Vehicle longitudinal and vertical coordination control method and system based on cloud data driving

Abstract

The invention belongs to the field of vehicle intelligent control, and relates to a vehicle longitudinal and vertical coordination control method and system based on cloud data driving. The system comprises a cloud vehicle communication module, a platform management module, a pavement information acquisition module, a vehicle-mounted unit module, a vehicle communication module, an active suspension control module, a longitudinal vehicle speed planning module, a longitudinal and vertical coordination control module and a positioning module. The method comprises the following steps: S1, acquiring road information; s2, road surface information collected by the vehicle is processed; s3, front vehicle information is obtained; s4, adjusting a vehicle suspension according to the navigation route and the corresponding road surface grade; s5, adjusting the vehicle speed according to the road information and the front vehicle information; s6, the vehicle speed is coordinated and controlled in the longitudinal direction and the vertical direction; and S7, ending navigation. According to the method, the road surface information is sent to the vehicle through the cloud control platform, the optimized longitudinal and vertical vehicle speed sequences are obtained by optimizing and solving the longitudinal and vertical performance indexes, and the popularization and development of the automatic driving vehicle are effectively promoted.

Description

Technical Field

[0001] The present invention belongs to the field of vehicle intelligent control, and in particular relates to a vehicle longitudinal and vertical coordinated control method and system driven by cloud data. Background Art

[0002] In recent years, with advancements in vehicle control technology and the development of artificial intelligence (AI), autonomous vehicle control technology has seen extensive development and progress. Against the backdrop of intelligent, connected, and new energy vehicles, the automotive industry will achieve converged development across fields such as computing, information communications, and AI. Based on next-generation information and communications technology—the intelligent connected vehicle cloud control system—this data-driven approach will enable cloud-controlled autonomous driving for new energy vehicles, providing innovative planning and control strategies for vehicle driving and powertrain systems. Intelligent connected vehicles, powered by real-time information communication, enable information exchange and sharing among drivers, vehicles, roads, and the cloud.

[0003] Vehicle safety and comfort are key considerations for autonomous vehicles. Autonomous vehicles rely entirely on machine perception, decision-making, and planning strategies to achieve vehicle control during driving. Safety, a fundamental requirement for the implementation of autonomous driving technology, is currently technologically feasible. Intelligent suspension systems are widely used in various vehicle models, and variable damping systems can effectively improve vehicle comfort and ride smoothness. However, most current vehicle suspension systems utilize a single control strategy or set of parameters. The development of adaptively adjustable suspension damping based on real-time road information has become a hot research topic.

[0004] At present, the promotion and development of cloud-based communication technology has promoted the development of emerging fields such as intelligent transportation and smart cities. If the real-time transmission and update of road data in the cloud-based control system can be used to effectively coordinate and control the longitudinal and vertical movements of the vehicle, and effectively improve the smoothness and comfort of the vehicle during driving, it will greatly promote the advancement of vehicle autonomous driving technology. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a vehicle longitudinal and vertical coordinated control method and system driven by cloud data, with the aim of more effectively improving a series of performance indicators such as vehicle comfort while ensuring driving safety.

[0006] To achieve the above objectives, the present invention discloses the following technical solutions: A vehicle longitudinal and vertical coordination control system driven by cloud data includes: a database, a cloud-vehicle communication module, a platform management module, a road information collection module, an on-board unit module, a vehicle communication module, an active suspension control module, a longitudinal vehicle speed planning module, a longitudinal and vertical coordination control module, and a positioning module; wherein the database, cloud-vehicle communication module, and platform management module are located in the cloud to form a cloud control platform, and the remaining modules are installed on the vehicle side; the vehicle communication module and the cloud-vehicle communication submodule are used for information exchange between the cloud control platform and the vehicle, and in the cloud control platform, the platform management module is connected to the cloud-vehicle communication submodule and the database respectively; the vehicle communication module is connected to the road information collection module, the active suspension control module, the longitudinal vehicle speed planning module, and the positioning module respectively, the active suspension control module is connected to the longitudinal and vertical coordination module, and the longitudinal vehicle speed planning module is connected to the on-board unit module and the longitudinal and vertical coordination module; specifically: The database includes a road information database and a vehicle information database. The road information database contains map information and corresponding road surface grades, and the vehicle information database includes the vehicle ID currently connected to the cloud control platform, as well as the vehicle's current location and current road surface information. The cloud-vehicle communication module and the vehicle communication module exchange information between the cloud control platform and the vehicle, including: sending the navigation route and corresponding road grade to the vehicle communication module, and receiving the vehicle's target position, current position and collected current road surface information from the vehicle communication module; The platform management module is used to process the information received from the cloud-car communication module; The positioning module is used to obtain the current location of the vehicle and send it to the vehicle communication module.

[0007] The road surface information collection module is used to collect road surface information and send the road surface information to the vehicle communication module; The on-board unit module is used to collect information about the preceding vehicle; The active suspension control module is used to regulate the vehicle suspension; The longitudinal speed planning module performs optimal speed planning based on the road surface grade obtained from the cloud control platform and the preceding vehicle information obtained from the vehicle-mounted unit module on the vehicle; The longitudinal and vertical coordination module couples and coordinates the active suspension control module and the longitudinal vehicle speed planning module.

