Control method and device of vehicle, vehicle and storage medium

Abstract

The application discloses a control method and device of a vehicle, the vehicle and a storage medium. The method comprises the following steps: acquiring a current planning path and a historical planning path; determining an adaptive parameter based on the current planning path and the historical planning path, wherein the adaptive parameter represents a deviation between the current planning path and the historical planning path; acquiring an actual state quantity; determining a planning control model based on the actual state quantity, the current planning path and the adaptive parameter; and performing longitudinal control on the vehicle based on the planning control model. Since the adaptive parameter for describing the fluctuation degree of the planning path is added to the planning control model, when the planning control model is optimized and solved, the adaptive parameter can constrain the determined control quantity to some extent, thereby reducing the influence of the large fluctuation of the planning path on the control quantity, improving the anti-interference ability of the control system of the vehicle to the fluctuation of the planning path, and making the longitudinal control of the vehicle more stable.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle control, and more particularly, to a vehicle control method and device, a vehicle, and a storage medium. BACKGROUND

[0002] With the popularization and development of intelligent driving technology, longitudinal tracking control of a vehicle has become a research focus of researchers. Specifically, in a case where an upstream Motion Planning (MP) module determines a planning path, a Motion Control (MC) module in the vehicle determines a control amount of the vehicle based on the planning path given by the MP module to achieve longitudinal tracking control of the vehicle.

[0003] In an existing technical solution, the MP module updates the planning path in real time based on current road condition information of the vehicle. For example, when a side vehicle suddenly changes lanes to enter a lane of the vehicle, the MP module plans an emergency deceleration planning path; for another example, when a front vehicle drives away from a lane where the vehicle is located, the MP module plans a relatively gentle planning path.

[0004] Therefore, once other traffic participants such as vehicles, pedestrians, and reverse two-wheel vehicles suddenly appear on the lane, the planning path of the vehicle will fluctuate greatly, and thus the control amount determined by the MC module will also change greatly, causing the vehicle to be unable to achieve relatively smooth longitudinal control during driving. SUMMARY

[0005] Embodiments of the present application provide a vehicle control method and device, a vehicle, and a storage medium.

[0006] In a first aspect, some embodiments of the present application provide a vehicle control method. The method includes: obtaining a current planning path and a historical planning path, the current planning path representing a planning path determined when the vehicle is at an actual position, and the historical planning path representing a planning path determined when the vehicle is at a historical position, the time when the vehicle drives to the historical position being earlier than the time when the vehicle drives to the actual position; determining an adaptive parameter based on the current planning path and the historical planning path, the adaptive parameter representing a deviation between the current planning path and the historical planning path; obtaining an actual state quantity, the actual state quantity including at least one of the following: an actual position, an actual speed, and an actual acceleration of the vehicle; determining a planning control model based on the actual state quantity, the current planning path, and the adaptive parameter; and performing longitudinal control on the vehicle based on the planning control model.

[0007] In a second aspect, some embodiments of the present application further provide a control device of a vehicle. The device comprises a first obtaining module, a first determining module, a second obtaining module, a second determining module, and a control module. The first obtaining module is configured to obtain a current planning path and a historical planning path. The current planning path represents a planning path determined when the vehicle is at an actual position. The historical planning path represents a planning path determined when the vehicle is at a historical position. The time when the vehicle travels to the historical position is earlier than the time when the vehicle travels to the actual position. The first determining module is configured to determine an adaptive parameter based on the current planning path and the historical planning path. The adaptive parameter represents a deviation between the current planning path and the historical planning path. The second obtaining module is configured to obtain an actual state quantity. The actual state quantity comprises at least one of the following: an actual position, an actual speed, and an actual acceleration of the vehicle. The second determining module is configured to determine a planning control model based on the actual state quantity, the current planning path, and the adaptive parameter. The control module is configured to perform longitudinal control on the vehicle based on the planning control model.

[0008] In a third aspect, some embodiments of the present application further provide a vehicle. The vehicle comprises one or more processors, a memory, and one or more application programs. The one or more application programs are stored in the memory and configured to be executed by the one or more processors, and configured to perform the method described above.

[0009] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium. The computer-readable storage medium stores computer program instructions. The computer program instructions can be invoked by a processor to perform the method described above.

[0010] In a fifth aspect, an embodiment of the present application further provides a computer program product. The computer program product, when executed, implements the method described above.

[0011] The present application provides a control method and device of a vehicle, a vehicle, and a storage medium. The control method describes the fluctuation degree of a planning path by obtaining a deviation between a current planning path and a historical planning path, and then determines an adaptive parameter based on the deviation. Finally, the vehicle determines a planning control model based on the current planning path, the adaptive parameter, and an actual state quantity, and performs longitudinal control on the vehicle based on the planning control model. Since the adaptive parameter for describing the fluctuation degree of the planning path is added to the planning control model, the adaptive parameter will constrain the determined control quantity when the planning control model is optimized and solved, thereby reducing the impact of large fluctuations of the planning path on the control quantity, improving the anti-interference ability of the control system of the vehicle to the fluctuations of the planning path, and making the longitudinal control of the vehicle more stable. BRIEF DESCRIPTION OF DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings described in the following embodiment are only some of the embodiments of the present application, and not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0013] Figure 1 A structural schematic diagram of a vehicle provided by an embodiment of the present application is shown.

[0014] Figure 2 A flowchart of a control method of a vehicle provided by the first embodiment of the present application is shown.

