Adaptive cruise control method and device, terminal equipment and computer program product

Abstract

The invention relates to an adaptive cruise control method and device, terminal equipment and a computer program product. The adaptive cruise control method comprises the steps that static parameters corresponding to a vehicle and driving data of the vehicle are acquired; based on the static parameters and the driving data, constructing a longitudinal dynamic model of the vehicle; based on the longitudinal dynamics model, a state equation and a measurement equation are established, and the state equation comprises the speed and the quality of the vehicle and the function relation between the road gradient and the static parameters and the running data; solving the state equation and the measurement equation by using an extended Kalman filtering algorithm to obtain a current quality estimation value and a road slope estimation value of the vehicle; and performing adaptive cruise control on the vehicle based on the mass estimation value and the road grade estimation value. According to the method and the device, the problem of relatively low adaptive cruise control performance of the commercial vehicle in related technologies is solved, and the technical effect of improving the adaptive cruise control performance is realized.

Description

Technical Field

[0001] This application belongs to the technical field of vehicle control, and particularly relates to an adaptive cruise control method, device, terminal device, and computer program product. Background Art

[0002] In recent years, with the comprehensive and in-depth development of the global economy, autonomous vehicles have been further developed. To improve the driving safety and comfort of drivers, some automotive intelligent systems have been proposed for the advanced driver assistance systems of autonomous vehicles. Among them, the research on Adaptive Cruise Control (ACC) has received extensive attention.

[0003] The ACC system measures the distance and relative speed between the host vehicle and the vehicle ahead through the use of ultrasonic radars, millimeter-wave radars, cameras, or lidar to achieve stable vehicle following, and adjusts the acceleration or deceleration of the vehicle through a control algorithm to ensure the distance between the host vehicle and the vehicle ahead, ensuring driving safety to the greatest extent. However, during the use of commercial vehicles, the load mass varies within a large range, and the operating road conditions are diverse. The vehicle mass and road gradient are crucial for the operation of the ACC system. If these parameters cannot be identified, the ride comfort, power performance, and safety of the vehicle cannot be guaranteed.

[0004] Currently, for the problem of the low performance of the adaptive cruise control of commercial vehicles in related technologies, no effective solution has been proposed. Summary of the Invention

[0005] Embodiments of this application provide an adaptive cruise control method, device, terminal device, and computer program product to at least solve the problem of the low performance of the adaptive cruise control of commercial vehicles in related technologies.

[0006] In a first aspect, embodiments of this application provide an adaptive cruise control method, including: obtaining static parameters corresponding to the vehicle and driving data of the vehicle; constructing a longitudinal dynamics model of the vehicle based on the static parameters and the driving data; establishing a state equation and a measurement equation based on the longitudinal dynamics model, where the state equation includes the functional relationships between the speed, mass, and road gradient of the vehicle and the static parameters and the driving data; using the extended Kalman filter algorithm to solve the state equation and the measurement equation to obtain the current mass estimation value and road gradient estimation value of the vehicle; and performing adaptive cruise control on the vehicle based on the mass estimation value and the road gradient estimation value.

[0007] In some embodiments, solving the state equation and the measurement equation using the extended Kalman filter algorithm to obtain the current mass estimate value and road slope estimate value of the vehicle includes: discretizing the state equation using a preset forward Euler algorithm to obtain a discretized state equation; solving the discretized state equation and the measurement equation using the extended Kalman filter algorithm to obtain the mass estimate value and the road slope estimate value.

[0008] In some embodiments, solving the discretized state equation and the measurement equation using the extended Kalman filter algorithm to obtain the mass estimate value and the road slope estimate value includes: determining a priori state estimate value and a priori error covariance according to the discretized state equation, and obtaining a Kalman gain according to the a priori error covariance; determining a posteriori state estimate value and a posteriori error covariance according to the measurement equation, the a priori state estimate value, the a priori error covariance and the Kalman gain, and outputting the mass estimate value and the road slope estimate value.

