adjusts the formation control strategy based on the communication quality assessment indicators. The control strategy switching employs switching logic with a hysteresis region. The hysteresis region width is 10% of the difference between the first and second thresholds to avoid frequent mode oscillations when the assessment indicators fluctuate near the thresholds. This unit includes at least the following three control modes: When the communication quality assessment index exceeds the first threshold, the first control mode is adopted, namely, a distributed collaborative control strategy based on full-state information sharing. The first threshold is a preset quantized value of the communication quality assessment index in the [0,1] interval, corresponding to the communication state in which the V2V link can reliably transmit the state information of all vehicles in the formation. The threshold is calibrated using a frequency domain analysis method based on the vehicle spacing error transfer function: with the formation queue stability (the H∞ norm of the spacing error transfer function is less than 1) as a constraint, different combinations of packet loss rate and delay are simulated and scanned to calculate the corresponding transfer function norm. The packet loss rate and delay boundary values ​​that satisfy the norm constraint are taken, and then the boundary values ​​are substituted into the weighted fusion rule to calculate the corresponding assessment index value as the first threshold. In this mode, the adaptive formation control unit constructs a dynamic model of the entire formation based on the motion state information of all vehicles in the formation. Based on this dynamic model, a model predictive control algorithm is used to calculate the expected acceleration of each vehicle, and the expected acceleration is sent to each corresponding vehicle as a formation control command. The objective function of the model predictive control algorithm is to minimize the weighted sum of the tracking errors of each vehicle to the desired speed and the distance errors with the vehicle in front within the prediction time domain. The prediction time domain is 1.5s, and the control time domain is 0.5s. When the communication quality assessment index is lower than the first threshold but higher than the second threshold, a second control mode is adopted, namely a reduced-order cooperative control strategy based on state prediction compensation. The second threshold is a preset quantized value of the communication quality assessment index in the range [0,1], corresponding to the communication state where some vehicle state information is missing due to packet loss or delayed arrival due to time delay. The calibration method of the second threshold is constrained by the stability of the single-vehicle car-following system under the condition of missing state information. The same simulation scanning method is used to determine the critical packet loss rate and critical time delay, and then mapped to the assessment index value. In this mode, the adaptive formation control unit uses the Kalman filter algorithm to predict and compensate for the unreceived vehicle state information based on historical motion state information and communication quality assessment index. Based on the predicted and compensated state information, the preset car-following model is used to calculate the expected acceleration of each vehicle, and the expected acceleration is sent to each corresponding vehicle as a formation control command. The Kalman filter algorithm is activated when the communication quality assessment index is between a first threshold and a second threshold. When the communication quality recovers to above the first threshold, prediction compensation stops and full-state information is restored. When a vehicle's state information is not received for several consecutive cycles, the prediction step of the Kalman filter continues to run, and the predicted compensation value replaces the missing value in the car-following model calculation. The preset car-following model is an Intelligent Driver Model (IDM), whose inputs are the desired headway (taken as 1.5s), the current speed of the vehicle, the relative distance and relative speed with the vehicle in front, and the output is the desired acceleration. When the communication quality assessment index falls below the second threshold, a third control mode is adopted, namely a follow-up control strategy based on autonomous vehicle decision-making. The second threshold corresponds to a state of severely deteriorated communication quality, at which point the V2V link can no longer support effective coordination, and each vehicle relies entirely on onboard sensors for follow-up control. In this mode, each vehicle's onboard perception and communication module obtains the relative distance and relative speed of the vehicle ahead through onboard sensors. Based on this relative distance and relative speed, a constant-distance follow-up strategy is used to calculate the vehicle's desired acceleration, and autonomous follow-up control is executed according to this desired acceleration. The signal light status obtained by the roadside communication and computing module is further used by the edge computing and collaborative control module to assist in correcting platoon control commands. The specific correction logic is as follows: Obtain the current position and speed of the platoon; calculate the estimated time window for the platoon to reach the intersection ahead at the current speed—the start time of this estimated time window is the current time plus the quotient of the platoon's distance from the intersection divided by the current average speed, and the end time is the start time plus the quotient of the platoon's overall length divided by the average speed; compare this estimated time window with the remaining time of the current signal phase and the start time of the next green light cycle; if the signal light is red within the estimated arrival time window, then the upper limit of the expected acceleration of the platoon leader vehicle is increased from 2.5 m / s². 2 Reduced to 1.0 m / s 2 This allows the formation to decelerate smoothly; if the traffic light is green within the expected arrival time window, no adjustment will be made to the upper limit of the desired acceleration. The correction is applied when the lead vehicle is 200m to 500m from the intersection. In addition, the system includes a formation disbandment management module, connected to the edge computing and collaborative control module, used to determine formation disbandment conditions and initiate disbandment when the conditions are met. The disbandment condition is: the communication quality assessment index of any vehicle in the formation and the lead vehicle remains below a second threshold for more than a preset time threshold. The lead vehicle refers to the specific vehicle at the front of the formation, responsible for guiding the overall speed and planning the path of the formation; the preset time threshold is a fixed value between 3 and 10 seconds; the time continuously below the second threshold is accumulated by a timer—the timer increments when the index falls below the second threshold and resets when the index rises above the second threshold. After disbandment, each vehicle switches to a third control mode and drives independently. The present invention achieves the following beneficial effects through the above configuration: A communication quality assessment unit and an adaptive formation control unit are set up in the edge computing and collaborative control module, establishing a mapping relationship between communication quality assessment indicators and formation control strategies. This allows the conservatism of the formation control strategy to increase as the communication quality assessment indicators decrease, thereby enabling the configuration of corresponding control modes based on actual communication carrying capacity when packet loss and latency changes occur in the communication link. This reduces the risk of queue stability caused by mismatch between control modes and communication states. In the second control mode, a Kalman filter algorithm is used to predict and compensate for unreceived vehicle status information, enabling the formation to maintain collaborative computing between vehicles within the transition interval where there is partial packet loss but a small amount of valid data can still be transmitted. This delays the critical trigger point for the formation to switch from collaborative control to independent single-vehicle control. The comprehensive evaluation subunit generates a single communication quality assessment indicator from packet loss rate and communication latency according to a preset weighted fusion rule. When this single indicator is used as the input variable for the mapping relationship, it avoids decision command conflicts that may occur due to the cross-reference of criteria when setting independent criteria for packet loss and latency parameters, and provides a single quantitative reference value for mode switching. The urban traffic platooning cooperative control method based on the above system includes the following steps: Step S1: Collect motion status information of each vehicle in the formation through the vehicle-side sensing and communication module, and share the motion status information among the vehicles in the formation through the vehicle network; Step S2: The edge computing and collaborative control module monitors the packet loss rate and communication latency of the vehicle network communication link in real time and generates communication quality assessment indicators. Step S3: The edge computing and collaborative control module dynamically selects a formation control strategy based on the communication quality assessment index: When the communication quality assessment index is higher than the first threshold, a distributed collaborative control strategy based on full-state information sharing is selected, and the expected acceleration of each vehicle is calculated based on the motion state information of all vehicles in the formation; when the communication quality assessment index is lower than the first threshold but higher than the second threshold, a reduced-order collaborative control strategy based on state prediction compensation is selected, and the unreceived vehicle state information is predicted and compensated based on historical motion state information, and the expected acceleration of each vehicle is calculated based on the predicted and compensated state information. The prediction compensation is implemented using the Kalman filter algorithm, which is activated only when the communication quality assessment index is between the first and second thresholds, and stops when the communication quality rises above the first threshold; when the communication quality assessment index is lower than the second threshold, a following control strategy based on single-vehicle autonomous decision-making is selected, and each vehicle obtains information about the vehicle in front through onboard sensors and autonomously calculates its own expected acceleration. In step S4, the edge computing and collaborative control module sends the generated desired acceleration as a formation control command to the vehicle-side perception and communication module of each corresponding vehicle, and each vehicle performs the corresponding longitudinal following action according to the formation control command.

