A hybrid vehicle group cooperative control method based on cloud-edge cooperative architecture

Abstract

The application relates to a hybrid vehicle group cooperative control method based on a cloud-edge cooperative architecture, and belongs to the technical field of intelligent transportation. The method comprises the following steps: S1. setting a single-lane heterogeneous traffic scene of a city road under the cloud-edge cooperative architecture; S2. respectively establishing a longitudinal kinematic model of a networked automatic vehicle and a networked human-driven vehicle based on the kinematic characteristics of the heterogeneous vehicles on a cloud server; S3. acquiring and analyzing vehicle state information data uploaded to the cloud server and the vehicle longitudinal kinematic model, and respectively constructing a networked human-driven vehicle cooperative driving model and a networked automatic vehicle control algorithm considering the mutual influence between vehicles on the cloud server in combination with a vehicle information topology structure; S4. establishing a state space equation of the heterogeneous vehicles, integrating the networked automatic vehicle control algorithm and the networked human-driven vehicle cooperative driving model, and constructing a hybrid vehicle group kinematic state equation; and S5. establishing a hybrid vehicle group cooperative control method. The application effectively guarantees that the hybrid vehicle group quickly realizes consistent and stable driving.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of intelligent transportation, and relates to a hybrid vehicle group cooperative control method based on a cloud-edge cooperative architecture. BACKGROUND

[0002] The fusion of intelligence and networking has become the mainstream of current intelligent driving technology. Because the networked automatic vehicle can more accurately perceive the road traffic environment, more effectively process complex information, and more accurately execute control strategies, it benefits a lot in the networked road. At the same time, with the gradual landing of intelligent driving technology, part of the human-driven vehicles also have networked cooperative functions, but in the future for a period of time, the networked human-driven vehicles and the networked automatic vehicles will coexist, forming a hybrid traffic scene.

[0003] Because the communication between vehicles in the hybrid traffic is realized through vehicle-mounted sensors and wireless communication, factors such as the environment and radio parameters will cause different communication delays between different vehicles, thereby affecting the effect of queue control. Considering that the cloud-edge cooperative technology has the advantages of low delay and can process a large amount of data, the cloud-edge cooperative technology can be fused with the intelligent driving technology to improve the stability of the hybrid vehicle group driving in the road traffic. After consulting existing literature and patents, few scholars combine the cloud-edge cooperative technology to solve the problem of hybrid vehicle group cooperative control. SUMMARY

[0004] Therefore, the purpose of the present application is to provide a hybrid vehicle group cooperative control method based on a cloud-edge cooperative architecture, which aims to reduce the influence of interactive information delay between vehicles on the stability of hybrid vehicle group driving and improve the cooperative control effect of hybrid vehicle group, especially when the number of vehicles is large.

[0005] To achieve the above purpose, the present application provides the following technical scheme:

[0006] A hybrid vehicle group cooperative control method based on a cloud-edge cooperative architecture, the method comprising the following steps:

[0007] S1. Setting a single-lane heterogeneous traffic scene of urban road under the cloud-edge cooperative architecture;

[0008] S2. Building a networked automatic vehicle and a networked human-driven vehicle longitudinal kinematic model based on the heterogeneous vehicle kinematic characteristics on the cloud server;

[0009] S3. According to the vehicle state information data uploaded to the cloud server after being acquired, analyzed and processed by the edge device in S1 and the vehicle longitudinal kinematic model built in S2, a networked human-driven vehicle cooperative driving model and a networked automatic vehicle control algorithm considering the mutual influence between vehicles are respectively built on the cloud server in combination with the vehicle information topology structure.

[0010] S4. Establishing a state space equation of heterogeneous vehicles at a cloud server according to S2 and S3, integrating a networked automatic vehicle control algorithm and a networked human-driven vehicle cooperative driving model, and constructing a unified hybrid vehicle group kinematics state equation;

[0011] S5. Establishing a hybrid vehicle group cooperative control method based on a cloud edge cooperative architecture according to S4.

