Hybrid vehicle group cooperative control method based on cloud edge cooperative architecture

Abstract

The invention relates to a hybrid vehicle group cooperative control method based on a cloud edge cooperative architecture, and belongs to the technical field of intelligent traffic. The method comprises the following steps: S1, setting an urban road single-lane heterogeneous traffic scene under a cloud edge collaborative architecture; s2, on the cloud server, based on the heterogeneous vehicle kinematics characteristics, establishing a longitudinal kinematics model for driving of the networked automatic vehicle and a longitudinal kinematics model for driving of the networked person; s3, the vehicle state information data and the vehicle longitudinal kinematics model uploaded to the cloud server are obtained and analyzed, and a networked person driving cooperative driving model and a networked automatic vehicle control algorithm considering mutual influence between the vehicles are constructed on the cloud server in combination with a vehicle information topological structure; s4, establishing a state-space equation of heterogeneous vehicles, integrating the networked automatic vehicle control algorithm and the networked person driving cooperative driving model, and constructing a hybrid vehicle group kinematics state equation; and S5, establishing a hybrid vehicle group cooperative control method. The method effectively ensures that the hybrid vehicle group rapidly realizes consistent and stable driving.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent transportation technology and relates to a hybrid vehicle group collaborative control method based on a cloud-edge collaborative architecture. Background Art

[0002] The integration of intelligence and networking has become the mainstream of the development of intelligent driving technology. Since networked automatic vehicles can perceive the road traffic environment more accurately, process complex information more effectively, and execute control strategies more accurately, they benefit a lot from networked roads. At the same time, with the gradual implementation of intelligent driving technology, some people's driving also has the function of networked coordination, but in the future, there will be a long-term coexistence of networked human driving and networked automatic vehicles, forming a mixed traffic scene.

[0003] Since the communication between vehicles in mixed traffic is achieved through on-board sensors and wireless communication, factors such as the environment and radio parameters will cause different communication delays between different vehicles, which in turn affects the queue control effect. This impact is more serious when the number of vehicles is large. Considering that cloud-edge collaborative technology has the advantages of low latency and can process large amounts of data, cloud-edge collaborative technology can be integrated with intelligent driving technology to improve the stability of mixed vehicle groups in road traffic. Looking at existing literature and patents, few scholars have combined cloud-edge collaborative technology to solve the problem of collaborative control of mixed vehicle groups. Summary of the invention

[0004] In view of this, the object of the present invention is to provide a hybrid vehicle group collaborative control method based on a cloud-edge collaborative architecture, with the aim of reducing the impact of the delay in interactive information between vehicles on the driving stability of the hybrid vehicle group, and improving the collaborative control effect of the hybrid vehicle group, especially when the number of vehicles is large.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

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

[0007] S1. Set up a single-lane heterogeneous traffic scenario on an urban road under the cloud-edge collaborative architecture;

[0008] S2. Establishing longitudinal kinematic models of connected automated vehicles and connected human drivers based on the kinematic characteristics of heterogeneous vehicles on the cloud server;

[0009] S3. Based on the vehicle status information data acquired, analyzed, processed and uploaded to the cloud server by the edge device in S1 and the vehicle longitudinal kinematics model constructed in S2, a connected human-driving cooperative driving model and a connected automatic vehicle control algorithm that consider the mutual influence between vehicles are constructed on the cloud server in combination with the vehicle information topology structure;

[0010] S4. Establish the state space equations of heterogeneous vehicles on the cloud server according to S2 and S3, and integrate the control algorithm of the connected automatic vehicle and the cooperative driving model of the connected human driver to construct a unified kinematic state equation of the hybrid vehicle group;

[0011] S5. According to S4, a hybrid vehicle group collaborative control method based on cloud-edge collaborative architecture is established.

