Vehicle predictive adaptive cruise control system and method

Abstract

The application relates to a vehicle predictive adaptive cruise control system and method, wherein the system comprises a cloud control platform and a vehicle terminal platform. The cloud control platform is used for receiving state information and position information sent by the vehicle terminal platform, determining elevation information of a road in front of a vehicle controlled by the vehicle terminal platform, and generating an economic speed planning strategy of the vehicle based on the state information, the position information and the elevation information. The vehicle terminal platform is used for collecting and sending the state information and the position information to the cloud control platform, receiving the economic speed planning strategy sent by the cloud control platform, and constructing a vehicle speed fusion planning model based on a current cruise state of the vehicle and the economic speed planning strategy, so as to adjust a target vehicle speed of the vehicle. Thus, the technical problems that in the prior art, the optimality and stability of the operation resource consumption and the strategy output of the vehicle are difficult to be considered, the economy is considered only from surface data, and the safe and economic vehicle speed is difficult to be planned are solved.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to a vehicle predictive adaptive cruise control system and method. Background Technology

[0002] The development of commercial vehicles still faces challenges such as high fuel consumption and frequent safety accidents. Therefore, achieving safe and energy-efficient driving for commercial vehicles, especially trucks, has become a key issue of concern for countries around the world.

[0003] Among related technologies, intelligent connected vehicle technology can be used to achieve vehicle economic speed planning by utilizing cloud resources through data exchange between the vehicle and the cloud; ACC (Adaptive Cruise Control) technology, which takes into account the following vehicle's economic efficiency, can also be used to achieve vehicle economic speed planning.

[0004] However, in related technologies, vehicle economic speed planning algorithms based on cloud control platforms and intelligent connected technologies all assume that the vehicle is driving unaffected by surrounding vehicles and that there are no communication interruptions or missing maps during vehicle operation. But under real traffic conditions, a vehicle's driving status may be affected by the vehicle in front, making it impossible to balance safety and economy. Furthermore, when vehicle communication is interrupted or road maps are missing, no corresponding solutions can be provided, and the vehicle cannot determine the optimal driving mode based on the current driving state.

[0005] In ACC technology that considers following-car economy, one part is based on road gradient and establishes an adaptive following nonlinear MPC (Model Predictive Control) optimal control problem. This consumes a lot of computing resources and takes a long time, which cannot meet the requirements of the controlled system with high real-time performance and makes it difficult to truly achieve economical following of the vehicle. Another part only considers the energy consumption and other weighted terms in the MPC cost, or considers fuel economy from the perspective of motor energy consumption or vehicle acceleration. It only considers economy from the surface data and is difficult to plan safe and economical speed.

[0006] In summary, the relevant technologies struggle to balance the vehicle's computational resource consumption with the stability of strategy output, and relying solely on superficial data for economic considerations makes it difficult to plan for safe and economical vehicle speeds, thus requiring further improvement. Summary of the Invention

[0007] This application provides a vehicle predictive adaptive cruise control system and method to solve the technical problems in related technologies, such as the difficulty in balancing the vehicle's computing resource consumption and the stability of the strategy output, and the difficulty in planning safe and economical vehicle speeds based solely on surface data for economic considerations.

[0008] The first aspect of this application provides a vehicle predictive adaptive cruise control system, comprising: a cloud control platform and a vehicle-side platform. The cloud control platform receives status information and location information sent by the vehicle-side platform, determines the elevation information of the road ahead of the vehicle controlled by the vehicle-side platform based on the location information, generates an economic speed planning strategy for the vehicle based on the status information, the location information, and the elevation information, and sends it to the vehicle-side platform. The vehicle-side platform collects and sends the status information and location information to a location outside the cloud control platform, receives the economic speed planning strategy sent by the cloud control platform, constructs a vehicle speed fusion planning model based on the vehicle's current cruise status and the economic speed planning strategy, and adjusts the vehicle's target speed based on the calculation results of the vehicle speed fusion planning model, so that the target speed meets preset following safety and energy-saving requirements.

[0009] Optionally, in one embodiment of this application, the cloud control platform includes: a positioning module, configured to parse the location information and obtain the current positioning information of the vehicle based on the location information; a high-precision map module, configured to determine a static road map of the road ahead based on the current positioning information and obtain the elevation information of the road ahead based on the static road map; and an economic speed planning module, configured to generate the economic speed planning strategy based on the current positioning information and the elevation information.

[0010] Optionally, in one embodiment of this application, the economic speed planning module is further configured to: obtain the longitudinal force of the vehicle during driving using a preset dynamics model based on the state information; obtain the fuel consumption rate of the vehicle using a preset engine fuel consumption model based on the state information; construct an objective function using the state information, the longitudinal force, and the fuel consumption rate, and set constraints on the objective function and the preset dynamics model according to the elevation information to obtain a discretized predictive cruise control mathematical model for dynamic programming solution; and solve the discretized predictive cruise control mathematical model to obtain the economic speed planning strategy.

[0011] Optionally, in one embodiment of this application, the discretized predictive cruise control mathematical model is,

[0012]

[0013] subjectto

[0014]

[0015] k = 0, 1, ..., N-1, v k >0

[0016] T min ≤T ek ≤T max

[0017] v min ≤v k ≤v max

[0018] ω min ≤ω ek ≤ω max

[0019] a min ≤a k ≤a max

[0020] Where w1 is the weight of fuel consumption, w2 is the weight of deviation between vehicle speed and reference speed, w3 is the weight of vehicle speed change, w4 is the weight of throttle opening change, w5 is the weight of driving efficiency, and v ref For reference speed, v k For the vehicle speed in stage k, α ck α represents the throttle opening value at stage k in cruise control mode. pk Let Δt(k,k+1) be the throttle opening value for stage k in predictive cruise mode, Δt(k,k+1) be the time taken from stage k to stage k+1, and Δs be the distance between two adjacent stages. Let C be the vehicle fuel consumption rate at time k. D Where A is the air resistance coefficient, g is the frontal area, f is the gravitational acceleration, δ is the rolling resistance coefficient, and T is the vehicle rotational mass conversion factor. ek For the engine torque at stage k, ω ek For the engine speed in stage k, a k For the vehicle acceleration in stage k, T min With T max v is the boundary between minimum and maximum torque. min With v max ω represents the boundary between the minimum and maximum vehicle speeds. min With ω max For the minimum and maximum speed boundaries, a min With a max These are the boundaries between the minimum and maximum accelerations.