[0015] Figure 3 A flowchart of a control method of a vehicle provided by the second embodiment of the present application is shown.

[0016] Figure 4 A flowchart of a control method of a vehicle provided by the third embodiment of the present application is shown.

[0017] Figure 5 A module block diagram of a control device of a vehicle provided by an embodiment of the present application is shown.

[0018] Figure 6 A module block diagram of a vehicle provided by an embodiment of the present application is shown.

[0019] Figure 7 A module block diagram of a computer readable storage medium provided by an embodiment of the present application is shown. DETAILED DESCRIPTION

[0020] The embodiments of the present application will be described in detail below, and examples of the embodiments are shown in the drawings, wherein the same or similar notations represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below are exemplary and are only used to explain the present application, and cannot be understood as a limitation of the present application.

[0021] In order to make the person skilled in the art better understand the scheme of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0022] The application provides a control method and device of a vehicle, the vehicle and a storage medium. The control method describes the fluctuation degree of a planning path by obtaining a deviation between a current planning path and a historical planning path, and then determines an adaptive parameter based on the deviation. Finally, the vehicle determines a planning control model based on the current planning path, the adaptive parameter and an actual state quantity, and realizes longitudinal control of the vehicle based on the planning control model. Since the adaptive parameter for describing the fluctuation degree of the planning path is added to the planning control model in the application, when the planning control model is optimized and solved, the adaptive parameter will constrain the determined control quantity to some extent, thereby reducing the influence of large fluctuation of the planning path on the control quantity, improving the anti-interference ability of the control system of the vehicle to the fluctuation of the planning path, and making the longitudinal control of the vehicle more stable.

[0023] In order to facilitate the detailed description of the application scheme, the application environment in the embodiments of the application will be introduced first in combination with the accompanying drawings. Please refer to Figure 1 The control method of the vehicle provided by the embodiments of the application is applied to a vehicle 100. The vehicle 100 refers to a traffic tool driven or pulled by a power device, used for people to ride or used for transporting goods, which includes but is not limited to a small car, a sport utility vehicle (SUV), a multi-purpose vehicle (MPV) and the like. Specifically, the vehicle 100 can include a center console 110 and an execution system 120.

[0024] The center console 110 is the control center of the vehicle 100, which is configured to process the data information acquired by the vehicle 100 during driving, and generate control instructions for controlling the vehicle 100. In the embodiment, the center console 110 can include a planning control module, based on which the center console 110 can determine the corresponding control quantity in the case of determining the current actual state quantity (i.e., at least one of the actual position, the actual speed and the actual acceleration), the current planning path and the adaptive parameter, and send the control quantity to the execution system 120. The execution system 120 works based on the control quantity to realize the tracking control of the planning path, i.e., realize the longitudinal control of the vehicle 100. In some possible embodiments, the planning control module can also be arranged in a server in communication connection with the vehicle 100, when the center console 110 needs to determine the control quantity, the current actual state quantity of the vehicle 100, the current planning path and the adaptive parameter can be sent to the server in real time, and the control quantity determined by the planning control module of the server is received. The server can be a server, a server cluster composed of multiple servers, or a cloud computing service center. In some possible embodiments, the server can be a background server corresponding to the intelligent driving function of the center console 110. Specifically, the working process of the planning control module and the determination process of the adaptive parameter are described in detail in the method embodiments below.

[0025] In the embodiment, the center console 110 further includes a path planning module, which can calculate the planning path based on the acquired actual position of the vehicle and the target endpoint. The planning path includes a plurality of planning state quantities, each of which can include planning position, planning speed, planning acceleration and the like. Specifically, the path planning module can be provided with a path planning algorithm, which can include a search algorithm (e.g., Dijkstra algorithm, A* algorithm, Weighted A* algorithm, etc.), which is not limited in the embodiment. Similarly, the path planning module can also be arranged in a server in communication connection with the vehicle 100, when the center console 110 needs to determine or update the planning path, the actual position of the vehicle and the target endpoint are sent to the server, and the planning path determined by the path planning module of the server is received.

[0026] The execution system 120 is electrically connected to the center console 110, and can work based on the control quantity output by the center console 110 to control the driving state of the vehicle 100. In the embodiment, the execution system 120 can receive the target acceleration control quantity determined by the center console 110 based on the planning control model, and adjust the rotation speed of the wheels based on the target acceleration control quantity to realize the acceleration or deceleration of the vehicle 100, i.e., realize the longitudinal control of the vehicle 100.

[0027] Please refer toFigure 2 , Figure 2 The control method of the vehicle provided by the first embodiment of the present application is schematically shown, which can include steps S210 to S250.

[0028] In step S210, a current planning path and a historical planning path are acquired.

[0029] In the present embodiment, the current planning path represents a planning path determined when the vehicle is at an actual position, and the historical planning path represents a planning path determined when the vehicle is at a historical position, where the vehicle travels to the historical position at a time earlier than the time when the vehicle travels to the actual position.

[0030] As an implementation form, the center console can acquire an actual position of the vehicle, and determine a current planning path of the vehicle based on a preset path planning algorithm. The current planning path can be a path corresponding to travel of the vehicle from the actual position to a target terminal point, or a path corresponding to travel of the vehicle at the actual position for a preset time length (for example, 10 seconds). Specifically, the center console can acquire the actual position of the vehicle based on a global positioning system (GPS), and the preset path planning algorithm can be a Dijkstra algorithm, an A* algorithm, a Weighted A* algorithm, etc., which is not limited in the present embodiment.