[0012] Further, the S1 is specifically:

[0013] A single-lane traffic scene of a city road containing heterogeneous vehicles, i.e., networked automatic vehicles and networked human-driven vehicles, is set up, roadside sensors and edge devices are installed on both sides of the road, a cloud server and a network base station are built to cover the traffic scene, the roadside sensors can collect the state information of all vehicles on the road, the edge devices can obtain, analyze and process the roadside sensor data in real time and upload the state information of each vehicle to the cloud server, the cloud server can send control input instructions to all networked automatic vehicles within the range of the network base station and send part of the vehicle information to all networked human-driven vehicles within the range of the network base station through relevant calculations, the networked automatic vehicles receive and execute the control input instructions, and the networked human-driven vehicles obtain the driving state information of the front and rear vehicles through the driver's perception and the cloud server to control.

[0014] Further, in the S2, the construction steps of the longitudinal kinematics model of the vehicle at the cloud server are as follows:

[0015] Because the networked automatic vehicles and the networked human-driven vehicles have different driving characteristics when driving, the longitudinal kinematics model of the networked automatic vehicle is established at the cloud server, and the expression is as follows:

[0016]

[0017] In the formula, x CAV (t), v CAV (t), a CAV (t), and u(t) represent the position, speed, acceleration and control input of the networked automatic vehicle at time t, respectively.

[0018]

[0019] In the formula, x CHV (t), v CHV (t), and a CHV (t) represent the position, speed and acceleration of the networked human-driven vehicle at time t, respectively.

[0020] Further, in the S3, the design steps of the cooperative control algorithm of the networked vehicle at the cloud server and the networked human-driven vehicle cooperative driving model are as follows:

[0021] S3.1 Considering that the connected and automated vehicle is affected by all vehicles in front and the immediately following vehicle, the longitudinal cooperative control algorithm of the ith connected and automated vehicle is designed in the cloud server combined with the connected and automated vehicle information topology, and the expression is as follows:

[0022]

[0023]

[0024] In the formula: α c , β c , γ c are the optimal speed sensitive coefficient, position difference sensitive coefficient and speed difference sensitive coefficient of the connected and automated vehicle respectively, V c (·) is the optimal speed function of the connected and automated vehicle, V f (·) and V b (·) are the optimal speed functions of the front vehicle and the immediately following vehicle respectively, is the position difference function of the connected and automated vehicle, is the speed difference function of the connected and automated vehicle, * represents the connected and automated vehicle or the connected and automated driver driving the vehicle; ρ is the influence weight of the optimal speed of the front vehicle on the main vehicle, 0≤ρ≤1, 1-ρ is the influence weight of the optimal speed of the immediately following vehicle on the main vehicle; ω is the influence weight of the position difference of the front vehicle on the main vehicle, 0≤ω≤1, 1-ω is the influence weight of the position difference of the immediately following vehicle on the main vehicle; is the influence weight of the speed difference of the front vehicle on the main vehicle, is the influence weight of the speed difference of the immediately following vehicle on the main vehicle; is the distance between the main vehicle and the jth vehicle in the current lane, h are the position and length of the jth vehicle at time t respectively; is the distance between the main vehicle and the following vehicle, d s is the minimum distance; is the speed difference between the main vehicle and the jth vehicle in the current lane; is the speed difference between the main vehicle and the immediately following vehicle; N is the total number of vehicles in front of the lane main vehicle; λ j is the weight coefficient of the jth vehicle in front of the current lane main vehicle on the main vehicle, λ j ≥0, The expression is as follows:

[0025]

[0026] In the formula: is the influence of vehicle i on vehicle j in the social field, A veh is the interaction strength between vehicles, b ijis the length of the short semi-axis of the social field, B ij is the range of the force between vehicles, is the distance vector between vehicle i and vehicle j, and Δt is the simulation step length, is a unit vector.