[0021] Optionally, in one embodiment of this application, the vehicle-side platform includes: a cruise mode switching module, used to determine the current cruise status of the vehicle based on the status information, the communication status between the vehicle and the cloud control platform, and the location information; a vehicle speed fusion planning module for following safety and energy saving, used to establish a vehicle speed fusion planning model based on the status information and the economic speed planning strategy, so as to obtain the target vehicle speed using the vehicle speed fusion planning model; and a vehicle module, used to obtain the status information based on the vehicle's operating parameters, obtain the vehicle's location information based on map information, upload the status information and the location information to the cloud control platform, and adjust the current driving speed of the vehicle according to the target vehicle speed.

[0022] Optionally, in one embodiment of this application, the cruise mode switching module is further configured to switch the current cruise state of the vehicle according to the status information, the communication status and the map information, wherein the current cruise state includes ACC adaptive cruise system mode cruise state, PCC predictive cruise control system mode cruise state, PACC predictive adaptive cruise system mode cruise state and manual driving mode driving state.

[0023] Optionally, in one embodiment of this application, the vehicle speed fusion planning module for following safety and energy saving is further used to obtain the actual following information of the current vehicle in the following state based on the state information; establish a vehicle speed fusion planning model according to the state information, the actual following information and the economic speed planning strategy; construct the actual dynamic constraints of the vehicle speed fusion planning model; and use the actual dynamic constraints to solve the vehicle speed fusion planning model to obtain the target vehicle speed.

[0024] Optionally, in one embodiment of this application, the state-space expression of the vehicle speed fusion planning model is:

[0025] x(k+1)=Ax(k)+Bu(k)+Gw(k)y(k)=Cx(k)-Z

[0026] y(k)=Cx(k)-Z

[0027] in,

[0028] x(k)=[Δd(k),v h (k),v e (k),a h (k),jerk(k)] T

[0029] y(k)=[δ(k),v e (k),v r (k),a h(k),j(k)] T

[0030] u(k)=a des (k)

[0031] w(k)=a p (k)

[0032] Where x(k) is the state variable, Δd(k) is the relative distance between the vehicle and the vehicle in front at time k, and v h (k) represents the vehicle speed, v e (k) represents the relative speed between the vehicle and the vehicle in front, a h (k) represents the vehicle's acceleration, jerk(k) represents the vehicle's jerk, x(k+1) represents the state variable at time k+1, y(k) represents the state variable, δ(k) represents the vehicle spacing error at time k, and v pcc For the economic speed sequence, v r (k) represents the difference between the vehicle's speed and its economical speed, j(k) represents the output variable formed by the vehicle's jerk, u(k) represents the control input variable at time k, and a des (k) represents the desired acceleration, w(k) represents the external disturbance at time k, and a p (k) represents the acceleration of the vehicle in front.

[0033] A second aspect of this application provides a vehicle predictive adaptive cruise control method applied to a cloud control platform. The method includes the following steps: receiving status information and location information sent by at least one vehicle-side platform; determining the elevation information of the road ahead of the vehicle controlled by the vehicle-side platform based on the location information; generating an economic speed planning strategy for the vehicle based on the status information, the location information, and the elevation information, and sending it to the vehicle-side platform.

[0034] A third aspect of this application provides a vehicle predictive adaptive cruise control method applied to a vehicle-side platform. The method includes the following steps: collecting vehicle status information and location information; sending the status information and location information to a cloud control platform; receiving an economic speed planning strategy sent by the cloud control platform; constructing a vehicle speed fusion planning model based on the vehicle's current cruise status and the economic speed planning strategy; adjusting the vehicle's target speed based on the calculation results of the vehicle speed fusion planning model, so that the target speed meets preset following safety and energy-saving requirements; wherein the economic speed planning strategy is obtained from the status information and the location information.

[0035] A fourth aspect of this application provides a server, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle predictive adaptive cruise control method as described in the above embodiments.

[0036] A fifth aspect of this application provides a vehicle, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle predictive adaptive cruise control method as described in the above embodiments.

[0037] A sixth aspect of this application provides a computer-readable storage medium storing computer instructions for causing the computer to perform the vehicle predictive adaptive cruise control method as described in the above embodiments.

[0038] This application embodiment utilizes a vehicle-side platform to collect vehicle status and location information, which is then uploaded to a cloud control platform. The cloud control platform can determine an economical speed planning strategy based on this information, allowing the vehicle-side platform to use this strategy as a reference for adjusting the target speed. This enables predictive adaptive cruise control, tightly integrating cloud and vehicle-side algorithms, reducing computational load on the vehicle, improving real-time performance, and adjusting speed planning based on vehicle status changes, thus balancing safety and economy. This solves the technical problems in related technologies, such as the difficulty in balancing vehicle computational resource consumption and strategy output stability, and the difficulty in planning safe and economical speeds based solely on surface-level data.

[0039] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0040] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0041] Figure 1 This is a schematic diagram of a vehicle predictive adaptive cruise control system according to an embodiment of this application;

[0042] Figure 2 This is a schematic diagram of longitudinal dynamic force analysis of a vehicle according to an embodiment of this application;

[0043] Figure 3 This is a schematic diagram of a fitted engine fuel consumption model according to an embodiment of this application;

[0044] Figure 4 This is a schematic diagram illustrating the switching logic between different cruise modes according to one embodiment of this application;

[0045] Figure 5 This is a schematic diagram of a following vehicle scenario according to an embodiment of this application;

[0046] Figure 6 This is a schematic diagram of a high-speed driving operation scenario according to an embodiment of this application;

[0047] Figure 7 This is a schematic diagram of a vehicle-cloud hierarchical control architecture according to an embodiment of this application;

[0048] Figure 8 This is a schematic diagram of one embodiment of the present application;

[0049] Figure 9 This is a flowchart of a vehicle predictive adaptive cruise control method according to an embodiment of this application;

[0050] Figure 10 This is a schematic diagram of the server structure provided according to an embodiment of this application;

[0051] Figure 11 This is a flowchart of another vehicle predictive adaptive cruise control method provided according to an embodiment of this application;

[0052] Figure 12 This is a structural schematic diagram of a vehicle provided according to an embodiment of this application. Detailed Implementation

[0053] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0054] The following description, with reference to the accompanying drawings, describes a vehicle predictive adaptive cruise control system and method according to embodiments of this application. Addressing the technical problems mentioned in the background art, such as the difficulty in balancing vehicle computational resource consumption and strategy output stability, and the difficulty in planning safe and economical speeds based solely on surface data for economic considerations, this application provides a vehicle multimedia sharing method. In this method, vehicle-side platform collects vehicle status and location information and uploads it to a cloud control platform. The cloud control platform can determine the vehicle's economical speed planning strategy based on the status and location information, allowing the vehicle-side platform to use this strategy as a reference for adjusting the target speed. This achieves predictive adaptive cruise control, tightly integrating cloud and vehicle-side algorithms, reducing vehicle-side computational load, improving algorithm real-time performance, and adjusting speed planning based on vehicle status changes, thus balancing safety and economy. Therefore, this solves the technical problems in related technologies, such as the difficulty in balancing vehicle computational resource consumption and strategy output stability, and the difficulty in planning safe and economical speeds based solely on surface data for economic considerations.