[0031] It should be noted that the current planning path and the historical planning path are both planning paths determined in the present travel process of the vehicle, that is, the center console will constantly update the determined planning path during travel of the vehicle, and then determine the latest determined planning path as the current planning path, and determine the previously determined planning path as the historical planning path. For example, the current planning path can be a planning path determined by the center console in the i-th iteration process, and the historical planning path can be a planning path determined by the center console in the (i-1)-th iteration process. Specifically, the historical planning path can be stored in a memory corresponding to the center console, and the center console can acquire the historical planning path by reading data in the memory.

[0032] In step S220, an adaptive parameter is determined based on the current planning path and the historical planning path.

[0033] In the embodiment, the adaptive parameter represents the deviation between the current planning path and the historical planning path. Here, the current planning path is the planning path determined by the center console in the i-th iteration process, and the historical planning path is the planning path determined by the center console in the (i-1)-th iteration process. Since the center console determines the control quantity corresponding to the longitudinal control in each iteration process, in order to ensure the real-time performance of the control quantity on the vehicle control, the time interval between the adjacent two iteration processes is small. For example, the time interval can be less than or equal to 100 ms. In this case, if the vehicle is stably driving on the lane, there will be little change between the current planning path and the historical planning path, that is, the deviation between the two is small. If other vehicles suddenly appear in front of the vehicle, for example, a vehicle in the adjacent lane suddenly changes lanes and enters the lane corresponding to the vehicle, the center console will determine a current planning path with a sudden deceleration trend, that is, the planning path of the vehicle suddenly changes. In this case, the deviation between the current planning path and the historical planning path is large.

[0034] The center console determines the adaptive parameter based on the deviation between the current planning path and the historical planning path. For example, the adaptive parameter can have a positive correlation with the above-mentioned deviation, that is, the larger the deviation, the larger the value of the adaptive parameter; otherwise, the smaller the deviation, the smaller the value of the adaptive parameter. Specifically, the determination process of the adaptive parameter is described in detail in the following embodiment.

[0035] In step S230, the actual state quantity is obtained.

[0036] In the embodiment, the actual state quantity includes at least one of the actual position, the actual speed, and the actual acceleration of the vehicle. As an implementation manner, the center console can obtain one or more of the actual position, the actual speed, and the actual acceleration of the vehicle every preset time length. The preset time length can be a default value in the center console, or can be adjusted by the developer based on the longitudinal control accuracy of the vehicle. Specifically, the higher the longitudinal control accuracy, the shorter the preset time length, so that the number of times of obtaining the actual state quantity is larger, that is, the number of iterations of the longitudinal control is larger.

[0037] As an implementation manner, the center console can obtain the actual position of the vehicle based on the GPS, obtain the actual speed of the vehicle through the vehicle-mounted speed sensor (for example, a magneto-electric speed sensor, a Hall speed sensor, etc.), and obtain the actual acceleration of the vehicle through the vehicle-mounted acceleration sensor (for example, a capacitive acceleration sensor, a strain acceleration sensor, etc.), which is not limited in the embodiment.

[0038] It should be noted that the current planning path of the vehicle in step S210 is determined based on the actual position of the vehicle. Therefore, step S230 can be performed earlier than step S210, or can be performed simultaneously with step S210.

[0039] In step S240, a planning control model is determined based on the actual state quantity, the current planning path, and the adaptive parameter.

[0040] In this embodiment, the planning control model can be an optimization model determined based on the actual state quantity, the current planning path, and the adaptive parameter. Therefore, the planning control model not only involves the actual state quantity (i.e., the actual displacement, the actual speed, and the actual acceleration) of the vehicle, but also involves the adaptive parameter for describing the fluctuation degree of the planning path. When the planning control model is subsequently optimized and solved by the central control console, the adaptive parameter will constrain the control quantity determined by the central control console, thereby reducing the influence of the large fluctuation of the planning path on the control quantity and improving the anti-interference ability of the central control console. Specifically, the determination process of the planning control model is described in detail in the following embodiments.

[0041] In step S250, the vehicle is longitudinally controlled based on the planning control model.

[0042] In this embodiment, the central control console can determine the control quantity (e.g., the target acceleration control quantity) corresponding to the longitudinal control based on the planning control model, and send the control quantity to the execution system of the vehicle. The execution system works based on the control quantity to achieve the longitudinal control of the vehicle. For example, the execution system can receive the target acceleration control quantity determined by the central control console based on the planning control model, and adjust the rotation speed of the wheel based on the target acceleration control quantity to achieve the acceleration or deceleration of the vehicle, i.e., to achieve the longitudinal control of the vehicle.

[0043] The embodiment provides a control method of a vehicle. Since the adaptive parameter for describing the fluctuation degree of the planning path is added to the planning control model in the present application, when the planning control model is optimized and solved, the adaptive parameter will constrain the determined control quantity, thereby reducing the influence of the large fluctuation of the planning path on the control quantity, improving the anti-interference ability of the control system of the vehicle to the fluctuation of the planning path, and making the longitudinal control of the vehicle more stable.

[0044] Please refer to Figure 3 , Figure 3The control method of the vehicle provided in the second embodiment of the present application is schematically shown, and the determination process of the adaptive parameter is specifically introduced in the embodiment. The current planning path includes N first planning state quantities, and each first planning state quantity includes at least one of a first planning position, a first planning speed and a first planning acceleration. The historical planning path includes N second planning state quantities, and each second planning state quantity includes at least one of a second planning position, a second planning speed and a second planning acceleration. Specifically, the method can include steps S310 to S360.

