Vehicle group formation transformation method based on vehicle-road cloud cooperative control architecture

Abstract

The invention relates to a vehicle group formation transformation method based on a vehicle-road cloud cooperative control architecture, and the method comprises the steps: obtaining the road limitation information of a vehicle and a vehicle group state, and determining the position of a limited lane and the position of the vehicle; dividing the vehicle group into a plurality of sub-vehicle groups, and determining a formation planning area of the vehicle group; a cost function is designed to evaluate the adaptation degree between the vehicle and the target position, and a distribution matrix is designed to solve the matching relation between the vehicle and the target position meeting the preset optimal condition; designing a vehicle group cooperative controller according to the vehicle motion model, the communication model, the vehicle group cooperative control protocol and the vehicle dynamics model; and generating a formation change behavior result of the vehicle group according to the distance adjusting stage and the transverse lane changing stage. Therefore, the problem that the related technology excessively depends on a pure distributed or pure centralized strategy and neglects the research of a vehicle cluster with a non-fixed structure, high plasticity and relatively high formation adjustment flexibility in a multi-lane scene is solved.

Description

Technical Field

[0001] The present application relates to the field of intelligent connected vehicle technology, and in particular to a method for changing vehicle group formation based on a vehicle-road-cloud collaborative control architecture. Background Art

[0002] Special scenarios such as restricted roads can lead to road congestion and even traffic accidents, which not only affects the safety of vehicle driving, but also reduces traffic efficiency. In this context, by utilizing multi-vehicle cooperative control technology, safety can be enhanced and traffic efficiency can be improved in the existing traffic environment. Among them, multi-lane, non-fixed formation vehicle clusters (vehicle groups) as a special research carrier of multi-vehicle cooperative control show their unique advantages. Such as the flexibility, plasticity and adaptability of formation adjustment to the traffic environment, these characteristics enable vehicle groups to effectively cope with multi-vehicle cooperation problems under complex road conditions. Therefore, in order to solve the problem of safe and efficient passage of vehicle groups in road restricted scenarios, it is particularly necessary to propose a planning and cooperative control method suitable for vehicle groups.

[0003] Among the related technologies, the multi-vehicle collaborative control method based on the vehicle-cloud collaborative architecture has become a research hotspot in the existing multi-vehicle collaborative control methods. In the networked environment, vehicles can directly receive real-time information instructions from the control center, accurately adjust the vehicle's operating status, and achieve the safety and flexibility requirements of multi-vehicle collaborative control.

[0004] However, in the case of restricted road scenarios, the research on multi-vehicle cooperative control mainly adopts two methods: distributed and centralized, but each has its own limitations. In the distributed framework, perception and information acquisition are limited, resulting in insufficient computational efficiency and optimality of multi-vehicle cooperative planning and decision-making; in the centralized method, the vehicle is only used as an execution unit, and the on-board perception and communication intelligence are not fully utilized. At the same time, in the process of vehicle behavior planning, linear models are usually used, ignoring the dynamic characteristics of vehicle motion, which needs to be improved urgently. Summary of the invention

[0005] The present application provides a vehicle group formation transformation method based on the vehicle-road-cloud collaborative control architecture to solve the current limitations of multi-vehicle collaborative control research methods in restricted road scenarios, excessive reliance on pure distributed or pure centralized strategies, and insufficient exploration of the possibility of combining the two strategies to achieve complementary advantages; most of them are carried out for multi-lane, fixed-structured vehicle queues, while ignoring the problem of research on vehicle clusters with non-fixed structures, high plasticity, and high formation adjustment flexibility in multi-lane scenarios.