[0055] Specifically, Figure 1 This is a schematic diagram of a vehicle predictive adaptive cruise control system provided in an embodiment of this application.

[0056] like Figure 1 As shown, the vehicle predictive adaptive cruise control system 10 includes a cloud control platform 100 and a vehicle-side platform 200.

[0057] Specifically, the cloud control platform 100 is used to receive status information and location information sent by the vehicle-end platform 200, determine the elevation information of the road ahead of the vehicle controlled by the vehicle-end platform 200 based on the location information, generate an economic speed planning strategy for the vehicle based on the status information, location information and elevation information, and send it to the vehicle-end platform 200.

[0058] In actual operation, the vehicle can use vehicle-to-cloud communication to exchange information with the edge cloud via wireless communication and request services from the cloud control platform 100 for the predictive adaptive cruise control application. The cloud control platform 100 can receive the vehicle status information and location information sent by the vehicle-side platform 200 based on the request. The status information may include the vehicle's current status information (such as current driving speed, current fuel level, etc.) and the current status information of the vehicle in front (such as current driving speed, distance between the vehicle in front, etc.). The location information may include the vehicle's current positioning information, etc.

[0059] This embodiment of the application can obtain a static road map of the road ahead of the vehicle based on location information, and obtain the elevation information of the road ahead, i.e., the slope information, based on the static road map. This embodiment of the application can also obtain the optimal economic speed sequence, i.e., the vehicle's economic speed planning strategy, based on status information and comprehensively considering vehicle fuel consumption, and send the obtained economic speed planning strategy to the vehicle-side platform 200. The vehicle-side platform 200 will be described below.

[0060] It is important to note that the cloud control platform 100 can include a cloud control infrastructure platform and cloud control applications. The infrastructure platform provides basic hardware and software platforms, standard components for perception fusion and decision-making, and can provide basic hardware, software and information support for the cloud control application platform.

[0061] Optionally, in one embodiment of this application, the cloud control platform 100 includes: a positioning module, a high-precision map module, and an economic speed planning module.

[0062] The positioning module is used to parse location information and obtain the vehicle's current location information based on the location information.

[0063] The high-precision map module is used to determine a static road map of the road ahead based on the current location information, and to obtain the elevation information of the road ahead based on the static road map.

[0064] The economic speed planning module is used to generate economic speed planning strategies based on current location and elevation information.

[0065] In some embodiments, the positioning module can parse the location information uploaded by the vehicle-side platform 200, locate the vehicle's current location information in the map of the cloud control platform 100, and further obtain the elevation information of the static road, i.e., the slope information, through the high-precision map module. This embodiment can transmit the above information to the economic speed planning module, thereby generating an economic speed planning strategy based on the current location information and elevation information, and solving for the economic speed of the vehicle type.

[0066] Optionally, in one embodiment of this application, the economic speed planning module is further configured to: obtain the longitudinal force of the vehicle during driving based on state information using a preset dynamics model; obtain the fuel consumption rate of the vehicle based on state information using a preset engine fuel consumption model; construct an objective function using state information, longitudinal force, and fuel consumption rate, and set constraints on the objective function and the preset dynamics model according to elevation information to obtain a discretized predictive cruise control mathematical model for dynamic programming solution; and solve the discretized predictive cruise control mathematical model to obtain an economic speed planning strategy.

[0067] Furthermore, the economic speed planning module also includes a preset dynamics model, a preset engine fuel consumption model, a set objective function and constraints for the preset dynamics model, and a solution model based on dynamic programming algorithm.

[0068] The economic speed planning module, after acquiring the vehicle's status information uploaded to the cloud control platform 100, uses a vehicle dynamics model (i.e., a preset dynamics model) to solve for the longitudinal force acting on the vehicle during its current driving process. It also uses a preset engine fuel consumption model to calculate the vehicle's speed and torque based on its current speed, thereby determining the vehicle's fuel consumption rate. The solved longitudinal force and fuel consumption rate are used to construct the vehicle's optimal control problem, i.e., setting the objective function and constraints of the preset dynamics model. The control objective of the optimal control problem is to achieve economic and efficient vehicle operation. Furthermore, this embodiment can use a dynamic programming algorithm to solve for the minimum cost function based on the optimal control problem, i.e., solving the discretized predictive cruise control mathematical model, and obtaining an optimal speed curve that meets the requirements, i.e., the economic speed planning strategy.

[0069] Specifically, the design of the preset dynamics model can be as follows: The PACC (Predictive Adaptive Cruise Control System) is mainly used in highway scenarios. The entire system rationally plans the following cruise speed by combining the motion information of the vehicle in front and the motion state of the vehicle itself, ensuring that the vehicle can improve fuel economy by utilizing road gradient information while driving safely. Since the system does not include lane change and steering control issues, this embodiment only needs to model the longitudinal dynamic characteristics of the vehicle.

[0070] By performing force analysis on the vehicle's motion, embodiments of this application can calculate information such as the driving speed required for the longitudinal dynamics model, such as... Figure 2 As shown, during the process of a vehicle climbing a hill, it is subjected to multiple forces in the longitudinal direction, including rolling resistance F. f air resistance F w , ramp resistance F i and the driving force F that drives the vehicle t To facilitate controller design, the following assumptions are made:

[0071] (1) Ignore dynamics such as tire slippage and half-shaft torsion.

[0072] (2) The clutch is not engaged / disengaged and gear shifting is not performed, that is, the vehicle is driven in a fixed gear.

[0073] According to Newton's second law, the force analysis of the vehicle yields the following equation for its motion:

[0074]

[0075]

[0076] Among them, T tq Where is the engine torque (Nm), and g is the acceleration due to gravity (m / s²). 2 f is the rolling resistance coefficient, θ is the road gradient (rad), and C D Where A is the air resistance coefficient and A is the frontal area (m²). 2 ), where v is the vehicle's speed (km / h), and i g i0 is the gear ratio of the transmission, i0 is the gear ratio of the main reducer, and η is the gear ratio of the gearbox. t Let m be the mechanical efficiency of the transmission system, m be the mass of the vehicle, and r be the wheel radius.