[0008] Preferably, the platform management module is used to process the information received from the cloud vehicle communication module, including: Based on the vehicle's target location and current location, the navigation route and corresponding road grade are retrieved from the road information database and sent to the cloud-vehicle communication module. The vehicle is then added to the vehicle information database to establish a connection between the vehicle and the cloud platform. Based on the current position of the vehicle and the collected current road surface information, the current road surface information is processed to obtain road surface roughness, and the road surface grade is obtained based on the road surface roughness. The road surface grade of the current vehicle position stored in the vehicle information database is verified and adjusted in real time; When the target position and current position of a vehicle coincide with each other, the vehicle information is deleted from the vehicle information database.

[0009] Preferably, the real-time verification and adjustment of the road surface grade of the current vehicle position stored in the vehicle information database is specifically as follows: When the obtained road surface grade is the same as the road surface grade in the road information database, no operation is performed; when the obtained road surface grade is different from the information in the road information database, the road surface grade in the road information database is updated, and the modified road surface grade is sent to the vehicles in the current navigation route in the vehicle information database that include this road section.

[0010] Preferably, the active suspension control module is used to regulate the vehicle suspension, specifically: The active suspension control module uses the suspension command control submodule to obtain suspension control instructions based on the road grade and current vehicle position received from the cloud control platform, and sends the suspension control instructions to the vibration reduction submodule in the active suspension module. After receiving the suspension control instructions, the vibration reduction submodule controls the valve control switch opening of the solenoid valve to adjust the softness or hardness of the suspension.

[0011] Preferably, the longitudinal speed planning module performs optimal speed planning based on the road grade obtained from the cloud control platform and the preceding vehicle information obtained by the on-board unit module on the vehicle, specifically: First, safety constraints are established during vehicle driving; then the vehicle's kinematic model is established; secondly, a model predictive controller is designed and a cost function based on the model predictive controller is constructed. Finally, the optimal solution in the planning process is obtained by solving the cost function.

[0012] The present invention also discloses a vehicle longitudinal and vertical coordinated control method based on cloud data drive, which includes the following steps: S1, obtain road information; The vehicle uses the vehicle communication module to send its current vehicle location and target location to the cloud-vehicle communication submodule. The platform management module retrieves the navigation route and corresponding road surface grade from the road information database, sends the navigation route and corresponding road surface grade to the vehicle communication module through the cloud-vehicle communication submodule, and adds the vehicle to the vehicle information database, establishing a connection between the vehicle and the cloud platform. S2, processing of road surface information collected by vehicles; The road surface information collection module is used to collect road surface information, and the positioning module is used to obtain the current position of the vehicle. The current position of the vehicle and road surface information are uploaded to the cloud control platform in real time. The platform management module in the cloud control platform processes the road surface information and finally obtains the road surface grade. The road surface grade of the current vehicle position stored in the road information database is verified and adjusted in real time. S3, obtain the preceding vehicle information; Use the vehicle-mounted unit module to obtain the preceding vehicle information, which is the relative position of the preceding vehicle and the current vehicle; S4, adjusts the vehicle suspension according to the navigation route and the corresponding road grade; An active suspension control module is used to regulate the vehicle suspension according to the navigation route obtained in S1 and the corresponding road surface grade, and a suspension control instruction is obtained by taking the root mean square value of the vehicle vertical vibration acceleration and the root mean square value of the wheel dynamic deformation as evaluation indicators; S5, adjusting the vehicle speed according to the road information and the information of the preceding vehicle; Based on the navigation route and corresponding road grade obtained in S1, and the preceding vehicle information obtained in S3, the longitudinal vehicle speed planning module is used to perform optimal speed planning; S6, longitudinal and vertical coordinated control of vehicle speed; Using the longitudinal and vertical coordinated control module, by coupling and coordinating the active suspension control module and the longitudinal vehicle speed planning module, the optimal longitudinal speed planning sequence is obtained while also obtaining the optimal damping force control sequence of the active suspension system; S7, end navigation; When the vehicle reaches the target location of the vehicle, the navigation ends and the platform management module deletes the current vehicle from the vehicle information database.

[0013] Preferably, the suspension control instruction is obtained by taking the root mean square value of the vehicle vertical vibration acceleration and the root mean square value of the wheel dynamic deformation as evaluation indicators, specifically: The root mean square value of the vertical vibration acceleration of the vehicle body is minimized, which can be expressed as: (1); in, Indicates the root mean square value of the vehicle's vertical vibration acceleration. RMS is the root mean square value calculation formula. is the vehicle vertical acceleration; Minimize the root mean square value of vehicle dynamic deformation, expressed as: (3); in, is the root mean square value of the wheel dynamic deformation, is the displacement of the suspension system, is the road surface excitation displacement; The comprehensive performance evaluation index of the suspension system is expressed as: (4); Where: , Respectively represent the evaluation index values of the passive suspension system’s ride comfort and road adhesion, where ; is the vehicle ride comfort adjustment coefficient, is the vehicle-road adhesion adjustment coefficient, Minimization is used as the comfort control strategy to obtain the suspension control instructions.