[0006] The first aspect of the present application provides a method for changing the formation of a vehicle group based on a vehicle-road-cloud collaborative control architecture, comprising the following steps: obtaining the road restriction information and vehicle group status of the vehicle, and integrating the road restriction information and the vehicle group status on the edge cloud to determine the restricted lane position and the vehicle position; dividing the vehicle group into multiple sub-vehicle groups based on the restricted lane position and the vehicle position, and taking the first sub-vehicle group as the object of formation planning to evenly plan the target position in the safe lane to determine the formation planning area of ​​the vehicle group; based on the formation planning area of ​​the vehicle group, designing a cost function to evaluate the fitness between the vehicle and the target position, and designing an allocation matrix to solve the vehicle and the target position to meet the preset optimal conditions, and determine the expected formation of the vehicle group according to the fitness degree and the matching relationship; calculate the corresponding vehicle group reference state according to the expected formation of the vehicle group, establish a vehicle motion model and a communication model based on the vehicle group reference state, and design a vehicle group cooperative controller according to the vehicle motion model and the communication model, the vehicle group cooperative control protocol and the vehicle dynamics model; based on the designed vehicle group cooperative controller, design a vehicle group formation transformation rule to constrain the vehicle cooperative behavior, and divide the formation transformation cooperative process of the vehicle group into a spacing adjustment stage and a lateral lane change stage according to the constrained vehicle cooperative behavior, so as to generate the formation transformation behavior result of the vehicle group according to the spacing adjustment stage and the lateral lane change stage.

[0007] Optionally, in one embodiment of the present application, the calculation formulas for the restricted lane position and the vehicle position are respectively:

[0008]

[0009] Among them, P rl,X (k), P rl,Y (k) represent the longitudinal starting position and lateral position of restricted lane k respectively, P c(m),X (i) P c(m),Y (i) represents the longitudinal and lateral global position of vehicle i in the mth sub-group, lane res , N represent the number of vehicles in the restricted lane and vehicle group respectively.

[0010] Optionally, in one embodiment of the present application, the calculation formula of the cost function is:

[0011] cost i,k =c pos,i (k)+c lc,i (k)-c ap,i (k)

[0012] c pos,i (k) = α pos abs(P(i)-P rli(k))

[0013] c l,i,i (k) = α lc abs(P Y (i)-P rl,Y (k))

[0014] c ap,i (k) = α ap abs(P Y (i)-P sl,Y )

[0015] i=1,2,…,N,k=1,2,…,lane.

[0016] Among them, c pos Considering the relative distance between the vehicle and the target position, c lc The number of lane changes required for the vehicle to reach the target location is taken into account, and c ap represents the avoidance priority of the vehicle, that is, the number of lane changes required for the vehicle to reach the nearest safe lane, α pos , α lc , α ap Both represent cost weights, P(i)=[P X (i),P Y (i)] T Represents the position of vehicle i in the relative coordinate system within the sub-vehicle group.

[0017] Optionally, in one embodiment of the present application, the calculation formula of the allocation matrix is:

[0018]

[0019] Among them, N * represents the number of target locations, x i,j Represents the allocation factor.

[0020] Optionally, in one embodiment of the present application, the calculating the corresponding vehicle group reference state according to the expected formation of the vehicle group includes: determining the longitudinal reference state of the vehicle based on the relative longitudinal position of the vehicle that meets a preset minimum subscript condition; taking the center line of the lane where the expected position is located as a lateral indicator, and determining the lateral reference state of the vehicle according to the lateral indicator; determining the vehicle group reference state according to the longitudinal reference state and the lateral reference state.

[0021] Optionally, in one embodiment of the present application, the calculation formula of the vehicle group cooperative control protocol is:

[0022]

[0023] Among them, dij =[D ij ,L ij ] is the expected vehicle spacing, v T is the expected speed, the protocol output is the reference acceleration, k0 is the distance error gain coefficient, k1 is the velocity, v i is the speed of vehicle i, s1 and s2 are the controller orders.

[0024] Optionally, in one embodiment of the present application, the longitudinal control formula in the vehicle group cooperative control protocol and the vehicle dynamics model is:

[0025]

[0026] Among them, r1 and r2 represent the controller gain coefficients, ||u g,i || is the reference acceleration u i (t) scalar, is the acceleration deflection angle, is the velocity deviation angle, β is the mass center side slip angle, v T is the expected speed, v i is the speed of the vehicle i.