[0077] It should be noted that when the engine torque T tq When the value is positive, the driving force is the traction force that propels the vehicle. Conversely, when the value is negative, the driving force is the drag force that hinders the vehicle's movement.

[0078] To accurately calculate fuel consumption during vehicle operation, this embodiment of the application can pre-establish a polynomial fuel consumption model for the engine. The polynomial fuel consumption model fits the fuel consumption model to a relationship between vehicle speed n and engine torque T. tq A quadratic polynomial function.

[0079]

[0080] Where, ξ i,j The fitting coefficients are shown below. Figure 3 As shown. Later, the fitting coefficients of the polynomial can be updated and modified according to the engine fuel consumption model of different vehicle models, and then updated and deployed in the application platform of Cloud Control Platform 100.

[0081] PCC (Predictive Cruise Control) utilizes pre-acquired elevation information, i.e., road slope information, to solve a dynamic programming algorithm on the cloud control platform 100, and then sends the optimal vehicle speed control sequence of the speed planning strategy to the vehicle-side platform 200 for control. ACC, based on model predictive control algorithms, establishes the state-space equations between the vehicle and the vehicle in front, taking into account multiple objectives such as economy and safety.

[0082] The PACC in this application embodiment can combine PCC and ACC to achieve predictive cruise in risk-free situations and anticipatory adaptive cruise in dangerous situations.

[0083] Setting the objective function and constraints of the preset dynamic model essentially involves constructing an optimization control problem using road map information from a high-precision map module, either predicted or acquired in advance. To simultaneously satisfy multiple performance requirements during vehicle operation, the objective function needs to consider fuel consumption, vehicle speed variations, throttle opening variations, and driving efficiency. The expression for the objective function is:

[0084]

[0085] Among them, ω1, ω2, ω3, ω4, and ω5 are the weights of various cost items, namely fuel consumption, deviation of vehicle speed from reference speed, vehicle speed change, throttle opening change, and driving efficiency, respectively; v ref v is the set reference speed t Let α be the vehicle speed at time t. ct and α pt These are the throttle opening values ​​at time t in both CC (Cruise Control System) and PCC modes, respectively. The system 10 described in this application needs to find an optimal control strategy to minimize the objective function under permissible control constraints. That is, the control strategy should enable the powertrain to achieve optimal driving performance when the vehicle is traveling on the road ahead, with minimizing fuel consumption being the primary control objective.

[0086] Since the road slope information in system 10 is given discretely according to a certain distance, that is, the slope information of each road segment is a discrete function of GPS (Global Positioning System) position, and in addition, in the actual control process, the controller controls the vehicle speed at a certain frequency, the system 10 of this application embodiment is a typical discrete control system. Dynamic programming algorithm is an effective method for solving multi-stage decision problems and can efficiently calculate the optimal decision sequence.

[0087] According to the calculation method of discrete dynamic programming, the embodiments of this application can discretize the preset dynamic model formula and objective function and set constraints to obtain a discretized predictive cruise control mathematical model for dynamic programming solution, as shown in the following formula.

[0088]

[0089]

[0090] In formulas (5) and (6), w1 is the weight of the fuel consumption term, w2 is the weight of the deviation between vehicle speed and reference speed, w3 is the weight of the vehicle speed change value, w4 is the weight of the throttle opening change value, w5 is the weight of the driving efficiency, and v ref For reference speed, α ckα represents the throttle opening value at stage k in cruise control mode. pk v represents the k-stage throttle opening value in predictive cruise mode. k (km / h) and v k+1 (km / h) represents the vehicle speed in stages k and k+1, respectively, and Δs(m) represents the distance between two adjacent stages. Let C be the vehicle fuel consumption rate at time k. D Let A be the air resistance coefficient, g be the frontal area, f be the gravitational acceleration, f be the rolling resistance coefficient, Δt(k,k+1) be the time taken from k to k+1, and i be the air resistance coefficient. tk Let T be the total transmission ratio of the transmission system at stage k. ek (Nm) represents the engine torque at stage k, σ k (rad) represents the road gradient angle at stage k, a k Let ω be the vehicle acceleration in stage k. ek For the engine speed in stage k, T min With T max v is the boundary between minimum and maximum torque. min With v max ω represents the boundary between the minimum and maximum vehicle speeds. min With ω max For the minimum and maximum speed boundaries, a min With a max The minimum and maximum acceleration boundaries are defined by δ, which is the conversion factor for the vehicle's rotating mass.

[0091] The solution based on dynamic programming is derived from the optimal control problem, which involves constructing the objective function and the constraints of the pre-defined dynamic model. It employs a "reverse calculation, forward optimization" method, indexing the values ​​of the cost function to determine if all values ​​represent the minimum global cost, and ultimately finding a minimum-cost economic speed sequence v. pcc This speed is then transmitted to the vehicle-side platform 200.

[0092] The PACC system uses the economic speed planning module on the cloud control platform to perform economic speed sequence v pcc The calculation is performed, and the cruise mode is determined on the vehicle-side platform 200. If the cruise mode is determined to be PCC mode, the T-BOX will receive the v pcc Send to VCU (Vehicular Communication Unit), VCU will... pcc The desired vehicle speed is used to directly control the vehicle's speed; if the cruise mode is determined to be PACC mode, then the speed sequence v pcc The speed 200 will be sent to the vehicle-side platform as a reference speed for following other vehicles, and will be considered as one of the target speeds, thereby planning a safe and economical following speed v.pacc Then, the vehicle speed is controlled.

[0093] The vehicle-side platform 200 is used to collect and send status information and location information to the cloud control platform 100, receive the economic speed planning strategy sent by the cloud control platform 100, and construct a vehicle speed fusion planning model based on the vehicle's current cruise status and economic speed planning strategy. Based on the calculation results of the vehicle speed fusion planning model, the target vehicle speed is adjusted so that the target vehicle speed meets the preset following safety and energy-saving requirements.

[0094] In actual implementation, the vehicle T-BOX of the vehicle-side platform 200 in this embodiment can determine the vehicle's current optimal cruise mode, i.e., the vehicle's current cruise state, based on the communication status and location information of the vehicle-cloud communication, and send the current cruise state determination result to the vehicle-side controller. The vehicle-side controller further calculates and determines the optimal control speed, i.e., the target speed, based on the speed planning strategy recommended by the cloud control platform 100 and the current cruise state determined by the T-BOX, and outputs the optimal control speed that meets the requirements of following safety and energy saving.

