Hierarchical Decision-making and Control Method and Device for Intelligent Connected Vehicles Based on Vehicle-Cloud Collaboration

Abstract

The present application discloses a hierarchical decision-making and control method, device, electronic device, and storage medium for an intelligent connected vehicle based on vehicle-cloud collaboration. Among them, the method includes: by establishing a relative coordinate system, planning the vehicle fleet movement plane as equally spaced grids in the relative coordinate system; using the equally spaced grids in the relative coordinate system to perform collaborative path planning for the vehicle to be planned, obtaining the planned path for the vehicle to be planned to reach the target position; projecting the relative coordinate system onto the absolute coordinate system, obtaining the absolute coordinate points of the planned path in the absolute coordinate system, and calculating the motion trajectory planning of the vehicle to be planned according to the absolute coordinate points and the target planning time of the vehicle to be planned, and controlling the vehicle to be planned to move to the target position according to the motion trajectory planning. Thus, the collaborative planning of the intelligent connected vehicle and the cloud platform is realized, and the overall performance of the traffic system is improved. Thereby, problems such as the collaborative planning of the intelligent cloud-connected vehicle and the platform are solved.

Description

Technical Field

[0001] This application relates to the technical field of vehicle collaborative planning, and particularly to a hierarchical decision-making and control method, device, electronic device, and storage medium for intelligent connected vehicles based on vehicle-cloud collaboration. Background Art

[0002] The driving efficiency and safety of autonomous vehicles in complex environments rely on reliable decision-making and control technologies. In a traffic environment with mixed traffic of multiple vehicles, autonomous driving methods relying on autonomous decision-making are difficult to take into account the optimization goals of multiple vehicles, and it is easy to have multi-vehicle decision-making conflicts that are difficult to resolve. With the help of intelligent connected cloud control technology, after centrally obtaining all vehicle information in a certain range of the traffic system, the cloud platform can perform centralized assignment and planning, comprehensively optimize the driving goals and operating performance of multiple vehicles, and improve the overall performance of the traffic system.

[0003] Existing centralized traffic optimization methods arrange all tasks in a centralized computing unit, with high computational complexity and difficulty in real-time application. The fundamental reason is that they model the behavior planning of multiple vehicles as a multi-objective optimization problem, which has a high dimension, numerous parameters, and complex constraints, severely restricting the solution efficiency and urgently needing to be solved. Summary of the Invention

[0004] This application provides a hierarchical decision-making and control method, device, electronic device, and storage medium for intelligent connected vehicles based on vehicle-cloud collaboration to solve problems such as collaborative planning of intelligent cloud-connected vehicles and platforms.

[0005] In a first aspect embodiment of this application, a hierarchical decision-making and control method for intelligent connected vehicles based on vehicle-cloud collaboration is provided, including the following steps: establishing a relative coordinate system using the cloud, and planning the vehicle fleet movement plane as equally spaced grids in the relative coordinate system; performing collaborative path planning for the vehicle to be planned using the equally spaced grids in the relative coordinate system to obtain a planned path for the vehicle to be planned to reach the target position; projecting the relative coordinate system to an absolute coordinate system using the vehicle end to obtain absolute coordinate points of the planned path in the absolute coordinate system, and calculating a motion trajectory plan for the vehicle to be planned according to the absolute coordinate points and the target planning time of the vehicle to be planned, and controlling the vehicle to be planned to move to the target position according to the motion trajectory plan.

[0006] Optionally, in an embodiment of this application, the performing collaborative path planning for the vehicle to be planned using the equally spaced grids in the relative coordinate system to obtain a planned path for the vehicle to be planned to reach the target position includes: matching the vehicle to be planned with the target position using the Hungarian algorithm; planning a relative path for the vehicle to be planned to reach the target position using the A* algorithm.

[0007] Optionally, in an embodiment of the present application, calculating the motion trajectory planning of the vehicle to be planned according to the absolute coordinate points and the target planning time of the vehicle to be planned includes: connecting a plurality of absolute coordinate points by using a Bezier curve, controlling the lateral corner input of the vehicle to be planned through a proportional-integral-derivative controller, and using the length of the Bezier curve to represent the driving distance of the vehicle to be planned; solving the longitudinal control input of the vehicle to be planned based on an optimal control method with a preset fixed initial state and a fixed final state to generate the motion trajectory planning of the vehicle to be planned.