[0014] Preferably, the , .

[0015] Preferably, the optimal speed planning is specifically: The objective function to be solved is as follows: (5); Where: is the objective function value to be solved, is the prediction step length, is the minimum time following vehicle weight factor, is the comfort weight factor, is the predicted passenger number at time k Motion sickness incidence rate at each moment, is the energy consumption weight factor, is the minimum acceleration weight factor, where ; is the vehicle speed at time k+n predicted at time k; is the energy consumption parameter of the vehicle at time k+n predicted at time k, is the lateral acceleration sequence at time k+n predicted at time k, is the longitudinal acceleration sequence at time k+n predicted at time k; The constraint set settings include road adhesion coefficient constraints, vehicle safety speed limits, and trajectory tracking constraints; According to the constraint set, the optimal control problem is transformed into the following expression, and the control variables are: , state variables for: .

[0016] The objective function of the solution process is: , where the constraints to be solved are as follows: (12); in, represents the lateral acceleration of the vehicle, represents the longitudinal acceleration of the vehicle, Represents the lateral coordinate value of the vehicle's trajectory, Represents the longitudinal coordinate value of the vehicle's trajectory, Represents the angle between the vehicle's direction of travel and the road centerline. Represents the lateral coordinate value of the vehicle reference driving trajectory, Represents the lateral coordinate value of the vehicle reference driving trajectory, is the gravitational acceleration coefficient.

[0017] Preferably, the constraint set is specifically: (1) Road adhesion coefficient constraint: (9); Where: is the lateral acceleration of the vehicle; is the vehicle's longitudinal acceleration; is the tire-road friction coefficient; is the gravitational acceleration coefficient; (2) Vehicle safety speed limit: (10); Where: is the lateral acceleration of the vehicle; and are the first adjustment coefficient and the second adjustment coefficient respectively; is the road curvature; is the maximum safe speed; (3) Trajectory tracking constraints: (11); Where: , ; The coordinates are relative to the center line The Jacobian of the coordinate arc length θ, is the vehicle lateral velocity, is the longitudinal velocity of the vehicle, Represents the angle between the vehicle's direction of travel and the road centerline.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention uses the road information collected by the vehicle to upload the vehicle's current position and road information to the cloud control platform in real time, and verifies and adjusts the road information database in the cloud control platform, ensuring the real-time nature of the road information in the cloud control platform.

[0019] 2. The present invention effectively utilizes the road surface information collected in advance and sends it back to the vehicles that are about to pass through the cloud control platform, so that the vehicles can realize the vertical motion control of the vehicles in advance according to the road information constructed by other vehicles.

[0020] 3. This method takes vehicle driving comfort as the goal, optimizes and solves the longitudinal and vertical performance indicators, and optimizes and controls the vertical active suspension system and the longitudinal vehicle speed sequence. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a schematic diagram of the vehicle longitudinal and vertical coordinated control system module based on cloud data drive of the present invention; Figure 2 This is a flow chart of the vehicle longitudinal and vertical coordinated control method based on cloud data drive of the present invention; Figure 3 This is a flow chart of road signal level identification according to the present invention; Figure 4 This is the optimal speed trajectory diagram of the vehicle on the local road of the present invention. DETAILED DESCRIPTION

[0022] The exemplary embodiments, features, and aspects of the present invention will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise indicated.

[0023] This application discloses a vehicle longitudinal and vertical coordination control system based on cloud data drive, such as Figure 1As shown, the system includes at least the following modules: database 1, cloud-vehicle communication module 2, platform management module 3, road surface information collection module 4, on-board unit (OBU) module 5, vehicle communication module 6, active suspension control module 7, longitudinal vehicle speed planning module 8, longitudinal and vertical coordination control module 9, and positioning module 10. The database 1, cloud-vehicle communication module 2, and platform management module 3 are located in the cloud, forming the cloud control platform. The road surface information collection module 4, on-board unit module 5, vehicle communication module 6, active suspension control module 7, longitudinal vehicle speed planning module 8, longitudinal and vertical coordination module 9, and positioning module 10 are installed on the vehicle. The vehicle communication module 6 and the cloud-vehicle communication submodule 2 are used for information exchange between the cloud control platform and the vehicle. In the cloud control platform, the platform management module 3 is connected to the cloud-vehicle communication submodule 2 and the database 1 respectively. The vehicle communication module 6 is connected to the road information acquisition module 4, the active suspension control module 7, the longitudinal vehicle speed planning module 8, and the positioning module 10 respectively. The active suspension control module 7 is connected to the longitudinal and vertical coordination module 9, and the longitudinal vehicle speed planning module 8 is connected to the vehicle unit module 5 and the longitudinal and vertical coordination module 9. The following is a detailed description of the vehicle longitudinal and vertical coordination control system driven by cloud data: Database 1 includes a road information database and a vehicle information database. The road information database contains map information and corresponding road surface grades, which can provide road surface grade data and map information data for vehicles in advance; the vehicle information database includes the vehicle ID currently connected to the cloud control platform, as well as the vehicle's current location and current road surface information.

