Control method and device and vehicle

Abstract

The invention provides a control method and device and a vehicle. The control method can be applied to the field of intelligent driving. The method comprises the following steps: acquiring information of a hybrid vehicle queue, wherein the hybrid vehicle queue comprises intelligent network connection vehicles and manual driving vehicles; establishing a dynamic model of the hybrid vehicle queue; decoupling the dynamic model to obtain a differential state equation and a nominal state equation; determining a reachable set of the nominal state equation according to the reachable set of the differential state equation; and controlling the intelligent networked vehicle according to the reachable set of the nominal state equation. The embodiment of the invention can be applied to an intelligent vehicle or an electric vehicle, the energy consumption of the intelligent network connection vehicle in the driving process of the hybrid vehicle queue is reduced, the energy consumption in traffic is reduced, the economy of the intelligent network connection vehicle is further improved, and a powerful supporting technology is provided for building a green and energy-saving intelligent traffic system.

Description

Technical Field

[0001] This application relates to the field of intelligent driving, and more particularly, to a control method, apparatus, and vehicle. Background Art

[0002] The intelligentization and networking of vehicles are the forefront directions of vehicle technology development. In recent years, intelligent connected vehicles have gradually achieved marketization and are expected to become the main means of transportation in the future, which is considered beneficial to solving problems such as huge energy consumption, traffic jams, and traffic accidents existing in current transportation. Among them, the control technology of intelligent connected vehicles has received extensive attention. Safe and efficient control technology can significantly improve the control performance of vehicles and further enhance the performance of the entire intelligent transportation system.

[0003] Currently, the control technology for a single intelligent connected vehicle has been widely studied. In addition, the control technology for a fully intelligent connected vehicle queue has also been widely studied. However, the gradual marketization process of intelligent connected vehicles cannot be completed in a short time. Therefore, there will be a mixed traffic scenario in which intelligent connected vehicles and human-driven vehicles coexist. In this scenario, a mixed vehicle queue including intelligent connected vehicles and human-driven vehicles will be a major traffic component. How to reduce the energy consumption of intelligent connected vehicles in the mixed vehicle queue has become an urgent problem to be solved. Summary of the Invention

[0004] This application provides a control method, apparatus, and vehicle, which helps to reduce the energy consumption of intelligent connected vehicles in a mixed vehicle queue.

[0005] In a first aspect, a control method is provided. The method includes: obtaining information of a mixed vehicle queue, where the mixed vehicle queue includes intelligent connected vehicles and human-driven vehicles; establishing a dynamic model of the mixed vehicle queue; decoupling the dynamic model to obtain a differential state equation and a nominal state equation; determining the reachable set of the nominal state equation according to the reachable set of the differential state equation; and controlling the intelligent connected vehicle according to the reachable set of the nominal state equation.

[0006] Based on the above technical solution, by constructing a dynamic model of a mixed vehicle queue including human-driven vehicles and intelligent connected vehicles, characterizing the reachable states of vehicles in the mixed vehicle queue based on the reachable set analysis method, and realizing the energy-saving control of intelligent connected vehicles through energy-saving predictive control technology. In this way, it helps to reduce the energy consumption of intelligent connected vehicles during the driving process of the mixed vehicle queue, reduce the energy consumption in traffic, further increase the economy of intelligent connected vehicles, and provide a strong supporting technology for building a green and energy-saving intelligent transportation system.

[0007] In some possible implementation manners, the uncertain input is determined by the noise collected by the sensors of the intelligent connected vehicle.

[0008] In some possible implementations, the intelligent connected vehicle can also be understood as a vehicle in an autonomous driving state.

[0009] In some possible implementations, a dynamic model of the hybrid vehicle queue is established, including: establishing the dynamic model of the hybrid vehicle queue according to the dynamic model of the human-driven vehicle, the dynamic model of the intelligent connected vehicle, and the uncertain input.

[0010] In some possible implementations, controlling the intelligent connected vehicle according to the reachable set of the nominal state equation can be understood as using the reachable set of the nominal state equation as a constraint to control the intelligent connected vehicle.

[0011] Combined with the first aspect, in some implementations of the first aspect, before controlling the intelligent connected vehicle according to the reachable set of the nominal state equation, the method further includes: obtaining an economic cost function according to the non-linear characteristics of the power system of the intelligent connected vehicle; wherein, controlling the intelligent connected vehicle according to the reachable set of the nominal state equation includes: controlling the intelligent connected vehicle according to the reachable set of the nominal state equation and the economic cost function.

[0012] Current research on hybrid vehicle queues directly uses speed or acceleration quantities for energy-saving control, which ignores the non-linear characteristics of the power system, and the energy-saving control effect of intelligent connected vehicles will be limited. Based on the above technical solutions, considering the non-linear characteristics of the power system of intelligent connected vehicles when designing the economic cost function helps to improve the energy-saving effect of intelligent connected vehicles.

[0013] In some possible implementations, the non-linear characteristics of the power system include the fuel economy characteristics of the engine.

[0014] In some possible implementations, the non-linear characteristics of the power system include the non-linear characteristics of the motor or battery.

[0015] Combined with the first aspect, in some implementations of the first aspect, the reachable set of the nominal state equation includes the reachable set of state variables and the reachable set of control variables. Determining the reachable set of the nominal state equation according to the reachable set of the differential state equation includes: determining the reachable set of state variables according to the reachable set of state variables of the differential state equation and the constraints of the state variables of the intelligent connected vehicle; determining the reachable set of control variables according to the reachable set of control variables of the differential state equation and the constraints of the control variables of the intelligent connected vehicle.

[0016] Based on the above technical solution, the reachable set of the state variables of the nominal state equation can be determined through the reachable set of the state variables of the differential state equation and the constraints of the state variables of the intelligent connected vehicle; the reachable set of the control variables of the nominal state equation can be determined through the reachable set of the control variables of the differential state equation and the constraints of the control variables of the intelligent connected vehicle. Through the reachable set of the state variables of the nominal state equation and the reachable set of the control variables of the nominal state equation, the nominal control variable can be determined, thereby controlling the actuator of the intelligent connected vehicle.

[0017] In some possible implementation manners, the constraints of the state variables of the intelligent connected vehicle include the constraints of the distance between vehicles.

[0018] In some possible implementation manners, when the speeds of the intelligent connected vehicles are different, the constraints of the distance between vehicles are different.

[0019] In some possible implementation manners, the constraints of the control variables of the intelligent connected vehicle include the constraints of acceleration.

[0020] For example, the acceleration range can be (-5m / s 2 , 5m / s 2 ).

[0021] Combined with the first aspect, in some implementation manners of the first aspect, the dynamic model is shown in the following formula (1):

[0022]

[0023] where x is the system state of the hybrid vehicle queue, u is the control input, w is the uncertainty input, A c is the state equation matrix, B c is the control input matrix, D c is the uncertainty input matrix; in some possible implementation manners, after discretizing the formula (1), the formula (2) can be obtained:

[0024] x(k + 1) = Ax(k) + Bu(k) + Dw(k) (2)

[0025] where x(k + 1) is the state of the hybrid vehicle queue at the (k + 1)-th moment, u(k) is the control input at the k-th moment, w(k) is the uncertainty input at the k-th moment, A is the state equation matrix, B is the control input matrix, and D is the uncertainty input matrix after separately deriving the model uncertainty.

[0026] Among them, decoupling the dynamic model to obtain a differential state equation and a nominal state equation includes: decoupling the discretized formula (2) to obtain the nominal state equation shown in formula (3) and the differential state equation shown in formula (4):

[0027] z(k + 1)= Az(k)+ Bu z (k) (3)

[0028] e(k + 1)= Ae(k)+ Bu e (k)+ Dw(k) (4)

[0029] Wherein, z(k + 1) represents the nominal state at the (k + 1)-th moment, z(k) represents the nominal state at the k-th moment, e(k + 1) represents the error state at the (k + 1)-th moment, e(k) represents the error state at the k-th moment, u z (k) and u e (k) represent the nominal control quantity and the differential control quantity respectively.