[0027] S3.2 Consider the influence of the vehicle immediately in front and behind the connected vehicle, and combine the connected vehicle information topology to establish a kth connected vehicle cooperative driving model on the cloud server, which is expressed as follows:

[0028]

[0029] In the formula: α h , β h , and γ h are the optimal speed sensitivity coefficient, position difference sensitivity coefficient, and speed difference sensitivity coefficient of the connected vehicle, respectively; u o,k is the connected cooperative term, V h (·) is the optimal speed function of the connected vehicle, is the position difference function of the connected vehicle, is the speed difference function of the connected vehicle; is the position difference between the host vehicle and the k-2th vehicle, is the speed difference between the host vehicle and the k-2th vehicle, μ is the connected cooperative position difference gain, and τ is the connected cooperative speed difference gain.

[0030] Further, in the S4, the construction steps of the kinematics equation of the mixed vehicle group on the cloud server are as follows:

[0031] S4.1 Construct the kinematics state equation of the ith connected autonomous vehicle on the cloud server, which is expressed as follows:

[0032]

[0033] In the formula: A and B are coefficient matrices, and a mixed vehicle group contains a connected autonomous vehicle. The state vector expression of all connected autonomous vehicles in the vehicle group is as follows:

[0034]

[0035] Considering the time delay of the transmission of control input instruction information on the cloud server, the kinematics state equation of all connected autonomous vehicles in a mixed vehicle group is constructed as follows:

[0036]

[0037] In the formula, I a×a = diag{i 00 ,i 11 ,i 22,…,i DD} represents the relative order of the connected automatic vehicles in the mixed vehicle group, D represents the number of all vehicles in the mixed vehicle group, and ζ is the time delay of the connected automatic vehicles obtaining the control input instruction information from the cloud server;

[0038] S4.2 Constructing the kinematic state equation of the kth connected human-driven vehicle in the cloud server, the expression is as follows:

[0039]

[0040] In the formula: ξ is the reaction time delay of the connected human-driven vehicle, and a mixed vehicle group contains b connected human-driven vehicles. The state vector expression of the b connected human-driven vehicles is as follows:

[0041]

[0042] Considering the reaction time delay of the connected human-driven vehicle and the time delay of the information transmission of the cloud server, the kinematic state equation of all connected human-driven vehicles in a mixed vehicle group is constructed:

[0043]

[0044] In the formula, I b×b = diag{i 00 ,i 11 ,i 22 ,…,i DD} represents the relative order of the connected human-driven vehicles in the mixed vehicle group, and ξ is the reaction time delay of the connected human-driven vehicle.

[0045] S4.3 Integrating the connected automatic vehicle control algorithm and the connected human-driven vehicle control algorithm into a unified kinematic equation of the mixed vehicle group in the cloud server, the expression is as follows:

[0046]

[0047] In the formula, I (a+b)×(a+b) is a D×D unit matrix, and the relative order of all vehicles in the mixed vehicle group is represented in the order from 1 to D.

[0048] S4.4 Setting the consistency constraint condition of the mixed vehicle group in the cloud server, the expression is as follows:

[0049]

[0050] Further, the S5, the roadside device collects the state information of all vehicles in the road, the edge device reads and sorts the driving state information of each vehicle in the mixed vehicle group on the road within the base station range through the roadside sensor in real time, and uploads the information to the cloud server through the network base station; in the cloud server, the obtained networked automatic vehicle and networked human-driven vehicle information is presented in the form of topology, based on this, considering the mutual influence between vehicles, analyzing the driving state of the mixed vehicle group, designing the networked automatic vehicle control algorithm, the networked human-driven vehicle cooperative driving model and the mixed vehicle group cooperative control algorithm in the cloud server, substituting the obtained data into the calculation to obtain the control input instruction of each networked automatic vehicle, and sending the control input instruction to the vehicle terminal of each networked automatic vehicle through the base station and sending part of the state information of the vehicle to the networked human-driven vehicle, then the vehicle terminal and the driver receive the control input instruction or the vehicle information and combine the consistency constraint and the safety constraint to perform corresponding control, so that the mixed vehicle group can quickly realize consistent and stable driving.