[0012] Further, the S1 is specifically:

[0013] A single-lane traffic scenario is set up on an urban road with mixed traffic of heterogeneous vehicles, namely connected automated vehicles and connected human drivers. Roadside sensors and edge devices are installed on both sides of the road. At the same time, cloud servers and network base stations are built to cover the traffic scenario. Roadside sensors can collect status information of all vehicles on the road. Edge devices can obtain, analyze and process roadside sensor data in real time and upload the status information of each vehicle to the cloud server. The cloud server can send control input instructions to all connected automated vehicles within the range of the network base station through relevant calculations, and send part of the vehicle information to all connected human drivers within the range of the network base station. The connected automated vehicles receive and execute the control input instructions, while the connected human drivers obtain the driving status information of the front and rear vehicles through driver perception and the cloud server for control.

[0014] Furthermore, in S2, the steps of constructing the longitudinal kinematic model of the vehicle on the cloud server are as follows:

[0015] Because connected autonomous vehicles and connected human drivers have different driving characteristics, the longitudinal kinematic model of the connected autonomous vehicle established on the cloud server is expressed as follows:

[0016]

[0017] Where: x CAV (t), v CAV (t), a CAV (t) and u(t) represent the position, velocity, acceleration and control input of the connected automatic vehicle at time t respectively; the longitudinal kinematic model of the connected human driving established on the cloud server is expressed as follows:

[0018]

[0019] Where: x CHV (t), v CHV (t), a CHV (t) respectively represent the position, speed and acceleration of the connected person driving at time t;

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

[0021] S3.1 Considering that the connected automatic vehicle will be affected by all vehicles in front and the vehicles immediately behind, the longitudinal cooperative control algorithm of the i-th connected automatic vehicle is designed on the cloud server in combination with the information topology of the connected automatic vehicle. The expression is as follows:

[0022]

[0023]

[0024] Where: α c , β c , γ c are the optimal speed sensitivity coefficient, position difference sensitivity coefficient, and speed difference sensitivity coefficient of the connected automatic vehicle, V c (·) is the optimal speed function of the connected automated vehicle, V f (·) and V b (·) are the optimal speed functions of the main vehicle to the preceding vehicle and the vehicle immediately behind, respectively. is the position difference function of the connected automatic vehicle, is the speed difference function of the connected automatic vehicle, * indicates a connected automatic vehicle or a connected human driving; ρ is the weight of the influence of the optimal speed of the preceding vehicle on the subject vehicle, 0≤ρ≤1, 1-ρ is the weight of the influence of the optimal speed of the immediately following vehicle on the subject vehicle; ω is the weight of the influence of the position difference of the preceding vehicle on the subject vehicle, 0≤ω≤1, 1-ω is the weight of the influence of the position difference of the immediately following vehicle on the subject vehicle; is the influence weight of the front vehicle speed difference on the subject vehicle, is the weight of the impact of the speed difference of the immediately following vehicle on the main vehicle; That is, the distance between the host vehicle in the current lane and the jth vehicle, h are the position and length of the jth vehicle at time t; That is, the distance between the main vehicle and the following vehicle, d s is the minimum vehicle spacing; That is, the speed difference between the host vehicle and the jth vehicle in the current lane; That is, the speed difference between the main vehicle in the current lane and the vehicle immediately behind; N is the total number of vehicles in front of the main vehicle in the lane; λ j is the weight coefficient of the jth vehicle in front of the main vehicle in the current lane to the main vehicle, λ j ≥0, The expression is as follows:

[0025]

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

[0027] S3.2 Considering that the connected human driving will be affected by the vehicles immediately in front and behind, the k-th connected human driving cooperative driving model is established on the cloud server in combination with the connected human driving information topology. The expression is as follows:

[0028]

[0029] Where: α h , β h , γ h are the optimal speed sensitivity coefficient, position difference sensitivity coefficient, and speed difference sensitivity coefficient of connected human driving respectively; u o,k is the network synergy term, V h (·) is the optimal speed function for connected human driving, is the position difference function of the connected person driving, It is the speed difference function of the connected person driving; is the position difference between the main vehicle and the k-2th vehicle, is the speed difference between the main vehicle and the k-2th vehicle, μ is the networked collaborative position difference gain, and τ is the networked collaborative speed difference gain.