[0030] Combined with the first aspect, in some implementation manners of the first aspect, controlling the intelligent connected vehicle according to the reachable set of the nominal state equation includes: obtaining the nominal control quantity according to the reachable set of the nominal state equation; obtaining the acceleration control quantity of the intelligent connected vehicle according to the nominal control quantity and the feedback control quantity; controlling the intelligent connected vehicle according to the acceleration control quantity of the intelligent connected vehicle.

[0031] Based on the above technical solution, the feedback control quantity (used to correct the error) can be considered when determining the final acceleration control quantity, which helps to improve the accuracy of the acceleration control quantity, and further improves the energy-saving effect of the intelligent connected vehicle.

[0032] The above nominal control quantity can be determined by the reachable set of the state quantity of the nominal state equation and the reachable set of the control quantity of the nominal state equation.

[0033] In some possible implementation manners, controlling the intelligent connected vehicle according to the reachable set of the nominal state equation and the economic cost function includes: controlling the intelligent connected vehicle according to the reachable set of the nominal state equation, the economic cost function and the stability cost function.

[0034] In a second aspect, a control device is provided, and the device includes: an acquisition unit, configured to acquire information of a mixed vehicle queue, where the mixed vehicle queue includes an intelligent connected vehicle and a human-driven vehicle; a model establishment unit, configured to establish a dynamic model of the mixed vehicle queue, where the dynamic model includes uncertain inputs; a decoupling unit, configured to decouple the dynamic model to obtain a differential state equation and a nominal state equation; a determination unit, configured to determine the reachable set of the nominal state equation according to the reachable set of the differential state equation; a control unit, configured to control the intelligent connected vehicle according to the reachable set of the nominal state equation.

[0035] In combination with the second aspect, in some implementation manners of the second aspect, the obtaining unit is further configured to: obtain an economic cost function according to the non-linear characteristics of the power system of the intelligent connected vehicle; the control unit is specifically configured to: control the intelligent connected vehicle according to the reachable set of the nominal state equation and the economic cost function.

[0036] In combination with the second aspect, in some implementation manners of the second aspect, the reachable set of the nominal state equation includes the reachable set of state variables and the reachable set of control variables. The determining unit is specifically configured to: determine the reachable set of state variables according to the reachable set of state variables of the difference state equation and the constraints of the state variables of the intelligent connected vehicle; determine the reachable set of control variables according to the reachable set of control variables of the difference state equation and the constraints of the control variables of the intelligent connected vehicle.

[0037] In combination with the second aspect, in some implementation manners of the second aspect, the dynamic model is shown in the following formula (1):

[0038]

[0039] where x is the system state of the hybrid vehicle queue, u is the control input, w is the uncertainty input, A c is the state equation matrix, B c is the control input matrix, D c is the uncertainty input matrix; where the decoupling unit is specifically configured to: decouple the discretized formula (1) to obtain the nominal state equation shown in formula (3) and the difference state equation shown in formula (4):

[0040] z(k + 1) = Az(k) + Bu z (k) (3)

[0041] e(k + 1) = Ae(k) + Bu e (k) + Dw(k) (4)

[0042] where z(k + 1) represents the nominal state at the (k + 1)-th moment, z(k) represents the nominal state at the k-th moment, e(k + 1) represents the error state at the (k + 1)-th moment, e(k) represents the error state at the k-th moment, u z (k) and u e (k) respectively represent the nominal control quantity and the difference control quantity.

[0043] In combination with the second aspect, in some implementations of the second aspect, the determining unit is specifically configured to: determine a nominal control quantity according to the reachable set of the nominal state equation; determine an acceleration control quantity of the intelligent connected vehicle according to the nominal control quantity and the feedback control quantity; the control unit is specifically configured to: control the intelligent connected vehicle according to the acceleration control quantity of the intelligent connected vehicle.

[0044] In a third aspect, the present application provides a control device, which includes a processor and a memory, where the memory is used to store instructions, and the processor executes the instructions stored in the memory so that the device executes any possible method in the first aspect.

[0045] In a fourth aspect, the present application provides a vehicle, which includes any possible device in the second aspect or the third aspect.

[0046] In a fifth aspect, the present application provides a computer program product, which includes: computer program code, when the computer program code runs on a computer, it causes the computer to execute any possible method in the first aspect.

[0047] It should be noted that the above computer program code can be stored in whole or in part on a first storage medium, where the first storage medium can be packaged together with the processor or separately packaged from the processor. The embodiments of the present application do not make specific limitations on this.

[0048] In a sixth aspect, the present application provides a computer-readable medium, which stores program code, when the computer program code runs on a computer, it causes the computer to execute any possible method in the first aspect.

[0049] In a seventh aspect, the present application provides a chip system, which includes a processor for calling computer programs or computer instructions stored in a memory, so that the processor executes any possible method in the first aspect.

[0050] In combination with the seventh aspect, in a possible implementation, the processor is coupled to the memory through an interface.

[0051] In combination with the seventh aspect, in a possible implementation, the chip system further includes a memory, and computer programs or computer instructions are stored in the memory.

[0052] In an eighth aspect, the present application provides a chip, and the chip system includes a circuit for executing any possible method in the first aspect. Description of the Drawings

[0053] Figure 1It is a schematic functional block diagram of a vehicle provided by an embodiment of the present application.

[0054] Figure 2 It is a flowchart of the technical solution provided by an embodiment of the present application.

[0055] Figure 3 It is a schematic flowchart of a decoupled reachable set analysis method for handling model uncertainty of a hybrid vehicle queue provided by an embodiment of the present application.

[0056] Figure 4 It is a schematic flowchart of an economic predictive control method for a hybrid vehicle queue provided by an embodiment of the present application.

[0057] Figure 5 It is a schematic flowchart of an energy-saving test and evaluation process for a hybrid vehicle queue provided by an embodiment of the present application.

[0058] Figure 6 It is a schematic diagram of an application scenario provided by an embodiment of the present application.

[0059] Figure 7 It is a schematic diagram of the fuel economy of an intelligent connected vehicle in a hybrid vehicle queue provided by an embodiment of the present application.

[0060] Figure 8 It is a schematic flowchart of a control method provided by an embodiment of the present application.

[0061] Figure 9 It is a schematic block diagram of a control device provided by an embodiment of the present application. Detailed implementation manners

[0062] Next, the technical solutions in the embodiments of the present application will be described with reference to the accompanying drawings in the embodiments of the present application. Among them, in the description of the embodiments of the present application, unless otherwise specified, " / " means "or". For example, A / B may mean A or B; herein, "and / or" is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may mean: A exists alone, A and B exist simultaneously, and B exists alone. "At least one item" means one or more than one item. For example, "at least one of A and B" is similar to "A and / or B", describing the association relationship of associated objects, indicating that three relationships may exist. For example, at least one of A and B may mean: A exists alone, A and B exist simultaneously, and B exists alone.

[0063] In the embodiments of the present application, prefix words such as "first" and "second" are only used to distinguish different described objects, and have no restrictive effect on the position, order, priority, quantity, content, etc. of the described objects. In the embodiments of the present application, the use of prefix words such as ordinal numbers for distinguishing described objects does not constitute a restriction on the described objects. The statements of the described objects refer to the descriptions in the context of the claims or embodiments, and should not constitute redundant restrictions due to the use of such prefix words. In addition, in the description of this embodiment, unless otherwise specified, the meaning of "a plurality" is two or more.