[0095] During a planning cycle, the cloud control platform 100 can receive vehicle commands again, and the vehicle will re-upload status and location information through the vehicle-side platform 200 to re-plan the vehicle's predictive adaptive cruise control and speed control, forming a rolling closed-loop control.

[0096] The communication and information processing functions between the vehicle-side platform 200 and the cloud control platform 100 can be realized by the vehicle-mounted intelligent remote information processing terminal T-BOX, which is equipped with a GPS positioning module, enabling it to obtain the vehicle's location in real time and upload it to the cloud control platform 100 to determine the vehicle's location.

[0097] Optionally, in one embodiment of this application, the vehicle-side platform 200 includes: a cruise mode switching module, a vehicle speed fusion planning module for following safety and energy saving, and a vehicle module.

[0098] The cruise mode switching module is used to determine the current cruise status of the vehicle based on status information, the communication status between the vehicle and the cloud control platform 100, and location information.

[0099] The vehicle speed fusion planning module for following safety and energy saving is used to establish a vehicle speed fusion planning model based on state information and economic speed planning strategy, so as to obtain the target vehicle speed using the vehicle speed fusion planning model.

[0100] The vehicle module is used to obtain status information based on the vehicle's operating parameters, obtain the vehicle's location information based on map information, upload the status information and location information to the cloud control platform 100, and adjust the vehicle's current driving speed according to the target vehicle speed.

[0101] The cruise mode switching module can determine the vehicle's current driving status based on the vehicle module's status information (vehicle speed, phase position, etc.), cloud network communication status, map information status, etc.

[0102] The vehicle speed fusion planning module, which considers economic efficiency, safety, and energy conservation, may also include a vehicle following control module, a system prediction model, and cost functions and constraints. The vehicle following control module uses the vehicle's driving status information uploaded by the vehicle module to solve for distance and speed errors when following another vehicle. The system prediction model, after receiving the vehicle speed planning strategy from the cloud control platform 100, establishes a predictive model for the actual control of system 10, i.e., the vehicle speed fusion planning model. The cost function and constraints comprehensively consider vehicle following safety, economy, driving smoothness, and actual vehicle dynamics constraints, ensuring that the vehicle speed planned by the vehicle speed fusion planning module meets the requirements of system 10. The vehicle module is the actual vehicle operation module and has the ability to communicate with the cloud.

[0103] In the cruise mode switching module, this embodiment first needs to determine the vehicle's current optimal driving mode. This embodiment mainly considers cruise control issues in scenarios of following another vehicle or when there is no vehicle ahead. Therefore, an event-driven switching strategy needs to be set between manual driving mode and cruise mode to enable the vehicle to maintain a safe and economical driving state during driving.

[0104] Optionally, in one embodiment of this application, the cruise mode switching module is further configured to switch the current cruise state of the vehicle according to status information, communication status and map information, wherein the current cruise state includes ACC adaptive cruise system mode cruise state, PCC predictive cruise control system mode cruise state, PACC predictive adaptive cruise system mode cruise state and manual driving mode driving state.

[0105] Specifically, the mode switching strategy can determine whether the state transition conditions are met by identifying GPS signal quality, network status, data format, relative distance between the vehicle and the vehicle in front, vehicle speed information, and the cruise mode activation button, ensuring the safe operation of the system. For example, mode switching is performed based on conditions such as the vehicle's relative status with the vehicle in front, network communication status, and whether map information is missing. The vehicle's initial default state is manual driving mode. When the driver presses the cruise mode switch, the vehicle enters the built-in ACC / CC mode, and then switches between cruise modes according to the vehicle's driving conditions. When the vehicle is in cruise mode, if the driver turns off the cruise mode switch or presses the brake / clutch pedal, it automatically exits cruise mode and switches back to manual driving mode. The switching logic between different cruise modes is as follows: Figure 4As shown, Table 1 is a table of corresponding condition values.

[0106] Table 1

[0107] Condition 1 Cruise switch = 1 Condition 2 Cruise control switch = 0 or driver pressing brake / clutch = 1 Condition 3 <![CDATA[Network status = 1 and map information = 0 and relative distance > d enter > Condition 4 Network status = 0 or map information = 0 Condition 5 <![CDATA[Network status = 1 and map information = 1 and relative distance < d enter and v h < v p > Condition 6 <![CDATA[Network status = 1 and map information = 1 and (relative distance > d enter or v p > v cc )]]> Condition 7 Network status = 0 or map information = 0 Condition 8 <![CDATA[Network status = 1 and map information = 1 and relative distance < d enter and v h < v p >

[0108] Understandably, good communication is a crucial condition for the integration of the cloud control platform 100's cloud control system with practical applications. For the cloud-controlled cruise system, network status represents the status of vehicle-to-cloud communication and other information. A normal network status (i.e., network status = 1) requires accurate vehicle GPS positioning information, a good T-BOX network status, and accurate data upload format from the vehicle. Normal map information (i.e., map information = 1) indicates that the cloud control platform 100 can acquire all slope information of the vehicle's current driving route. Relative distance is measured by the vehicle's radar sensors and transmitted to the T-BOX for mode determination; max d is the maximum following distance (m) when the vehicle exits the following lane. enter The set distance (m) for entering following mode; v h v represents the vehicle's speed (km / h). p v represents the speed of the vehicle in front (km / h). cc The desired cruising speed (km / h) set for the driver.

[0109] Optionally, in one embodiment of this application, the vehicle speed fusion planning module for following safety and energy saving is further used to obtain the actual following information of the current vehicle in the following state based on the state information; establish a vehicle speed fusion planning model based on the state information, the actual following information and the economic speed planning strategy; construct the actual dynamic constraints of the vehicle speed fusion planning model; and use the actual dynamic constraints to solve the vehicle speed fusion planning model to obtain the target vehicle speed.

[0110] Furthermore, the vehicle speed fusion planning module for following safety and energy saving in the vehicle platform 200 can use the vehicle speed planning strategy issued by the cloud control platform 100 as one of the inputs of the module, and use the speed as one of the reference values ​​and target speeds of the vehicle speed. Figure 5 This illustrates the longitudinal kinematic relationship between the vehicle and the vehicle in front in this embodiment.

[0111] A vehicle speed fusion planning module for following safety and energy conservation is established based on a vehicle kinematic model in a following scenario. This model describes the relative distance and relative speed between the two vehicles, the difference between the vehicle's speed and the economical speed, and the vehicle's motion state. The state equation expression is as follows.