[0051] The beneficial effects of the present application are:

[0052] (1) The present application considers the influence factors of multiple front and rear vehicles, and improves the control algorithm of the networked automatic vehicle by using the influence model in the social field to quantitatively analyze the influence weight of the front vehicle on the host vehicle according to the inter-vehicle distance and speed information of the front vehicle.

[0053] (2) The present application considers the influence of communication delay on the driving stability of the mixed vehicle group, and designs a mixed vehicle group cooperative control method using cloud edge cooperative technology. The method of the present application can effectively ensure the consistent and stable driving of the mixed vehicle group, especially when the number of vehicles is large, and improve the overall efficiency of road traffic.

[0054] Other advantages, objects, and features of the present application will be apparent to those skilled in the art from the following specification, and it is intended to be covered by the following claims. The objects and other advantages of the present application can be achieved and obtained by the following specification. BRIEF DESCRIPTION OF DRAWINGS

[0055] In order to make the objects, technical solutions and advantages of the present application clearer, the preferred detailed description of the present application will be combined with the drawings to describe the present application, in which:

[0056] Fig. 1 is the flowchart of the present application;

[0057] Fig. 2 is the heterogeneous traffic scene graph of the urban road single lane under the cloud edge architecture provided by the present application;

[0058] Fig. 3 is the mixed vehicle group cooperative control principle diagram under the cloud edge architecture provided by the present application. Detailed Implementation

[0059] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0060] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0061] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0062] See appendix Figs. 1-3 This embodiment provides a hybrid vehicle group cooperative control method based on a cloud-edge collaborative architecture, including the following steps:

[0063] S1. Set up a heterogeneous traffic scenario for a single lane on an urban road under a cloud-edge collaborative architecture;

[0064] Step S1 sets up a single-lane urban road traffic scenario involving heterogeneous vehicles, namely connected autonomous vehicles and connected human-driven vehicles. For example... Fig. 2As shown, the lane of the present embodiment has a leading connected automatic vehicle, a-1 connected automatic vehicle and b connected human-driven vehicle, a+b=D, D is the total number of vehicles in the lane; Roadside sensors and edge devices are installed on both sides of the road, and cloud servers and network base stations are built to cover the traffic scene, wherein the roadside sensors can collect the state information of all vehicles on the road, the edge devices can real-time acquire, analyze and process the roadside sensor data, and upload the state information of each vehicle to the cloud server, the cloud server sends control input instructions to the connected automatic vehicle and sends part of the vehicle information to all connected human-driven vehicles within the network base station range, the connected automatic vehicle receives and executes the control input instructions, and the connected human-driven vehicle controls through the driving perception and cloud server to obtain the driving state information of the front and rear vehicles.

[0065] S2. The cloud server establishes a longitudinal kinematic model of the connected automatic vehicle and the connected human-driven vehicle based on the heterogeneous vehicle kinematic characteristics, as follows:

[0066] Because the connected automatic vehicle and the connected human-driven vehicle have different driving characteristics when driving, the longitudinal kinematic model expression of the connected automatic vehicle established in the cloud server is as follows:

[0067]

[0068] In the formula: x CAV (t), v CAV (t), a CAV (t), u(t) respectively represent the position, speed, acceleration and control input of the connected automatic vehicle at time t; The longitudinal kinematic model of the connected human-driven vehicle established in the cloud server is expressed as follows:

[0069]

[0070] In the formula: x CHV (t), v CHV (t), a CHV (t) respectively represent the position, speed and acceleration of the connected human-driven vehicle at time t;

[0071] S3. According to the vehicle state information data uploaded to the cloud server by the edge device after acquisition, analysis and processing in step S1 and the vehicle longitudinal kinematic model constructed in step S2, the cloud server constructs a connected human-driven vehicle cooperative driving model and a connected automatic vehicle control algorithm considering the mutual influence between vehicles based on the vehicle information topology, as follows:

[0072] S3.1 Considering that the connected automatic vehicle will be affected by all vehicles in front and the immediately adjacent rear vehicle, as follows: Fig. 3As shown, a longitudinal cooperative control algorithm for the i-th connected autonomous vehicle is designed on a cloud server based on the connected autonomous vehicle information topology. The expression is as follows:

[0073]

[0074]

[0075] In the formula: α c β c γ c These are the optimal speed sensitivity coefficient, position difference sensitivity coefficient, and speed difference sensitivity coefficient for the connected autonomous vehicle, respectively. c (·) represents the optimal speed function for the connected autonomous vehicle, V f (·) and V b (·) represent the optimal speed functions of the main vehicle relative to the preceding vehicle and the immediately following vehicle, respectively. Let the position difference function be the function for connected autonomous vehicles. Let ρ be the speed difference function of the connected autonomous vehicle, * represents the connected autonomous vehicle or connected human-driven vehicle; ρ is the influence weight of the optimal speed of the preceding vehicle on the main vehicle, 0≤ρ≤1, 1-ρ is the influence weight of the optimal speed of the immediately following vehicle on the main vehicle; ω is the influence weight of the position difference of the preceding vehicle on the main vehicle, 0≤ω≤1, 1-ω is the influence weight of the position difference of the immediately following vehicle on the main vehicle. The weight of the impact of the speed difference of the preceding vehicle on the main vehicle. The weight of the impact of the speed difference between adjacent vehicles on the main vehicle; That is, the distance between the current lane's main vehicle and the j-th vehicle. h represents the position and length of the j-th vehicle at time t, respectively; That is, the distance between the main vehicle and the following vehicle, d s Minimum vehicle spacing; That is, the speed difference between the current lane's main vehicle and the j-th vehicle; This represents the speed difference between the current vehicle in the lane and the vehicle immediately following it; N is the total number of vehicles ahead of the current vehicle in the lane; λ j λ represents the weighting coefficient of the j-th vehicle ahead of the current vehicle in the main lane relative to the main vehicle. j ≥0, The expression is as follows:

[0076]

[0077] In the formula: A represents the degree of influence that vehicle i receives from vehicle j in the social field. veh b represents the strength of the interaction force between the vehicles. ij B is the length of the short semi-axis of the social field. ij The range of forces between vehicles is the distance vector between vehicle i and vehicle j, and Δt is the simulation step size, is a unit vector.

[0078] S3.2 Considering the influence of the immediately preceding and following vehicles on the connected and autonomous vehicle, the kth connected and autonomous vehicle cooperative driving model is established in the cloud server based on the connected and autonomous vehicle information topology, and the expression is as follows:

[0079]

[0080] In the formula: α h , β h , and γ h are the optimal speed sensitivity coefficient, the position difference sensitivity coefficient, and the speed difference sensitivity coefficient of the connected and autonomous vehicle, respectively; u o,k is the connected and autonomous cooperative term, V h (·) is the optimal speed function of the connected and autonomous vehicle, is the position difference function of the connected and autonomous vehicle, is the speed difference function of the connected and autonomous vehicle; is the position difference between the host vehicle and the k-2th vehicle, is the speed difference between the host vehicle and the k-2th vehicle, μ is the connected and autonomous cooperative position difference gain, and τ is the connected and autonomous cooperative speed difference gain.

[0081] S4. According to steps S2 and S3, the state space equation of the heterogeneous vehicle is established in the cloud server, and the connected and autonomous vehicle control algorithm and the connected and autonomous vehicle cooperative driving model are integrated to construct a unified hybrid vehicle group kinematics state equation, and the steps are as follows:

[0082] S4.1 The kinematics state equation of the ith connected and autonomous vehicle is constructed in the cloud server, and the expression is as follows:

[0083]

[0084] In the formula: A and B are coefficient matrices, and a connected and autonomous vehicles are included in a hybrid vehicle group. The state vector expression of all connected and autonomous vehicles in this vehicle group is as follows:

[0085]

[0086] Considering the time delay of the cloud server in transmitting control input instruction information, the kinematics state equation of all connected and autonomous vehicles in a hybrid vehicle group is constructed:

[0087]

[0088] In the formula, I a×a = diag{i 00 ,i 11 ,i 22,…,i DD} represents the relative order of the connected automatic vehicles in the mixed vehicle group, D represents the number of all vehicles in the mixed vehicle group, and ζ is the time delay of the connected automatic vehicle obtaining the control input instruction information of the cloud server.