[0045] In step S310, the current planning path and the historical planning path are acquired.

[0046] Specifically, the specific implementation of step S310 can refer to the detailed introduction in step S210, which will not be repeated here.

[0047] In step S320, the difference between the first planning state quantity and the second planning state quantity is acquired.

[0048] In the embodiment, the first planning state quantity can include one or more parameters (for example, the first planning position, the first planning speed, the first planning acceleration, etc.), the second planning state quantity includes the same number of parameters as the first planning state quantity, and the parameters have the same attribute. For example, in the case where the first planning state quantity includes the first planning position and the first planning speed, the second planning state quantity also includes the second planning position and the second planning speed. The center console calculates the difference between the parameters of the same attribute in the first planning state quantity and the second planning state quantity, and determines the difference between the first planning state quantity and the second planning state quantity based on the difference.

[0049] In some embodiments, the center console can determine the difference between the first planning state quantity and the second planning state quantity based on any one parameter in the first planning state quantity and the second planning state quantity. For example, in the case where the first planning state quantity includes the first planning position and the second planning state quantity includes the second planning position, step S320 can include steps S3210 and S3220.

[0050] In step S3210, the first difference between the first planning position and the second planning position is acquired.

[0051] In the embodiment, the current planning path includes N first planning positions, and the historical planning path includes N second planning positions. The center console calculates the distance between each first planning position and its corresponding second planning position, and determines the sum of the N distances as the first difference between the first planning position and the second planning position.

[0052] It should be noted that the N first planning positions can be sequentially arranged according to their corresponding time points to form a first sequence. The N second planning positions can be sequentially arranged according to their corresponding time points to form a second sequence. Therefore, “the first planning position and the corresponding second planning position” means that when the sequence number of the first planning position in the first sequence is k, the sequence number of the corresponding second planning position in the second sequence is also k.

[0053] In step S3220, the first difference is determined as the difference between the first planning state quantity and the second planning state quantity.

[0054] In this embodiment, the head unit determines the first difference as the difference between the first planning state quantity and the second planning state quantity.

[0055] It should be noted that the above embodiment is only exemplary, and the head unit can also determine the second difference between the first planning speed and the second planning speed as the difference between the first planning state quantity and the second planning state quantity, or determine the third difference between the first planning acceleration and the second planning acceleration as the difference between the first planning state quantity and the second planning state quantity, which is not limited in this embodiment.

[0056] In other embodiments, the head unit can determine the difference between the first planning state quantity and the second planning state quantity based on any two or all three parameters in the first planning state quantity and the second planning state quantity. For example, when the first planning state quantity includes the first planning position, the first planning speed and the first planning acceleration, and the second planning state quantity includes the second planning position, the second planning speed and the second planning acceleration, step S320 can include step S3240 and step S3270.

[0057] In step S3240, the first difference between the first planning position and the second planning position is obtained.

[0058] Specifically, the specific implementation of step S3240 can refer to the detailed introduction in step S3210, which will not be repeated here.

[0059] In step S3250, the second difference between the first planning speed and the second planning speed is obtained.

[0060] In this embodiment, the current planning path includes N first planning speeds, and the historical planning path includes N second planning speeds. The head unit calculates the speed difference between each first planning speed and the corresponding second planning speed, and determines the sum of the N speed differences as the second difference between the first planning speed and the second planning speed.

[0061] It should be noted that the N first planning speeds can be sequentially arranged according to their corresponding time points to form a third sequence. The N second planning speeds can be sequentially arranged according to their corresponding time points to form a fourth sequence. Therefore, “the first planning speed and the corresponding second planning speed” means that when the sequence number of the first planning speed in the third sequence is k, the sequence number of the corresponding second planning speed in the fourth sequence is also k.

[0062] In step S3260, a third difference between the first planning acceleration and the second planning acceleration is obtained.

[0063] In the embodiment, the current planning path includes N first planning accelerations, and the historical planning path includes N second planning accelerations. The central control platform calculates the acceleration difference between each first planning acceleration and the corresponding second planning acceleration, and determines the sum of the N acceleration differences as the third difference between the first planning speed and the second planning speed.

[0064] It should be noted that the N first planning accelerations can be sequentially arranged according to their corresponding time points to form a fifth sequence. The N second planning accelerations can be sequentially arranged according to their corresponding time points to form a sixth sequence. Therefore, “the first planning acceleration and the corresponding second planning acceleration” means that when the sequence number of the first planning acceleration in the fifth sequence is k, the sequence number of the corresponding second planning acceleration in the sixth sequence is also k.

[0065] In step S3270, the sum of the first difference, the second difference and the third difference is determined as the difference between the first planning state quantity and the second planning state quantity.

[0066] In the embodiment, the central control platform determines the sum of the first difference, the second difference and the third difference as the difference between the first planning state quantity and the second planning state quantity.

[0067] It should be noted that the above embodiment is only exemplary, and the central control platform can also determine the sum of any two of the first difference, the second difference and the third difference as the difference between the first planning state quantity and the second planning state quantity. For example, the sum of the first difference and the second difference is determined as the difference between the first planning state quantity and the second planning state quantity; or the sum of the second difference and the third difference is determined as the difference between the first planning state quantity and the second planning state quantity; the sum of the first difference and the third difference is determined as the difference between the first planning state quantity and the second planning state quantity, which is not limited in the embodiment.