[0008] Optionally, in an embodiment of the present application, controlling the vehicle to be planned to move to the target position according to the motion trajectory planning includes: generating a speed sequence or an acceleration sequence and a front wheel corner sequence for the movement of the vehicle to be planned according to the motion trajectory planning; controlling the vehicle to be planned to move to the target position according to the speed sequence or the acceleration sequence and the front wheel corner sequence.

[0009] An embodiment of the second aspect of the present application provides an intelligent connected vehicle hierarchical decision-making and control device based on vehicle-cloud collaboration, including: an establishment module, configured to establish a relative coordinate system by using the cloud and plan the vehicle fleet movement plane as equally spaced grids in the relative coordinate system; a planning module, configured to perform collaborative path planning on the vehicle to be planned by using the equally spaced grids in the relative coordinate system to obtain a planned path for the vehicle to be planned to reach the target position; a control module, configured to project the relative coordinate system to an absolute coordinate system by using the vehicle terminal to obtain absolute coordinate points of the planned path in the absolute coordinate system, calculate the motion trajectory planning of the vehicle to be planned according to the absolute coordinate points and the target planning time of the vehicle to be planned, and control the vehicle to be planned to move to the target position according to the motion trajectory planning.

[0010] Optionally, in an embodiment of the present application, the planning module includes: a matching unit, configured to match the vehicle to be planned with the target position by using the Hungarian algorithm; a path planning unit, configured to plan the relative path for the vehicle to be planned to reach the target position by using the A* algorithm.

[0011] Optionally, in an embodiment of the present application, calculating the motion trajectory planning of the vehicle to be planned according to the absolute coordinate points and the target planning time of the vehicle to be planned includes: connecting a plurality of absolute coordinate points by using a Bezier curve, controlling the lateral corner input of the vehicle to be planned through a proportional-integral-derivative controller, and using the length of the Bezier curve to represent the driving distance of the vehicle to be planned; solving the longitudinal control input of the vehicle to be planned based on an optimal control method with a preset fixed initial state and a fixed final state to generate the motion trajectory planning of the vehicle to be planned.

[0012] Optionally, in an embodiment of the present application, controlling the vehicle to be planned to move to the target position according to the motion trajectory planning includes: generating a speed sequence or an acceleration sequence and a front wheel steering angle sequence for the movement of the vehicle to be planned according to the motion trajectory planning; controlling the vehicle to be planned to move to the target position according to the speed sequence or the acceleration sequence and the front wheel steering angle sequence.

[0013] An embodiment of the third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the program to perform the method for hierarchical decision-making and control of an intelligent networked vehicle based on vehicle-cloud collaboration as described in the above embodiments.

[0014] An embodiment of the fourth aspect of the present application provides a computer-readable storage medium, on which a computer program is stored, and the program is executed by a processor to perform the method for hierarchical decision-making and control of an intelligent networked vehicle based on vehicle-cloud collaboration as described in the above embodiments.

[0015] Therefore, the present application has at least the following beneficial effects:

[0016] By establishing a relative coordinate system, the movement plane of the vehicle fleet is planned as equally spaced grids in the relative coordinate system; the vehicle to be planned is collaboratively path-planned using the equally spaced grids in the relative coordinate system to obtain a planned path for the vehicle to be planned to reach the target position; the relative coordinate system is projected onto the absolute coordinate system to obtain the absolute coordinate points of the planned path in the absolute coordinate system, and the motion trajectory planning of the vehicle to be planned is calculated based on the absolute coordinate points and the target planning time of the vehicle to be planned, and the vehicle to be planned is controlled to move to the target position according to the motion trajectory planning. Thus, the collaborative planning of intelligent networked vehicles and cloud platforms is realized, and the overall performance of the traffic system is improved. Thereby, problems such as the collaborative planning of intelligent cloud networked vehicles and platforms are solved.

[0017] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be understood through the practice of the present application. Description of the Drawings

[0018] The above and / or additional aspects and advantages of the present application will become apparent and easy to understand from the following description of the embodiments in conjunction with the drawings, where:

[0019] Figure 1 is a flowchart of a method for hierarchical decision-making and control of an intelligent networked vehicle based on vehicle-cloud collaboration according to an embodiment of the present application;

[0020] Figure 2 is a schematic diagram of the relationship between relative coordinates and absolute coordinates according to an embodiment of the present application;

[0021] Figure 3 It is an example diagram of a hierarchical decision-making and control device for an intelligent connected vehicle based on vehicle-cloud collaboration according to an embodiment of the present application;

[0022] Figure 4 It is a schematic structural diagram of an electronic device provided by an embodiment of the application.