[0089] S4.2 constructing the kinematic state equation of the kth connected human-driven vehicle in the cloud server, the expression is as follows:

[0090]

[0091] In the formula: ξ is the reaction time delay of the connected human-driven vehicle, and the mixed vehicle group contains b connected human-driven vehicles, and the state vector expression of the b connected human-driven vehicles is as follows:

[0092]

[0093] Considering the reaction time delay of the connected human-driven vehicle and the time delay of the cloud server transmitting information, the kinematic state equation of all connected human-driven vehicles in the mixed vehicle group is constructed:

[0094]

[0095] In the formula, I b×b =diag{i 00 ,i 11 ,i 22 ,…,i DD} represents the relative order of the connected human-driven vehicle in the mixed vehicle group, and ξ is the reaction time delay of the connected human-driven vehicle.

[0096] S4.3 integrating the connected automatic vehicle control algorithm and the connected human-driven vehicle control algorithm into a unified kinematic equation of the mixed vehicle group in the cloud server, the expression is as follows:

[0097]

[0098] In the formula, I (a+b)×(a+b) is a DxD unit matrix, and the relative order of all vehicles in the mixed vehicle group is represented in the order from 1 to D.

[0099] S4.4 setting the consistency constraint condition of the mixed vehicle group in the cloud server, the expression is as follows:

[0100]

[0101] S5. According to step S4, a mixed vehicle group cooperative control method based on the cloud edge cooperative architecture is established.

[0102] The application starts from the cloud edge cooperative architecture, and the mixed vehicle group cooperative control method based on the cloud edge cooperative architecture is established by Fig. 3It can be known that the roadside device collects the state information of all vehicles on the road, the edge device reads and sorts the driving state information of each vehicle in the mixed vehicle group in the base station range through the roadside sensor in real time, and then uploads the information to the cloud server through the network base station; in the cloud server, the obtained information of the networked automatic vehicle and the networked human-driven vehicle is presented in the form of topology, based on which, considering the mutual influence between vehicles, analyzing the driving state of the mixed vehicle group, designing the networked automatic vehicle control algorithm, the networked human-driven vehicle cooperative driving model and the mixed vehicle group cooperative control algorithm in the cloud server, substituting the obtained data into the calculation to obtain the control input instruction of each networked automatic vehicle, and sending the control input instruction to the vehicle terminal of each networked automatic vehicle through the base station and sending part of the vehicle state information to the networked human-driven vehicle, then the vehicle terminal and the driver receive the control input instruction or the vehicle information and make corresponding control combining the consistency constraint and the safety constraint, so that the mixed vehicle group can quickly realize consistent and stable driving.

[0103] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced equivalently without departing from the purpose and scope of the technical solutions, which should be covered in the scope of the claims of the present application.