[0027] Optionally, in one embodiment of the present application, the lateral control formula in the vehicle group cooperative control protocol and the vehicle dynamics model is:

[0028]

[0029] Among them, C f , C r is the tire cornering stiffness, β f , β r is the side slip angle. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0031] Figure 1 A flowchart of a vehicle group formation transformation method based on a vehicle-road-cloud collaborative control architecture provided according to an embodiment of the present application;

[0032] Figure 2 This is a schematic diagram of a vehicle group formation change control method according to an embodiment of the present application;

[0033] Figure 3 A design diagram of a vehicle group formation change scene according to an embodiment of the present application;

[0034] Figure 4A schematic diagram of a desired formation planning according to an embodiment of the present application;

[0035] Figure 5 A schematic diagram of a reference state planning according to an embodiment of the present application;

[0036] Figure 6 A schematic diagram of a control method combined with a vehicle dynamics model according to an embodiment of the present application;

[0037] Figure 7 A schematic diagram of the division of a formation change phase according to an embodiment of the present application;

[0038] Figure 8 A schematic diagram of a formation change according to an embodiment of the present application. DETAILED DESCRIPTION

[0039] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0040] The following describes a vehicle group formation transformation method based on a vehicle-road-cloud collaborative control architecture in an embodiment of the present application with reference to the accompanying drawings. In view of the fact that the current research methods for multi-vehicle collaborative control in restricted road scenarios mentioned in the above background technology have certain limitations, excessive reliance on purely distributed or purely centralized strategies, and have not fully explored the possibility of combining the two strategies to achieve complementary advantages; most of them are carried out on multi-lane, fixed-structured vehicle queues, while ignoring the research problems of vehicle clusters with non-fixed structures, high plasticity, and high formation adjustment flexibility in multi-lane scenarios. The present application provides a vehicle group formation transformation method based on a vehicle-road-cloud collaborative control architecture. In this method, the roadside unit obtains road information and vehicle group status through beyond-visual-range perception technology, and the edge cloud performs dimensionality reduction processing on the planning problem of the desired formation, and determines the vehicle group. Formation planning area; by designing cost functions and optimization objectives, the optimal matching relationship between vehicles and target positions is solved, and then the optimal expected formation of the vehicle group is obtained, and finally the reference state of the vehicle group is calculated; then, the vehicle motion and communication model is established, combined with the vehicle group formation control objectives, and the vehicle group cooperative controller is designed based on multi-agent consistency and vehicle dynamics model; in addition, the vehicle group formation transformation rules are designed to constrain the vehicle cooperative behavior, and the various stages of the vehicle group formation transformation are divided to achieve conflict-free formation transformation, ensuring that the formation transformation control of the CAV vehicle group is carried out in the restricted road section, so that the vehicle group can complete the formation adjustment under the planning of the edge cloud, and safely pass through the restricted road section with the final expected formation. In this way, the current research methods of multi-vehicle cooperative control in restricted road scenarios have certain limitations, and the excessive reliance on pure distributed or pure centralized strategies has not been fully explored. The possibility of combining the two strategies to achieve complementary advantages has not been fully explored; most of them are carried out for multi-lane, fixed-structure vehicle queues, while ignoring the research on vehicle clusters with non-fixed structures, high plasticity, and high formation adjustment flexibility in multi-lane scenarios.

[0041] Specifically, Figure 1 A flow chart of a method for changing a vehicle group formation based on a vehicle-road-cloud collaborative control architecture provided in an embodiment of the present application.

[0042] like Figure 1 As shown, the vehicle group formation transformation method based on the vehicle-road-cloud collaborative control architecture includes the following steps:

[0043] In step S101, the road restriction information and vehicle group status of the vehicle are obtained, and the road restriction information and vehicle group status are integrated in the edge cloud to determine the restricted lane position and vehicle position.

[0044] In the actual implementation process, Figure 2As shown, in the embodiment of the present application, the restricted area information and vehicle group status can be obtained by RSU (roadside unit) using beyond-visual-range sensing technology, and integrated in the edge cloud to determine the restricted lane position P rl (k) and the vehicle position P C(m) (i).

[0045] In one embodiment of the present application, the calculation formulas for the restricted lane position and the vehicle position are respectively:

[0046]

[0047] Among them, P rl,X (k), P rl,Y (k) represent the longitudinal starting position and lateral position of restricted lane k respectively, P c(m),X (i) P c(m),Y (i) respectively represent the longitudinal and lateral global positions of vehicle i in the mth sub-group. Similarly, the position information P of the safe lane can be obtained. sl =[P sl,X ,P sl,Y ], lane res , N represent the number of vehicles in the restricted lane and vehicle group respectively.