[0023] Explanation of reference numerals: Establishment module - 100, Planning module - 200, Control module - 300, Memory - 401, Processor - 402, Communication interface - 403. Detailed implementation manners

[0024] The embodiments of the present application will be described in detail below. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present application, and should not be construed as a limitation to the present application.

[0025] The hierarchical decision-making and control method, device, electronic device and storage medium of an intelligent connected vehicle based on vehicle-cloud collaboration according to an embodiment of the present application will be described below with reference to the accompanying drawings. In response to the problems mentioned in the above background art, the present application provides a hierarchical decision-making and control method for an intelligent connected vehicle based on vehicle-cloud collaboration. In this method, by decoupling the multi-vehicle behavior planning problem into an upper-layer path planning problem and a lower-layer trajectory planning problem, the upper-layer path planning is run in a cloud centralized manner, and the lower-layer trajectory planning problem is run in a vehicle-side distributed manner. This can not only make full use of the large-scale computing power of the cloud, but also give play to the distributed parallel acceleration ability of the vehicle side, which is an important means to solve the dilemmas of the prior art. In addition, by establishing a relative coordinate system for vehicle movement, planning the movement of the vehicle in the relative coordinate, discretizing the vehicle movement plane into equally spaced grids, and using a multi-agent motion planning method for collaborative path planning, it is ensured that there are no spatio-temporal conflicts in the planning results, that is, collisions during the movement of multiple vehicles are avoided. At the same time, after obtaining the key point coordinates provided by cloud computing, the present application projects the relative coordinates into absolute coordinates, and performs vehicle motion trajectory planning according to the position of the coordinate points and the required arrival time, including the speed sequence (or acceleration sequence) and front wheel steering angle sequence required for the vehicle to continuously pass through the specified coordinate points. Thus, problems such as collaborative planning of intelligent cloud-connected vehicles and platforms are solved.

[0026] To achieve the collaborative planning of intelligent cloud-connected vehicles and platforms, the present application decouples the vehicle motion planning task, that is, performs centralized multi-vehicle conflict-free path planning in the cloud and distributed single-vehicle multi-stage trajectory planning at the vehicle side.

[0027] Specifically, Figure 1It is a flowchart of a hierarchical decision-making and control method for an intelligent connected vehicle based on vehicle-cloud collaboration provided by an embodiment of the present application.

[0028] As Figure 1 shown, the hierarchical decision-making and control method for an intelligent connected vehicle based on vehicle-cloud collaboration includes the following steps:

[0029] In step S101, a relative coordinate system is established using the cloud, and the fleet movement plane is planned as equally spaced grids in the relative coordinate system.

[0030] In the embodiment of the present application, a relative coordinate system for vehicle movement of the above fleet is established through the cloud, and the movement of the vehicle is planned in the relative coordinates. At the same time, in the relative coordinate system, the vehicle movement plane is discretized into equally spaced grids to determine the coordinate position of the vehicle in the above relative coordinate system, so as to perform subsequent path and trajectory planning.

[0031] It can be understood that the relative coordinate system moves forward with the vehicle, and the relative coordinates of a vehicle moving at a constant speed remain unchanged in the relative coordinate system. For example, both vehicle A and vehicle B are moving at a constant speed of 30 km / h. On the premise of taking the coordinate of vehicle A as the reference point, a relative coordinate system is established. Vehicle B is in a relatively stationary state relative to vehicle A, so the coordinates of vehicle B in this relative coordinate system remain unchanged.

[0032] In step S102, collaborative path planning is performed on the vehicle to be planned using the equally spaced grids in the relative coordinate system to obtain the planned path for the vehicle to be planned to reach the target position.

[0033] After establishing a relative coordinate system for the fleet and discretizing the vehicle movement plane into equally spaced grids, in the relative coordinate system, according to the lateral and longitudinal movement requirements of the fleet vehicles, multiple expected position data of each vehicle in the fleet are obtained. And the vehicles in the fleet are matched one by one according to the above-mentioned expected position preference requirements generated in the relative coordinate system to search for the best target position, and then the cloud plans the path for the vehicle to smoothly drive to this target position.