Claims

1. A hybrid vehicle group cooperative control method based on a cloud-edge cooperative architecture, characterized in that: The method comprises the following steps: S1: setting a single-lane heterogeneous traffic scene of urban road under a cloud-edge collaborative architecture; the S1 is specifically: Setting a single-lane traffic scene of urban road containing heterogeneous vehicles, i.e. mixed driving of networked automatic vehicles and networked human-driven vehicles, installing roadside sensors and edge devices on both sides of the road, simultaneously building a cloud server and a network base station to cover the traffic scene, the roadside sensors collecting the state information of all vehicles on the road, the edge devices acquiring, analyzing and processing the roadside sensor data in real time and uploading the state information of each vehicle to the cloud server, the cloud server sending control input instructions to all networked automatic vehicles within the range of the network base station and sending part of vehicle information to all networked human-driven vehicles within the range of the network base station through relevant calculations, the networked automatic vehicles receiving and executing the control input instructions, and the networked human-driven vehicles acquiring the driving state information of the front and rear vehicles through the driver's perception and the cloud server to control; S2: respectively establishing a longitudinal kinematic model of the networked automatic vehicle and the networked human-driven vehicle based on the kinematic characteristics of the heterogeneous vehicles on the cloud server; S3: according to the vehicle state information data uploaded to the cloud server after being acquired, analyzed and processed by the edge device in S1 and the vehicle longitudinal kinematic model constructed in S2, respectively constructing a networked human-driven vehicle cooperative driving model and a networked automatic vehicle control algorithm on the cloud server in combination with the vehicle information topology, wherein S3 is specifically as follows: S3.1 considering that the networked automatic vehicle will be affected by all vehicles in front and the immediately adjacent rear vehicle, designing a longitudinal cooperative control algorithm for the ith networked automatic vehicle on the cloud server in combination with the networked automatic vehicle information topology, and the expression is as follows: In the formula: α c , β c , γ c are optimal speed sensitivity coefficient, position difference sensitivity coefficient, and speed difference sensitivity coefficient of the connected and automatic vehicle respectively, V c (·) is the optimal speed function of the connected and automatic vehicle, V f (·) and V b (·) are optimal speed functions of the front vehicle and the immediately following vehicle respectively, is the position difference function of the connected and automatic vehicle, is the speed difference function of the connected and automatic vehicle, * indicates the connected and automatic vehicle or the connected and automatic person driving the vehicle; ρ is the influence weight of the optimal speed of the front vehicle on the subject vehicle, 0≤ρ≤1, 1-ρ is the influence weight of the optimal speed of the immediately following vehicle on the subject vehicle; ω is the influence weight of the position difference of the front vehicle on the subject vehicle, 0≤ω≤1, 1-ω is the influence weight of the position difference of the immediately following vehicle on the subject vehicle; is the influence weight of the speed difference of the front vehicle on the subject vehicle, is the influence weight of the speed difference of the immediately following vehicle on the subject vehicle; is the distance between the subject vehicle and the jth vehicle in the current lane, h are the position and the length of the jth vehicle at t; is the distance between the subject vehicle and the following vehicle, d s is the minimum distance; is the speed difference between the subject vehicle and the jth vehicle in the current lane; is the speed difference between the subject vehicle and the immediately following vehicle; N is the total number of vehicles in front of the subject vehicle in the lane; λ j is the weight coefficient of the jth vehicle in front of the subject vehicle in the current lane on the subject vehicle, λ j ≥0, The expression is as follows: wherein: is the influence of vehicle j on vehicle i in the social field, A veh is the strength of the interaction between vehicles, b ij is the length of the semi-major axis of the social field, B ij is the range of the interaction between vehicles, is the distance vector between vehicle i and vehicle j, and Δt is the simulation step size, is the unit vector; S3.2 considering that the networked human-driven vehicle will be affected by the immediately adjacent front and rear vehicles, establishing a cooperative driving model for the kth networked human-driven vehicle on the cloud server in combination with the networked human-driven vehicle information topology, and the expression is as follows: In the formula: α h β h γ h These are the optimal speed sensitivity coefficient, position difference sensitivity coefficient, and speed difference sensitivity coefficient for connected human-driven vehicles, respectively; u o,k For connected and collaborative projects, V h (·) represents the optimal speed function for connected human-driven vehicles. Let the location difference function be the location of the connected vehicle driver. Let the speed difference function be defined for connected vehicles. The position difference between the main vehicle and the (k-2)th vehicle. The speed difference between the master vehicle and the (k-2)th vehicle is μ, the network cooperative position difference gain is τ, and the network cooperative speed difference gain is τ. S4: establishing a state space equation of the heterogeneous vehicles on the cloud server according to S2 and S3, and integrating the networked automatic vehicle control algorithm and the networked human-driven vehicle cooperative driving model to construct a unified hybrid vehicle group kinematic state equation; S5: establishing a hybrid vehicle group cooperative control method based on the cloud-edge collaborative architecture according to S4. 