[0009] In some embodiments, the longitudinal dynamics model is expressed as: Where is the longitudinal acceleration of the vehicle, F t is the driving force of the vehicle, F i is the ramp resistance when the vehicle is traveling, F w is the air resistance, F f is the rolling resistance when the vehicle is traveling.

[0010] In some embodiments, F t is expressed as F i is expressed as F i = mgi, F w is expressed as F f is expressed as F f = mgf; where, T tq is the actual torque input from the engine of the vehicle to the transmission, i g is the transmission ratio of the vehicle, i o is the final drive ratio of the vehicle, η T is the mechanical efficiency of the driveline of the vehicle, r is the rolling radius of the vehicle's wheels, m is the mass of the vehicle, g is the acceleration due to gravity, i is the road slope, C D is the air resistance coefficient, A is the frontal area, ρ is the air density, v is the speed of the vehicle, f is the rolling resistance coefficient.

[0011] In some embodiments, the discretized state equation is expressed as: The measurement equation is expressed as: where x k is the state vector at time k, v k is the speed of the vehicle at time k, m k is the mass of the vehicle at time k, i k is the road slope at time k, Δt is the discrete step length, and W k-1 is the process noise at time (k - 1), Z k is the measurement vector at time k, and V k is the measurement noise at time k.

[0013] In some embodiments, based on the mass estimation value and the road slope estimation value, performing adaptive cruise control on the vehicle includes: correcting the longitudinal acceleration of the vehicle based on the mass estimation value and the road slope estimation value to obtain a corrected longitudinal acceleration; performing adaptive cruise control on the vehicle based on the corrected longitudinal acceleration so that the vehicle maintains a target vehicle speed and a target vehicle distance.

[0014] In a second aspect, an embodiment of the present application provides an adaptive cruise control device, including: an acquisition module configured to acquire static parameters corresponding to the vehicle and driving data of the vehicle; a first construction module configured to construct a longitudinal dynamics model of the vehicle based on the static parameters and the driving data; a second construction module configured to establish a state equation and a measurement equation based on the longitudinal dynamics model, where the state equation includes a functional relationship between the speed, mass, and road slope of the vehicle and the static parameters and the driving data; a calculation module configured to solve the state equation and the measurement equation using an extended Kalman filter algorithm to obtain a current mass estimation value and a road slope estimation value of the vehicle; and a control module configured to perform adaptive cruise control on the vehicle based on the mass estimation value and the road slope estimation value.

[0015] In a third aspect, an embodiment of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the adaptive cruise control method according to any one of the first aspects is implemented.

[0016] In a fourth aspect, an embodiment of the present application provides a computer program product, including a computer program. When the computer program is run, the adaptive cruise control method according to any one of the first aspects is executed.

[0017] Compared with the related art, the adaptive cruise control method, device, terminal device, and computer program product provided by the embodiments of the present application construct a longitudinal dynamics model of a vehicle by obtaining static parameters and driving data of the vehicle; based on the longitudinal dynamics model, the extended Kalman filter algorithm is used to jointly estimate the mass of the vehicle and the road slope; based on the mass estimate value and the road slope estimate value obtained by the extended Kalman filter algorithm, adaptive cruise control is performed on the vehicle. In this way, by estimating the mass of the vehicle and the road slope during the vehicle driving process in real time, the adaptability of the vehicle's adaptive cruise control can be improved; in the face of complex and changeable driving conditions, the adaptive cruise control system can respond in a timely manner to changes in the vehicle mass or road slope, thereby improving the driving performance of the adaptive cruise control; at the same time, the embodiments of the present application do not require additional sensors to be installed to obtain information such as the mass of the vehicle and the road slope, but based on the optimal estimation theory and combined with the longitudinal dynamics model of the vehicle, the vehicle mass and road slope are calculated in real time, while improving the overall performance of the adaptive cruise control, the design cost is also reduced. Through the present application, the problem of low performance of the adaptive cruise control of commercial vehicles in the related art is solved, and the technical effect of improving the performance of the adaptive cruise control is achieved.

