A method for vehicle formation transformation based on vehicle-road-cloud cooperative control architecture

Abstract

This application relates to a vehicle platoon formation transformation method based on a vehicle-road-cloud cooperative control architecture. The method includes: acquiring road restriction information and vehicle platoon status, determining the location of restricted lanes and vehicle positions; dividing the vehicle platoon into multiple sub-platoons, determining the formation planning area of ​​each platoon; designing a cost function to evaluate the fit between vehicles and target positions, and designing an allocation matrix to solve for the matching relationship between vehicles and target positions satisfying preset optimal conditions; designing a vehicle platoon cooperative controller based on vehicle motion and communication models, a vehicle platoon cooperative control protocol, and a vehicle dynamics model; and generating the formation transformation behavior results of the vehicle platoon based on the spacing adjustment stage and the lateral lane-changing stage. This addresses the problem of related technologies over-relying on purely distributed or purely centralized strategies, neglecting the research on vehicle platoons with non-fixed structures, high plasticity, and high formation adjustment flexibility in multi-lane scenarios.

Description

Technical Field

[0001] This application relates to the field of intelligent connected vehicle technology, and in particular to a method for vehicle formation transformation based on a vehicle-road-cloud collaborative control architecture. Background Technology

[0002] Special scenarios such as road restrictions can lead to traffic congestion and even traffic accidents, affecting both vehicle safety and traffic efficiency. Against this backdrop, multi-vehicle cooperative control technology can enhance safety and improve traffic efficiency in existing traffic environments. Among these, multi-lane, non-fixed-formation vehicle groups (vehicle platoons) serve as a special research vehicle for multi-vehicle cooperative control, demonstrating unique advantages. These advantages include flexibility, adaptability, and resilience to traffic conditions, enabling vehicle platoons to effectively address multi-vehicle cooperation issues under complex road conditions. Therefore, proposing a planning and cooperative control method suitable for vehicle platoons is particularly necessary to solve the problem of safe and efficient passage of vehicle platoons in road-restricted scenarios.

[0003] In related technologies, multi-vehicle cooperative control methods based on vehicle-cloud collaborative architecture have become a research hotspot. In a connected environment, vehicles can directly receive real-time information commands from the control center, accurately adjust their operating status, and meet the safety and flexibility requirements of multi-vehicle cooperative control.

[0004] However, in restricted road scenarios, research on multi-vehicle cooperative control mainly employs two methods: distributed and centralized, each with its own limitations. In the distributed framework, limited perception and information acquisition lead to insufficient computational efficiency and optimality in multi-vehicle cooperative planning and decision-making. In the centralized method, vehicles are merely execution units, failing to fully utilize onboard perception and communication intelligence. Furthermore, the use of linear models in vehicle behavior planning often neglects the dynamic characteristics of vehicle motion, which urgently needs improvement. Summary of the Invention

[0005] This application provides a vehicle formation transformation method based on a vehicle-road-cloud cooperative control architecture to address the limitations of current research methods for multi-vehicle cooperative control in restricted road scenarios. These methods rely excessively 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 these methods focus on multi-lane vehicle platoons with fixed structures, while neglecting the research on vehicle clusters with non-fixed structures, high plasticity, and high flexibility in formation adjustment in multi-lane scenarios.

[0006] The first aspect of this application provides a vehicle group formation transformation method based on a vehicle-road-cloud cooperative control architecture, comprising the following steps: acquiring road restriction information and vehicle group status, and integrating the road restriction information and vehicle group status in the edge cloud to determine the restricted lane position and vehicle position; based on the restricted lane position and vehicle position, dividing the vehicle group into multiple sub-vehicle groups, and uniformly planning the target position within a safe lane using the first sub-vehicle group as the formation planning object 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 fit between the vehicle and the target position, and designing an allocation matrix to solve for the vehicle and the target position satisfying a preset optimal... The system determines the desired formation of the vehicle group based on the matching relationship of the conditions and the fitness and the matching relationship; calculates the corresponding vehicle group reference state based on the desired formation of the vehicle group; establishes a vehicle motion model and a communication model based on the vehicle group reference state; designs a vehicle group cooperative controller based on the vehicle motion model, the communication model, the vehicle group cooperative control protocol, and the vehicle dynamics model; designs a vehicle group formation transformation rule based on the designed vehicle group cooperative controller to constrain vehicle cooperative behavior; and divides the formation transformation cooperative process of the vehicle group into a spacing adjustment stage and a lateral lane change stage based on the constrained vehicle cooperative behavior, so as to generate the formation transformation behavior result of the vehicle group based on the spacing adjustment stage and the lateral lane change stage.