[0034] It should be noted that the above path refers to planning each vehicle to reach a specified position at a specified time point, only restricting the coordinates of the time point and the position point, and not restricting the movement process of the vehicle between multiple coordinates, and ensuring that as long as the vehicle moves continuously between multiple positions according to the given requirements, there will be no collision.

[0035] Specifically, in the embodiments of the present application, an equal-distance grid in a relative coordinate system is used to perform cooperative path planning on the vehicle to be planned, and a planned path for the vehicle to be planned to reach the target position is obtained, including: using the Hungarian algorithm to match the vehicles in the formation with their target positions expected in the formation, and then using the A* algorithm to plan the relative path (i.e., a sequence of coordinate points in the relative coordinate system) for the vehicle to reach the target position. Among the vehicles that may collide, collisions are avoided by exchanging their target positions or keeping the relative coordinates of some vehicles unchanged so that some other vehicles can pass with restrictions.

[0036] It should be noted that when performing target assignment, the assignment cost matrix between the vehicle and the target position will be calculated first. For example, if the defined cost matrix is C = [c i,j , where the element in the i-th row and j-th column is the cost for vehicle i to be assigned to target j. Thus, the cost for each vehicle to be assigned to each target position is obtained. Then, an assignment matrix A = [a i,j needs to be calculated, where if the element in the i-th row and j-th column is 1, it means vehicle i is assigned to target j, and if it is 0, it means no assignment. In fact, a mathematical integer programming problem needs to be solved, as follows:

[0037]

[0038]

[0039]

[0040]

[0041] It should be noted that the task of the above target assignment algorithm is to find a one-to-one assignment relationship that minimizes the sum of the assignment costs between the assigned vehicles and the targets. The Hungarian algorithm and the simplex algorithm can both be used to solve the above target assignment problem, which is specifically set by those skilled in the art according to the actual situation and will not be specifically limited herein.

[0042] For example, first, the cloud establishes a relative coordinate system for the movement of a normally moving vehicle fleet and plans the movement of the vehicles in the relative coordinates. One vehicle in the fleet wants to make a U-turn at the intersection ahead. Through cloud computing, only this vehicle has the expectation of making a U-turn ahead. Before making the U-turn, this vehicle needs to change lanes in sequence to drive onto the left-turn lane for the U-turn. Therefore, the cloud will generate 6 expected positions at a preset interval, such as 3 meters, from front to back in the lane required for the lane-changing process of this vehicle. Thus, the cloud calculates the costs for this vehicle to be assigned to the 6 expected positions respectively and constructs a cost matrix based on the costs. Among them, when this vehicle is not allowed to be assigned to any target position, the cost is infinite; according to the cost matrix and the Hungarian algorithm, a corresponding target position is matched for this vehicle. The method for planning the relative path of this vehicle from the current position to the target position can use the conflict-based search method to perform relative path planning. Its main steps include:

[0043] Step 1: After determining the assignment relationship with the target, without considering collisions first, apply the A* algorithm to plan the relative paths required for all vehicles to reach their target positions. The path planning result at this time is the root node;

[0044] Step 2: Calculate the conflicts generated when all vehicles move in the first step. This conflict means that multiple vehicles reach the same position at the same time, or the paths of multiple vehicles cross within the same time period;

[0045] Step 3: Apply the conflicts detected in Step 2 as constraints to the conflicting vehicles in sequence. This constraint means that a certain vehicle is prohibited from performing a certain action at a certain moment. In this way, one conflict can be resolved because one of the two conflicting vehicles is prohibited from performing the conflicting behavior, while the other vehicle maintains its original planned path. The constraint imposed by the root node will generate a branch behind the root node as a sub-node;

[0046] Step 4: For each branch that still has conflicts, continue to apply the A* algorithm to each vehicle to plan the relative path required for it to reach the target position only considering the currently imposed constraints, continue to detect the conflicts among them, and continue to turn the conflicts into constraints and apply them to the conflicting vehicles in the current node. By analogy, the new path planning result will be used as the sub-node of the node to which the constraint is imposed to continue to expand the conflict tree;

[0047] Step 5: Repeat Step 4 until the operation time reaches the specified maximum operation time, or there are no more conflicting nodes in the conflict tree. At this time, the relative path corresponding to the node with the minimum total distance among all conflict-free nodes is the optimal path obtained currently.