[0048] In this embodiment of the application, a high-speed one-way three-lane scene and a group of 8 intelligent connected vehicles are used as an example. The formation change scene of the group of vehicles is as follows: Figure 3 As shown, this method is explained and introduced.

[0049] In step S102, based on the restricted lane position and the vehicle position, the vehicle group is divided into multiple sub-vehicle groups, and the first sub-vehicle group is used as the object of formation planning to evenly plan the target position in the safe lane to determine the formation planning area of ​​the vehicle group.

[0050] As a possible implementation method, the embodiment of the present application can divide the vehicle group into multiple sub-vehicle groups based on the restricted lane position and the vehicle position by introducing the concept of sub-cluster, and take the first sub-vehicle group as the object of formation planning. At the same time, based on the relative coordinate system and according to the difference in relative longitudinal position, the possible target position P pos Evenly plan in the safe lane to determine the formation planning zone of the vehicle group def ,like Figure 4 shown.

[0051] Among them, the desired formation planning in the embodiment of the present application is reduced in dimension and simplified, the vehicle group is divided into 3 similar sub-vehicle groups, and the first sub-vehicle group is selected as the formation planning object. Because the original formation of the vehicle group is an interlaced structure, it is repetitive, so the planning results of the remaining sub-vehicle groups are consistent with the formation planning results of the first sub-vehicle group, and the optimization problem after dimensionality reduction significantly reduces the calculation dimension.

[0052] In step S103, based on the formation planning area of ​​the vehicle group, a cost function is designed to evaluate the fitness between the vehicle and the target position, and an allocation matrix is ​​designed to solve the matching relationship between the vehicle and the target position that meets the preset optimal conditions, and the desired formation of the vehicle group is determined based on the fitness and the matching relationship.

[0053] In the actual implementation process, the embodiment of the present application can design a cost cost to evaluate the compatibility between vehicle i and target position k. i,k .

[0054] In one embodiment of the present application, the calculation formula of the cost function is:

[0055] cost i,k =c pos,i (k)+c lc,i (k)-c ap,i (k)

[0056] c pos,i (k) = α pos abs(P(i)-P rli (k))

[0057] c l,i,i (k) = α lc abs(P Y (i)-P rl,Y (k))

[0058] c ap,i (k) = α ap abs(P Y (i)-P sl,Y )

[0059] i=1,2,…,N,k=1,2,…,lane.

[0060] Among them, c pos Considering the relative distance between the vehicle and the target position, c lc The number of lane changes required for the vehicle to reach the target location is taken into account, and c ap represents the avoidance priority of the vehicle, that is, the number of lane changes required for the vehicle to reach the nearest safe lane, α pos , α lc , α apBoth represent cost weights, P(i)=[P X (i),P Y (i)] T represents the position of vehicle i in the relative coordinate system within the sub-vehicle group, P sl (i) = [P sl,X (i),P sl,Y (i)] T Represents the relative position of the safety lane in the local coordinates of the vehicle group.

[0061] The formation planning problem in the embodiment of the present application is transformed into the optimal matching problem between the vehicle and the target position. def , when the optimal matching relationship is formed between the vehicle and the target position, all successfully matched target positions form a set, which together constitute the expected formation. At this point, the planning problem of the expected formation of the vehicle group can be transformed into the optimal matching problem between the vehicle and the target position.

[0062] Furthermore, in order to solve the allocation problem, the embodiment of the present application may design an allocation matrix X.

[0063] In one embodiment of the present application, the calculation formula of the allocation matrix is:

[0064]

[0065] Among them, N * represents the number of target locations, x i,j represents the allocation factor. The matching problem of the desired target location requires the allocation cost cost i,k Minimum, the objective function is defined as the sum of the product of the cost function and the decision variables, and each target position can only be assigned to one vehicle. The objective function and constraints are as follows:

[0066]

[0067] Based on the above work, the problem is optimized and solved through the Hungarian algorithm, and the optimal expected formation of the vehicle group is obtained.