[0112] x(k+1)=Ax(k)+Bu(k)+Gw(k) (7)

[0113] y(k)=Cx(k)-Z (8)

[0114] in,

[0115] x(k)=[Δd(k),v h (k),v e (k),a h (k),jerk(k)] T (9)

[0116] y(k)=[δ(k),v e (k),v r (k),a h (k),j(k)] T (10)

[0117] u(k)=a des (k) (11)

[0118] w(k)=a p (k) (12)

[0119] In the above formula, the selected state variables x(k) are the relative distance Δd(k) between the vehicle and the vehicle in front, and the vehicle speed v. h (k) Relative speed v between the vehicle and the vehicle in front e (k) Vehicle acceleration a h (k) represents the vehicle's jerk(k). y(k) represents the distance error δ(k) and the relative speed v with respect to the vehicle in front. e (k) Vehicle and economic speed v pcc Difference v r (k) Vehicle acceleration a h (k) Output variable j(k) is composed of vehicle judder. u(k) is the control input variable at time k, which in this embodiment refers to the desired acceleration a. des w(k) represents the external disturbance, specifically the acceleration a of the vehicle in front. p (k).

[0120] The system matrix can be represented as follows

[0121]

[0122]

[0123] Using the workshop motion model of the PACC system established by formulas (7) and (8) as the prediction model, the future behavior of the PACC system is predicted in p steps. The set of state variables and the set of output variables for the p-step prediction at discrete time k are defined as follows:

[0124]

[0125]

[0126] in,

[0127]

[0128]

[0129]

[0130]

[0131]

[0132] The output variable should move along the reference trajectory. In order to reduce fuel consumption and minimize the occurrence of sudden changes in the variable, the reference trajectory is set as an exponential decay function.

[0133] y ref (k+i)=[ρ δ ,ρ ve ,ρ vr ,ρ ah ,ρ jerk ] i y(k) (15)

[0134]

[0135] Where, α δ Let be the time constant, and ρ be the decay coefficient. A larger ρ value indicates a slower response and more robust system behavior. Comfort, economy, and safety are combined as the objective function of the PACC following control system.

[0136]

[0137] Among them, Y ref (k+p) represents the set of performance vector reference trajectories predicted at discrete time k and p steps, where Q and R represent the weight matrices of the target and control, respectively.

[0138]

[0139] Q = diag(q) δ ,q ve ,q vr ,q ah ,q jerk (19)

[0140] Wherein, the coefficients q in the Q matrix δ ,q ve ,q vr ,q ah ,q jerkThese represent the weights for vehicle spacing error, relative speed between the vehicle and the vehicle in front, difference between the vehicle and the economic speed, vehicle acceleration, and vehicle jump, respectively.

[0141] Further simplification of formula (17), and taking into account the physical characteristics of the vehicle and the requirements for safety and comfort during vehicle operation, yields the following result:

[0142]

[0143] stΩU(k+m)≤T (21)

[0144] Formulas (20) and (21) above represent the cost function and constraints of the vehicle speed fusion planning module for vehicle following safety and energy conservation. Among them,

[0145]

[0146]

[0147]

[0148]

[0149]

[0150]

[0151]

[0152]

[0153]

[0154] In summary, the vehicle speed problem in the PACC speed fusion planning module for following safety and energy saving is transformed into an online quadratic programming problem. By applying the first control component u(k) of the calculated control sequence U(k) to the vehicle module, and repeating the above process at the next time step, the optimal control of the vehicle for safety and economy can be achieved.

[0155] Combination Figures 6-8 As shown, the working principle of the vehicle predictive adaptive cruise control system 10 of this application is explained in detail with reference to one embodiment.

[0156] like Figure 6 The illustration shows an application scenario of this application embodiment, as follows: Figure 7 As shown, this is a vehicle-cloud hierarchical control architecture according to an embodiment of this application. Figure 8The diagram shown is a schematic diagram of the principle of an embodiment of this application. The system 10 of this embodiment may include: a cloud control platform 100 and a vehicle-side platform 200.

[0157] The cloud control platform 100 includes: a positioning module 101, a high-precision map module 102, and an economic speed planning module 103;

[0158] The vehicle-side platform 200 includes: a cruise mode switching module 201, a speed fusion planning module for following safety and energy saving 202, and a vehicle module 203.

[0159] In actual implementation, such as Figures 6-8 As shown, the cloud control platform 100 can obtain the vehicle's status and location information, and acquire a static road map ahead of the vehicle through the high-precision map module 102. Taking into account the vehicle's fuel consumption, the economic speed planning algorithm of the economic speed planning module 103 solves the problem and sends the recommended optimal economic speed to the vehicle's terminal controller on the vehicle-side platform 200. The T-BOX, considering communication status and map information, determines the vehicle's current optimal cruise mode through the cruise mode switching module 201 and sends the cruise mode determination result to the vehicle-side controller. The vehicle-side controller further calculates and judges based on the economic speed and cruise mode recommended by the cloud control platform 100, and outputs the optimal control speed for the vehicle. During a planning cycle, the cloud control platform 100 re-accepts the vehicle's cruise request service command, and the vehicle re-uploads its own status information and the relevant driving status of the vehicle ahead, re-planning the vehicle's predictive adaptive cruise control and speed control, forming a rolling closed-loop control.

[0160] The control described in this application is a real-time application of a cloud control system, requiring deployment on an edge cloud application platform. Through vehicle-to-cloud communication, information exchange occurs between the edge cloud and the vehicle via wireless communication. Uploaded information includes speed and location information of the main vehicle and the vehicle ahead. Downloaded information is the economic speed sequence planned by the cloud control platform 100. The cloud control platform 100's basic platform provides the static map information required for the algorithm, and its application platform deploys the vehicle's longitudinal dynamics model, fuel consumption model, and cloud-based economic speed planning algorithm. The vehicle-side platform 200 deploys the vehicle's following safety control algorithm, and the vehicle's cruise mode switching control algorithm is deployed in the vehicle's T-BOX within the cloud control platform 100. This vehicle-cloud collaborative control enables predictive adaptive cruise control.

[0161] In summary, the overall architecture, vehicle-to-cloud communication method, and specific implementation algorithm of the system 10 in this application embodiment fully utilize the static road map resources of the cloud control platform 100 to obtain the road gradient information required for economic speed planning. Furthermore, the cloud control platform 100 can perform rapid real-time planning and decision calculations, which can significantly alleviate the computational pressure on the vehicle platform, making economic speed planning more forward-looking. It can quickly plan the economic speed based on the long-term static road map information in the cloud, and a speed planning algorithm based on the rolling distance domain is designed, enabling the algorithm to quickly adjust the planned economic speed according to the vehicle's driving status. This achieves vehicle-to-cloud hierarchical collaborative control and realizes safe and economical vehicle cruising.