[0007] Optionally, in one embodiment of this 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) represents the longitudinal starting position and the lateral position of the restricted lane k, respectively. P c(m),X (i), P c(m),Y (i) represent the longitudinal and lateral global positions of vehicle i in the m-th subgroup, respectively. res N and N represent the number of vehicles in the restricted lane and the group of vehicles, respectively.

[0010] Optionally, in one embodiment of this application, the cost function is calculated as follows:

[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 Taking into account the relative distance between the vehicle and the target location, c lc The number of lane changes required for the vehicle to reach the target location was taken into account, and c ap This indicates the vehicle's avoidance priority, specifically 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 This represents the position of vehicle i in the relative coordinate system within the subgroup of vehicles.

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

[0018]

[0019] Where, N * x represents the number of target locations. i,j This represents the allocation factor.

[0020] Optionally, in one embodiment of this application, the step of calculating the corresponding vehicle group reference state based on the desired formation of the vehicle group includes: determining the longitudinal reference state of the vehicle based on the relative longitudinal position of the vehicle that satisfies the preset minimum index condition; using the centerline of the lane where the desired position is located as the lateral index, and determining the lateral reference state of the vehicle based on the lateral index; and determining the vehicle group reference state based on the longitudinal reference state and the lateral reference state.

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

[0022]

[0023] Where, dij =[D ij ,L ij ] represents the desired vehicle spacing, v T For the desired speed, the protocol output item For reference acceleration, k0 is the distance error gain coefficient, k1 is the velocity, and v i Let be the speed of vehicle i, and s1 and s2 be the controller orders.

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

[0025]

[0026] Where r1 and r2 represent the controller gain coefficients, ||u g,i || is the reference acceleration u i (t) scalar, For acceleration deflection angle, β is the velocity deflection angle, β is the sideslip angle of the center of mass, v T For the desired speed, v i Let i be the speed of the vehicle.

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

[0028]

[0029] Among them, C f C r For tire lateral stiffness, β f β r It is the sideslip angle. Attached Figure Description

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

[0031] Figure 1 This is a flowchart of a vehicle formation transformation method based on a vehicle-road-cloud cooperative control architecture provided in an embodiment of this application;

[0032] Figure 2 This is an architecture diagram of a vehicle formation change control method according to an embodiment of this application;

[0033] Figure 3 This is a design diagram of a vehicle formation transformation scenario according to an embodiment of this application;

[0034] Figure 4This is a schematic diagram of a desired formation planning according to one embodiment of this application;

[0035] Figure 5 This is a schematic diagram of a reference state planning according to an embodiment of this application;

[0036] Figure 6 This is a schematic diagram of a vehicle dynamics model control method according to an embodiment of this application;

[0037] Figure 7 This is a schematic diagram illustrating the division of formation transformation stages according to an embodiment of this application;

[0038] Figure 8 This is a schematic diagram of a formation transformation according to an embodiment of this application. Detailed Implementation

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

[0040] The following description, with reference to the accompanying drawings, illustrates a vehicle formation transformation method based on a vehicle-road-cloud cooperative control architecture, according to an embodiment of this application. Addressing the limitations of current research methods for multi-vehicle cooperative control in restricted road scenarios mentioned in the background, which over-rely on purely distributed or purely centralized strategies and haven't fully explored the possibility of combining these two strategies to achieve complementary advantages; and focusing primarily on multi-lane vehicle platoons with fixed structures while neglecting the challenges of studying vehicle clusters with non-fixed structures, high flexibility, and greater platooning adjustment flexibility in multi-lane scenarios, this application provides a vehicle formation transformation method based on a vehicle-road-cloud cooperative control architecture. In this method, roadside units acquire road information and vehicle group status through beyond-line-of-sight perception technology, and the edge cloud performs dimensionality reduction processing on the desired formation planning problem and determines the vehicle group's... The formation planning area involves designing a cost function and optimization objective to solve for the optimal matching relationship between vehicles and target positions, thereby obtaining the optimal expected formation of the vehicle group and finally calculating the reference state of the vehicle group. Next, a vehicle motion and communication model is established, combined with the vehicle group formation control objective, and a vehicle group cooperative controller is designed based on multi-agent consensus and vehicle dynamics models. Furthermore, vehicle group formation transformation rules are designed to constrain vehicle cooperative behavior and divide the formation transformation into various stages, thereby achieving conflict-free formation transformation. This ensures that in restricted road sections, formation transformation control for CAV vehicle groups is performed, allowing the vehicle group to complete formation adjustment under edge cloud planning and safely pass through the restricted road section with the final expected formation. This addresses the limitations of current multi-vehicle cooperative control research methods in restricted road scenarios, such as over-reliance on purely distributed or purely centralized strategies, insufficient exploration of the possibility of combining the two strategies to achieve complementary advantages, and a focus on multi-lane, fixed-structure vehicle platoons while neglecting research on multi-lane vehicle clusters with non-fixed structures, high plasticity, and high formation adjustment flexibility.