[0064] Figure 1 FIG. is a schematic functional block diagram of a vehicle 100 provided by an embodiment of the present application. The vehicle 100 may include a sensing system 110, a computing platform 120, and a display device 130. Among them, the sensing system 110 may include one or more sensors for sensing information about the environment around the vehicle 100. For example, the sensing system 110 may include a positioning system, which may be a global positioning system (GPS), or a Beidou system or other positioning systems. For another example, the sensing system 110 may include one or more of an inertial measurement unit (IMU), an acceleration sensor, a lidar, a millimeter wave radar, an ultrasonic radar, and a camera device. Exemplarily, the acceleration sensor may include a sensor for detecting the acceleration signal of the air suspension system, or may also include a sensor for the acceleration signal of the ESC.

[0065] Some or all functions of vehicle 100 can be controlled by computing platform 120. Computing platform 120 may include one or more processors, such as processors 121 to 12n (n is a positive integer). A processor is a circuit with signal processing capabilities. In one implementation, a processor can be a circuit with the ability to read and execute instructions, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a type of microprocessor), or a digital signal processor (DSP), etc.; in another implementation, a processor can achieve certain functions through the logical relationship of a hardware circuit, and the logical relationship of the hardware circuit is fixed or can be reconfigured. For example, the processor is a hardware circuit implemented by an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as a field programmable gate array (FPGA). In a reconfigurable hardware circuit, the process of the processor loading a configuration document to implement the configuration of the hardware circuit can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units. In addition, the processor can also be a hardware circuit designed for artificial intelligence, which can be understood as a type of ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), etc. In addition, computing platform 120 may further include a memory for storing instructions, and some or all of processors 121 to 12n can call the instructions in the memory to implement corresponding functions.

[0066] The display device 130 in the cockpit is mainly divided into two categories. The first category is the vehicle display screen; the second category is the projection display screen, such as a head up display (HUD). The vehicle display screen is a physical display screen and an important part of the vehicle infotainment system. Multiple display screens can be set in the cockpit, such as a digital instrument display screen, a central control screen, a display screen in front of the passenger in the co-pilot seat (also called the front passenger), a display screen in front of the left rear passenger, and a display screen in front of the right rear passenger. Even the window can be used as a display screen for display. Head-up display, also known as a head-up display system. It is mainly used to display driving information such as speed and navigation on a display device in front of the driver (such as a windshield). To reduce the driver's line of sight transfer time, avoid pupil changes caused by the driver's line of sight transfer, and improve driving safety and comfort. HUD includes, for example, a combined head-up display (C-HUD) system, a windshield head-up display (W-HUD) system, and an augmented reality head-up display system (AR-HUD). It should be understood that other types of HUD systems may appear as technology evolves, and this application is not limited to this.

[0067] The above display device 130 is described by taking a vehicle-mounted display screen and a projection display screen as examples, and the embodiments of the present application are not limited thereto. For example, the display device 130 can also be a light display screen or a projection screen.

[0068] The vehicle in the embodiments of the present application may be a vehicle in a broad sense, and may be a means of transportation (such as a commercial vehicle, a passenger car, a motorcycle, a flying car, a train, etc.), an industrial vehicle (such as a forklift, a trailer, a tractor, etc.), an engineering vehicle (such as an excavator, a bulldozer, a crane, etc.), agricultural equipment (such as a lawn mower, a harvester, etc.), amusement equipment, a toy vehicle, etc. The embodiments of the present application do not specifically limit the type of vehicle.

[0069] The intelligence and networking of vehicles are the forefront of the development of automotive technology. In recent years, intelligent networked vehicles have gradually been commercialized and are expected to become the main means of transportation in the future. They are believed to be conducive to solving the current problems of huge energy consumption, traffic jams and traffic accidents in transportation. Among them, the control technology of intelligent networked vehicles has received extensive attention. Safe and efficient control technology can significantly improve the control performance of vehicles and further improve the performance of the entire intelligent transportation system.

[0070] At present, the control technology of a single intelligent connected vehicle has been widely studied. In addition, the platoon control technology of fully intelligent connected vehicle platoons has also been widely studied. However, the gradual marketization process of intelligent connected vehicles cannot be completed in a short time. Therefore, there will be a mixed traffic scenario in which intelligent connected vehicles and human-driven vehicles coexist. In this scenario, a mixed vehicle platoon including intelligent connected vehicles and human-driven vehicles will be a major traffic component. Existing research on mixed vehicle platoons is scarce. The few existing studies mainly focus on the control of indicators such as vehicle stability, and do not focus on the energy-saving problem of intelligent connected vehicles in mixed vehicle platoons. In addition, existing research ignores the uncertainty problem of the human-driven vehicle model in the mixed vehicle platoon, and the model uncertainty problem will significantly affect the actual implementation efficiency of vehicle control algorithms. Therefore, the model uncertainty problem can be mainly considered in the control of mixed vehicle platoons.

[0071] In summary, in the current research on mixed vehicle platoons including intelligent connected vehicles and human-driven vehicles, the model uncertainty problem of vehicles is not considered in detail, relying too much on deterministic dynamic models, and it is impossible to ensure the vehicle driving stability during the driving process of intelligent connected vehicles under the condition of model uncertainty. Moreover, the existing discussions on mixed vehicle problems do not focus on the economic problem of intelligent connected vehicles in mixed vehicle platoons, and the energy-saving problem of intelligent connected vehicles is one of the important topics in vehicle safety, energy conservation, environmental protection, and comfort, so it can be mainly discussed. The existing control methods for mixed vehicle platoons have technical bottlenecks in incomplete consideration of uncertainty and difficult coordination of control objectives.

[0072] In order to improve the stability and energy-saving performance of the control of mixed vehicle platoons, in the embodiments of this application, the reachable states of vehicle states in the mixed vehicle platoon are characterized based on the reachable set analysis method, and the energy-saving control of intelligent connected vehicles is realized through energy-saving predictive control technology. In this way, it helps to reduce the energy consumption of intelligent connected vehicles during the driving process of mixed vehicle platoons, reduce the energy consumption in traffic, further increase the economy of intelligent connected vehicles, and provide a powerful supporting technology for building a green and energy-saving intelligent transportation system.

[0073] The above intelligent connected vehicle can also be understood as a vehicle in an autonomous driving state, or, the intelligent connected vehicle can also be understood as a vehicle with an autonomous driving level of L3 or above. Under different autonomous driving levels (L0-L5), different levels of autonomous driving assistance can be achieved based on the information obtained by artificial intelligence algorithms and multi-sensors. The above autonomous driving levels (L0-L5) are based on the grading standards of the Society of Automotive Engineers (SAE). Among them, L0 represents no automation; L1 represents driving assistance; L2 represents partial automation; L3 represents conditional automation; L4 represents high automation; L5 represents full automation. For the tasks of monitoring road conditions and making responses at L1 to L3 levels, both the driver and the system complete them together, and the driver needs to take over the dynamic driving tasks. At L4 and L5 levels, the driver can completely transform into the role of a passenger.

[0074] Figure 2 The flowchart of the technical solution provided by the embodiment of the present application is shown.

[0075] Regarding the problem that the existing energy-saving control technology for intelligent connected vehicles does not consider the problem of hybrid vehicle queue control, does not consider the problem of the uncertainty of the human-driven vehicle model in the hybrid vehicle queue, and does not establish a complete energy-saving evaluation for the hybrid vehicle queue. The embodiment of the present application proposes a reachable set analysis method for the model uncertainty in the hybrid vehicle queue, and separately applies the influence of uncertainty on the system to the differential equation of the state, realizing the transformation to the standard problem.

[0076] In the embodiment of the present application, an economic predictive control method is proposed for the hybrid vehicle queue. Different from the existing methods, the embodiment of the present application focuses on considering the vehicle dynamics characteristics, and can more comprehensively and effectively improve the economy of the hybrid vehicle queue system. By means of constraints, the stability and safety of the system are ensured, and the multi-objective control of the hybrid vehicle queue system is realized.