[0030] Furthermore, in S4, the steps of constructing the kinematic equation of the hybrid vehicle group on the cloud server are specifically as follows:

[0031] S4.1 constructs the kinematic state equation of the i-th connected automatic vehicle on the cloud server, and the expression is as follows:

[0032]

[0033] Where: A and B are coefficient matrices. A mixed vehicle group contains a connected automatic vehicles. The state vector expressions of all connected automatic vehicles in the vehicle group are as follows:

[0034]

[0035] Considering the time delay of cloud server transmitting control input command information, a kinematic state equation of all connected automatic vehicles in a hybrid vehicle group is constructed:

[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 vehicle in the hybrid vehicle group, D represents the number of all vehicles in the hybrid vehicle group, and ζ is the delay for the connected automatic vehicle to obtain the control input command information from the cloud server;

[0038] S4.2 constructs the k-th connected human driving kinematic state equation on the cloud server, which is expressed as follows:

[0039]

[0040] Where: ξ is the reaction delay of the connected car. A mixed car group contains b connected cars. The state vector expression of b connected cars is as follows:

[0041]

[0042] Considering the reaction delay of the connected human driver and the information transmission delay of the cloud server, a kinematic state equation of all connected human drivers 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 driver in the mixed vehicle group, ξ is the reaction delay of the connected human driver;

[0045] S4.3 integrates the connected automatic vehicle control algorithm and the connected human driving control algorithm into a unified hybrid vehicle group kinematic equation on the cloud server, which is expressed as follows:

[0046]

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

[0048] S4.4 sets the consistency constraint of the mixed vehicle group on the cloud server, and the expression is as follows:

[0049]

[0050] Further, in S5, the roadside equipment will collect the status information of all vehicles on the road, and the edge device will read and organize the driving status information of each vehicle in the mixed vehicle group on the road within the base station range in real time through the roadside sensor, and then upload the information to the cloud server through the network base station; on the cloud server, the obtained networked automatic vehicle and networked human driving information will be presented in the form of topology. Based on this, considering the mutual influence between vehicles and analyzing the driving status of the mixed vehicle group, the cloud server will design the networked automatic vehicle control algorithm, the networked human driving cooperative driving model and the mixed vehicle group cooperative control algorithm, substitute the obtained data into the calculation to obtain the control input instruction of each networked automatic vehicle, and send the control input instruction to the vehicle terminal of each networked automatic vehicle through the base station, and at the same time send the status information of some vehicles to the networked human driving, and then the vehicle terminal and the driver receive the control input instruction or vehicle information and perform corresponding control in combination with consistency constraints and safety constraints, so that the mixed vehicle group can quickly achieve consistent and stable driving.

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

[0052] (1) The present invention considers the influencing factors of multiple leading and trailing vehicle information, and uses the influence model in the social field to quantitatively analyze the influence weight of the leading vehicle on the host vehicle based on the distance and speed information of the leading vehicle, thereby improving the control algorithm of the connected automatic vehicle;

[0053] (2) The present invention takes into account the impact of communication delay on the driving stability of a mixed vehicle group and designs a mixed vehicle group collaborative control method using cloud-edge collaborative technology. The method of the present invention can effectively ensure the consistent and stable driving of a mixed vehicle group, especially when there are a large number of vehicles, and improve the overall efficiency of road traffic.

[0054] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0056] Figure 1 It is a flow chart of the present invention;

[0057] Figure 2 A heterogeneous traffic scene graph of a single lane of an urban road under the cloud-edge architecture provided by the present invention;

[0058] Figure 3 This is a schematic diagram of the collaborative control principle of a hybrid vehicle group under the cloud-edge architecture provided by the present invention. DETAILED DESCRIPTION

[0059] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways 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 only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0060] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on the present invention. In order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0061] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "front", "rear", etc. indicate the orientation or position relationship, they are based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0062] See attached Figure 1 to Figure 3 , this embodiment provides a hybrid vehicle group collaborative control method based on cloud-edge collaborative architecture, including the following steps:

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

[0064] Step S1 sets a single-lane traffic scene on an urban road with heterogeneous vehicles, i.e., connected automated vehicles and connected human vehicles. Figure 2As shown, in the lane of this embodiment, there is a leading connected automatic vehicle, a-1 connected automatic vehicles and b connected human drivers, a+b=D, D is the total number of vehicles on the lane; roadside sensors and edge devices are installed on both sides of the road, and cloud servers and network base stations are set up to cover the traffic scene, wherein the roadside sensors can collect status information of all vehicles on the road, and the edge devices can obtain, analyze and process the roadside sensor data in real time and upload the status information of each vehicle to the cloud server after sorting. The cloud server sends the control input command to the connected automatic vehicle through relevant calculations, and sends part of the vehicle information to all connected human drivers within the range of the network base station. The connected automatic vehicle receives and executes the control input command, while the connected human driver obtains the driving status information of the front and rear vehicles through the driver's perception and the cloud server for control.

[0065] S2. On the cloud server, longitudinal kinematic models of connected automated vehicles and connected human drivers are established based on the kinematic characteristics of heterogeneous vehicles. The steps are as follows:

[0066] Because connected autonomous vehicles and connected human drivers have different driving characteristics, the longitudinal kinematic model of the connected autonomous vehicle established on the cloud server is expressed as follows:

[0067]

[0068] Where: x CAV (t), v CAV (t), a CAV (t) and u(t) represent the position, velocity, acceleration and control input of the connected automatic vehicle at time t respectively; the longitudinal kinematic model of the connected human driving established on the cloud server is expressed as follows:

[0069]

[0070] Where: x CHV (t), v CHV (t), a CHV (t) respectively represent the position, speed and acceleration of the connected person driving at time t;

[0071] S3. Based on the vehicle status information data acquired, analyzed, processed and uploaded to the cloud server by the edge device in step S1 and the vehicle longitudinal kinematics model constructed in step S2, a connected human-driving cooperative driving model and a connected automatic vehicle control algorithm considering the mutual influence between vehicles are constructed on the cloud server in combination with the vehicle information topology structure. The steps are as follows:

[0072] S3.1 Consider that the connected automated vehicle will be affected by all vehicles in front and immediately behind it, such as Figure 3As shown in the figure, the longitudinal cooperative control algorithm of the i-th connected automatic vehicle is designed on the cloud server in combination with the information topology of the connected automatic vehicle. The expression is as follows:

[0073]

[0074]

[0075] Where: α c , β c , γ c are the optimal speed sensitivity coefficient, position difference sensitivity coefficient, and speed difference sensitivity coefficient of the connected automatic vehicle, V c (·) is the optimal speed function of the connected automated vehicle, V f (·) and V b (·) are the optimal speed functions of the main vehicle to the preceding vehicle and the vehicle immediately behind, respectively. is the position difference function of the connected automatic vehicle, is the speed difference function of the connected automatic vehicle, * indicates a connected automatic vehicle or a connected human driving; ρ is the weight of the influence of the optimal speed of the preceding vehicle on the subject vehicle, 0≤ρ≤1, 1-ρ is the weight of the influence of the optimal speed of the immediately following vehicle on the subject vehicle; ω is the weight of the influence of the position difference of the preceding vehicle on the subject vehicle, 0≤ω≤1, 1-ω is the weight of the influence of the position difference of the immediately following vehicle on the subject vehicle; is the influence weight of the front vehicle speed difference on the subject vehicle, is the weight of the impact of the speed difference of the immediately following vehicle on the main vehicle; That is, the distance between the host vehicle in the current lane and the jth vehicle, h are the position and length of the jth vehicle at time t; That is, the distance between the main vehicle and the following vehicle, d s is the minimum vehicle spacing; That is, the speed difference between the host vehicle and the jth vehicle in the current lane; That is, the speed difference between the main vehicle in the current lane and the vehicle immediately behind; N is the total number of vehicles in front of the main vehicle in the lane; λ j is the weight coefficient of the jth vehicle in front of the main vehicle in the current lane to the main vehicle, λ j ≥0, The expression is as follows:

[0076]

[0077] Where: is the influence of vehicle i on vehicle j in the social field, A veh is the interaction force strength between vehicles, b ij is the length of the minor semi-axis of the social field, B ij The range of forces between vehicles, is the distance vector between vehicle i and vehicle j, Δt is the simulation step length, is a unit vector.