[0068] In some possible embodiments, before step S3210 and step S3240, step S320 further includes step S3200.

[0069] Step S3200, obtaining the idle computing resource of the vehicle.

[0070] In this embodiment, the idle computing resource can refer to the available memory space in the processor. The central control console can obtain the idle computing resource of the vehicle by obtaining the current working parameter of the processor.

[0071] In the subsequent steps, the central control console executes steps S3240 to S3270 when the idle computing resource is greater than or equal to a specified value. That is, when the idle computing resource is sufficient, the central control console comprehensively considers multiple items in the first difference, the second difference and the third difference, so that the difference between the determined first planning state quantity and the second planning state quantity can more accurately reflect the deviation of the planning path, and thus the subsequently determined adaptive parameter can be more reasonable. Conversely, the central control console executes steps S3210 to S3220 when the idle computing resource is less than the specified value. Specifically, the specified value can be a default value in the central control console, or can be adjusted by the R&D personnel based on the actual driving conditions of the vehicle, and the present embodiment does not make specific limitations.

[0072] Step S330, determining the adaptive parameter based on the difference between the first planning state quantity and the second planning state quantity.

[0073] In this embodiment, the adaptive parameter and the difference are positively correlated, that is, the greater the difference between the first planning state quantity and the second planning state quantity, the greater the adaptive parameter; conversely, the smaller the difference between the first planning state quantity and the second planning state quantity, the smaller the adaptive parameter.

[0074] As an implementation manner, the central control console can pre-store a mapping relationship between the difference between the first planning state quantity and the second planning state quantity and the adaptive parameter, which can be induced and summarized by the R&D personnel based on a large amount of experimental data. The central control console can determine the adaptive parameter based on the above mapping relationship when the difference between the first planning state quantity and the second planning state quantity is determined. Specifically, the above mapping relationship can be embodied by a mapping table or a mapping function, and the present embodiment does not make specific limitations.

[0075] Step S340, obtaining the actual state quantity.

[0076] Specifically, the specific implementation manner of step S340 can refer to the detailed introduction in step S230, which will not be repeated here.

[0077] Step S350, determining the planning control model based on the actual state quantity, the current planning path and the adaptive parameter.

[0078] At step S360, the vehicle is longitudinally controlled based on the planning control model.

[0079] The specific implementation of steps S350 and S360 is described in detail in the following embodiments.

[0080] The embodiment provides a control method of a vehicle, and the determination process of the adaptive parameter is described in detail in the embodiment. Since the adaptive parameter for describing the fluctuation degree of the planning path is added in the planning control model in the application, when the planning control model is optimized and solved, the adaptive parameter will constrain the determined control quantity to some extent, thereby reducing the influence of large fluctuations of the planning path on the control quantity, improving the anti-interference ability of the control system of the vehicle to the fluctuations of the planning path, and making the longitudinal control of the vehicle more stable.

[0081] Please refer to Figure 4 , Figure 4 The control method of the vehicle provided in the third embodiment of the application is schematically shown, and the planning control model and the longitudinal control process of the vehicle are described in detail in the embodiment. The current planning path includes N first planning state quantities and N corresponding planning acceleration control quantities, the N predicted accelerations included in the control sequence are sequentially arranged according to their corresponding time, and the planning control model is characterized by a cost function. Specifically, the method can include steps S410 to S490.

[0082] At step S410, the current planning path and the historical planning path are obtained.

[0083] At step S420, the adaptive parameter is determined based on the current planning path and the historical planning path.

[0084] At step S430, the actual state quantity is obtained.

[0085] Specifically, the specific implementation of steps S410 to S430 can refer to the detailed description in steps S210 to S230, which will not be repeated here.

[0086] At step S440, the control sequence is obtained.

[0087] The control sequence includes N predicted acceleration control quantities. The N predicted acceleration control quantities represent the predicted acceleration control quantities of the center console at different times after the vehicle travels at the current position. Specifically, the N predicted acceleration control quantities are sequentially arranged according to their corresponding time. As an implementation, the center console can determine a default value as the initial value of the control sequence. The center console can also determine the control sequence determined in the historical iteration process as the control sequence in the current iteration process, and the embodiment does not specifically limit the determination manner of the control sequence.

[0088] Step S450, determining N-1 predicted state quantities based on the control sequence and the actual state quantity.

[0089] In this embodiment, the central control console can determine the N-1 predicted state quantities based on a preset vehicle dynamics model. The vehicle dynamics model can be pre-stored in the central control console, and specifically, the vehicle dynamics model is as follows.

[0090] x k+1 = Ax k + Bu k + c.

[0091] wherein x k represents the kth predicted state quantity, and when k is equal to 1, x1 is the actual state quantity obtained by the central control console. Specifically, wherein s represents the kth predicted position, v represents the kth predicted speed, and a represents the kth predicted acceleration. When k is equal to 1, s is the actual position, v is the actual speed, and a is the actual acceleration. k u represents the kth predicted acceleration control quantity, i.e., the input quantity. x k+1 represents the k+1 predicted state quantity. A and B are respectively a state matrix and an input matrix with known parameters, and c is a disturbance matrix for representing the disturbance of external noise on the predicted state quantity, and the disturbance matrix c can be measured.

[0092] Here, the determination process of the predicted state quantity is introduced with N taking a value of 3. At this time, the control sequence includes three predicted acceleration control quantities, i.e., u1, u2 and u3. Since x1 is a known quantity, i.e., the actual state quantity, therefore, the two predicted state quantities (x2 and x3) to be determined by the central control console can be represented by the following formulas.