[0077] In the embodiment of the present application, based on application simulation or experimental results, through the energy consumption test evaluation device and process of the hybrid vehicle queue, simulation experiments and real vehicle experiments are carried out under various working conditions to objectively and comprehensively evaluate various aspects of the performance of the hybrid vehicle queue system and visualize it. The proposed energy-saving control method for the hybrid vehicle queue based on reachable set analysis under the condition of model uncertainty has the potential to improve the overall fuel economy of the hybrid vehicle queue system, can solve the problem of hybrid vehicle queue control in the mixed traffic scenario, and can provide a reference for the energy-saving control of the hybrid vehicle queue in the intelligent transportation system in terms of methods.

[0078] Exemplarily, Figure 3Fig. 0 shows a schematic flowchart of a decoupled reachable set analysis method 300 for handling model uncertainties of a hybrid vehicle queue provided by an embodiment of the present application. The method 300 includes:

[0079] S301, modeling the hybrid vehicle queue system.

[0080] Under the framework of the intelligent transportation system, in an embodiment of the present application, information such as the position and speed of each vehicle on the road is obtained through devices such as the cloud system, roadside sensors, and in-vehicle sensors. Then this information is sent to the connected intelligent vehicle as input for control. This ensures the feasibility of implementing the hybrid vehicle queue. However, there are uncertainties in the model of human-driven vehicles in the hybrid vehicle system. Regarding the modeling problem of human-driven vehicles, there is currently no unified method, resulting in an inevitable problem of model uncertainties. Therefore, in the process of modeling the hybrid vehicle queue, model uncertainties can be analyzed and processed with emphasis. In an embodiment of the present application, the model uncertainties are decomposed and transformed into a standard model uncertainty problem.

[0081] The above decoupled reachable set analysis method 300 for handling model uncertainties can be executed by a connected intelligent vehicle (e.g., the above vehicle 100), or by a cloud server. In the following embodiments, the example of being executed by a connected intelligent vehicle is used for illustration.

[0082] When dealing with the decoupled reachable set analysis method for model uncertainties, first, a state equation of the hybrid vehicle queue is constructed. Exemplarily, the state equation of the hybrid vehicle queue (or, the system model or dynamic model of the hybrid vehicle queue) can be as formula (1):

[0083]

[0084] where x is the system state of the hybrid vehicle queue, u is the control input, w is the uncertainty input, A c is the state equation matrix, B c is the control input matrix, D c is the uncertainty input matrix after separately deriving the model uncertainties.

[0085] Exemplarily, the system state of the hybrid vehicle queue includes but is not limited to the states of human-driven vehicles and connected intelligent vehicles, such as speed, inter-vehicle distance, acceleration, etc.

[0086] Exemplarily, the uncertainty input includes but is not limited to the noise in the data acquisition process of the sensors of the connected intelligent vehicle.

[0087] The above formula (1) is discretized to obtain the discrete state equation of the hybrid vehicle queue, as follows formula (2):

[0088] x(k + 1) = Ax(k) + Bu(k) + Dw(k) (2)

[0089] Where x(k + 1) is the state of the hybrid vehicle queue at time k + 1, u(k) is the control input at time k, w(k) is the uncertainty input at time k, A is the state equation matrix, B is the control input matrix, and D is the uncertainty input matrix after separately deriving the model uncertainty.

[0090] S302. Decouple the hybrid vehicle queue system model.

[0091] Exemplarily, the above formula (2) can be decoupled into a nominal state equation and a differential state equation, as shown in formulas (3) and (4) respectively.

[0092] z(k + 1) = Az(k) + Bu z (k) (3)

[0093] e(k + 1) = Ae(k) + Bu e (k) + Dw(k) (4)

[0094] Where z(k + 1) represents the nominal state at time k + 1, z(k) represents the nominal state at time k, e(k + 1) represents the error state at time k + 1, e(k) represents the error state at time k, and u z (k) and u e (k) represent the nominal control quantity and the differential control quantity respectively.

[0095] The above nominal control quantity u z (k) can be determined by the reachable set of the state quantity of the nominal state equation and the reachable set of the control quantity of the nominal state equation.

[0096] The above nominal state equation reflects the ideal vehicle dynamics model in the absence of external disturbances (e.g., no noise). The differential state equation reflects the difference between the ideal vehicle dynamics model and the actual vehicle dynamics model in the presence of external disturbances.

[0097] S303. Analyze the reachable set of the differential state equation.

[0098] According to the description of the differential state equation, first determine the number of steps within a single period of predictive control, and then calculate the reachable set of the differential state equation. The reachable set of this differential state equation can be set as a very small set of polytopes at the initial moment to facilitate subsequent calculations. In the embodiments of the present application, the differential control quantity u e (k) in the differential state equation can be determined first, and this differential control quantity u e(k) can vary, but to ensure the efficiency of online calculation, it can be set to linear constant feedback, provided that the resulting difference state equation is stable, i.e., the eigenvalues of A + BK are inside the unit circle. The linear quadratic regulator (LQR) method or other pole placement methods can be used. Then, based on the initial difference reachable set, the reachable sets of the state variables in the difference state equation at each step within the prediction steps of the difference state equation (such as e(k) in the above formula (4)) are calculated in the form of a polyhedron set.

[0099] In one embodiment, the method 300 further includes: calculating the difference control quantity u e (k) at each step within the prediction step length. Thus, the establishment and calculation of the reachable set of the difference state equation are completed.

[0100] The above difference control quantity can be understood as the input of the model, and the reachable set of the state variables of the difference state equation can be understood as the output of the model.

[0101] In one embodiment, the method 300 further includes: constructing a constraint set for the state variables of the intelligent connected vehicle in combination with the state constraints of the intelligent connected vehicle and converting it into the form of a polyhedron set.

[0102] In one embodiment, the method 300 further includes: calculating a constraint set for the control quantity of the intelligent connected vehicle based on the limitations of the control input during the control process of the intelligent connected vehicle, which is also expressed in the form of a polyhedron set.

[0103] The above constraint set for the state variables can be understood as a constraint on the model output, and the constraint set for the control quantity can be understood as a constraint on the model input.

[0104] S304, analyzing the reachable set of the nominal state equation.

[0105] Exemplarily, the reachable set of the nominal state equation includes the reachable set of the state variables of the nominal state equation and the reachable set of the control quantity of the nominal state equation.

[0106] Exemplarily, the Minkowski difference between the constraint set of the state variables of the intelligent connected vehicle and the reachable set of the state variables of the difference state equation can be used to calculate the reachable set of the state variables of the nominal state equation, which is also expressed in the form of a polyhedron set.

[0107] Exemplarily, subtracting the difference control quantity u e (k) at each step of the difference state equation from the constraint set of the control quantity of the intelligent connected vehicle at each step to obtain the reachable set of the control quantity of the nominal state equation. Thus, the design and calculation of the state constraint and control quantity constraint of the nominal state equation are completed.

[0108] Differential control quantity u e (k) can be understood as the reachable set of the control quantity (or, control input quantity) of the differential state equation. e(k) can be understood as the reachable set of the state quantity (or, state output quantity) of the differential state equation.

[0109] Exemplarily, Figure 4 Fig. 8 shows a schematic flowchart of an economic predictive control method 400 for a hybrid vehicle queue provided by an embodiment of the present application. The method 400 includes:

[0110] S401, construct an economic model prediction problem.

[0111] In one embodiment, the nonlinear characteristics of the power system can be considered during the construction of the economic model prediction problem. Taking the intelligent connected vehicle as a fuel vehicle as an example, the nonlinear characteristics of the engine can be considered during the construction of the economic model prediction problem.

[0112] First, establish a dynamic model of the hybrid vehicle queue, which is generally expressed in the form of a state equation. Existing studies on hybrid vehicle queues directly use speed or acceleration quantities for energy-saving control, which ignores the nonlinear characteristics of the engine, and the energy-saving control effect will be limited. Different from such methods, the embodiments of the present application consider the fuel economy characteristics of the engine, express the acceleration quantity as a function of the vehicle longitudinal dynamics, and detail the consideration of the vehicle dynamics characteristics. Then, based on the reachable set of the state quantity of the nominal state equation and the reachable set of the control quantity of the nominal state equation obtained in the above method 300, set the constraints of the optimization problem. By setting an economic cost function, generally directly select the fuel consumption model of the engine, and this model is generally in the form of a polynomial of torque and speed.