[0068] In step S104, the corresponding vehicle group reference state is calculated according to the desired formation of the vehicle group, and based on the vehicle group reference state, a vehicle motion model and a communication model are established. In addition, a vehicle group cooperative controller is designed according to the vehicle motion model and the communication model, the vehicle group cooperative control protocol and the vehicle dynamics model.

[0069] Specifically, if Figure 5 As shown, the embodiment of the present application can calculate the corresponding vehicle group reference state S according to the desired formation of the vehicle group. ref , based on the vehicle group reference state S ref, establish the vehicle motion model and communication model, and design the vehicle group cooperative controller based on the vehicle motion model and communication model, the vehicle group cooperative control protocol and the vehicle dynamics model, such as Figure 6 shown.

[0070] Optionally, in one embodiment of the present application, a corresponding vehicle group reference state is calculated according to the expected formation of the vehicle group, including: determining the longitudinal reference state of the vehicle based on the relative longitudinal position of the vehicle that meets a preset minimum index condition; taking the center line of the lane where the expected position is located as a lateral index, and determining the lateral reference state of the vehicle according to the lateral index; determining the vehicle group reference state according to the longitudinal reference state and the lateral reference state.

[0071] It can be understood that in the embodiment of the present application, the relative longitudinal position of the vehicle that meets the preset minimum subscript condition may be the relative longitudinal position of the minimum subscript vehicle car1.

[0072] In the actual implementation process, the embodiment of the present application can select the vehicle with the closest relative position as the minimum index vehicle car1 according to the distribution of the expected position in the vehicle group, and update the vehicle's communication number and establish a communication topology. In the longitudinal direction, the vehicle determines the longitudinal reference state based on the relative longitudinal position with car1, that is, maintains a longitudinal spacing of nD; in the lateral direction, the center line of the lane where the expected position is located is used as the lateral reference, and maintains a lateral spacing of mL with the adjacent vehicle. Among them, S ref =[nD,mL], where n and m represent the longitudinal and lateral relative position differences of the vehicle with respect to car1, respectively. The longitudinal spacing D adopts a constant spacing strategy, and the lateral spacing L is set to the width of a single lane.

[0073] It should be noted that the preset minimum subscript condition can be set by those skilled in the art according to actual conditions and is not specifically limited here.

[0074] In one embodiment of the present application, a vehicle motion model and a communication model are established, and a calculation formula for establishing a vehicle group cooperative control protocol is established as follows:

[0075]

[0076] Among them, d ij =[D ij ,L ij ] is the expected vehicle spacing, v T is the expected speed, the protocol output is the reference acceleration, k0 is the distance error gain coefficient, k1 is the velocity, v i is the speed of vehicle i, s1 and s2 are the controller orders. Combined with the vehicle kinematic model, the vehicle acceleration a is output as the vehicle control quantity. x,veh and the front wheel turning angle δi , thus realizing the design of the controller.

[0077] In one embodiment of the present application, the swarm collaborative controller combines multi-agent consistency and vehicle dynamics model. i (t), and combined with the vehicle dynamics model to achieve the following longitudinal and lateral control. Among them, the longitudinal control is based on the longitudinal motion equation. The longitudinal control formula in the vehicle group cooperative control protocol and the vehicle dynamics model is:

[0078]

[0079] Among them, r1 and r2 represent the controller gain coefficients, ||u g,i || is the reference acceleration u i (t) scalar, is the acceleration deflection angle, is the velocity deviation angle, β is the mass center side slip angle, v T is the expected speed, v i is the speed of vehicle i.

[0080] In one embodiment of the present application, the lateral control is established based on the steady-state steering motion equation, and the lateral control formula in the vehicle group cooperative control protocol and the vehicle dynamics model is:

[0081]

[0082] Among them, C f , C r is the tire cornering stiffness, β f , β r is the side slip angle.

[0083] In step S105, based on the design of the vehicle group cooperative controller, the vehicle group formation change rules are designed to constrain the vehicle cooperative behavior, and the vehicle group formation change cooperative process is divided into a spacing adjustment stage and a lateral lane change stage according to the constrained vehicle cooperative behavior, so as to generate the vehicle group formation change behavior results according to the spacing adjustment stage and the lateral lane change stage.