[0024] The cloud-vehicle communication module 2 is used to transmit information with the vehicle communication module, including sending the navigation route and the corresponding road surface grade to the vehicle communication module, and receiving the vehicle's target position, current position and collected current road surface information from the vehicle communication module.

[0025] The platform management module 3 is used to process the information received from the cloud-vehicle communication module, including: based on the vehicle's target position and current position, retrieving the navigation route and the corresponding road surface grade from the road information library and sending them to the cloud-vehicle communication module, adding the vehicle to the vehicle information library, and establishing a connection between the vehicle and the cloud platform; based on the vehicle's current position and the collected current road surface information, processing the current road surface information to obtain the road surface roughness, obtaining the road surface grade based on the road surface roughness, and performing real-time verification and adjustment on the road surface grade of the current vehicle position stored in the vehicle information library; when the vehicle's target position and current position coincide, sending a navigation end message to the cloud-vehicle communication module to end the interaction and delete the vehicle information from the vehicle information library. Of course, the positioning module in the vehicle can also be used to end the interaction. When the positioning module determines that the vehicle's target position and current position coincide, it sends a navigation end message to the platform management module. When the platform management module receives the navigation end message, the interaction ends and the vehicle information is deleted from the vehicle information library.

[0026] The platform management module 3 performs real-time verification and adjustment on the road surface grade of the current vehicle position stored in the road information database. Specifically, when the obtained road surface grade is the same as the road surface grade in the road information database, no operation is performed; when the obtained road surface grade is different from the information in the road information database, the road surface grade in the road information database is updated, and the modified road surface grade is sent to the vehicles in the current navigation route in the vehicle information database that include this road section.

[0027] Furthermore, in order to prevent frequent modifications of the road surface grade, the present invention adopts a threshold method. When the road surface grades at the same location sent by more than N vehicles in a row are different from the road surface grades in the road information database, the road surface grade in the road information database is modified, and the modified road surface grade is the average value of N vehicles, where N is a positive integer, for example, N can be set to 5.

[0028] The positioning module 10 is used to obtain the current location of the vehicle and send it to the vehicle communication module.

[0029] The road surface information collection module 4 is used to collect road surface information and send the road surface information to the vehicle communication module.

[0030] The vehicle communication module 6 transmits information to the cloud vehicle communication module, including sending the vehicle's target location, current location, and collected current road surface information, and receives the navigation route and corresponding road surface grade from the cloud vehicle communication module.

[0031] The vehicle-mounted unit module 5 is used to collect the preceding vehicle information, which at least includes the relative position of the preceding vehicle and the current vehicle.

[0032] The active suspension control module 7 is used to control the vehicle's suspension. This module utilizes a preview control framework to implement suspension control. Based on the road surface grade and the vehicle's current position received from the cloud control platform, the preview control framework uses the suspension command control submodule to obtain suspension control commands based on the road surface grade in front of the vehicle. These commands are then sent to the damping submodule (CDC damper) within the active suspension module. Upon receiving the suspension control commands from the suspension command control submodule, the damping submodule controls the valve-controlled switch opening of the solenoid valve, adjusting the suspension's firmness and firmness to enhance vehicle comfort. By adjusting the valve-controlled switch opening of the damping submodule within the suspension control module in real time, the vehicle's ride comfort and ride quality can be effectively improved on various road surfaces. Because the present invention eliminates the need to process collected road surface information in real time to determine the road surface grade, the suspension control submodule directly obtains suspension control commands based on pre-stored road surface grades, ensuring the timeliness of suspension control commands.

[0033] The longitudinal speed planning module 8 plans the optimal speed based on the road surface grade obtained from the cloud control platform and the preceding vehicle information obtained from the vehicle's onboard unit module. The optimal speed is primarily guided by vehicle comfort during driving. For example, when passing discrete impact surfaces such as speed bumps or potholes, a strategy of decelerating first and then accelerating can be used to optimize the speed during the discrete impact, effectively improving vehicle comfort. Furthermore, by controlling the vehicle's load offset during deceleration and the occurrence of braking nodding, the vehicle's ride comfort can be effectively improved. The longitudinal speed planning module is described as follows: First, safety constraints are established to ensure vehicle safety during driving; then, a vehicle kinematic model is established to prepare for the speed planning algorithm; then, a model predictive controller is designed and a cost function based on the model predictive controller is constructed. The cost function is then solved to obtain the optimal solution for the planning process. Vehicle comfort is considered as a primary factor in the cost function, so the optimal speed planned will fully consider vehicle comfort while ensuring driving safety.