[0018] Details of one or more embodiments of the present application are set forth in the following drawings and description, so that other features, objects, and advantages of the present application will become more apparent and understandable. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0020] Figure 1 is a flowchart of an adaptive cruise control method according to an embodiment of the present application;

[0021] Figure 2 is a schematic diagram of the longitudinal force of a vehicle when driving on a slope according to an embodiment of the present application;

[0022] Figure 3 is a flowchart of the extended Kalman filter algorithm according to an embodiment of the present application;

[0023] Figure 4 is a schematic structural diagram of an adaptive cruise control device according to an embodiment of the present application;

[0024] Figure 5 is a schematic structural diagram of a terminal device according to an embodiment of the present application. Detailed implementation manners

[0025] In the following description, specific details such as specific system architectures, technologies, etc. are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0026] It should be understood that when used in the specification of the present application and the appended claims, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0027] It should also be understood that the term "and / or" used in the specification of the present application and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0028] As used in the specification of the present application and the appended claims, the term "if" can be interpreted as "when", "once", "in response to determining", or "in response to detecting" depending on the context. Similarly, the phrase "if determined" or "if detecting [the described condition or event]" can be interpreted as meaning "once determined", "in response to determining", "once detecting [the described condition or event]", or "in response to detecting [the described condition or event]" depending on the context.

[0029] In addition, in the description of the specification of the present application and the appended claims, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0030] The reference to "one embodiment" or "some embodiments" etc. described in the specification of the present application means that a specific feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, the statements "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "comprising", "including", "having", and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0031] In recent years, with the comprehensive and in-depth development of the global economy, autonomous vehicles have been further developed. In order to improve the driving safety and comfort of drivers, some automotive intelligent systems have been proposed for the advanced driver assistance systems of autonomous vehicles. Among them, the research on Adaptive Cruise Control (ACC) has received extensive attention.

[0032] The ACC system realizes stable car following by using ultrasonic radar, millimeter-wave radar, camera or lidar to measure the distance and relative speed between the host vehicle and the preceding vehicle, and adjusts the acceleration or deceleration of the vehicle through a control algorithm to ensure the distance between the host vehicle and the preceding vehicle, ensuring driving safety to the greatest extent. However, during the use of commercial vehicles, the load mass varies within a large range, and the operating road conditions are diverse. Vehicle mass and road gradient are crucial for the working process of the ACC system. If these parameters cannot be identified, the ride comfort, power performance, and safety of the vehicle cannot be guaranteed.

[0033] Currently, there is no effective solution to the problem of the low performance of the adaptive cruise control of commercial vehicles in related technologies.

[0034] In view of this, the embodiments of the present application provide an adaptive cruise control method. A longitudinal dynamics model of the vehicle is constructed by obtaining the static parameters and driving data of the vehicle; based on this longitudinal dynamics model, the extended Kalman filter algorithm is used to jointly estimate the mass and road gradient of the vehicle; based on the mass estimate value and road gradient estimate value obtained by the extended Kalman filter algorithm, adaptive cruise control is performed on the vehicle. In this way, by estimating the vehicle mass and road gradient during the vehicle driving process in real time, the adaptability of the vehicle's adaptive cruise control can be improved; in the face of complex and variable driving conditions, the adaptive cruise control system can respond in a timely manner to changes in vehicle mass or road gradient, thereby improving the safety and stability of the adaptive cruise control; at the same time, the embodiments of the present application do not require additional sensors to obtain information such as vehicle mass and road gradient, but based on the optimal estimation theory and combined with the longitudinal dynamics model of the vehicle, the vehicle mass and road gradient are calculated in real time, while improving the overall performance of the adaptive cruise control, reducing the design cost. Through the present application, the problem of the low performance of the adaptive cruise control of commercial vehicles in related technologies is solved, and the technical effect of improving the performance of the adaptive cruise control is achieved.

[0035] The following will be combined with Figure 1 to illustrate an adaptive cruise control method provided by an embodiment of the present application. Please refer to Figure 1 , Figure 1 which is a flowchart of the adaptive cruise control method according to an embodiment of the present application. As shown in Figure 1As shown, the method includes:

[0036] Step S101, obtaining static parameters corresponding to the vehicle and driving data of the vehicle.