[0093] x2 = Ax1 + Bu1 + c;

[0094] x3 = Ax2 + Bu2 + c = A(Ax1 + Bu1 + c) + Bu2 + c.

[0095] Therefore, under the condition that x1, A, B and c are known, the two predicted state quantities (x2 and x3) can be represented by the control sequence.

[0096] Step S460, determining a planning control model based on the actual state quantity, the N-1 predicted state quantities, the N first planning state quantities, the N planning acceleration control quantities, the control sequence and the adaptive parameter.

[0097] In this embodiment, the planning control model is represented by a cost function. Specifically, step S460 can include steps S4610 to S4660.

[0098] In step S4610, the first planning term is determined based on the actual state quantity, the N-1 predicted state quantities, and the N first planning state quantities.

[0099] As an implementation, the central control console can first determine a first error between the actual state quantity and the N-1 predicted state quantities and the corresponding N first planning state quantities. Then, a product between the first error and a first scale factor is determined as the first planning term. Specifically, the first planning term corresponds to the following calculation formula.

[0100]

[0101] wherein J1 is the first planning term, ref k is the kth first planning state quantity, x k is the actual state quantity; when k is greater than 1, x k is the kth predicted state quantity. That is, ref k -x k is the first error between the actual state quantity and the N-1 predicted state quantities and the corresponding N first planning state quantities. Q is the first scale factor, wherein Q can be represented by a matrix form, and the values of Q are all greater than 0, that is, the first planning term is positively correlated with the first error. Specifically, Q can be a default parameter, or can be adjusted by the R&D personnel based on the actual driving condition of the vehicle.

[0102] In step S4620, the second planning term is determined based on the N planning acceleration control quantities and the corresponding N predicted acceleration control quantities in the control sequence.

[0103] As an implementation, the central control console can first determine a second error between the N planning acceleration control quantities and the corresponding N predicted acceleration control quantities in the control sequence. Then, a product between the second error and a second scale factor is determined as the second planning term. Specifically, the second planning term corresponds to the following calculation formula.

[0104]

[0105] wherein J2 is the second planning term, u r is the planning acceleration control quantity, u k is the kth predicted acceleration control quantity. That is, u r -u k is the second error between the N planning acceleration control quantities and the corresponding N predicted acceleration control quantities in the control sequence. R is the second scale factor, wherein R can be represented by a matrix form, and the values of R are all greater than 0, that is, the second planning term is positively correlated with the second error. Specifically, R can be a default parameter, or can be adjusted by the R&D personnel based on the actual driving condition of the vehicle.

[0106] Step S4630, determining a third planning item based on a difference between the predicted accelerations of any two adjacent time points in the control sequence.

[0107] As an implementation manner, the central control console can determine a product between the difference between the predicted accelerations of any two adjacent time points in the control sequence and the second proportional factor as the third planning item. Specifically, the calculation formula corresponding to the third planning item is as shown below.

[0108]

[0109] wherein, J3 is the third planning item, Δu k is the difference between the predicted accelerations of any two adjacent time points in the control sequence, that is, Δu k = u k+1 - u k . R is the second proportional factor, wherein R can be represented in the form of a matrix, and the values of R are all greater than 0, that is, the third planning item is positively correlated with Δu k .

[0110] Step S4640, determining a fourth planning item based on a ratio between the actual state quantity and the N-1 predicted state quantities and an adaptive parameter.

[0111] Specifically, the calculation formula corresponding to the fourth planning item is as shown below.

[0112]

[0113] wherein, J4 is the fourth planning item, γ is the adaptive parameter, x k is the actual state quantity when k is equal to 1; x k is the kth predicted state quantity when k is greater than 1.

[0114] Step S4650, determining a fifth planning item based on a product between a difference between any two adjacent state quantities in the actual state quantity and the N-1 predicted state quantities and an adaptive parameter.

[0115] Specifically, the calculation formula corresponding to the fifth planning item is as shown below.

[0116] J5 = γd T d.

[0117] wherein, J5 is the fifth planning item, γ is the adaptive parameter, and d is the difference between any two adjacent state quantities in the actual state quantity and the N-1 predicted state quantities, that is, d = x k+1 - x k .

[0118] Step S4660, determine the sum of the first planning item, the second planning item, the third planning item, the fourth planning item and the fifth planning item as the planning control model.

[0119] In this embodiment, the planning control model is characterized by a cost function. After determining the first planning item, the second planning item, the third planning item, the fourth planning item and the fifth planning item, the central control console determines the sum of the first planning item, the second planning item, the third planning item, the fourth planning item and the fifth planning item as the cost function. Specifically, the calculation formula corresponding to the cost function is as follows.

[0120]

[0121] s.t.x k+1 =Ax k +Bu k +c,

[0122] x lb ≤x k ≤x ub ,u lb ≤u k ≤u ub ,Δu lb ≤Δu k ≤Δu ub ,0≤γ≤γ ub 。

[0123] Wherein, J represents the cost function value corresponding to the cost function, x lb and x ub are the lower limit value and the upper limit value of x k , respectively, u lb and u ub are the lower limit value and the upper limit value of u k , respectively, Δu lb and Δu ub are the lower limit value and the upper limit value of Δu l , respectively, 0 and γ ub are the lower limit value and the upper limit value of γ, respectively.

[0124] Step S470, optimize and solve the control sequence and the adaptive parameter in the planning control model to determine the target control sequence and the target adaptive parameter.