[0041] Specifically, Figure 1 This is a flowchart illustrating a vehicle formation transformation method based on a vehicle-road-cloud cooperative control architecture provided in an embodiment of this application.

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

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

[0044] In actual implementation, such as Figure 2As shown, this embodiment of the application can use the Roadside Unit (RSU) to acquire information on restricted areas and vehicle group status through beyond-line-of-sight perception technology, and integrate this information at the edge cloud to determine the location P of the restricted lane. rl (k) and vehicle position P C(m) (i).

[0045] In one embodiment of this application, the formulas for calculating the restricted lane position and the vehicle position are as follows:

[0046]

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

[0048] In this embodiment, a vehicle group consisting of eight intelligent connected vehicles is used as an example in a high-speed, one-way, three-lane scenario. The formation change scenario of the vehicle group is as follows: Figure 3 As shown, this method will be explained and introduced.

[0049] In step S102, based on the restricted lane position and vehicle position, the vehicle group is divided into multiple sub-vehicle groups, and the target position is evenly planned within 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.

[0050] As one possible implementation, embodiments of this application can divide a vehicle group into multiple sub-groups based on the location of the restricted lane and the vehicle location, using the first sub-group as the object of formation planning. Simultaneously, based on a relative coordinate system and according to the difference in relative longitudinal position, the possible target position P is determined. pos The vehicles are evenly spaced within the safe lanes to determine the platooning zone. def ,like Figure 4 As shown.

[0051] In this embodiment, the desired formation planning is reduced in dimensionality and simplified by dividing the vehicle group into three similar sub-groups and selecting the first sub-group as the formation planning object. Since the original formation of the vehicle group is an interleaved structure with repetition, the planning results of the remaining sub-groups are consistent with the formation planning results of the first sub-group. The optimization problem after dimensionality reduction significantly reduces the computational dimension.

[0052] In step S103, based on the formation planning area of ​​the vehicle group, a cost function is designed to evaluate the 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 the preset optimal conditions. The desired formation of the vehicle group is determined according to the fit and the matching relationship.

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

[0054] In one embodiment of this application, the formula for calculating the cost function is as follows:

[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 Taking into account the relative distance between the vehicle and the target location, c lc The number of lane changes required for the vehicle to reach the target location was taken into account, and c ap This indicates the vehicle's yield priority, specifically the number of lane changes required for a 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 P represents the position of vehicle i in the relative coordinate system within the subgroup of vehicles. sl (i)=[P sl,X (i),P sl,Y (i)] T This represents the relative position of the safe lane in the local coordinates of the vehicle group.

[0061] The formation planning problem in this application embodiment is transformed into an optimal matching problem between vehicles and target positions, based on the designed formation planning zone. def When an optimal matching relationship is formed between a vehicle and a target location, all successfully matched target locations form a set, which together constitutes the desired formation. Thus, the problem of planning the desired formation of the vehicle group can be transformed into the problem of optimal matching between vehicles and target locations.

[0062] Furthermore, in this embodiment of the application, an allocation matrix X can be designed to solve the allocation problem.

[0063] In one embodiment of this application, the formula for calculating the allocation matrix is:

[0064]

[0065] Where, N * x represents the number of target locations. i,j This represents the allocation factor. The matching problem for the desired target location requires allocating a cost. i,k The objective function is defined as the sum of the product of the cost function and the decision variables, and each target location 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 solved by optimizing 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. Based on the vehicle group reference state, a vehicle motion model and a communication model are established. A vehicle group cooperative controller is designed based on the vehicle motion model, the communication model, the vehicle group cooperative control protocol, and the vehicle dynamics model.

[0069] Specifically, such as Figure 5 As shown, in this embodiment of the application, the corresponding vehicle group reference state S can be calculated based on the desired formation of the vehicle group. ref Based on the vehicle group reference state S refEstablish vehicle motion and communication models, and design a vehicle group cooperative controller based on the vehicle motion and communication models, vehicle group cooperative control protocol, and vehicle dynamics model, such as... Figure 6 As shown.