[0084] In the actual implementation process, the embodiment of the present application can design a vehicle group formation change rule to constrain the coordinated behavior between the vehicle groups, and based on this constraint, divide the vehicle group formation change coordination process into a spacing adjustment stage and a lateral lane change stage to achieve conflict-free formation change behavior, such as Figure 7 shown.

[0085] In the embodiment of the present application, in a restricted road section, a formation change control is performed on a CAV vehicle group, so that the vehicle group completes the formation adjustment under the planning of the edge cloud and safely passes through the restricted road section with the final desired formation.

[0086] Furthermore, in order to achieve a conflict-free vehicle group formation change process, a vehicle group formation change rule is proposed to constrain the cooperative behavior, as shown below:

[0087] Rule 1: Before receiving the formation change command sent by the RSU, the vehicle group maintains its original motion state;

[0088] Rule 2: Before a vehicle in a group changes lanes laterally, it must first ensure a longitudinal distance D from the vehicle in front of it in the target lane. ij Greater than the minimum safety distance d safe Otherwise, the longitudinal distance should be adjusted first;

[0089] Rule 3: After passing through the restricted road area, the vehicle group will restore the original staggered formation through the reverse process after the RSU issues a formation reorganization command.

[0090] Based on the constraints of the above rules, the formation change coordination process of the vehicle group is divided into the spacing adjustment stage and the lateral lane change stage, such as Figure 8 As shown in the figure. In the interval adjustment stage, after the RSU issues the interval adjustment command, within the sub-cluster, according to the relative position of the expected formation planned by the edge cloud, each vehicle coordinates the longitudinal interval to the expected interval D through the collaborative controller deployed on the vehicle side. In addition, the distance between the front and rear sub-clusters is coordinated by the last vehicle and the first vehicle respectively, so that the longitudinal interval between the sub-clusters is the expected interval D. At the same time, during the whole process, the vehicle takes the expected speed as a reference and finally adjusts to the expected speed v T .

[0091] When the edge cloud observes that the vehicle group has completed the spacing adjustment, it sends a lateral lane change command to the vehicle group through the RSU. In the lateral lane change phase, the vehicles change lanes autonomously at the same time through the vehicle group collaborative controller according to the planned lateral reference lane. When all vehicles reach the target position, the formation change of the vehicle group is completed.

[0092] According to a vehicle group formation transformation method based on a vehicle-road-cloud collaborative control architecture proposed in an embodiment of the present application, the roadside unit obtains road information and vehicle group status through beyond-visual-range perception technology, and the edge cloud performs dimensionality reduction processing on the planning problem of the desired formation and determines the formation planning area of ​​the vehicle group; by designing a cost function and an optimization objective, the optimal matching relationship between the vehicle and the target position is solved, and then the optimal desired formation of the vehicle group is obtained, and finally the reference state of the vehicle group is calculated; then, a vehicle motion and communication model is established, combined with the vehicle group formation control objective, and a vehicle group collaborative controller is designed based on multi-agent consistency and vehicle dynamics model; in addition, a vehicle group formation transformation rule is designed to constrain the vehicle collaborative behavior and divide the various stages of the vehicle group formation transformation, so as to achieve conflict-free formation transformation. This solves the problem that the current research methods of multi-vehicle cooperative control in restricted road scenarios have certain limitations, such as excessive reliance on purely distributed or purely centralized strategies, and the possibility of combining the two strategies to achieve complementary advantages has not been fully explored; most of them are carried out on multi-lane, fixed-structured vehicle queues, while ignoring the problem of research on vehicle clusters with non-fixed structures, high plasticity, and high formation adjustment flexibility in multi-lane scenarios.