2.The hybrid vehicle group cooperative control method based on the cloud-edge cooperative architecture according to claim 1, wherein: The S2 is specifically: The expression of the longitudinal kinematic model of the networked automatic vehicle established on the cloud server is as follows: where x CAV (t), v CAV (t), a CAV (t), u(t) represent the position, velocity, acceleration and control input of the connected and automated vehicle at time t, respectively; the expression of the longitudinal kinematic model of the connected and human-driven vehicle established in the cloud server is as follows: where x CHV (t), v CHV (t), a CHV (t) denote the position, velocity and acceleration of the connected driver at time t, respectively. 3.The hybrid vehicle group cooperative control method based on the cloud-edge cooperative architecture of claim 1, wherein: In the S4, the construction steps of the hybrid vehicle group kinematic equation on the cloud server are specifically as follows: S4.1 constructing a kinematic state equation of the ith networked automatic vehicle on the cloud server, and the expression is as follows: In the formula: A, B are coefficient matrices, a mixed fleet contains a vehicles of connected and autonomous vehicles, and the state vector expression of all connected and autonomous vehicles in the fleet is as follows: Considering the time delay of the cloud server in transmitting control input instruction information, a kinematic state equation of all networked automatic vehicles in the hybrid vehicle group is constructed: In the formula, I a×a = diag{ i 00 ,i 11 ,i 22 ,…, i DD} represents the relative order of the connected automatic vehicle in the mixed vehicle group, D represents the number of all vehicles in the mixed vehicle group, and ζ is the time delay of the connected automatic vehicle obtaining the control input instruction information from the cloud server. S4.2 constructing a kinematic state equation of the kth networked human-driven vehicle on the cloud server, and the expression is as follows: In the formula: ξ is the reaction time delay of the connected driver, and a mixed vehicle group contains b connected drivers. The state vector expression of b connected drivers is as follows: Considering the reaction time delay of the networked human-driven vehicle and the time delay of the cloud server in transmitting information, a kinematic state equation of all networked human-driven vehicles in the hybrid vehicle group is constructed: In the formula, I b×b = diag{ i 00 ,i 11 ,i 22 ,…, i DD} represents the relative order of the net-connected driver in the mixed vehicle group, and ξ is the reaction delay of the net-connected driver. S4.3 integrating the networked automatic vehicle control algorithm and the networked human-driven vehicle control algorithm into a unified hybrid vehicle group kinematic equation on the cloud server, and the expression is as follows: where I (a+b)×(a+b) is a D x D identity matrix, and r represents the relative rank of all vehicles in the mixed platoon in order from 1 to D; S4.4 Set the consistency constraint condition of the mixed vehicle group in the cloud server, and the expression is as follows:

4. The hybrid vehicle group cooperative control method based on the cloud-edge cooperative architecture according to claim 1, characterized in that: In S5, the roadside device collects the state information of all vehicles in the road, and the edge device reads and sorts the driving state information of each vehicle in the mixed vehicle group in the road within the base station range through the roadside sensor, and then uploads the information to the cloud server through the network base station; In the cloud server, the obtained information of the networked automatic vehicle and the networked human-driven vehicle is presented in the form of topology. Based on this, the mutual influence between vehicles is considered, the driving state of the mixed vehicle group is analyzed, the networked automatic vehicle control algorithm, the networked human-driven vehicle cooperative driving model and the mixed vehicle group cooperative control algorithm are designed in the cloud server, the obtained data is substituted and calculated to obtain the control input instruction of each networked automatic vehicle, and the control input instruction is sent to the vehicle terminal of each networked automatic vehicle through the base station, and the state information of part of the vehicles is sent to the networked human-driven vehicle. Then the vehicle terminal and the driver receive the control input instruction or the vehicle information and make corresponding control combining the consistency constraint and the safety constraint, so that the mixed vehicle group can quickly realize consistent and stable driving.

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