[0125] In this embodiment, the central control console can optimize and solve the control sequence based on a preset iterative optimization algorithm, and then determine the target control sequence and the target adaptive parameter. Specifically, the iterative optimization algorithm can be Newton method, gradient descent algorithm, etc., which is not limited in the embodiment.

[0126] Step S480, in a case where the target adaptive parameter is less than or equal to a first preset value and the cost function value corresponding to the cost function is less than or equal to a second preset value, determining the first value in the target control sequence as the target acceleration control quantity.

[0127] In the embodiment, the head unit determines the first value in the target control sequence as the target acceleration control quantity in a case where the target adaptive parameter is less than or equal to a first preset value and the cost function value corresponding to the cost function is less than or equal to a second preset value. The first preset value and the second preset value can be default values in the head unit, or can be adjusted by the R&D personnel based on the optimization accuracy of the control sequence. Specifically, the higher the optimization accuracy of the control sequence, the smaller the first preset value and the second preset value, which are not specifically limited in the embodiment.

[0128] On the contrary, the head unit repeatedly executes step S470 in a case where the target adaptive parameter is greater than the first preset value or the cost function value corresponding to the cost function is greater than the second preset value.

[0129] Step S490, performing longitudinal control on the vehicle based on the target acceleration control quantity.

[0130] In the embodiment, the head unit can send the target acceleration control quantity to the execution system of the vehicle, and the execution system works based on the target acceleration control quantity to achieve the longitudinal control on the vehicle.

[0131] The embodiment provides a control method of a vehicle, and the planning control model and the longitudinal control process of the vehicle are introduced in detail in the embodiment. Since the adaptive parameter for describing the fluctuation degree of the planning path is added in the planning control model in the embodiment, the adaptive parameter can constrain the determined control quantity when the planning control model is optimized and solved, thereby reducing the influence of the large fluctuation of the planning path on the control quantity, improving the anti-interference ability of the control system of the vehicle to the fluctuation of the planning path, and making the longitudinal control of the vehicle more stable.

[0132] Please refer to Figure 5 , Figure 5A control device 500 of a vehicle is shown schematically. The control device 500 of the vehicle includes a first obtaining module 510, a first determining module 520, a second obtaining module 530, a second determining module 540, and a control module 550. The first obtaining module 510 is configured to obtain a current planning path and a historical planning path. The current planning path represents a planning path determined when the vehicle is at an actual position. The historical planning path represents a planning path determined when the vehicle is at a historical position. The vehicle travels to the historical position at a time earlier than the vehicle travels to the actual position. The first determining module 520 is configured to determine an adaptive parameter based on the current planning path and the historical planning path. The adaptive parameter represents a deviation between the current planning path and the historical planning path. The second obtaining module 530 is configured to obtain an actual state quantity. The actual state quantity includes at least one of an actual position, an actual speed, and an actual acceleration of the vehicle. The second determining module 540 is configured to determine a planning control model based on the actual state quantity, the current planning path, and the adaptive parameter. The control module 550 is configured to perform longitudinal control on the vehicle based on the planning control model.

[0133] In some embodiments, the current planning path includes N first planning state quantities, and the historical planning path includes N second planning state quantities. The first determining module 520 is further configured to obtain a difference between the first planning state quantity and the second planning state quantity; and determine the adaptive parameter based on the difference between the first planning state quantity and the second planning state quantity. The adaptive parameter and the difference are in a positive correlation.

[0134] In some embodiments, each of the first planning state quantity includes a first planning position, a first planning speed, and a first planning acceleration. Each of the second planning state quantity includes a second planning position, a second planning speed, and a second planning acceleration. The first determining module 520 is further configured to obtain a first difference between the first planning position and the second planning position; obtain a second difference between the first planning speed and the second planning speed; obtain a third difference between the first planning acceleration and the second planning acceleration; and determine a sum of the first difference, the second difference, and the third difference as the difference between the first planning state quantity and the second planning state quantity.

[0135] In some embodiments, the current planning path includes N first planning state quantities and N planning acceleration control quantities. The second determining module 540 is further configured to obtain a control sequence including N predicted acceleration control quantities; determine N-1 predicted state quantities based on the control sequence and the actual state quantity; and determine the planning control model based on the actual state quantity, the N-1 predicted state quantities, the N first planning state quantities, the N planning acceleration control quantities, the control sequence, and the adaptive parameter.

[0136] In some embodiments, the second determining module 540 is further configured to determine a first planning item based on the actual state quantity, the N-1 predicted state quantities and the N first planning state quantities; determine a second planning item based on the N planning acceleration control quantities and the control sequence; determine a third planning item based on a difference between any two adjacent predicted accelerations in the control sequence; determine a fourth planning item as a ratio between the actual state quantity and the N-1 predicted state quantities and the adaptive parameter; determine a fifth planning item as a product between a difference between any two adjacent state quantities in the actual state quantity and the N-1 predicted state quantities and the adaptive parameter; and determine the planning control model as a sum of the first planning item, the second planning item, the third planning item, the fourth planning item and the fifth planning item.

[0137] In some embodiments, the second determining module 540 is further configured to determine a first error between the actual state quantity and the N-1 predicted state quantities and the corresponding N first planning state quantities; determine a first planning item as a product between the first error and a first scale factor; determine a second error between the N planning acceleration control quantities and the corresponding N predicted acceleration control quantities in the control sequence; determine a second planning item as a product between the second error and a second scale factor; and determine a third planning item as a product between a difference between any two adjacent predicted accelerations in the control sequence and the second scale factor.