[0070] Optionally, in one embodiment of this application, calculating the corresponding vehicle group reference state based on the desired formation of the vehicle group includes: determining the longitudinal reference state of the vehicles based on the relative longitudinal positions of the vehicles that meet the preset minimum index condition; using the centerline of the lane where the desired position is located as the lateral index, and determining the lateral reference state of the vehicles based on the lateral index; and determining the vehicle group reference state based on the longitudinal reference state and the lateral reference state.

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

[0072] In actual implementation, this embodiment can select the vehicle with the highest relative position as the minimum index vehicle car1 based on the distribution of the desired position in the vehicle group, update the vehicle's communication number, and establish a communication topology. Longitudinally, the vehicle determines its longitudinal reference state based on its relative longitudinal position to car1, maintaining a longitudinal distance of nD. Laterally, the centerline of the lane where the desired position is located is used as a lateral reference, and a lateral distance of mL is maintained with adjacent vehicles. Wherein, S... ref = [nD,mL], where n and m represent the longitudinal and lateral relative position differences of the vehicle relative 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 the actual situation, and no specific restrictions are imposed here.

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

[0075]

[0076] Where, d ij =[D ij ,L ij ] represents the desired vehicle spacing, v T For the desired speed, the protocol output item For reference acceleration, k0 is the distance error gain coefficient, k1 is the velocity, and v i Let be the vehicle velocity i, and s1 and s2 be the controller orders. Based on the vehicle kinematics model, the vehicle acceleration 'a' is output as the vehicle control variable. x,veh With front wheel steering angle δi This allows for the design of the controller.

[0077] In one embodiment of this application, the vehicle group cooperative controller combines multi-agent consensus and a vehicle dynamics model. A vehicle group control protocol is designed based on multi-agent consensus. i (t), and combined with the vehicle dynamics model, the following longitudinal and lateral control are achieved. The longitudinal control is based on the longitudinal motion equation, and the longitudinal control formula in the vehicle group cooperative control protocol and vehicle dynamics model is:

[0078]

[0079] Where r1 and r2 represent the controller gain coefficients, ||u g,i || is the reference acceleration u i (t) scalar, For acceleration deflection angle, β is the velocity deflection angle, β is the sideslip angle of the center of mass, v T For the desired speed, v i Let i be the speed of vehicle i.

[0080] In one embodiment of this 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 vehicle dynamics model is as follows:

[0081]

[0082] Among them, C f C r For tire lateral stiffness, β f β r It is the sideslip angle.

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

[0084] In actual implementation, embodiments of this application can design vehicle formation change rules to constrain the cooperative behavior between vehicle groups. Based on these constraints, the vehicle group's formation change coordination process is divided into a spacing adjustment stage and a lateral lane-changing stage to achieve conflict-free formation change behavior, such as... Figure 7 As shown.

[0085] In this embodiment of the application, formation change control is performed on CAV vehicle groups in restricted road sections, so that the vehicle groups complete formation adjustment under the planning of edge cloud and safely pass through the restricted road section in the final desired formation.

[0086] Furthermore, in order to achieve conflict-free vehicle formation transformation, vehicle formation transformation rules are proposed to constrain cooperative behavior, as follows:

[0087] Rule 1: The vehicle group shall maintain its original state of motion until it receives a formation change instruction from the RSU;

[0088] Rule 2: Before making a lateral lane change, vehicles in a group of vehicles must first ensure a longitudinal distance D from the vehicle in front 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 a restricted area, the vehicle group shall restore its original staggered formation by reversing the process after the RSU issues a formation reorganization instruction.

[0090] Based on the constraints of the above rules, the formation change coordination process of the vehicle group is divided into a spacing adjustment stage and a lateral lane-changing stage, such as... Figure 8 As shown. During the spacing adjustment phase, after the RSU issues the spacing adjustment command, within the sub-cluster, based on the relative positions of the desired formation planned in the edge cloud, each vehicle coordinates its longitudinal spacing to the desired spacing D through the cooperative controller deployed on the vehicle. Furthermore, the spacing between the two sub-clusters is coordinated through the last vehicle and the first vehicle, respectively, to achieve the desired longitudinal spacing D between the sub-clusters. Simultaneously, throughout the entire process, the vehicles use the desired speed as a reference and ultimately adjust to the desired speed v. T .

[0091] Once the edge cloud observes that the vehicle group has completed its spacing adjustment, it issues a lateral lane-changing command to the vehicle group via the RSU. During the lateral lane-changing phase, vehicles autonomously change lanes simultaneously using the vehicle group cooperative controller, based on the planned lateral reference lane. Once all vehicles have reached their target positions, the formation change of the vehicle group is complete.