[0113] In one embodiment, to ensure the stability of the hybrid vehicle queue system, stability constraints can be imposed in the optimization problem. The stability constraints can be selected to decrease the square value of the state quantity as the prediction step increases for constraint. Thus, the construction of the economic model prediction problem is completed.

[0114] S402, solve the economic model prediction problem.

[0115] Exemplarily, when solving the economic model prediction problem, the calculation step size can be selected, and the number of prediction steps within a single period can be selected. The number of prediction steps can ensure the solvability of the problem. By applying the satisfiability modulo theory to solve the optimization problem, the satisfiability modulo theory transforms the constraint problem into a logical language problem, and through iterative loops, the numerical optimal solutions of convex optimization and non-convex optimization problems can be obtained, and the control sequence of the nominal system optimization problem can be obtained.

[0116] The control sequence for the above nominal system optimization problem can be understood as the acceleration control amount of the intelligent connected vehicle within each future time step.

[0117] S403. Based on the control sequence of the nominal system optimization problem, control the actuator to perform corresponding operations.

[0118] Exemplarily, the intelligent connected vehicle selects the first control amount in the optimized control sequence plus the feedback control amount according to the obtained control sequence of the nominal system optimization problem to obtain the actual control amount and act on the actuator of the intelligent connected vehicle, so as to realize the execution of the control amount in the hybrid vehicle queue system.

[0119] Exemplarily, the feedback control amount can be as shown in formula (5):

[0120] u e =Ke(k) (5)

[0121] Where, u e is the feedback control amount, K is the gain of the feedback control, and e(k) is the state quantity of the difference state equation.

[0122] Exemplarily, taking the example of obtaining the optimized control sequence within the next 10 seconds by solving the economic model, each time step is 0.5s. The optimized control sequence can be expressed as:

[0123]

[0124] Where, U(k) is the optimized control sequence, u(k + 1) is the first control amount and u(k + 1) is the acceleration control amount at the (k + 1)-th moment.

[0125] The intelligent connected vehicle can obtain the acceleration control amount of the intelligent connected vehicle at the (k + 1)-th moment based on u(k + 1) and the above u e This acceleration control amount can also be understood as the acceleration control amount that the intelligent connected vehicle expects to reach at the (k + 1)-th moment.

[0126] In one embodiment, the method 400 further includes: performing rolling horizon control, obtaining and saving the actual states of the vehicles in the hybrid vehicle queue for analysis.

[0127] The above rolling horizon control can be understood as a cyclic mechanism, and the actual states include information such as the speed, acceleration, and position of the vehicles in the hybrid vehicle queue.

[0128] Exemplarily, Figure 5 shows a schematic flowchart of the energy-saving test and evaluation process 500 for a hybrid vehicle queue provided by an embodiment of the present application. This test and evaluation process 500 includes:

[0129] S501. Save the system data of the hybrid vehicle queue.

[0130] The test and evaluation process in the embodiments of this application may include two main parts: simulation testing and experimental testing. Based on the proposed energy-saving control method for hybrid vehicle platoons using reachable set analysis, simulation testing is carried out. Before the simulation testing, system identification of the real vehicle can be performed to establish an accurate vehicle dynamics model. Vehicle control is carried out based on the established model considering vehicle model uncertainties, and the simulation experiment results are obtained and saved. Then, real vehicle experimental testing is carried out, and the real vehicle test results are also saved.

[0131] S502, analyze the system performance of the hybrid vehicle platoon.

[0132] For the simulation and experimental result data in the energy-saving control of hybrid vehicle platoons, system performance evaluation of the hybrid vehicle platoon is carried out. The evaluation mainly focuses on the energy consumption performance indicators in the hybrid platoon, and at the same time focuses on the platoon stability and safety indicators of the vehicles. Taking intelligent connected vehicles as fuel vehicles as an example, the energy consumption performance indicators can be evaluated based on the fuel consumption per 100 kilometers. Divide the fuel consumption of the vehicle in the experiment by the driving distance, and then perform unit conversion to obtain the fuel consumption per 100 kilometers.

[0133] Exemplarily, the fuel consumption per 100 kilometers can be as shown in formula (6):

[0134]

[0135] where, δ is the unit conversion coefficient, C t is the fuel consumption, and S is the driving distance of the vehicle.

[0136] Exemplarily, for the stability evaluation index of the hybrid vehicle platoon, the vehicle speed can be selected for evaluation, and it is evaluated by calculating the sum of the absolute values of the differences between the speeds of each vehicle in the platoon and the average speed. As shown in formula (7):

[0137]

[0138] where, v i represents the vehicle speed, i represents the i-th vehicle in the hybrid platoon, represents the average speed of all vehicles at each moment, N represents the number of vehicles in the hybrid platoon, and T represents the total time of the simulation or experiment.

[0139] Exemplarily, the safety index can consider the spacing error of the vehicles. Generally, if the cumulative spacing error is too large, it indicates a higher risk. The safety index can be as shown in formula (8):

[0140]

[0141] where, si represents the distance between the \(i\)-th vehicle and the \((i - 1)\)-th vehicle, \(s\) * represents the desired headway, \(N\) represents the number of vehicles in the mixed queue, and \(T\) represents the total time of the simulation or experiment. \(s\) * It can be the desired headway under the condition of a fixed time headway or a fixed following distance.

[0142] Calculate the results of the simulation experiment and the real vehicle experiment through formulas (6), (7) and (8), analyze the results, and obtain the comprehensive evaluation results of the energy consumption of the mixed vehicle queue.

[0143] In one embodiment, by increasing the number of driving conditions, verify the energy-saving control method for the mixed vehicle queue under typical or atypical driving conditions, and evaluate the control method more comprehensively. Thus, the energy consumption evaluation of the mixed vehicle queue is completed.

[0144] Exemplarily, Figure 6 shows a schematic diagram of the application scenario provided by the embodiment of the present application. Taking the scenario of a mixed vehicle queue on a highway as an example, the practical application of the energy-saving control method for the mixed vehicle queue based on reachable set analysis under the condition of model uncertainty proposed by the embodiment of the present application is described. Figure 6 is a mixed vehicle queue scenario, where the leading vehicle and the trailing vehicle are connected vehicles, and the second vehicle and the third vehicle are human-driven vehicles.

[0145] First, a connected vehicle dynamics model can be established. This model can consider engine characteristics to achieve economic predictive control. Taking the connected vehicle as a fuel vehicle as an example, its powertrain mainly consists of an internal combustion engine, a torque converter, and a continuously variable transmission (or automatic transmission). When modeling, factors such as the response delay of each component are ignored. The driving torque of the vehicle comes from the engine, denoted as \(T\) e . Exemplarily, the driving force of the driving wheels of the connected vehicle can be as shown in formula (9):

[0146]

[0147] where \(r\) g is the transmission ratio of the gearbox, \(r\) t is the transmission ratio of the final drive. \(\eta\) is the mechanical efficiency of the transmission system, and \(R\) is the wheel radius.

[0148] Calculating the resistance during vehicle driving can include wind resistance \(F\) w , rolling resistance \(F\) f , and gradient resistance \(F\) p . Assuming the vehicle is driving on a flat road, the gradient resistance can be ignored. Exemplarily, the wind resistance and rolling resistance can be as shown in formulas (10) and (11):

[0149]

[0150] F f = mgf (11)

[0151] where ρ is the air density, A is the frontal area of the connected and automated vehicle, C a is the rolling resistance coefficient of the connected and automated vehicle, v is the speed of the connected and automated vehicle, m is the mass of the connected and automated vehicle, g is the acceleration due to gravity, and f is the rolling resistance coefficient.