[0078] S3.2 Considering that the connected human driving will be affected by the vehicles immediately in front and behind, the k-th connected human driving cooperative driving model is established on the cloud server in combination with the connected human driving information topology. The expression is as follows:

[0079]

[0080] Where: α h , β h , γ h are the optimal speed sensitivity coefficient, position difference sensitivity coefficient, and speed difference sensitivity coefficient of connected human driving respectively; u o,k is the network synergy term, V h (·) is the optimal speed function for connected human driving, is the position difference function of the connected person driving, It is the speed difference function of the connected person driving; is the position difference between the main vehicle and the k-2th vehicle, is the speed difference between the main vehicle and the k-2th vehicle, μ is the networked collaborative position difference gain, and τ is the networked collaborative speed difference gain.

[0081] S4. According to step S2 and step S3, the state space equations of heterogeneous vehicles are established on the cloud server, and the control algorithm of the networked automatic vehicle and the networked human-driven cooperative driving model are integrated to construct a unified kinematic state equation of the hybrid vehicle group. The steps are as follows:

[0082] S4.1 constructs the kinematic state equation of the i-th connected automatic vehicle on the cloud server, and the expression is as follows:

[0083]

[0084] Where: A and B are coefficient matrices. A mixed vehicle group contains a connected automatic vehicles. The state vector expressions of all connected automatic vehicles in the vehicle group are as follows:

[0085]

[0086] Considering the time delay of cloud server transmitting control input command information, a kinematic state equation of all connected automatic 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 vehicle in the hybrid vehicle group, D represents the number of all vehicles in the hybrid vehicle group, and ζ is the delay for the connected automatic vehicle to obtain the control input command information from the cloud server;

[0089] S4.2 constructs the k-th connected human driving kinematic state equation on the cloud server, which is expressed as follows:

[0090]

[0091] Where: ξ is the reaction delay of the connected car. A mixed car group contains b connected cars. The state vector expression of b connected cars is as follows:

[0092]

[0093] Considering the reaction delay of the connected human driver and the information transmission delay of the cloud server, a kinematic state equation of all connected human drivers in a 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 driver in the mixed vehicle group, ξ is the reaction delay of the connected human driver;

[0096] S4.3 integrates the connected automatic vehicle control algorithm and the connected human driving control algorithm into a unified hybrid vehicle group kinematic equation on the cloud server, which is expressed as follows:

[0097]

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

[0099] S4.4 sets the consistency constraint of the mixed vehicle group on the cloud server, and the expression is as follows:

[0100]

[0101] S5. According to step S4, a hybrid vehicle group collaborative control method based on a cloud-edge collaborative architecture is established.