[0025] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection, an electrical connection, or a connection that allows communication between them; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0026] Obviously, the embodiments described above are merely some embodiments of the present invention, not all embodiments. The accompanying drawings show preferred embodiments of the present invention, but do not limit the patent scope of the present invention. The present invention can be implemented in many different forms; rather, these embodiments are provided to provide a more thorough and complete understanding of the disclosure of the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the patent protection scope of this invention.

Claims

1. A vehicle-to-everything (V2X)-based urban traffic platooning cooperative control system, characterized in that, include: The vehicle-mounted sensing and communication module is installed on each vehicle in the formation to collect the vehicle's motion status information and interact with other vehicles in the formation through the vehicle network; The edge computing and collaborative control module is communicatively connected to the vehicle-side perception and communication module. It is used to receive motion status information of each vehicle, generate formation control commands based on the information, and send the formation control commands to the vehicle-side perception and communication modules of each vehicle. The edge computing and collaborative control module includes: The communication quality assessment unit is used to monitor the packet loss rate and communication latency of the vehicle-to-everything (V2X) communication link in real time and generate communication quality assessment indicators. An adaptive formation control unit, connected to the communication quality assessment unit, is used to dynamically adjust the formation control strategy according to the communication quality assessment index. The adaptive formation control unit includes at least: In the first control mode, when the communication quality evaluation index is higher than the first threshold, a distributed collaborative control strategy based on full-state information sharing is adopted. In the second control mode, when the communication quality evaluation index is lower than the first threshold but higher than the second threshold, a reduced-order collaborative control strategy based on state prediction compensation is adopted. In the third control mode, when the communication quality assessment index is lower than the second threshold, a follow control strategy based on vehicle autonomous decision-making is adopted.

2. The urban traffic platooning cooperative control system based on vehicle-to-everything (V2X) as described in claim 1, characterized in that, The vehicle-side sensing and communication module includes: The vehicle status acquisition unit is used to acquire the vehicle's real-time position, speed, acceleration, heading angle, and accelerator / brake pedal opening information through on-board sensors; The V2V communication unit is used to interact with other vehicles in the formation via the vehicle-to-vehicle network, receive motion status information from other vehicles, and broadcast the motion status information of its own vehicle.