[0048] It can be understood that by discretizing the vehicle motion plane into equidistant grids in the relative coordinate system and using the multi-agent motion planning method for collaborative path planning, it is ensured that there are no spatio-temporal conflicts in the planning results, avoiding collisions during the movement of multiple vehicles.

[0049] In step S103, the vehicle end is used to project the relative coordinate system onto the absolute coordinate system to obtain the absolute coordinate points of the planned path in the absolute coordinate system, and the motion trajectory planning of the vehicle to be planned is calculated according to the absolute coordinate points and the target planning time of the vehicle to be planned, and the vehicle to be planned is controlled to move to the target position according to the motion trajectory planning.

[0050] It should be noted that after obtaining the key point coordinates provided by the above cloud computing, the relative coordinates are projected into the absolute coordinates. In order to enable the vehicle to continuously pass through the above specified coordinate points, and according to the position of the coordinate points and the required arrival time, the corresponding speed sequence, acceleration sequence, front wheel steering angle sequence, etc. are input to perform the motion trajectory planning of the vehicle. Among them, the trajectory refers to the speed and front wheel steering angle control inputs required for the vehicle to continuously reach the specified position at the specified time after the path planning is completed.

[0051] Specifically, in an embodiment of the present application, first, a Bezier curve is used to connect multiple absolute coordinate points, the lateral steering angle input of the vehicle to be planned is controlled by a proportional-integral-derivative controller, and the length of the Bezier curve represents the driving distance of the vehicle to be planned; based on the optimal control method with preset fixed initial state and fixed final state, the longitudinal control input of the vehicle to be planned is solved to generate the motion trajectory planning of the vehicle to be planned. Then, according to the motion trajectory planning, a speed sequence, acceleration sequence, and front wheel steering angle sequence for the movement of the vehicle to be planned are generated, and the vehicle to be planned is controlled to move to the target position according to the speed sequence, acceleration sequence, and front wheel steering angle sequence.

[0052] As Figure 2 shown, it shows the corresponding relationship between the establishment of relative coordinates (middle part), the planning of relative motion (upper part), and the motion trajectory planning of the vehicle after projection into the absolute coordinate system (lower part) when a vehicle (dark gray) joins four vehicles in front (light gray) to form a formation. The green vehicle in the figure reaches the target formation position after two stages and three states. In the figure, vF represents the unified running speed of the formation, T represents the time limit for the vehicle to switch between two states, and dg is the discrete distance of the relative coordinates.

[0053] The intelligent connected vehicle hierarchical decision-making and control method based on vehicle-cloud collaboration proposed according to the embodiments of the present application establishes a relative coordinate system, and plans the vehicle platoon motion plane as equidistant grids in the relative coordinate system; uses the equidistant grids in the relative coordinate system to perform collaborative path planning on the vehicle to be planned, and obtains the planned path for the vehicle to be planned to reach the target position; projects the relative coordinate system onto the absolute coordinate system to obtain the absolute coordinate points of the planned path in the absolute coordinate system, and calculates the motion trajectory planning of the vehicle to be planned according to the absolute coordinate points and the target planning time of the vehicle to be planned, and controls the vehicle to be planned to move to the target position according to the motion trajectory planning. Thereby realizing the collaborative planning of intelligent connected vehicles and cloud platforms and improving the overall performance of the traffic system.

[0054] Next, a vehicle-cloud collaboration-based intelligent connected vehicle hierarchical decision-making and control device according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0055] Figure 3 It is a block diagram of a vehicle-cloud collaboration-based intelligent connected vehicle hierarchical decision-making and control device according to an embodiment of the present application.

[0056] As Figure 3 shown, the vehicle-cloud collaboration-based intelligent connected vehicle hierarchical decision-making and control device 10 includes: a establishment module 100, a planning module 200, and a control module 300.

[0057] Among them, the establishment module 100 is used to establish a relative coordinate system by using the cloud, and plan the vehicle platoon motion plane as equidistant grids in the relative coordinate system; the planning module 200 is used to perform collaborative path planning on the vehicle to be planned by using the equidistant grids in the relative coordinate system, and obtain the planned path for the vehicle to be planned to reach the target position; the control module 300 is used to project the relative coordinate system onto the absolute coordinate system by using the vehicle end, obtain the absolute coordinate points of the planned path in the absolute coordinate system, and calculate the motion trajectory planning of the vehicle to be planned according to the absolute coordinate points and the target planning time of the vehicle to be planned, and control the vehicle to be planned to move to the target position according to the motion trajectory planning.