[0034] The longitudinal and vertical coordination module 9 couples and coordinates the active suspension control module and the longitudinal speed planning module to obtain the optimal longitudinal speed planning sequence and the optimal damping force control sequence of the active suspension system, thereby fully improving the smoothness, comfort, and safety of the vehicle during driving. In the longitudinal and vertical motion coordination control module, which is guided by vehicle comfort, the active suspension control module and the longitudinal speed planning module respectively control the vertical and longitudinal motion states of the vehicle. Coordinating the motion in both directions will further effectively improve the comfort of the vehicle during driving. In this coordination control module, when the vehicle is turning, by planning the turning speed and adjusting the active suspension system, it can effectively prevent the vehicle from excessively rolling during turns. Excessive rolling will cause the occupants to feel the centrifugal effect, which will cause great discomfort. By achieving coordinated control of the longitudinal speed and vertical suspension, the comfort of the vehicle is maximized while ensuring safety and energy saving, effectively preventing motion sickness.

[0035] The present invention also discloses a vehicle longitudinal and vertical coordinated control method based on cloud data drive, such as Figure 2 As shown, the following steps are included: S1, obtain road information.

[0036] The vehicle uses the vehicle communication module to send the current vehicle position and target position to the cloud-vehicle communication sub-module. The cloud-vehicle communication sub-module sends the vehicle position and target position to the platform management module. The platform management module retrieves the navigation route and the corresponding road surface grade from the road information database, sends the navigation route and the corresponding road surface grade to the vehicle communication module through the cloud-vehicle communication sub-module, and adds the vehicle to the vehicle information database to establish a connection between the vehicle and the cloud platform.

[0037] S2, processing of road surface information collected by vehicles.

[0038] like Figure 3 As shown, a road surface information collection module is used to collect road surface information, a positioning module is used to obtain the current position of the vehicle, and the current position of the vehicle and road surface information are uploaded to the cloud control platform in real time. The platform management module in the cloud control platform processes the road surface information and finally obtains the road surface grade. The road surface grade of the current vehicle position stored in the road information library is verified and adjusted in real time. Specifically, when the obtained road surface grade is the same as the road surface grade in the road information library, no operation is performed; when the obtained road surface grade is different from the information in the road information library, the road surface grade in the road information library is updated, and the modified road surface grade is sent to the vehicles in the current navigation route in the vehicle information library that include this road section.

[0039] In order to further prevent frequent modifications of road surface grades, the present invention adopts a threshold method. When the road surface grades at the same location sent by more than N vehicles in a row are different from the road surface grades in the road information database, the road surface grade in the road information database will be modified, and the modified road surface grade is the average value of N vehicles.

[0040] S3, obtain the preceding vehicle information.

[0041] The vehicle-mounted unit module is used to obtain the preceding vehicle information, which is the relative position of the preceding vehicle and the current vehicle.

[0042] S4 adjusts the vehicle suspension according to the navigation route and the corresponding road grade.

[0043] An active suspension control module is used to regulate the vehicle suspension according to the navigation route obtained in S1 and the corresponding road surface grade.

[0044] The suspension command control submodule mainly generates suspension control commands in the following ways: Suspension control commands are generated based on the vehicle's vertical motion comfort, which is primarily determined by suspension vibration evaluation indicators and vibration control targets. The most commonly used suspension vibration evaluation indicators are the vehicle's vertical RMS acceleration and the wheel's RMS dynamic deformation. The vehicle's vertical RMS acceleration is used to evaluate ride comfort, while the wheel's RMS dynamic deformation is used to assess road adhesion.

[0045] The root mean square value of the vehicle's vertical vibration acceleration is: , where: Indicates the root mean square value of the vehicle's vertical vibration acceleration. The smaller the better. RMS is the root mean square value calculation formula. is the vehicle's vertical acceleration. One of the vibration control objectives of the suspension system is to minimize the root mean square value of the vehicle's vertical vibration acceleration to achieve the best ride comfort. It can be expressed as: (1).

[0046] The vehicle's road adhesion is evaluated by the root mean square value of the wheel dynamic deformation, which can be expressed as: Where: is the root mean square value of the wheel dynamic deformation, The smaller the vehicle, the better its road adhesion. is the displacement of the suspension system, is the road excitation displacement. Another vibration control goal of the suspension system is to minimize the root mean square value of the vehicle dynamic deformation to obtain the best road adhesion, which can be expressed as: (3).

[0047] Therefore, the comprehensive performance evaluation index of the suspension system can be expressed as: (4); Where: , Respectively represent the corresponding passive suspension system's ride comfort and road adhesion evaluation indexes, and each evaluation index is compared with its corresponding passive suspension system evaluation index to eliminate the influence of different dimensions; ; is the vehicle ride comfort adjustment coefficient, is the vehicle-road adhesion adjustment coefficient. If there is no preference requirement for the vehicle suspension system evaluation index, it is usually set , , then The smaller the indicator value, the better. Minimization as a comfort control strategy.

[0048] The suspension command control submodule uses the navigation line and the corresponding road surface level to Minimization is used as the comfort control strategy to obtain the suspension control instructions.

[0049] S5, adjusting the vehicle speed according to the road information and the preceding vehicle information.