[0162] Meanwhile, this application embodiment also considers mode switching strategies based on events such as network status. Compared with related technologies, it adds PACC and ACC modes, taking into account more cruise modes. The algorithm combines the driving status of the vehicle and the vehicle in front, while considering issues such as whether the network communication status is good and whether map information is missing. It quickly determines the driving mode in the T-BOX on the vehicle side and sends the result to the vehicle-side VCU.

[0163] This application's embodiments also combine PCC and ACC, realizing predictive cruise in risk-free situations and anticipatory adaptive cruise in dangerous situations. This solves the problems of predictive cruise in related technologies being unable to handle obstruction from vehicles ahead and the lack of economic considerations in adaptive cruise, thus balancing vehicle driving economy and safety.

[0164] Next, with reference to the accompanying drawings, a vehicle predictive adaptive cruise control method according to an embodiment of this application is described.

[0165] Figure 9 This is a flowchart of a vehicle predictive adaptive cruise control method according to an embodiment of this application.

[0166] like Figure 9 As shown, the vehicle predictive adaptive cruise control method is applied to a cloud control platform, and the method includes the following steps:

[0167] In step S901, status information and location information sent by at least one vehicle-side platform are received.

[0168] In step S902, the elevation information of the road ahead of the vehicle controlled by the vehicle-end platform is determined based on the location information.

[0169] In step S903, an economic speed planning strategy for the vehicle is generated based on the status information, location information, and elevation information, and then sent to the vehicle-side platform.

[0170] It should be noted that the foregoing explanation of the vehicle predictive adaptive cruise control system embodiment also applies to the vehicle predictive adaptive cruise control method of this embodiment, and will not be repeated here.

[0171] The vehicle predictive adaptive cruise control method proposed in this application can collect vehicle status and location information using a vehicle-side platform and upload this information to a cloud control platform. The cloud control platform can then determine an economical speed planning strategy for the vehicle based on this information. This allows the vehicle-side platform to use the economical speed planning strategy as a reference for adjusting the target speed, thereby achieving predictive adaptive cruise control. This method tightly integrates cloud and vehicle-side algorithms, reducing the computational load on the vehicle side, improving the real-time performance of the algorithm, and adjusting speed planning based on changes in vehicle status, thus balancing safety and economy. This solves the technical problems in related technologies, such as the difficulty in balancing vehicle computational resource consumption and strategy output stability, and the difficulty in planning safe and economical speeds based solely on surface data for economic considerations.

[0172] Figure 10 A schematic diagram of the structure of a server provided in an embodiment of this application. The server may include:

[0173] The memory 1001, the processor 1002, and the computer program stored on the memory 1001 and capable of running on the processor 1002.

[0174] When the processor 1002 executes the program, it implements the vehicle predictive adaptive cruise control method provided in the above embodiments.

[0175] Furthermore, the server also includes:

[0176] Communication interface 1003 is used for communication between memory 1001 and processor 1002.

[0177] The memory 1001 is used to store computer programs that can run on the processor 1002.

[0178] The memory 1001 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0179] If the memory 1001, processor 1002, and communication interface 1003 are implemented independently, then the communication interface 1003, memory 1001, and processor 1002 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 10 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0180] Optionally, in a specific implementation, if the memory 1001, processor 1002, and communication interface 1003 are integrated on a single chip, then the memory 1001, processor 1002, and communication interface 1003 can communicate with each other through an internal interface.

[0181] The processor 1002 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0182] The vehicle predictive adaptive cruise control method according to embodiments of this application is described again with reference to the accompanying drawings.

[0183] Figure 11 This is a flowchart of a vehicle predictive adaptive cruise control method according to an embodiment of this application.

[0184] like Figure 11 As shown, the vehicle predictive adaptive cruise control method is applied to a vehicle-side platform, and the method includes the following steps:

[0185] In step S1101, the vehicle's status information and location information are collected.

[0186] In step S1102, status information and location information are sent to the cloud control platform.

[0187] In step S1103, the vehicle receives the economic speed planning strategy sent by the cloud control platform, and constructs a vehicle speed fusion planning model based on the vehicle's current cruise state and the economic speed planning strategy. The vehicle's target speed is adjusted based on the calculation results of the vehicle speed fusion planning model so that the target speed meets the preset following safety and energy-saving requirements. The economic speed planning strategy is obtained from the state information and the position information.

[0188] It should be noted that the foregoing explanation of the vehicle predictive adaptive cruise control system embodiment also applies to the vehicle predictive adaptive cruise control method of this embodiment, and will not be repeated here.

[0189] The vehicle predictive adaptive cruise control method proposed in this application can collect vehicle status and location information using a vehicle-side platform and upload this information to a cloud control platform. The cloud control platform can then determine an economical speed planning strategy for the vehicle based on this information. This allows the vehicle-side platform to use the economical speed planning strategy as a reference for adjusting the target speed, thereby achieving predictive adaptive cruise control. This method tightly integrates cloud and vehicle-side algorithms, reducing the computational load on the vehicle side, improving the real-time performance of the algorithm, and adjusting speed planning based on changes in vehicle status, thus balancing safety and economy. This solves the technical problems in related technologies, such as the difficulty in balancing vehicle computational resource consumption and strategy output stability, and the difficulty in planning safe and economical speeds based solely on surface data for economic considerations.

[0190] Figure 12 A schematic diagram of the structure of a vehicle provided in an embodiment of this application. The vehicle may include:

[0191] The memory 1201, the processor 1202, and the computer program stored on the memory 1201 and executable on the processor 1202.

[0192] When the processor 1202 executes the program, it implements the vehicle predictive adaptive cruise control method provided in the above embodiments.

[0193] Furthermore, the vehicle also includes:

[0194] Communication interface 1203 is used for communication between memory 1201 and processor 1202.

[0195] The memory 1201 is used to store computer programs that can run on the processor 1202.

[0196] The memory 1201 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0197] If the memory 1201, processor 1202, and communication interface 1203 are implemented independently, then the communication interface 1203, memory 1201, and processor 1202 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 12 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0198] Optionally, in a specific implementation, if the memory 1201, processor 1202, and communication interface 1203 are integrated on a single chip, then the memory 1201, processor 1202, and communication interface 1203 can communicate with each other through an internal interface.