[0093] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0094] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0095] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

Claims

1. A vehicle group formation transformation method based on a vehicle-road-cloud collaborative control architecture, characterized in that: The following steps are involved: Obtaining road restriction information and vehicle group status of the vehicle, and integrating the road restriction information and the vehicle group status in the edge cloud to determine the restricted lane position and vehicle position; Based on the restricted lane position and the vehicle position, the vehicle group is divided into a plurality of sub-vehicle groups, and the target position is evenly planned in the safe lane with the first sub-vehicle group as the object of formation planning, so as to determine the formation planning area of ​​the vehicle group; Based on the formation planning area of ​​the vehicle group, a cost function is designed to evaluate the degree of fit between the vehicle and the target position, and an allocation matrix is ​​designed to solve the matching relationship between the vehicle and the target position that satisfies a preset optimal condition, and the desired formation of the vehicle group is determined according to the degree of fit and the matching relationship; Calculating a corresponding vehicle group reference state according to the desired formation of the vehicle group, establishing a vehicle motion model and a communication model based on the vehicle group reference state, and designing a vehicle group cooperative controller according to the vehicle motion model and the communication model, the vehicle group cooperative control protocol and the vehicle dynamics model; Based on the designed vehicle group cooperative controller, a vehicle group formation change rule is designed to constrain the vehicle cooperative behavior, and the vehicle group formation change cooperative process is divided into a spacing adjustment stage and a lateral lane change stage according to the constrained vehicle cooperative behavior, so as to generate the vehicle group formation change behavior result according to the spacing adjustment stage and the lateral lane change stage.

2. The method according to claim 1, characterized in that The calculation formulas for the restricted lane position and the vehicle position are respectively: Among them, P rl,X (k), P rl,Y (k) represent the longitudinal starting position and lateral position of restricted lane k respectively, P c(m),X (i) P c(m),Y (i) represents the longitudinal and lateral global position of vehicle i in the mth sub-group, lane res , N represent the number of vehicles in the restricted lane and vehicle group respectively.

3. The method according to claim 1, characterized in that The calculation formula of the cost function is: cost i,k =c pos,i (k)+c lc,i (k)-c ap,i (k) c pos,i (k)=α pos abs(P(i)-P rli (k)) c l,i,i (k)=α lc abs(P Y (i)-P rl,Y (k)) c ap,i (k)=α ap abs(P Y (i)-P sl,Y ) i=1,2,…,N,k=1,2,…,lane. Among them, c pos Considering the relative distance between the vehicle and the target position, c lc The number of lane changes required for the vehicle to reach the target location is taken into account, and c ap represents the avoidance priority of the vehicle, that is, the number of lane changes required for the vehicle to reach the nearest safe lane, α pos , α lc , α ap Both represent cost weights, P(i)=[P X (i),P Y (i)] T Represents the position of vehicle i in the relative coordinate system within the sub-vehicle group.

4. The method according to claim 1, characterized in that: The calculation formula of the allocation matrix is: Among them, N * represents the number of target locations, x i,j Represents the allocation factor.

5. The method according to claim 1, characterized in that The calculating a corresponding vehicle group reference state according to the desired formation of the vehicle group includes: Determining a longitudinal reference state of the vehicle based on a relative longitudinal position of the vehicle that satisfies a preset minimum index condition; Taking the center line of the lane where the expected position is located as a lateral index, and determining the lateral reference state of the vehicle according to the lateral index; The vehicle group reference state is determined according to the longitudinal reference state and the lateral reference state.

6. The method according to claim 1, characterized in that The calculation formula of the vehicle group cooperative control protocol is: Among them, d ij =[D ij ,L ij ] is the expected vehicle spacing, v T is the expected speed, the protocol output is the reference acceleration, k0 is the distance error gain coefficient, k1 is the velocity, v i is the speed of vehicle i, s1 and s2 are the controller orders.

7. The method according to claim 6, characterized in that The longitudinal control formula in the vehicle group cooperative control protocol and the vehicle dynamics model is: Among them, r1 and r2 represent the controller gain coefficients, ||u g,i || is the reference acceleration u i (t) scalar, is the acceleration deflection angle, is the velocity deviation angle, β is the mass center side slip angle, v T is the expected speed, v i is the speed of the vehicle i.

8. The method according to claim 1, characterized in that: The lateral control formula in the vehicle group cooperative control protocol and the vehicle dynamics model is: Among them, C f , C r is the tire cornering stiffness, β f , β r is the side slip angle.

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