[0050] Based on the navigation route and corresponding road grade obtained in S1, as well as the preceding vehicle information obtained in S3, the longitudinal speed planning module performs optimal speed planning. The optimal speed is primarily guided by vehicle comfort during driving. For example, when navigating discrete impact surfaces such as speed bumps or potholes, a strategy of decelerating first and then accelerating can be used to optimize the speed during the discrete impact, effectively improving vehicle comfort. Furthermore, by controlling vehicle load offset and braking nodding during deceleration, vehicle ride comfort can be effectively improved. The implementation of the longitudinal speed planning module is described as follows: First, safety constraints are established to ensure vehicle safety during driving; then, a vehicle kinematic model is established to prepare for the speed planning algorithm; then, a model predictive controller is designed and a cost function based on the model predictive controller is constructed. The cost function is then solved to obtain the optimal solution for the planning process. Vehicle comfort is considered as a primary factor in the cost function, so the optimal speed planned will fully consider vehicle comfort while ensuring driving safety. Figure 4 The optimal speed trajectory of a vehicle on a local road is shown as an example.

[0051] According to the above description of optimal speed planning, the specific implementation process is as follows: To solve the corresponding multi-objective optimization problem, a corresponding nonlinear model predictive controller is designed for the target system. By solving the cost function under different weight coefficients, the speed sequence of vehicles under different driving styles can be obtained. The specific objective function to be solved in this invention is as follows: (5); Where: is the objective function value to be solved, is the prediction step length, is the minimum time following vehicle weight factor, is the comfort weight factor, is the predicted passenger number at time k Motion sickness incidence rate at each moment, is the energy consumption weight factor, is the minimum acceleration weight factor, where . is the longitudinal velocity at time k+n predicted at time k. is the energy consumption parameter of the vehicle at time k+n predicted at time k, is the lateral acceleration sequence at time k+n predicted at time k, is the longitudinal acceleration sequence at time k+n predicted at time k; , , and By setting several weight adjustment parameters, we can obtain cost functions with different weight tendencies.

[0052] Constraint Set Settings: (1) Road adhesion coefficient constraint: During vehicle driving, the vehicle's driving state is closely related to the road adhesion coefficient. Too large or too small an adhesion coefficient will cause the vehicle to roll or slip, directly affecting the vehicle's driving safety. Therefore, in the design process of the model predictive controller, the road adhesion coefficient is set within a reasonable range. The constraint expression (9) is as follows: (9); Where: is the lateral acceleration of the vehicle; is the vehicle's longitudinal acceleration; is the tire-road friction coefficient; is the gravitational acceleration coefficient.

[0053] (2) Vehicle safety speed limit: In ACC (adaptive cruise control) driving scenarios, if a safety speed limit is not set, the vehicle may collide. According to formula (10), the specific vehicle safety speed constraint is expressed as follows: (10) Where: is the lateral acceleration of the vehicle; and are the first adjustment coefficient and the second adjustment coefficient respectively; is the road curvature, obtained according to the navigation route; is the vehicle speed, is the maximum safe speed.

[0054] (3) Trajectory tracking constraints: During the vehicle following process, the trajectory of the vehicle is given in real time based on the movement position of the vehicle in front. The relative position of the vehicle in front and the current vehicle is obtained through the OBU module. Since the vehicle is traveling within a certain constraint range, the trajectory tracking performance of the vehicle must be guaranteed.

[0055] (11); Where: , The vehicle's distance traveled and speed on the centerline will be updated. The coordinates are relative to the center line Jacobian of the coordinate arc length θ. is the vehicle lateral velocity, is the longitudinal velocity of the vehicle, Represents the angle between the vehicle's direction of travel and the road centerline.

[0056] Solve the optimization predictive control problem under the premise of a well-designed constraint set: The optimal control problem is transformed into the following expression, and the control variables are: , state variables for: .

[0057] The objective function of the solution process is: , where the constraints to be solved are as follows: (12); in, represents the lateral acceleration of the vehicle, represents the longitudinal acceleration of the vehicle, Represents the lateral coordinate value of the vehicle's trajectory, Represents the longitudinal coordinate value of the vehicle's trajectory, Represents the lateral coordinate value of the vehicle reference driving trajectory, Represents the lateral coordinate value of the vehicle's reference driving trajectory.

[0058] Here, by solving the expression of the objective function, under the expression of the constraint set of the prediction problem, at the end of the optimal solution routine, by the first prediction state Assign as the new state value, the first expected input Specify new input values to update the system.

[0059] S6. Coordinate vehicle speed control in longitudinal and vertical directions.

[0060] By using the longitudinal and vertical coordinated control module, the active suspension control module and the longitudinal vehicle speed planning module are coupled and coordinated to obtain the optimal longitudinal speed planning sequence and the optimal damping force control sequence of the active suspension system.

[0061] S7, end navigation.

[0062] When the vehicle reaches the target location of the vehicle, the navigation ends and the platform management module deletes the current vehicle from the vehicle information database.

[0063] To determine whether the vehicle has reached its target location, the platform management module may determine the target location and current location of the vehicle in the vehicle information database, or the positioning module may determine the location.