[0037] In this embodiment, the static parameters may include parameters such as the transmission ratio of the vehicle, the wheel rolling radius, the mechanical efficiency of the driveline, the frontal area, the air density, the air drag coefficient, the rolling resistance coefficient, etc.; the driving data may include parameters such as the actual torque input from the engine of the vehicle to the transmission, the speed of the vehicle, the longitudinal acceleration of the vehicle, etc.

[0038] In one embodiment, the driving data generated during the driving of the vehicle may be obtained through a Controller Area Network (CAN) bus.

[0039] Step S102, constructing a longitudinal dynamics model of the vehicle based on the static parameters and the driving data.

[0040] In this embodiment, the longitudinal force on the vehicle when driving on a slope may be analyzed, and then the longitudinal dynamics model of the vehicle may be constructed.

[0041] Figure 2 is a schematic diagram of the longitudinal force on a vehicle when driving on a slope according to an embodiment of the present application. As Figure 4 shown, when the vehicle is driving on a slope, it is mainly affected by four forces, namely the driving force F of the vehicle t , the slope resistance F i , the air resistance F w , and the rolling resistance F f .

[0042] Based on this longitudinal force analysis diagram, the longitudinal dynamics model of the vehicle can be expressed as the following equation of longitudinal force balance:

[0043]

[0044] Wherein, is the longitudinal acceleration of the vehicle, F t is the driving force of the vehicle, F i is the slope resistance when the vehicle is driving, F w is the air resistance, and F f is the rolling resistance when the vehicle is driving.

[0045] It should be noted that F t can be expressed as F i can be expressed as F i = mgi, and F w can be expressed as Ff It can be expressed as F F = mgf;

[0046] wherein, T tq is the actual torque input from the vehicle's engine to the transmission, i g is the transmission ratio of the vehicle's transmission, i o is the reduction ratio of the vehicle's final drive, η T is the mechanical efficiency of the vehicle's driveline, r is the rolling radius of the vehicle's wheels, m is the mass of the vehicle, g is the acceleration due to gravity, i is the road gradient = tanα (as Figure 2 shown, α is the road gradient angle), C D is the air resistance coefficient, A is the frontal area, ρ is the air density, v is the speed of the vehicle, and f is the rolling resistance coefficient.

[0047] In this embodiment, after obtaining the longitudinal dynamics model of the vehicle, the Extended Kalman Filter (EKF) algorithm can be used subsequently to estimate the mass of the vehicle and the road gradient. It should be noted that the adaptive cruise control method provided in the embodiments of the present application can be applied to the adaptive cruise control of commercial vehicles. Therefore, the mass of the vehicle estimated using the Extended Kalman Filter algorithm should be the sum of the mass of the vehicle itself, the mass of the passengers carried by the vehicle, and the mass of the goods carried by the vehicle.

[0048] During the use of commercial vehicles, the mass of the goods they carry varies within a large range, and the mass of commercial vehicles and the road gradient are crucial for the working process of the ACC system. Therefore, the Extended Kalman Filter algorithm can be used to estimate the mass of the vehicle and the road gradient to improve the overall performance of the adaptive cruise control.

[0049] In addition, the adaptive cruise control method provided in the embodiments of the present application can also be applied to passenger cars or other vehicle models. In this case, the mass of the vehicle estimated using the Extended Kalman Filter algorithm should also be the sum of the mass of the vehicle itself, the mass of the passengers carried by the vehicle, and the mass of the goods carried by the vehicle. It should be understood that the present application does not limit the specific vehicle models.

[0050] Step S103, based on the longitudinal dynamics model, establish a state equation and a measurement equation, wherein the state equation includes the functional relationships between the speed, mass, and road gradient of the vehicle and the static parameters and driving data.

[0051] In this embodiment, the extended Kalman filter algorithm is used to estimate the vehicle mass and the road slope. To this end, the state equation of the system needs to be established. The vehicle speed v, the vehicle mass m, and the road slope i are selected as state variables. Then, the state vector of the entire system can be expressed as:

[0052]

[0053] where x k is the state vector at time k, v k is the vehicle speed at time k, m k is the vehicle mass at time k, and i k is the road slope at time k.