[0152] After that, the longitudinal dynamics model of the connected and automated vehicle can be constructed, ma = F e - F w - F f . Substituting the detailed models of F e , F w , F f gives the following formula (12):

[0153]

[0154] where a is the longitudinal acceleration of the connected and automated vehicle. It can be seen that the acceleration of the connected and automated vehicle is affected by the entire vehicle transmission system, mainly including the engine, transmission, final drive, and wheel characteristics.

[0155] When performing energy-saving control, the engine characteristics of the connected and automated vehicle can be considered. Energy-saving control focuses on optimizing the fuel consumption rate of the engine, and the engine fuel consumption rate is directly related to the engine speed and torque. Exemplarily, the engine fuel consumption rate can be shown by the following formula (13):

[0156]

[0157] where C represents the instantaneous fuel consumption, W e is the engine speed, T e is the engine torque, and p 00 , p 10 , p 01 , p 20 , p 11 , p 02 , p 30 , p 21 , p 12 , p 03 are the coefficients fitted according to the actual engine data.

[0158] After that, the relationship between the engine speed and the vehicle longitudinal speed can be established from the vehicle dynamics relationship. Exemplarily, the relationship between the engine speed W e and the longitudinal speed v can be shown by the following formula (14):

[0159]

[0160] Based on formulas (13) and (14), the relationship among the engine's instantaneous fuel consumption rate, vehicle speed, and torque can be established. Thus, the dynamic modeling of the intelligent connected vehicle is completed.

[0161] The above formulas (6)-(14) are the dynamic modeling process of the intelligent connected vehicle.

[0162] After that, the dynamic model of the human-driven vehicle can be established. Currently, there are various vehicle following models used for the modeling research of human-driven vehicles. Exemplarily, the intelligent driver model (IDM) is a form of the human-driven vehicle following model in the study of mixed traffic problems, and its expression can be as shown in formulas (15) and (16):

[0163]

[0164]

[0165] where a is the acceleration of the human-driven vehicle, a max is the maximum acceleration of the human-driven vehicle, v0 is the free flow speed, s * is the desired headway, s is the actual headway, l is the length of the human-driven vehicle, T is the desired time headway, and b is the maximum comfortable deceleration. Exemplarily, the detailed parameters can be taken as follows: a = 2, v0 = 30, s0 = 2, T = 1.5, l = 5.

[0166] Linearizing the expression (16) of the following model near the equilibrium state, the following formula (17) can be obtained:

[0167]

[0168] where, are the partial derivatives of formula (16) with respect to the headway, speed difference, and speed at the equilibrium state, s * and v * are the equilibrium headway and vehicle speed respectively. Solve the three partial derivative formulas (18) when applying the IDM model to the human-driven vehicle:

[0169]

[0170] Let

[0171]

[0172] The simplified form of the car-following model for a manually driven vehicle is shown in Equation (20):

[0173] a = α1s - α2v i + α3v i-1 (20)

[0174] After combining the dynamic model of the connected vehicle and the dynamic model of the manually driven vehicle, the dynamic model of the hybrid vehicle queue system is obtained, as shown in Equation (21):

[0175]

[0176] The above Equation (21) can be an implementation of the above Equation (1).

[0177] where s is the inter-vehicle distance, v is the vehicle speed, α1, α2, α3 are as shown in Equation (19), u h is the control variable of the first connected vehicle (the vehicle at the head of the hybrid vehicle queue as shown Figure 6 ), u t is the control variable of the last connected vehicle (the vehicle at the tail of the hybrid vehicle queue as shown Figure 6 ), v0 is the speed of the vehicle ahead. Taking the 4 vehicles shown above as an example, the first and last vehicles are connected vehicles. By considering the influence of uncertainties, the following Equation (22) is obtained: Figure 6 Taking the 4 vehicles shown above as an example, the first and last vehicles are connected vehicles. By considering the influence of uncertainties, the following Equation (22) is obtained:

[0178]

[0179] where Δ1, Δ2, Δ3 respectively represent the nominal values of α1, α2, α3, respectively represent the maximum deviation values of α1, α2, α3. Dividing the above Equation (22) into a standard part and an uncertainty part, Equation (23) is obtained:

[0180]

[0181] Simplifying Equation (23) gives the following Equation (24), where is the upper bound of the distance and speed.

[0182]

[0183] Equation (24) can be further simplified to Equation (25), where

[0184]

[0185] After differencing the model, the difference state equation (26) is obtained:

[0186]

[0187] Among them, represents the control quantity of the differential state equation of the first intelligent connected vehicle, represents the control quantity of the differential state equation of the last intelligent connected vehicle. and can be understood as u e (k) in the above formula (4).

[0188] After that, subtract formula (26) from formula (25), and subtract the two formulas to obtain the nominal state equation (27):

[0189]

[0190] Among them, represents the control quantity of the nominal state equation of the first intelligent connected vehicle, represents the control quantity of the nominal state equation of the last intelligent connected vehicle, and can be understood as u z (k) in the above formula (3). In the differential state equation, uses a fixed K feedback gain.

[0191] By calculating the reachable set of the differential state equation (26), after the calculation is completed, further subtract the reachable set of the state quantity of the differential state equation according to the constraint of the state quantity to obtain the reachable set of the state quantity of the nominal state equation (27). Use the constraint of the control quantity to subtract the differential control quantity u e (k) at each step in the differential state equation to obtain the reachable set of the control quantity of the nominal state equation. Through the reachable set of the state quantity of the nominal state equation and the reachable set of the control quantity of the nominal state equation, solve the optimization problem.

[0192] Regarding the solution of the optimization problem, the modulo theory can be applied for the solution. Optionally, other optimization problem solvers can also be used for the solution. After the solution is completed, the solution of the optimal control is obtained, that is, the control quantity and After that, the control quantity of the actual system is further obtained and

[0193] Compare the measured values with the data obtained from the simulation to more comprehensively verify the effectiveness of the algorithm. The performance result graph of the hybrid vehicle queue is as Figure 7As shown, where the vertical axis represents a certain index to be evaluated, such as fuel economy, queue stability, safety, etc. Taking fuel economy as an example for the vertical axis, the horizontal axis represents different working conditions. For example, working condition one is the highway condition, working condition two is the urban road condition, and working condition three is the mountain road condition. The bar chart represents the comparison of fuel economy obtained by not using the method in the embodiment of the present application and using the method in the embodiment of the present application. From this figure, the effectiveness of the energy-saving control algorithm provided by the embodiment of the present application can be visually evaluated.

[0194] Figure 8 FIG. shows a schematic flowchart of a control method 800 provided by an embodiment of the present application. This method 800 can be executed by the above-mentioned vehicle 100, or this method 800 can be executed by the above-mentioned computing platform 120, or this method 800 can be executed by a system composed of the computing platform 120 and the perception system 110, or this method 800 can be executed by a system-on-a-chip (SoC) in the above-mentioned computing platform 120, or this method 800 can be executed by a processor, chip or circuit in the computing platform 120. This method 800 includes:

[0195] S810, obtain information of a mixed vehicle queue, where the mixed vehicle queue includes connected and autonomous vehicles and human-driven vehicles.

[0196] Exemplarily, taking the execution subject of method 800 as a connected and autonomous vehicle as an example, the mixed vehicle queue includes connected and autonomous vehicles. The connected and autonomous vehicle can add human-driven vehicles that are in the same lane as the connected and autonomous vehicle and the distance between them is less than or equal to a preset distance to the mixed vehicle queue.

[0197] Exemplarily, different preset distances can correspond to the speeds of different connected and autonomous vehicles.

[0198] S820, establish a dynamic model of the mixed vehicle queue, where the dynamic model includes uncertain inputs.

[0199] Optionally, the dynamic model is as shown in the above formula (1).

[0200] Exemplarily, the above formula (21) is an expression of formula (1).

[0201] S830, decouple the dynamic model to obtain a differential state equation and a nominal state equation.