[0102] The present invention starts from the cloud-edge collaborative architecture. Figure 3It can be seen that the roadside equipment will collect the status information of all vehicles on the road. The edge equipment reads and organizes the driving status information of each vehicle in the mixed vehicle group on the road within the base station range in real time through the roadside sensor, and then uploads the information to the cloud server through the network base station; on the cloud server, the obtained connected automatic vehicle and connected human driving information are presented in the form of topology. Based on this, considering the mutual influence between vehicles and analyzing the driving status of the mixed vehicle group, the connected automatic vehicle control algorithm, the connected human driving cooperative driving model and the mixed vehicle group cooperative control algorithm are designed on the cloud server. The obtained data is substituted into the calculation to obtain the control input instructions of each connected automatic vehicle, and the control input instructions are sent to the on-board terminal of each connected automatic vehicle through the base station. At the same time, the status information of some vehicles is sent to the connected human driving. Then the on-board terminal and the driver receive the control input instructions or vehicle information and perform corresponding control in combination with consistency constraints and safety constraints, so that the mixed vehicle group can quickly achieve consistent and stable driving.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A hybrid vehicle group collaborative control method based on cloud-edge collaborative architecture, characterized by: The method comprises the following steps: S1: Set up a single-lane heterogeneous traffic scenario on an urban road under a cloud-edge collaborative architecture; S2: Establish longitudinal kinematic models of connected automated vehicles and connected human drivers based on heterogeneous vehicle kinematic characteristics on the cloud server; S3: Based on the vehicle status information data acquired, analyzed, processed and uploaded to the cloud server by the edge device in S1 and the vehicle longitudinal kinematics model constructed in S2, a connected human-driving cooperative driving model and a connected automatic vehicle control algorithm that consider the mutual influence between vehicles are constructed on the cloud server in combination with the vehicle information topology structure; S4: Establish the state space equations of heterogeneous vehicles on the cloud server based on S2 and S3, and integrate the control algorithm of the connected automatic vehicle and the cooperative driving model of the connected human driver to construct a unified kinematic state equation of the hybrid vehicle group; S5: According to S4, a hybrid vehicle group collaborative control method based on cloud-edge collaborative architecture is established.

2. The hybrid vehicle group collaborative control method based on cloud-edge collaborative architecture according to claim 1 is characterized by: The S1 is specifically: A single-lane traffic scenario is set up on an urban road with mixed traffic of heterogeneous vehicles, namely connected automated vehicles and connected human drivers. 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 scenario. Roadside sensors collect status information of all vehicles on the road. Edge devices acquire, analyze and process roadside sensor data in real time and upload the status information of each vehicle to the cloud server. The cloud server sends control input instructions to all connected automated vehicles within the range of the network base station through relevant calculations, and sends part of the vehicle information to all connected human drivers within the range of the network base station. The connected automated vehicles receive and execute the control input instructions, while the connected human drivers obtain the driving status information of the front and rear vehicles through driver perception and the cloud server for control.

3. The hybrid vehicle group collaborative control method based on cloud-edge collaborative architecture according to claim 1 is characterized by: The S2 is specifically: The longitudinal kinematic model of the connected autonomous vehicle established on the cloud server is expressed as follows: Where: x CAV (t), v CAV (t), a CAV (t) and u(t) represent the position, velocity, acceleration and control input of the connected automatic vehicle at time t respectively; the longitudinal kinematic model of the connected human driving established on the cloud server is expressed as follows: Where: x CHV (t), v CHV (t), a CHV (t) represent the position, speed and acceleration of the connected person driving at time t respectively.

4. The hybrid vehicle group collaborative control method based on cloud-edge collaborative architecture according to claim 1 is characterized by: The S3 is as follows: S3.1 Considering that the connected automatic vehicle will be affected by all vehicles in front and the vehicles immediately behind, the longitudinal cooperative control algorithm of the i-th connected automatic vehicle is designed on the cloud server in combination with the information topology of the connected automatic vehicle. The expression is as follows: Where: α c , β c , γ c are the optimal speed sensitivity coefficient, position difference sensitivity coefficient, and speed difference sensitivity coefficient of the connected automatic vehicle, V c (·) is the optimal speed function of the connected automated vehicle, V f (·) and V b (·) are the optimal speed functions of the main vehicle to the preceding vehicle and the vehicle immediately behind, respectively. is the position difference function of the connected automatic vehicle, is the speed difference function of the connected automatic vehicle, * indicates a connected automatic vehicle or a connected human driving; ρ is the influence weight of the optimal speed of the preceding 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 leading 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 front vehicle speed difference on the subject vehicle, is the weight of the impact of the speed difference of the immediately following vehicle on the main vehicle; That is, the distance between the host vehicle in the current lane and the jth vehicle, h are the position and length of the jth vehicle at time t; That is, the distance between the main vehicle and the following vehicle, d s is the minimum vehicle spacing; That is, the speed difference between the host vehicle and the jth vehicle in the current lane; That is, the speed difference between the main vehicle in the current lane and the vehicle immediately behind; N is the total number of vehicles in front of the main vehicle in the lane; λ j is the weight coefficient of the jth vehicle in front of the main vehicle in the current lane to the main vehicle, λ j ≥0, The expression is as follows: Where: is the influence of vehicle i on vehicle j in the social field, A veh is the interaction force strength between vehicles, b ij is the length of the minor semi-axis of the social field, B ij The range of forces between vehicles, is the distance vector between vehicle i and vehicle j, Δt is the simulation step length, is a unit vector; S3.2 Considering that the connected human driving will be affected by the vehicles immediately in front and behind, the k-th connected human driving cooperative driving model is established on the cloud server in combination with the connected human driving information topology. The expression is as follows: Where: α h , β h , γ h are the optimal speed sensitivity coefficient, position difference sensitivity coefficient, and speed difference sensitivity coefficient of connected human driving respectively; u o,k is the network synergy term, V h (·) is the optimal speed function for connected human driving, is the position difference function of the connected person driving, It is the speed difference function of the connected person driving; is the position difference between the main vehicle and the k-2th vehicle, is the speed difference between the main vehicle and the k-2th vehicle, μ is the networked collaborative position difference gain, and τ is the networked collaborative speed difference gain.