[0092] According to an embodiment of this application, a vehicle group formation transformation method based on a vehicle-road-cloud cooperative control architecture is proposed. The roadside unit acquires road information and vehicle group status through beyond-line-of-sight perception technology. The edge cloud performs dimensionality reduction processing on the desired formation planning problem and determines the vehicle group's formation planning area. By designing a cost function and optimization objective, the optimal matching relationship between vehicles and target positions is solved to obtain the optimal desired formation of the vehicle group, and finally, the reference state of the vehicle group is calculated. Next, a vehicle motion and communication model is established, combined with the vehicle group formation control objective, and a vehicle group cooperative controller is designed based on multi-agent consensus and vehicle dynamics models. Furthermore, vehicle group formation transformation rules are designed to constrain vehicle cooperative behavior and divide the various stages of vehicle group formation transformation, thereby achieving conflict-free formation transformation. This addresses the limitations of current research methods for multi-vehicle cooperative control in restricted road scenarios, which rely excessively 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 these methods focus on multi-lane vehicle platoons with fixed structures, while neglecting the research on vehicle clusters with non-fixed structures, high plasticity, and high flexibility in formation adjustment in multi-lane scenarios.

[0093] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

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

[0095] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

Claims

1. A method for vehicle formation transformation based on a vehicle-road-cloud cooperative control architecture, characterized in that, Includes the following steps: Acquire road restriction information and vehicle group status for vehicles, and integrate the road restriction information and vehicle group status in the edge cloud to determine the location of restricted lanes and vehicle locations; Based on the restricted lane location and the vehicle location, the vehicle group is divided into multiple sub-vehicle groups, and the target location is evenly planned within the safe lane using 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 vehicle formation planning area, a cost function is designed to evaluate the fit between the vehicles and the target positions, and an allocation matrix is ​​designed to solve for the matching relationship between the vehicles and the target positions that satisfies the preset optimal conditions. The desired formation of the vehicle group is then determined based on the fit and the matching relationship. The calculation formula for the cost function is as follows: in, c pos The relative distance between the vehicle and the target location was taken into account. c lc The number of lane changes required for the vehicle to reach the target location was taken into account, and c ap This indicates the vehicle's avoidance priority, which 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. Representative vehicle i Position in the relative coordinate system within the sub-vehicle group; The formula for calculating the allocation matrix is ​​as follows: in, This indicates the number of target locations. Indicates the allocation factor; Calculate the corresponding vehicle group reference state based on the desired 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 based on the vehicle motion model, the communication model, the vehicle group cooperative control protocol and the vehicle dynamics model. Based on the designed vehicle group cooperative controller, vehicle group formation transformation rules are designed to constrain vehicle cooperative behavior. According to the constrained vehicle cooperative behavior, the formation transformation cooperative process of the vehicle group is divided into a spacing adjustment stage and a lateral lane change stage, 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.

2. The method according to claim 1, characterized in that, The formulas for calculating the restricted lane position and the vehicle position are as follows: in, , These represent restricted lanes. The starting position in the vertical direction and its horizontal position. , They represent the first m Vehicles in a group i The global position in both the vertical and horizontal directions. , These represent the number of vehicles in the restricted lane and the number of vehicles in the group, respectively.

3. The method according to claim 1, characterized in that, The step of calculating the corresponding vehicle group reference state based on the desired formation of the vehicle group includes: The longitudinal reference state of the vehicle is determined based on the relative longitudinal position of the vehicle that meets the preset minimum subscript condition. Using the centerline of the lane where the desired position is located as a lateral reference, the lateral reference state of the vehicle is determined based on the lateral reference. The vehicle group reference state is determined based on the longitudinal reference state and the lateral reference state.

4. The method according to claim 1, characterized in that, The calculation formula for the vehicle group cooperative control protocol is as follows: in, d ij = [ D ij , L ij [This represents the desired vehicle spacing.] v T For the desired speed, the protocol output item For reference acceleration, This is the distance error gain coefficient. For speed, For vehicles i speed, This represents the controller order.

5. The method according to claim 4, characterized in that, The longitudinal control formula in the vehicle group cooperative control protocol and the vehicle dynamics model is as follows: in, r 1. r 2 represents the controller gain coefficient. For reference acceleration scalar For acceleration deflection angle, For velocity deflection angle, The sideslip angle is the angle of the centroid. v T For the desired speed, For the vehicle i speed.

6. 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 as follows: in, C f , C r For tire lateral stiffness, β f 、β r It is the sideslip angle.

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