[0199] The processor 1202 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0200] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described vehicle predictive adaptive cruise control method.

[0201] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0202] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0203] "Computer-readable medium" can be any means that can contain, store, communicate, propagate or transmit programs for use by or in conjunction with an instruction execution system, apparatus or device.

[0204] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A vehicle predictive adaptive cruise control system, characterized in that, include: Cloud control platform and vehicle-side platform, among which, The cloud control platform is used to receive status information and location information sent by the vehicle-end platform, determine the elevation information of the road ahead of the vehicle controlled by the vehicle-end platform based on the location information, generate an economic speed planning strategy for the vehicle based on the status information, the location information and the elevation information, and send it to the vehicle-end platform. The vehicle-side platform is used to collect and send the status information and the location information to the cloud control platform, receive the economic speed planning strategy sent by the cloud control platform, and construct a vehicle speed fusion planning model based on the current cruise status of the vehicle and the economic speed planning strategy. The vehicle speed is adjusted based on the calculation results of the vehicle speed fusion planning model so that the target speed meets the preset following safety and energy saving requirements. The vehicle-side platform includes: The cruise mode switching module is used to determine the current cruise status of the vehicle based on the status information, the communication status between the vehicle and the cloud control platform, and the location information. The vehicle speed fusion planning module for following safety and energy saving is used to establish the vehicle speed fusion planning model based on the state information and the economic speed planning strategy, so as to obtain the target vehicle speed using the vehicle speed fusion planning model; The vehicle module is used to obtain the status information based on the vehicle's operating parameters, obtain the vehicle's location information based on map information, upload the status information and the location information to the cloud control platform, and adjust the vehicle's current driving speed according to the target vehicle speed. The cruise mode switching module is further configured to switch the current cruise state of the vehicle according to the status information, the communication status, and the map information, wherein the current cruise state includes ACC adaptive cruise control system mode cruise state, PCC predictive cruise control system mode cruise state, PACC predictive adaptive cruise control system mode cruise state, and manual driving mode driving state.

2. The system according to claim 1, characterized in that, The cloud control platform includes: A positioning module is used to parse the location information and obtain the current location information of the vehicle based on the location information; The high-precision map module is used to determine a static road map of the road ahead based on the current positioning information, and to obtain the elevation information of the road ahead based on the static road map; The economic speed planning module is used to generate the economic speed planning strategy based on the current positioning information and the elevation information.

3. The system according to claim 2, characterized in that, The economic speed planning module is further used to obtain the longitudinal force of the vehicle during driving based on the state information and using a preset dynamic model. Based on the state information, the fuel consumption rate of the vehicle is obtained using a preset engine fuel consumption model; an objective function is constructed using the state information, the longitudinal force, and the fuel consumption rate, and constraints on the objective function and the preset dynamic model are set according to the elevation information to obtain a discretized predictive cruise control mathematical model for dynamic programming solution; the economic speed planning strategy is obtained by solving the discretized predictive cruise control mathematical model.

4. The system according to claim 3, characterized in that, The discrete predictive cruise control mathematical model is as follows: in, As the weight of fuel consumption, The weight of the deviation term between vehicle speed and reference speed. As the weight of the vehicle speed change value, As the weight of the throttle opening change value, Weighted by driving efficiency, For reference speed, for k Phase speed, For cruise control mode k Stage throttle opening value, For predictive cruise mode k Stage throttle opening value, for k arrive k The time required for phase +1 This represents the distance between two adjacent stages. for k Vehicle fuel consumption rate at any given time. The air drag coefficient, A For windward area, It is the acceleration due to gravity. The rolling resistance coefficient, This is the conversion factor for vehicle rotational mass. For the stage Engine torque, for Engine speed during the phase, For the vehicle acceleration in stage k, and The boundary between minimum and maximum torque, and The boundary between minimum and maximum vehicle speeds and The minimum and maximum speed boundaries, and The minimum and maximum acceleration boundaries are given by m, where m is the mass of the vehicle and r is the wheel radius. The mechanical efficiency of the transmission system. Let k be the total transmission ratio of the transmission system at stage k. Let k be the road slope angle at stage k.

5. The system according to claim 1, characterized in that, The vehicle speed fusion planning module for following safety and energy saving is further used to obtain the actual following information of the vehicle in the following state based on the state information; to establish a vehicle speed fusion planning model based on the state information, the actual following information and the economic speed planning strategy; to construct the actual dynamic constraints of the vehicle speed fusion planning model; and to solve the vehicle speed fusion planning model using the actual dynamic constraints to obtain the target vehicle speed.

6. The system according to claim 5, characterized in that, The state-space expression of the vehicle speed fusion planning model is as follows: in, in, For state variables, for k The relative distance between the vehicle and the vehicle in front at any given time. Let be the speed of the vehicle. The relative speed between the vehicle and the vehicle in front is [the speed of the vehicle]. Let be the acceleration of the vehicle. Let be the jump of the vehicle. for The state variable at time t, For state variables, for k Inter-vehicle spacing error at any given time For economic speed sequence, The difference between the vehicle's speed and its economical speed. The output variable is composed of the jump of the vehicle. for Constantly control input variables, For the desired acceleration, for External disturbance amount at any given time This represents the acceleration of the vehicle in front.

7. A vehicle predictive adaptive cruise control method, characterized in that, Applied to the vehicle predictive adaptive cruise control system as described in any one of claims 1-6, wherein the method comprises the following steps: Receive status and location information from at least one vehicle-side platform; The elevation information of the road ahead of the vehicle controlled by the vehicle-end platform is determined based on the location information. Based on the status information, location information, and elevation information, an economic speed planning strategy for the vehicle is generated and sent to the vehicle-side platform.

8. A vehicle predictive adaptive cruise control method, characterized in that, Applied to the vehicle predictive adaptive cruise control system as described in any one of claims 1-6, wherein the method comprises the following steps: Collect vehicle status and location information; Send the status information and location information to the cloud control platform; The system receives an economic speed planning strategy sent by the cloud control platform, and constructs a vehicle speed fusion planning model based on the vehicle's current cruise status and the economic speed planning strategy. The system then adjusts the vehicle's target speed based on the calculation results of the vehicle speed fusion planning model, so that the target speed meets preset following safety and energy-saving requirements. The economic speed planning strategy is obtained from the status information and the location information.

9. A server, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the vehicle predictive adaptive cruise control method as described in claim 7.

10. A vehicle, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the vehicle predictive adaptive cruise control method as described in claim 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the vehicle predictive adaptive cruise control method as described in claim 7 or 8.

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