5. The hybrid vehicle group collaborative control method based on cloud-edge collaborative architecture according to claim 1 is characterized by: In S4, the steps for constructing the kinematic equation of the hybrid vehicle group on the cloud server are as follows: S4.1 constructs the kinematic state equation of the i-th connected automatic vehicle on the cloud server, and the expression is as follows: Where: A and B are coefficient matrices. A mixed vehicle group contains a connected automatic vehicles. The state vector expressions of all connected automatic vehicles in the vehicle group are as follows: Considering the time delay of cloud server transmitting control input command information, a kinematic state equation of all connected automatic vehicles in a 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 hybrid vehicle group, D represents the number of all vehicles in the hybrid vehicle group, and ζ is the delay for the connected automatic vehicle to obtain the control input command information from the cloud server; S4.2 constructs the k-th connected human driving kinematic state equation on the cloud server, which is expressed as follows: Where: ξ is the reaction delay of the connected car. A mixed car group contains b connected cars. The state vector expression of b connected cars is as follows: Considering the reaction delay of the connected human driver and the information transmission delay of the cloud server, a kinematic state equation of all connected human drivers in a mixed 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 connected human driver in the mixed vehicle group, ξ is the reaction delay of the connected human driver; S4.3 integrates the networked automatic vehicle control algorithm and the networked human driving control algorithm into a unified hybrid vehicle group kinematic equation on the cloud server, which is expressed as follows: In the formula, I (a+b)×(a+b) is a D×D identity matrix, which represents the relative order of all vehicles in the mixed vehicle group in the order from 1 to D; S4.4 sets the consistency constraint of the mixed vehicle group on the cloud server, and the expression is as follows:

6. The hybrid vehicle group collaborative control method based on cloud-edge collaborative architecture according to claim 1 is characterized by: In S5, the roadside equipment will collect the status information of all vehicles on the road. The edge device reads and organizes the driving status information of each vehicle in the mixed vehicle group on the road within the base station range in real time through the roadside sensor, and then uploads the information to the cloud server through the network base station; On the cloud server, the obtained information of connected automatic vehicles and connected human drivers is presented in the form of topology. Based on this, the mutual influence between vehicles is considered and the driving status of the mixed vehicle group is analyzed. The connected automatic vehicle control algorithm, the connected human driver cooperative driving model and the mixed vehicle group cooperative control algorithm are designed on the cloud server. The obtained data is substituted into the calculation to obtain the control input instructions for each connected automatic vehicle, and the control input instructions are sent to the on-board terminal of each connected automatic vehicle through the base station. At the same time, the status information of some vehicles is sent to the connected human driver. Then the on-board terminal and the driver receive the control input instructions or vehicle information and perform corresponding control in combination with consistency constraints and safety constraints, so that the mixed vehicle group can quickly achieve consistent and stable driving.

Images