[0058] Optionally, in an embodiment of the present application, the planning module 200 includes: a matching unit for matching the vehicle to be planned with the target position by using the Hungarian algorithm; a path planning unit for planning the relative path of the vehicle to be planned to reach the target position by using the A* algorithm.

[0059] Optionally, in an embodiment of the present application, calculating the motion trajectory planning of the vehicle to be planned according to the absolute coordinate points and the target planning time of the vehicle to be planned includes: connecting multiple absolute coordinate points using a Bezier curve, controlling the lateral corner input of the vehicle to be planned through a proportional-integral-derivative controller, and using the length of the Bezier curve to represent the driving distance of the vehicle to be planned; solving the longitudinal control input of the vehicle to be planned based on an optimal control method with preset fixed initial state and fixed final state to generate the motion trajectory planning of the vehicle to be planned.

[0060] Optionally, in an embodiment of the present application, controlling the vehicle to be planned to move to the target position according to the motion trajectory planning includes: generating a speed sequence or an acceleration sequence and a front wheel corner sequence for the movement of the vehicle to be planned according to the motion trajectory planning; controlling the vehicle to be planned to move to the target position according to the speed sequence or the acceleration sequence and the front wheel corner sequence.

[0061] It should be noted that the foregoing explanation of the embodiments of the hierarchical decision-making and control method for intelligent connected vehicles based on vehicle-cloud collaboration also applies to the device for hierarchical decision-making and control of intelligent connected vehicles based on vehicle-cloud collaboration in this embodiment, and will not be elaborated here.

[0062] The device for hierarchical decision-making and control of intelligent connected vehicles based on vehicle-cloud collaboration proposed according to the embodiments of the present application performs centralized multi-vehicle conflict-free path planning in the cloud and distributed single-vehicle multi-stage trajectory planning at the vehicle end. By establishing a relative coordinate system for vehicle movement, planning the movement of the vehicle in the relative coordinates, discretizing the vehicle movement plane into equally spaced grids, and using a multi-agent motion planning method for collaborative path planning, it is ensured that there are no spatio-temporal conflicts in the planning results. In addition, after obtaining the key point coordinates provided by cloud computing, the relative coordinates are projected into absolute coordinates, and the motion trajectory of the vehicle is planned according to the position of the coordinate points and the required arrival time, thereby realizing the collaborative planning of intelligent connected vehicles and the cloud platform and improving the overall performance of the traffic system.

[0063] Figure 4 It is a schematic structural diagram of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0064] A memory 401, a processor 402, and a computer program stored on the memory 401 and executable on the processor 402.

[0065] When the processor 402 executes the program, it implements the hierarchical decision-making and control method for intelligent connected vehicles based on vehicle-cloud collaboration provided in the foregoing embodiments.

[0066] Further, the electronic device further includes:

[0067] A communication interface 403 for communication between the memory 401 and the processor 402.

[0068] A memory 401 for storing a computer program that can run on a processor 402.

[0069] The memory 401 may include a high-speed RAM memory and may also include non-volatile memory, such as at least one disk memory.

[0070] If the memory 401, the processor 402, and the communication interface 403 are implemented independently, the communication interface 403, the memory 401, and the processor 402 can be interconnected through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of simplicity of representation, Figure 4 only a thick line is used to represent it in the figure, but it does not mean that there is only one bus or one type of bus.

[0071] Optionally, in a specific implementation, if the memory 401, the processor 402, and the communication interface 403 are integrated on a chip, the memory 401, the processor 402, and the communication interface 403 can communicate with each other through an internal interface.

[0072] The processor 402 may be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0073] This embodiment also provides a computer-readable storage medium, on which a computer program is stored, characterized in that when the program is executed by a processor, the above-mentioned hierarchical decision-making and control method for an intelligent networked vehicle based on vehicle-cloud collaboration is implemented.

[0074] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this 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 can be combined in any one or N embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0075] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of this application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0076] Any process or method description shown in a flowchart or described in other ways herein can be understood to represent a module, segment, or part of code including one or more N executable instructions for implementing a customized logical function or process, and the scope of the preferred embodiments of this application includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in a reverse order according to the involved functions, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of this application belong.