3. The urban traffic platooning cooperative control system based on vehicle-to-everything (V2X) as described in claim 1, characterized in that, It also includes a roadside communication and computing module, which is set up along the roadside of urban roads to obtain the status of traffic lights at intersections and to communicate with the vehicle-side perception and communication module and the edge computing and collaborative control module. The roadside communication and computing module includes: Roadside sensing units are used to collect the status of traffic lights at intersections; The roadside communication unit is used to interact with the vehicle-side perception and communication modules of the platooned vehicles within the coverage area; The roadside computing unit is used to preprocess the intersection traffic light status collected by the roadside sensing unit, generate roadside traffic environment information, and send it to the edge computing and collaborative control module. The edge computing and collaborative control module is also used to assist in correcting the formation control commands based on the roadside traffic environment information.

4. The urban traffic platooning cooperative control system based on vehicle-to-everything (V2X) as described in claim 1, characterized in that, The communication quality assessment unit includes: The packet loss rate calculation subunit is used to calculate the packet loss rate of vehicle-to-everything (V2X) communication according to a preset time window. The delay measurement subunit is used to measure the round-trip delay and one-way delay of data packet transmission in vehicle-to-everything (V2X) communication. The comprehensive evaluation subunit is used to generate the communication quality evaluation index according to the packet loss rate and the communication delay, based on a preset weighted fusion rule.

5. The urban traffic platooning cooperative control system based on vehicle-to-everything (V2X) as described in claim 1, characterized in that, In the first control mode, the adaptive formation control unit implements distributed cooperative control in the following manner: Based on the motion state information of all vehicles in the formation, construct the overall dynamic model of the formation; Based on the aforementioned dynamic model, the model predictive control algorithm is used to calculate the desired acceleration of each vehicle; The desired acceleration is sent as the formation control command to each corresponding vehicle.

6. The urban traffic platooning cooperative control system based on vehicle-to-everything (V2X) as described in claim 1, characterized in that, In the second control mode, the adaptive formation control unit implements reduced-order cooperative control in the following manner: Based on historical motion state information and the aforementioned communication quality evaluation indicators, a Kalman filter algorithm is used to predict and compensate for unreceived vehicle state information. Based on the predicted and compensated state information, the expected acceleration of each vehicle is calculated using a pre-set car-following model; The desired acceleration is sent as the formation control command to each corresponding vehicle.

7. The urban traffic platooning cooperative control system based on vehicle-to-everything (V2X) as described in claim 1, characterized in that, In the third control mode, the adaptive formation control unit achieves autonomous decision-making and following control for individual vehicles in the following manner: Each vehicle's onboard perception and communication module obtains the relative distance and relative speed of the vehicle ahead through onboard sensors. Each vehicle calculates its desired acceleration based on the relative distance and relative speed using a constant distance following strategy; Each vehicle performs autonomous car-following control based on the desired acceleration.

8. The urban traffic platooning cooperative control system based on vehicle-to-everything (V2X) as described in claim 1, characterized in that, Also includes: The formation disbanding management module is connected to the edge computing and collaborative control module. It is used to determine the formation disbanding conditions and initiate formation disbanding when the formation disbanding conditions are met. The formation disbandment condition is: the communication quality assessment index of any vehicle in the formation and the lead vehicle is continuously lower than the second threshold for a period of time exceeding a preset time threshold.

9. A method for coordinated urban traffic platooning control based on vehicle-to-everything (V2X) networks, applied to the system according to any one of claims 1-9, characterized in that, Includes the following steps: Step S1: Collect motion status information of each vehicle in the formation through the vehicle-side sensing and communication module, and share the motion status information among the vehicles in the formation through the vehicle network; Step S2: The edge computing and collaborative control module monitors the packet loss rate and communication latency of the vehicle network communication link in real time and generates communication quality assessment indicators. Step S3: The edge computing and collaborative control module dynamically selects a formation control strategy based on the communication quality evaluation index. When the communication quality evaluation index is higher than the first threshold, a distributed cooperative control strategy based on full-state information sharing is selected, and the expected acceleration of each vehicle is calculated according to the motion state information of all vehicles in the formation. When the communication quality evaluation index is lower than the first threshold but higher than the second threshold, a reduced-order cooperative control strategy based on state prediction compensation is selected. The unreceived vehicle state information is predicted and compensated according to historical motion state information. The expected acceleration of each vehicle is calculated based on the predicted and compensated state information. The prediction compensation is implemented using the Kalman filter algorithm. When the communication quality evaluation index is lower than the second threshold, a follow control strategy based on single-vehicle autonomous decision-making is selected. Each vehicle obtains information about the vehicle in front through on-board sensors and autonomously calculates its own expected acceleration. In step S4, the edge computing and collaborative control module sends the generated desired acceleration as a formation control command to the vehicle-side perception and communication module of each corresponding vehicle, and each vehicle performs the corresponding longitudinal following action according to the formation control command.

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