[0054] The preset forward Euler algorithm can be used to discretize the state equation to obtain the discretized state equation:

[0055]

[0056] The measurement equation is expressed as:

[0057] where Δt is the discretization step size, W k-1 is the process noise at time (k - 1), Z k is the measurement vector at time k, V k is the measurement noise at time k. The process noise and the measurement noise can be independent of each other and can be Gaussian white noise with a mean of zero.

[0058] In addition, the discretized state equation can be simplified to:

[0059] Step S104: Use the extended Kalman filter algorithm to solve the state equation and the measurement equation to obtain the current vehicle mass estimate and road slope estimate.

[0060] In this embodiment, the extended Kalman filter algorithm can be used to solve the discretized state equation and the measurement equation to obtain the mass estimate and the road slope estimate. The extended Kalman filter algorithm includes extended Kalman filter prediction (time update) and extended Kalman filter update (measurement update). The time update and the measurement update are alternately processed to form a loop, continuously calculating the optimal mass estimate and road slope estimate, effectively improving the accuracy of vehicle mass estimation and road slope estimation.

[0061] Figure 3 is a flowchart of the extended Kalman filter algorithm according to an embodiment of the present application, as shown in Figure 3As shown, using the extended Kalman filter algorithm to solve the discretized state equation and measurement equation, and obtaining the mass estimation value and road slope estimation value includes the following steps:

[0062] Step 1, determine the prior state estimate and prior error covariance according to the discretized state equation, and obtain the Kalman gain according to the prior error covariance.

[0063] This step is the time update process of the extended Kalman filter algorithm and is calculated using the time update equation. The time update equation can be expressed as:

[0064]

[0065] Among them, is the prior state estimate of the state variable, is the optimal estimate (posterior state estimate) of the state variable at the previous moment, is the prior error covariance, P k-1 is the error covariance (posterior error covariance) at the previous moment, J f is the Jacobian matrix obtained by taking the partial derivative of the state vector function f with respect to the state variable, Q k-1 is the process noise covariance.

[0066] Step 2, determine the posterior state estimate and posterior error covariance according to the measurement equation, prior state estimate, prior error covariance and Kalman gain, and output the mass estimation value and road slope estimation value.

[0067] This step is the measurement update process of the extended Kalman filter algorithm and is calculated using the measurement update equation. The measurement update equation can be expressed as:

[0068]

[0069] Among them, K k is the Kalman gain, is the posterior state estimate of the state variable, P k is the posterior error covariance, and I is the identity matrix.

[0070] According to the above time update process and measurement update process, continuously perform rolling filtering estimation, and the mass estimation value and road slope estimation value of the vehicle can be output. Specifically, in the embodiment of the present application, the posterior state estimate can be selected, that is, the second item in the state variable in the above steps is the mass estimation value, and the state variable in the above steps The third item in [description] is the road slope estimation value, and the posterior estimation quality and the posterior estimated road slope are output, completing a calculation of estimating the vehicle mass and the road slope. The extended Kalman filter algorithm is carried out recursively. Only by obtaining the posterior state estimation value of the state variable at the previous moment and the prior state estimation value of the current state variable can the posterior state estimation value of the current state variable be obtained. Through update iteration, the new posterior estimated mass and the posterior estimated road slope are output, and the optimal mass estimation value and road slope estimation value are obtained, thereby effectively improving the accuracy of mass estimation and road slope estimation.

[0071] Step S105, perform adaptive cruise control on the vehicle based on the mass estimation value and the road slope estimation value.

[0072] In this embodiment, performing adaptive cruise control on the vehicle based on the mass estimation value and the road slope estimation value includes: correcting the longitudinal acceleration of the vehicle based on the mass estimation value and the road slope estimation value to obtain the corrected longitudinal acceleration; performing adaptive cruise control on the vehicle based on the corrected longitudinal acceleration so that the vehicle maintains the target vehicle speed and the target vehicle distance.