[0202] Optionally, the dynamic model is decoupled to obtain a difference state equation and a nominal state equation, including: decoupling the discretized formula (1) to obtain formula (2). Decoupling the obtained formula (2) through discretization to obtain the nominal state equation shown in formula (3) and the difference state equation shown in formula (4).

[0203] Exemplarily, formula (26) is an implementation manner of the above formula (4).

[0204] Exemplarily, formula (27) is an implementation manner of the above formula (3).

[0205] S840. Determine the reachable set of the nominal state equation according to the reachable set of the difference state equation.

[0206] Optionally, the reachable set of the nominal state equation includes the reachable set of state variables and the reachable set of control variables. Determining the reachable set of the nominal state equation according to the reachable set of the difference state equation includes: determining the reachable set of state variables according to the reachable set of state variables of the difference state equation and the constraints of the state variables of the intelligent connected vehicle; determining the reachable set of control variables according to the reachable set of control variables of the difference state equation and the constraints of the control variables of the intelligent connected vehicle.

[0207] Exemplarily, the constraints of the state variables of the intelligent connected vehicle include the constraints of the distance between vehicles.

[0208] Exemplarily, when the speeds of intelligent connected vehicles are different, the constraints of the distance between vehicles are different.

[0209] Exemplarily, the constraints of the control variables of the intelligent connected vehicle include the constraints of acceleration.

[0210] For example, the acceleration range of the intelligent connected vehicle can be (-5m / s 2 , 5m / s 2 ).

[0211] S850. Control the intelligent connected vehicle according to the reachable set of the nominal state equation.

[0212] Optionally, before controlling the intelligent connected vehicle according to the reachable set of the nominal state equation, the method 800 further includes: obtaining an economic cost function according to the nonlinear characteristics of the power system of the intelligent connected vehicle; wherein, controlling the intelligent connected vehicle according to the reachable set of the nominal state equation includes: controlling the intelligent connected vehicle according to the reachable set of the nominal state equation and the economic cost function.

[0213] Exemplarily, taking the intelligent connected vehicle as a fuel vehicle as an example, the determination process of the economic cost function can refer to the above formulas (9)-(14).

[0214] Exemplarily, taking the intelligent connected vehicle as an electric vehicle as an example, the economic cost function can also be determined by the nonlinear characteristics of the motor.

[0215] Optionally, controlling the intelligent connected vehicle according to the reachable set of the nominal state equation and the economic cost function includes: controlling the intelligent connected vehicle according to the reachable set of the nominal state equation, the economic cost function, and the stability cost function.

[0216] Optionally, controlling the intelligent connected vehicle according to the reachable set of the nominal state equation includes: obtaining a nominal control quantity according to the reachable set of the nominal state equation; obtaining an acceleration control quantity of the intelligent connected vehicle according to the nominal control quantity and the feedback control quantity; and controlling the intelligent connected vehicle according to the acceleration control quantity of the intelligent connected vehicle.

[0217] The above nominal control quantity can be determined by the reachable set of the state quantity of the nominal state equation and the reachable set of the control quantity of the nominal state equation.

[0218] Exemplarily, the nominal control quantity can be u z (k) in the above formula (3). u z (k) can be determined by the reachable set of the state quantity of the nominal state equation and the reachable set of the control quantity of the nominal state equation.

[0219] Exemplarily, the feedback control quantity can be determined by the above formula (5), and the feedback control quantity can be the product of the gain of the feedback control and e(k) in the above differential state equation (4).

[0220] Optionally, the acceleration control quantity of the intelligent connected vehicle can be the acceleration control quantity of the intelligent connected vehicle at the next moment, or the acceleration control quantity of the intelligent connected vehicle can be the desired acceleration control quantity.

[0221] Figure 9Fig. 0 shows a schematic block diagram of a control device 900 provided by an embodiment of the present application. The device 900 includes: an acquisition unit 910, configured to acquire information of a mixed vehicle queue, where the mixed vehicle queue includes connected and autonomous vehicles (CAVs) and human-driven vehicles; a model establishment unit 920, configured to establish a dynamic model of the mixed vehicle queue, where the dynamic model includes uncertain inputs; a decoupling unit 930, configured to decouple the dynamic model to obtain a differential state equation and a nominal state equation; a determination unit 940, configured to determine the reachable set of the nominal state equation according to the reachable set of the differential state equation; and a control unit 950, configured to control the CAVs according to the reachable set of the nominal state equation.

[0222] Optionally, the acquisition unit 910 is further configured to: acquire an economic cost function according to the non-linear characteristics of the powertrain of the CAVs; and the control unit 950 is specifically configured to: control the CAVs according to the reachable set of the nominal state equation and the economic cost function.

[0223] Optionally, the reachable set of the nominal state equation includes a reachable set of state variables and a reachable set of control variables, and the determination unit 940 is specifically configured to: determine the reachable set of the state variables according to the reachable set of the state variables of the differential state equation and the constraints of the state variables of the CAVs; and determine the reachable set of the control variables according to the reachable set of the control variables of the differential state equation and the constraints of the control variables of the CAVs.

[0224] Optionally, the dynamic model is shown in the following formula (1); where the decoupling unit 930 is specifically configured to: decouple the discretized formula (1) to obtain the nominal state equation shown in formula (3) and the differential state equation shown in formula (4).

[0225] Optionally, the determination unit 940 is specifically configured to: determine a nominal control variable according to the reachable set of the nominal state equation; determine an acceleration control variable of the CAVs according to the nominal control variable and a feedback control variable; and the control unit 950 is specifically configured to: control the CAVs according to the acceleration control variable of the CAVs.

[0226] It should be understood that the division of each unit in the above device is only a division of logical functions. In actual implementation, all or part of them can be integrated into a physical entity, or physically separated. In addition, the units in the device can be implemented in the form of a processor calling software. For example, the device includes a processor, the processor is connected to a memory, and instructions are stored in the memory. The processor calls the instructions stored in the memory to implement any of the above methods or the functions of each unit of the device. The processor is, for example, a general-purpose processor, such as a CPU or a microprocessor, and the memory is a memory inside or outside the device. Alternatively, the units in the device can be implemented in the form of a hardware circuit, and the functions of some or all of the units can be implemented by designing the hardware circuit. The hardware circuit can be understood as one or more processors. For example, in one implementation, the hardware circuit is an ASIC, and the functions of some or all of the above units are implemented by designing the logical relationship of the components in the circuit. Again, for example, in another implementation, the hardware circuit can be implemented by a PLD. Taking an FPGA as an example, it can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured through a configuration file to implement the functions of some or all of the above units. All the units of the above device can be all implemented in the form of a processor calling software, or all implemented in the form of a hardware circuit, or part implemented in the form of a processor calling software, and the remaining part implemented in the form of a hardware circuit.

[0227] In the embodiments of the present application, a processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction reading and running capabilities, such as a CPU, a microprocessor, a GPU, or a DSP, etc. In another implementation, the processor can implement certain functions through the logical relationship of a hardware circuit, and the logical relationship of the hardware circuit is fixed or can be reconfigured. For example, the processor is a hardware circuit implemented by an ASIC or a PLD, such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document to implement the configuration of the hardware circuit can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as a type of ASIC, such as an NPU, a TPU, a DPU, etc.

[0228] It can be seen that each unit in the above device can be one or more processors (or processing circuits) configured to implement the above method. For example: a CPU, a GPU, an NPU, a TPU, a DPU, a microprocessor, a DSP, an ASIC, an FPGA, or a combination of at least two of these processor forms.

[0229] In addition, all or part of the units in the above device can be integrated together or can be implemented independently. In one implementation, these units are integrated together and implemented in the form of a SoC. The SoC may include at least one processor for implementing any of the above methods or implementing the functions of the units of the device. The types of the at least one processor may be different, such as including a CPU and an FPGA, a CPU and an artificial intelligence processor, a CPU and a GPU, etc.