[0077] It should be understood that each part of this application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0078] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the method for implementing the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

Claims

1. A hierarchical decision-making and control method for intelligent connected vehicles based on vehicle-cloud collaboration, characterized in that, Including the following steps: Using the cloud to establish a relative coordinate system, and planning the vehicle fleet movement plane as equally-spaced grids in the relative coordinate system; Using the equally-spaced grids in the relative coordinate system to perform collaborative path planning on the vehicle to be planned, and obtaining the planned path for the vehicle to be planned to reach the target position; Using the vehicle terminal to project the relative coordinate system onto the absolute coordinate system, obtaining the absolute coordinate points of the planned path in the absolute coordinate system, and calculating the motion trajectory planning of the vehicle to be planned according to the absolute coordinate points and the target planning time of the vehicle to be planned, and controlling the vehicle to be planned to move to the target position according to the motion trajectory planning; The calculating the motion trajectory planning of the vehicle to be planned according to the absolute coordinate points and the target planning time of the vehicle to be planned includes: Connecting multiple absolute coordinate points using a Bezier curve, controlling the lateral corner input of the vehicle to be planned through a proportional-integral-derivative controller, and using the length of the Bezier curve to represent the driving distance of the vehicle to be planned; Solving the longitudinal control input of the vehicle to be planned based on an optimal control method with preset fixed initial state and fixed final state, and generating the motion trajectory planning of the vehicle to be planned; The controlling the vehicle to be planned to move to the target position according to the motion trajectory planning includes: Generating a speed sequence or an acceleration sequence and a front wheel corner sequence for the movement of the vehicle to be planned according to the motion trajectory planning; Controlling the vehicle to be planned to move to the target position according to the speed sequence or the acceleration sequence and the front wheel corner sequence.

2. The method according to claim 1, characterized in that, The using the equally-spaced grids in the relative coordinate system to perform collaborative path planning on the vehicle to be planned, and obtaining the planned path for the vehicle to be planned to reach the target position includes: Using the Hungarian algorithm to match the vehicle to be planned with the target position; Using the A* algorithm to plan the relative path for the vehicle to be planned to reach the target position.

3. An intelligent networked vehicle hierarchical decision-making and control device based on vehicle-cloud collaboration, characterized in that, Including: A establishing module, configured to use the cloud to establish a relative coordinate system, and plan the vehicle fleet movement plane as equally-spaced grids in the relative coordinate system; A planning module, configured to use the equally-spaced grids in the relative coordinate system to perform collaborative path planning on the vehicle to be planned, and obtain the planned path for the vehicle to be planned to reach the target position; A control module, configured to use the vehicle terminal to project the relative coordinate system onto the absolute coordinate system, obtain the absolute coordinate points of the planned path in the absolute coordinate system, and calculate the motion trajectory planning of the vehicle to be planned according to the absolute coordinate points and the target planning time of the vehicle to be planned, and control the vehicle to be planned to move to the target position according to the motion trajectory planning; The calculating the motion trajectory planning of the vehicle to be planned according to the absolute coordinate points and the target planning time of the vehicle to be planned includes: Connecting multiple absolute coordinate points using a Bezier curve, controlling the lateral corner input of the vehicle to be planned through a proportional-integral-derivative controller, and using the length of the Bezier curve to represent the driving distance of the vehicle to be planned; Solve the longitudinal control input of the vehicle to be planned based on the optimal control method with preset fixed initial state and fixed final state, and generate the motion trajectory planning of the vehicle to be planned; Controlling the vehicle to be planned to move to the target position according to the motion trajectory planning includes: Generating a speed sequence or an acceleration sequence and a front wheel steering angle sequence for the movement of the vehicle to be planned according to the motion trajectory planning; Controlling the vehicle to be planned to move to the target position according to the speed sequence or the acceleration sequence and the front wheel steering angle sequence.

4. The device according to claim 3, characterized in that, The planning module includes: A matching unit for matching the vehicle to be planned with the target position by using the Hungarian algorithm; A path planning unit for planning the relative path of the vehicle to be planned to reach the target position by using the A* algorithm.

5. An electronic device, characterized in that, It includes: A memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the program to implement the vehicle-cloud collaborative intelligent connected vehicle hierarchical decision-making and control method according to any one of claims 1-2.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to be used to implement the vehicle-cloud collaborative intelligent connected vehicle hierarchical decision-making and control method according to any one of claims 1-2.

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