[0064] The embodiments described above are merely descriptions of preferred implementations of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should fall within the scope of protection determined by the claims of the present invention.

Claims

1. A vehicle longitudinal and vertical coordinated control system driven by cloud data, characterized by: It includes a database, a cloud-vehicle communication module, a platform management module, a road information collection module, an on-board unit module, a vehicle communication module, an active suspension control module, a longitudinal speed planning module, a longitudinal and vertical coordination control module, and a positioning module; wherein the database, cloud-vehicle communication module, and platform management module are located in the cloud to form a cloud control platform, and the remaining modules are installed on the vehicle side; the vehicle communication module and the cloud-vehicle communication submodule are used for information exchange between the cloud control platform and the vehicle, and in the cloud control platform, the platform management module is connected to the cloud-vehicle communication submodule and the database respectively; the vehicle communication module is connected to the road information collection module, the active suspension control module, the longitudinal speed planning module, and the positioning module respectively, the active suspension control module is connected to the longitudinal and vertical coordination module, and the longitudinal speed planning module is connected to the on-board unit module and the longitudinal and vertical coordination module; specifically: The database includes a road information database and a vehicle information database. The road information database contains map information and corresponding road surface grades, and the vehicle information database includes the vehicle ID currently connected to the cloud control platform, as well as the vehicle's current location and current road surface information. The cloud-vehicle communication module and the vehicle communication module exchange information between the cloud control platform and the vehicle, including: sending the navigation route and corresponding road grade to the vehicle communication module, and receiving the vehicle's target position, current position and collected current road surface information from the vehicle communication module; The platform management module is used to process the information received from the cloud-car communication module; The positioning module is used to obtain the current location of the vehicle and send it to the vehicle communication module; The road surface information collection module is used to collect road surface information and send the road surface information to the vehicle communication module; The on-board unit module is used to collect information about the preceding vehicle; The active suspension control module is used to regulate the vehicle suspension; The longitudinal speed planning module performs optimal speed planning based on the road surface grade obtained from the cloud control platform and the preceding vehicle information obtained from the vehicle-mounted unit module on the vehicle; The longitudinal and vertical coordination module couples and coordinates the active suspension control module and the longitudinal vehicle speed planning module.

2. The vehicle longitudinal and vertical coordinated control system based on cloud data drive according to claim 1, characterized in that: The platform management module is used to process the information received from the cloud vehicle communication module, including: Based on the vehicle's target location and current location, the navigation route and corresponding road grade are retrieved from the road information database and sent to the cloud-vehicle communication module. The vehicle is then added to the vehicle information database to establish a connection between the vehicle and the cloud platform. Based on the current position of the vehicle and the collected current road surface information, the current road surface information is processed to obtain road surface roughness, and the road surface grade is obtained based on the road surface roughness. The road surface grade of the current vehicle position stored in the vehicle information database is verified and adjusted in real time; When the target position and current position of a vehicle coincide with each other, the vehicle information is deleted from the vehicle information database.

3. The cloud data-driven vehicle longitudinal and vertical coordinated control system according to claim 2, characterized in that: The real-time verification and adjustment of the road surface grade of the current vehicle position stored in the vehicle information database is specifically as follows: When the obtained road surface grade is the same as the road surface grade in the road information database, no operation is performed; when the obtained road surface grade is different from the information in the road information database, the road surface grade in the road information database is updated, and the modified road surface grade is sent to the vehicles in the current navigation route in the vehicle information database that include this road section.

4. The vehicle longitudinal and vertical coordinated control system based on cloud data drive according to claim 1, characterized in that: The active suspension control module is used to regulate the vehicle suspension, specifically: The active suspension control module uses the suspension command control submodule to obtain suspension control instructions based on the road grade and current vehicle position received from the cloud control platform, and sends the suspension control instructions to the vibration reduction submodule in the active suspension module. After receiving the suspension control instructions, the vibration reduction submodule controls the valve control switch opening of the solenoid valve to adjust the softness or hardness of the suspension.

5. The vehicle longitudinal and vertical coordinated control system based on cloud data drive according to claim 1, characterized in that: The longitudinal speed planning module performs optimal speed planning based on the road surface grade obtained from the cloud control platform and the preceding vehicle information obtained from the on-board unit module on the vehicle, specifically: First, safety constraints are established during vehicle driving; then the vehicle's kinematic model is established; secondly, a model predictive controller is designed and a cost function based on the model predictive controller is constructed. Finally, the optimal solution in the planning process is obtained by solving the cost function.