[0230] An embodiment of the present application further provides a device, which includes a processing unit and a storage unit, where the storage unit is used to store instructions, and the processing unit executes the instructions stored in the storage unit so that the device executes the method or steps executed in the above embodiment.

[0231] Optionally, if the device is located in a vehicle, the above processing unit may be Figure 1 the processors 121-12n shown.

[0232] An embodiment of the present application further provides a control system, which may include a computing platform and a sensing system, and the computing platform may include the above control device 900.

[0233] An embodiment of the present application further provides a vehicle, which may include the above control device 900, or includes the above control system.

[0234] An embodiment of the present application further provides a computer program product, which includes: computer program code, when the computer program code runs on a computer, it causes the computer to execute the method in the above embodiment.

[0235] An embodiment of the present application further provides a computer-readable medium, which stores program code, and when the computer program code runs on a computer, it causes the computer to execute the method in the above embodiment.

[0236] An embodiment of the present application further provides a chip, which includes a circuit for executing the control method in the above embodiment.

[0237] In the implementation process, each step of the above method can be completed by the integrated logic circuit in the hardware of the processor or the instructions in the form of software. The method disclosed in combination with the embodiments of the present application can be directly embodied as being executed and completed by the hardware processor, or executed and completed by the combination of the hardware and software modules in the processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above method. To avoid repetition, it will not be described in detail here.

[0238] It should be understood that in the embodiments of the present application, the memory may include a read-only memory and a random access memory, and provide instructions and data to the processor.

[0239] It should also be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0240] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0241] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0242] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces. The indirect coupling or communication connection of the devices or units may be in an electrical, mechanical, or other form.

[0243] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0244] In addition, in each embodiment of the present application, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0245] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0246] As described above, the above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application shall be subject to the protection scope of the claims.

Claims

1. A control method, characterized in that, including: Obtaining information of a mixed vehicle queue, where the mixed vehicle queue includes connected and automated vehicles (CAVs) and human-driven vehicles; Establishing a dynamic model of the mixed vehicle queue, where the dynamic model includes uncertain inputs; Decoupling the dynamic model to obtain a difference state equation and a nominal state equation; Determining the reachable set of the nominal state equation according to the reachable set of the difference state equation; Controlling the CAVs according to the reachable set of the nominal state equation.

2. The method according to claim 1, wherein Before controlling the CAVs according to the reachable set of the nominal state equation, the method further includes: Obtaining an economic cost function according to the nonlinear characteristics of the power system of the CAVs; where controlling the CAVs according to the reachable set of the nominal state equation includes: Controlling the CAVs according to the reachable set of the nominal state equation and the economic cost function.

3. The method according to claim 1 or 2, characterized in that, The reachable set of the nominal state equation includes the reachable set of state variables and the reachable set of control variables. Determining the reachable set of the nominal state equation according to the reachable set of the difference state equation includes: Determining the reachable set of the state variables according to the reachable set of the state variables of the difference state equation and the constraints of the state variables of the CAVs; Determining the reachable set of the control variables according to the reachable set of the control variables of the difference state equation and the constraints of the control variables of the CAVs.

4. The method according to any one of claims 1 to 3, characterized in that, The dynamic model is shown in the following formula (2): x(k + 1) = Ax(k) + Bu(k) + Dw(k) (2) where x(k + 1) is the state of the mixed vehicle queue at time k + 1, u(k) is the control input at time k, w(k) is the uncertain input at time k, A is the state equation matrix, B is the control input matrix, and D is the uncertain input matrix after separately deriving the model uncertainty; where decoupling the dynamic model to obtain a difference state equation and a nominal state equation includes: Decoupling the formula (2) to obtain the nominal state equation shown in formula (3) and the difference state equation shown in formula (4): z(k + 1) = Az(k) + Bu z (k) (3) e(k + 1)=Ae(k)+Bu e (k)+Dw(k) (4) Among them, z(k + 1) represents the nominal state at the (k + 1)-th moment, z(k) represents the nominal state at the k-th moment, e(k + 1) represents the error state at the (k + 1)-th moment, e(k) represents the error state at the k-th moment, u z (k) and u e (k) represent the nominal control quantity and the differential control quantity respectively.

5. The method according to any one of claims 1 to 4, characterized in that Controlling the CAVs according to the reachable set of the nominal state equation includes: Obtaining a nominal control quantity according to the reachable set of the nominal state equation; Obtaining the acceleration control quantity of the CAVs according to the nominal control quantity and the feedback control quantity; Controlling the CAVs according to the acceleration control quantity of the CAVs.

6. A control device, characterized in that, including: An acquisition unit, configured to obtain information of a mixed vehicle queue, where the mixed vehicle queue includes connected and automated vehicles (CAVs) and human-driven vehicles; A model establishment unit, configured to establish a dynamic model of the mixed vehicle queue, where the dynamic model includes uncertain inputs; A decoupling unit, configured to decouple the dynamic model to obtain a difference state equation and a nominal state equation; A determination unit, configured to determine the reachable set of the nominal state equation according to the reachable set of the difference state equation; A control unit for controlling the intelligent connected vehicle according to the reachable set of the nominal state equation.

7. The device according to claim 6, characterized in that, The obtaining unit is further configured to: Obtain an economic cost function according to the non-linear characteristics of the power system of the intelligent connected vehicle; The control unit is specifically configured to: control the intelligent connected vehicle according to the reachable set of the nominal state equation and the economic cost function.

8. The device according to claim 6 or 7, characterized in that, The reachable set of the nominal state equation includes the reachable set of state variables and the reachable set of control variables. The determining unit is specifically configured to: Determine the reachable set of state variables according to the reachable set of state variables of the differential state equation and the constraints of the state variables of the intelligent connected vehicle; Determine the reachable set of control variables according to the reachable set of control variables of the differential state equation and the constraints of the control variables of the intelligent connected vehicle.

9. The device according to any one of claims 6 to 8, characterized in that The dynamic model is shown in the following formula (2): x(k + 1) = Ax(k) + Bu(k) + Dw(k) (2) Where, x(k + 1) is the state of the hybrid vehicle queue at time k + 1, u(k) is the control input at time k, w(k) is the uncertainty input at time k, A is the state equation matrix, B is the control input matrix, and D is the uncertainty input matrix after separately deriving the model uncertainty; Wherein, the decoupling unit is specifically configured to: Decouple the formula (2) to obtain the nominal state equation shown in formula (3) and the differential state equation shown in formula (4): z(k + 1) = Az(k) + Bu z (k) (3) e(k + 1)=Ae(k)+Bu e (k)+Dw(k) (4) Among them, z(k + 1) represents the nominal state at the (k + 1)-th moment, z(k) represents the nominal state at the k-th moment, e(k + 1) represents the error state at the (k + 1)-th moment, e(k) represents the error state at the k-th moment, u z (k) and u e (k) represent the nominal control quantity and the differential control quantity respectively.

10. The device according to any one of claims 6 to 9, characterized in that The determining unit is specifically configured to: determine the nominal control variable according to the reachable set of the nominal state equation; Determine the acceleration control variable of the intelligent connected vehicle according to the nominal control variable and the feedback control variable; The control unit is specifically configured to: Control the intelligent connected vehicle according to the acceleration control variable of the intelligent connected vehicle.

11. A control device, characterized in that, Comprising: A memory for storing a computer program; A processor for executing the computer program stored in the memory, so that the device executes the method according to any one of claims 1 to 5.

12. A vehicle, characterized in that, Including the control device according to any one of claims 6 to 11.

13. A computer-readable storage medium, characterized in that, Instructions are stored thereon, and when the instructions are executed by the processor, the processor is caused to implement the method according to any one of claims 1 to 5.

14. A computer program product, characterized in that, The computer program product includes computer program code, and when the computer program code runs on a computer, the computer is caused to implement the method according to any one of claims 1 to 5.

15. A chip, characterized in that, The chip includes a circuit for executing the method according to any one of claims 1 to 5.

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