[0073] In this way, after the adaptive cruise control method provided by the embodiment of the present application controls the vehicle loaded with goods, the vehicle can more accurately and quickly track the preset cruise vehicle speed during the cruise process. Moreover, during the following process, its braking performance is more reasonable, and the ability to avoid collisions is greatly improved. During the uphill and downhill processes, its speed regulation is faster and more accurate. This can fully demonstrate the advantages of the adaptive cruise control method provided by the embodiment of the present application in terms of vehicle safety performance, handling stability, and traffic efficiency.

[0074] Through the above steps S101 to S105, a longitudinal dynamics model of the vehicle is constructed by obtaining the static parameters and driving data of the vehicle; based on this longitudinal dynamics model, the extended Kalman filter algorithm is used to jointly estimate the mass of the vehicle and the road slope; based on the mass estimation value and the road slope estimation value obtained by the extended Kalman filter algorithm, adaptive cruise control is performed on the vehicle. In this way, by estimating the mass of the vehicle and the road slope during the vehicle driving process in real time, the adaptability of the adaptive cruise control of the vehicle can be improved; in the face of complex and changeable driving conditions, the adaptive cruise control system can respond in a timely manner to changes in the vehicle mass or road slope, thereby improving the safety and stability of the adaptive cruise control; at the same time, the embodiments of the present application do not require additional sensors to be installed to obtain information such as the mass of the vehicle and the road slope, but based on the optimal estimation theory and combined with the longitudinal dynamics model of the vehicle, the vehicle mass and road slope are calculated in real time, while improving the overall performance of the adaptive cruise control, the design cost is also reduced. Through the present application, the problem of low performance of the adaptive cruise control of commercial vehicles in the related art is solved, and the technical effect of improving the performance of the adaptive cruise control is achieved.

[0075] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution, and the execution order 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.

[0076] Corresponding to the adaptive cruise control method in the above embodiments, Figure 4 The structural schematic diagram of an adaptive cruise control device according to an embodiment of the present application is shown. For the sake of convenience of description, only the parts related to the embodiments of the present application are shown.

[0077] Please refer to Figure 4 , the adaptive cruise control device 4 includes: an acquisition module 40, configured to acquire static parameters corresponding to the vehicle and driving data of the vehicle; a first construction module 41, configured to construct a longitudinal dynamics model of the vehicle based on the static parameters and the driving data; a second construction module 42, configured to establish a state equation and a measurement equation based on the longitudinal dynamics model, wherein the state equation includes the functional relationship between the speed, mass, and road slope of the vehicle and the static parameters and the driving data; a calculation module 43, configured to solve the state equation and the measurement equation using the extended Kalman filter algorithm to obtain the current mass estimation value and road slope estimation value of the vehicle; a control module 44, configured to perform adaptive cruise control on the vehicle based on the mass estimation value and the road slope estimation value.

[0078] In one embodiment, the calculation module 43 is further configured to discretize the state equation using a preset forward Euler algorithm to obtain a discretized state equation; and solve the discretized state equation and the measurement equation using an extended Kalman filter algorithm to obtain a mass estimation value and a road slope estimation value.

[0079] In one embodiment, the calculation module 43 is further configured to determine a prior state estimate value and a prior error covariance according to the discretized state equation, and obtain a Kalman gain according to the prior error covariance; determine a posterior state estimate value and a posterior error covariance according to the measurement equation, the prior state estimate value, the prior error covariance and the Kalman gain, and output a mass estimation value and a road slope estimation value.

[0080] In one embodiment, the longitudinal dynamics model is expressed as: Where is the longitudinal acceleration of the vehicle, F t is the driving force of the vehicle, F i is the ramp resistance when the vehicle is driving, F w is the air resistance, F f is the rolling resistance when the vehicle is driving.

[0081] In one embodiment, F t is expressed as F i is expressed as F i = mgi, F w is expressed as F f is expressed as F f = mgf; where, T tq is the actual torque input from the vehicle's engine to the transmission, i g is the transmission ratio of the vehicle's transmission, i o is the final drive ratio of the vehicle, η T is the mechanical efficiency of the vehicle's driveline, r is the rolling radius of the vehicle's wheels, m is the mass of the vehicle, g is the acceleration due to gravity, i is the road slope, C D is the air resistance coefficient, A is the frontal area, ρ is the air density, v is the speed of the vehicle, f is the rolling resistance coefficient.