6. A vehicle longitudinal and vertical coordinated control method based on cloud data drive, characterized by: It includes the following steps: S1, obtain road information; The vehicle uses the vehicle communication module to send its current vehicle location and target location to the cloud-vehicle communication submodule. The platform management module retrieves the navigation route and corresponding road surface grade from the road information database, sends the navigation route and corresponding road surface grade to the vehicle communication module through the cloud-vehicle communication submodule, and adds the vehicle to the vehicle information database, establishing a connection between the vehicle and the cloud platform. S2, processing of road surface information collected by vehicles; The road surface information collection module is used to collect road surface information, and the positioning module is used to obtain the current position of the vehicle. The current position of the vehicle and road surface information are uploaded to the cloud control platform in real time. The platform management module in the cloud control platform processes the road surface information and finally obtains the road surface grade. The road surface grade of the current vehicle position stored in the road information database is verified and adjusted in real time. S3, obtain the preceding vehicle information; Use the vehicle-mounted unit module to obtain the preceding vehicle information, which is the relative position of the preceding vehicle and the current vehicle; S4, adjusts the vehicle suspension according to the navigation route and the corresponding road grade; An active suspension control module is used to regulate the vehicle suspension according to the navigation route obtained in S1 and the corresponding road surface grade, and a suspension control instruction is obtained by taking the root mean square value of the vehicle vertical vibration acceleration and the root mean square value of the wheel dynamic deformation as evaluation indicators; S5, adjusting the vehicle speed according to the road information and the information of the preceding vehicle; Based on the navigation route and corresponding road grade obtained in S1, and the preceding vehicle information obtained in S3, the longitudinal vehicle speed planning module is used to perform optimal speed planning; S6, longitudinal and vertical coordinated control of vehicle speed; Using the longitudinal and vertical coordinated control module, by coupling and coordinating the active suspension control module and the longitudinal vehicle speed planning module, the optimal longitudinal speed planning sequence is obtained while also obtaining the optimal damping force control sequence of the active suspension system; S7, end navigation; When the vehicle reaches the target location of the vehicle, the navigation ends and the platform management module deletes the current vehicle from the vehicle information database.

7. The vehicle longitudinal and vertical coordinated control method based on cloud data drive according to claim 6 is characterized in that: The suspension control command is obtained by using the root mean square value of the vehicle vertical vibration acceleration and the root mean square value of the wheel dynamic deformation as evaluation indicators, specifically: The root mean square value of the vertical vibration acceleration of the vehicle body is minimized, which can be expressed as: （1）； in, Indicates the root mean square value of the vehicle's vertical vibration acceleration. RMS is the root mean square value calculation formula. is the vehicle vertical acceleration; Minimize the root mean square value of vehicle dynamic deformation, expressed as: （3）； in, is the root mean square value of the wheel dynamic deformation, is the displacement of the suspension system, is the road surface excitation displacement; The comprehensive performance evaluation index of the suspension system is expressed as: （4）； Where: , Respectively represent the evaluation index values of the ride smoothness and road adhesion of the passive suspension system, where ; is the vehicle ride comfort adjustment coefficient, is the vehicle-road adhesion adjustment coefficient, Minimization is used as the comfort control strategy to obtain the suspension control instructions.

8. The vehicle longitudinal and vertical coordinated control method based on cloud data drive according to claim 7 is characterized in that: described , .

9. The vehicle longitudinal and vertical coordinated control method based on cloud data drive according to claim 6, characterized in that: The optimal speed planning is specifically as follows: The objective function to be solved is as follows: （5）； Where: is the objective function value to be solved, is the prediction step length, is the minimum time following vehicle weight factor, is the comfort weight factor, is the predicted passenger number at time k Motion sickness incidence rate at each moment, is the energy consumption weight factor, is the minimum acceleration weight factor, where ; is the longitudinal velocity at time k+n predicted at time k; is the energy consumption parameter of the vehicle at time k+n predicted at time k, is the lateral acceleration sequence at time k+n predicted at time k, is the longitudinal acceleration sequence at time k+n predicted at time k; The constraint set settings include road adhesion coefficient constraints, vehicle safety speed limits, and trajectory tracking constraints; According to the constraint set, the optimal control problem is transformed into the following expression, and the control variables are: , state variables for: ; The objective function of the solution process is: , where the constraints to be solved are as follows: （12）； in, represents the lateral acceleration of the vehicle, represents the longitudinal acceleration of the vehicle, Represents the lateral coordinate value of the vehicle's trajectory, Represents the longitudinal coordinate value of the vehicle's trajectory, Represents the angle between the vehicle's direction of travel and the road centerline. Represents the lateral coordinate value of the vehicle reference driving trajectory, Represents the lateral coordinate value of the vehicle reference driving trajectory, is the gravitational acceleration coefficient.

10. The vehicle longitudinal and vertical coordinated control method based on cloud data drive according to claim 9, characterized in that: The constraint set is specifically: (1) Road adhesion coefficient constraint: （9）； Where: is the lateral acceleration of the vehicle; is the vehicle's longitudinal acceleration; is the tire-road friction coefficient; is the gravitational acceleration coefficient; (2) Vehicle safety speed limit: （10）； Where: is the lateral acceleration of the vehicle; is the vehicle speed, and are the first adjustment coefficient and the second adjustment coefficient respectively; is the road curvature; is the maximum safe speed; (3) Trajectory tracking constraints: （11）； Where: , ; The coordinates are relative to the center line The Jacobian of the coordinate arc length θ, is the vehicle lateral velocity, is the longitudinal velocity of the vehicle, Represents the angle between the vehicle's direction of travel and the road centerline.

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