[0082] In one embodiment, the discretized state equation is expressed as: The measurement equation is expressed as: Where, x k is the state vector at time k, v k is the speed of the vehicle at time k, m k is the mass of the vehicle at time k, i k is the road slope at time k, Δt is the discretization step size, Wk-1 is the process noise at time (k - 1), Z k is the measurement vector at time k, V k is the measurement noise at time k.

[0084] In one embodiment, performing adaptive cruise control on a vehicle based on a mass estimation value and a road slope estimation value includes: correcting a longitudinal acceleration of the vehicle based on the mass estimation value and the road slope estimation value to obtain a corrected longitudinal acceleration; performing adaptive cruise control on the vehicle based on the corrected longitudinal acceleration so that the vehicle maintains a target vehicle speed and a target vehicle distance.

[0085] It should be noted that for the information interaction, execution process, etc. between the above-mentioned device / units, since they are based on the same concept as the method embodiment of the present application, for their specific functions and the technical effects brought, reference can be specifically made to the method embodiment part, and details will not be elaborated here.

[0086] Those skilled in the art can clearly understand that for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above-mentioned functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of the present application. The specific working process of the units and modules in the above system can refer to the corresponding process in the foregoing method embodiment, and details will not be elaborated here.

[0087] Figure 5 is a schematic structural diagram of a terminal device according to an embodiment of the present application. As Figure 5 shown, the terminal device 5 includes: at least one processor 50 ( Figure 5 only one is shown in the figure), a memory 51, and a computer program 52 stored in the memory 51 and executable on at least one processor 50. When the processor 50 executes the computer program 52, the steps in any of the above-mentioned adaptive cruise control method embodiments are implemented.

[0088] The terminal device 5 can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device 5 may include, but is not limited to, the processor 50 and the memory 51. Those skilled in the art can understand that Figure 5The above are merely examples of the terminal device 5, which do not constitute a limitation to the terminal device 5. It may include more or fewer components than those shown in the figure, or combine certain components, or different components. For example, it may also include input / output devices, network access devices, etc.

[0089] The processor 50 may be a central processing unit (CPU). The processor 50 may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0090] In some embodiments, the memory 51 may be an internal storage unit of the terminal device 5, such as the hard disk or memory of the terminal device 5. In some other embodiments, the memory 51 may also be an external storage device of the terminal device 5, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device 5. In other embodiments, the memory 51 may also include both the internal storage unit and the external storage device of the terminal device 5. The memory 51 is used to store an operating system, application programs, a boot loader, data, and other programs, such as the program code of the computer program 52. The memory 51 may also be used to temporarily store data that has been output or will be output.

[0091] An embodiment of the present application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement the steps in the above embodiments of various adaptive cruise control methods.

[0092] An embodiment of the present application provides a computer program product, which, when running on a mobile terminal, enables the mobile terminal to implement the steps in the above embodiments of various adaptive cruise control methods when executed.

[0093] The implementation of all or part of the processes in the method of the above embodiments in this application can be completed by instructing relevant hardware through a computer program. This computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can at least include: any entity or device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium that can carry the computer program code to the adaptive cruise control device or terminal device. For example, a USB flash drive, a mobile hard disk, a magnetic disk, or an optical disc, etc.

[0094] In the above embodiments, the descriptions of the various embodiments each have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0095] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or by 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. A professional technician 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 this application.

[0096] In the embodiments provided in this application, it should be understood that the disclosed device / network device and method can be implemented in other ways. For example, the device / network device embodiments described above are only illustrative. For example, the division of modules or 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 coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical, mechanical, or other form.

[0097] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or 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.

[0098] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. An adaptive cruise control method, characterized in that: include: Acquiring static parameters corresponding to a vehicle and driving data of the vehicle; constructing a longitudinal dynamics model of the vehicle based on the static parameters and the driving data; Based on the longitudinal dynamics model, establishing a state equation and a measurement equation, wherein the state equation includes a functional relationship between the speed, mass and road slope of the vehicle and the static parameters and the driving data; Using an extended Kalman filter algorithm to solve the state equation and the measurement equation to obtain a current mass estimate of the vehicle and a road slope estimate; Based on the mass estimate and the road gradient estimate, adaptive cruise control is performed on the vehicle.

2. The method according to claim 1, characterized in that The state equation and the measurement equation are solved by using an extended Kalman filter algorithm to obtain the current mass estimation value and the road slope estimation value of the vehicle, including: Discretize the state equation using a preset forward Euler algorithm to obtain a discretized state equation; The extended Kalman filter algorithm is used to solve the discretized state equation and the measurement equation to obtain the mass estimation value and the road slope estimation value.

3. The method according to claim 2, characterized in that Solving the discretized state equation and the measurement equation using the extended Kalman filter algorithm to obtain the mass estimation value and the road slope estimation value includes: Determine a priori state estimation value and a priori error covariance according to the discretized state equation, and obtain a Kalman gain according to the priori error covariance; According to the measurement equation, the priori state estimate, the priori error covariance and the Kalman gain, the a posteriori state estimate and the a posteriori error covariance are determined, and the quality estimate and the road slope estimate are output.

4. The method according to claim 2 or 3, characterized in that: The longitudinal dynamics model is expressed as: in, is the longitudinal acceleration of the vehicle, F t is the driving force of the vehicle, F i is the slope resistance of the vehicle when it is traveling, F w is the air resistance, F f is the rolling resistance of the vehicle when it is running.

5. The method according to claim 4, characterized in that F t Expressed as F i Indicated as F i = mgi, F w Expressed as F f Indicated as F f = mgf; Among them, T tq is the actual torque input from the engine of the vehicle to the transmission, i g is the transmission ratio of the vehicle, i o is the final reducer transmission ratio of the vehicle, η T is the mechanical efficiency of the vehicle's powertrain, r is the rolling radius of the vehicle's wheels, m is the vehicle's mass, g is the acceleration of gravity, i is the road slope, C D is the air resistance coefficient, A is the frontal area, ρ is the air density, v is the speed of the vehicle, and f is the rolling resistance coefficient.

6. The method according to claim 5, characterized in that The discretized state equation is expressed as: The measurement equation is expressed as: Among them, x k is the state vector at time k, v k is the speed of the vehicle at time k, m k is the mass of the vehicle at time k, i k is the road slope at time k, Δt is the discrete step length, W k-1 is the process noise at time (k-1), Z k is the measurement vector at time k, V k is the measurement noise at time k.

7. The method according to any one of claims 1 to 3, characterized in that Based on the mass estimation value and the road gradient estimation value, performing adaptive cruise control on the vehicle comprises: Based on the mass estimation value and the road gradient estimation value, correcting the longitudinal acceleration of the vehicle to obtain a corrected longitudinal acceleration; Adaptive cruise control is performed on the vehicle based on the corrected longitudinal acceleration so that the vehicle maintains a target vehicle speed and a target vehicle distance.

8. An adaptive cruise control device, characterized in that: include: An acquisition module, used to acquire static parameters corresponding to a vehicle and driving data of the vehicle; A first building module, configured to build a longitudinal dynamics model of the vehicle based on the static parameters and the driving data; A second building module is used to establish a state equation and a measurement equation based on the longitudinal dynamics model, wherein the state equation includes a functional relationship between the speed, mass and road slope of the vehicle and the static parameters and the driving data; A calculation module, used for solving the state equation and the measurement equation by using an extended Kalman filter algorithm to obtain a current mass estimation value and a road slope estimation value of the vehicle; A control module is configured to perform adaptive cruise control on the vehicle based on the mass estimation value and the road gradient estimation value.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the adaptive cruise control method according to any one of claims 1 to 7 is implemented.

10. A computer program product, characterized in that The invention comprises a computer program, and when the computer program is executed, the adaptive cruise control method according to any one of claims 1 to 7 is executed.

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