[0138] In some embodiments, the N predicted accelerations in the control sequence are arranged in a time sequence according to their corresponding time points, the planning control model is characterized by a cost function, and the control module 550 is further configured to optimize and solve the control sequence and the adaptive parameter in the planning control model to determine a target control sequence and a target adaptive parameter; in a case where the target adaptive parameter is less than or equal to a first preset value and a cost function value corresponding to the cost function is less than or equal to a second preset value, determine a first value in the target control sequence as the target acceleration control quantity; and perform longitudinal control on the vehicle based on the target acceleration control quantity.

[0139] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described devices and modules can refer to the corresponding process in the foregoing method embodiments, which will not be described herein.

[0140] In the several embodiments provided in the present application, the coupling between the modules can be electrical, mechanical or other forms of coupling.

[0141] In addition, each functional module in each embodiment of the present application can be integrated in one processing module, or each module can exist physically independently, or two or more modules can be integrated in one module. The above integrated module can be realized in the form of hardware or in the form of a software functional module.

[0142] The embodiment provides a control device of a vehicle, which describes a planning path fluctuation degree by obtaining a deviation between a current planning path and a historical planning path, and determines an adaptive parameter based on the deviation. Finally, the vehicle determines a planning control model based on the current planning path, the adaptive parameter and an actual state quantity, and realizes longitudinal control of the vehicle based on the planning control model. Since the adaptive parameter for describing the planning path fluctuation degree is added to the planning control model in the embodiment, when the planning control model is optimized and solved, the adaptive parameter will constrain the determined control quantity to some extent, thereby reducing the influence of large fluctuation of the planning path on the control quantity, improving the anti-interference ability of the control system of the vehicle to the planning path fluctuation, and making the longitudinal control of the vehicle more stable.

[0143] Please refer to Figure 6 , Figure 6 The embodiment of the application also provides a vehicle 600, which comprises one or more processors 610, a memory 620 and one or more application programs. The one or more application programs are stored in the memory and configured to be executed by the one or more processors, and the one or more application programs are configured to execute the method described in the above embodiment.

[0144] The processor 610 can include one or more processing cores. The processor 610 connects various parts in the battery management system through various interfaces and lines, and performs various functions of the battery management system and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 620, and calling data stored in the memory 620. Optionally, the processor 610 can be realized in at least one of a hardware form of a digital signal processing (Digital Signal Processing, DSP), a field-programmable gate array (Field-Programmable Gate Array, FPGA) and a programmable logic array (Programmable Logic Array, PLA). The processor 610 can be integrated with a combination of one or more of a central processing unit (Central Processing Unit, CPU), a graphics processor (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes operating systems, user interfaces and application programs; the GPU is responsible for rendering and drawing display content; and the modem is used for processing wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 610, but can be realized by a separate communication chip.

[0145] The memory 620 can include a random access memory (RAM) and can also include a read-only memory (ROM). The memory 620 can be used to store instructions, programs, codes, code sets, or instruction sets. The memory 620 can include a program storage area and a data storage area, where the program storage area can store instructions for implementing an operating system, instructions for implementing at least one function (e.g., a touch function, a sound playing function, an image playing function, etc.), instructions for implementing various method embodiments described above, etc. The data storage area can also store data created by the electronic device in use (e.g., a phonebook, audio / video data, chat log data), etc.

[0146] Referring to Figure 7 , Figure 7 The computer readable storage medium 700 can be, for example, a flash memory, an electrically erasable programmable read-only memory (EEPROM), an electrically programmable read-only memory (EPROM), a hard disk, or a read-only memory (ROM). Alternatively, the computer readable storage medium includes a non-transitory computer readable storage medium. The computer readable storage medium 700 has a storage space for the computer program instructions 710 to execute any of the method steps described above. These computer program instructions 710 can be read out from or written into one or more computer program products.

[0147] The computer readable storage medium 700 can be, for example, a flash memory, an electrically erasable programmable read-only memory (EEPROM), an electrically programmable read-only memory (EPROM), a hard disk, or a read-only memory (ROM). Alternatively, the computer readable storage medium includes a non-transitory computer readable storage medium. The computer readable storage medium 700 has a storage space for the computer program instructions 710 to execute any of the method steps described above. These computer program instructions 710 can be read out from or written into one or more computer program products.

[0148] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application in any form. Although the present application has been disclosed with the preferred embodiments as above, it is not intended to limit the present application, and any person skilled in the art can make some more changes or modifications to the equivalent embodiments with the disclosed technical content, as long as the changes or modifications do not deviate from the technical solution of the present application. Any brief modification, equivalent change and modification made to the above embodiments according to the technical essence of the present application still falls within the scope of the technical solution of the present application.

Claims

1. A control method of a vehicle, characterized by, The method comprises: acquiring a current planning path and a historical planning path, the current planning path representing a planning path determined when the vehicle is at an actual position, and the historical planning path representing a planning path determined when the vehicle is at a historical position, the time when the vehicle travels to the historical position being earlier than the time when the vehicle travels to the actual position; wherein the current planning path comprises N first planning state quantities and corresponding N planning acceleration control quantities; determining an adaptive parameter based on the current planning path and the historical planning path, the adaptive parameter representing a deviation between the current planning path and the historical planning path; acquiring an actual state quantity, the actual state quantity comprising at least one of the following: an actual position, an actual speed and an actual acceleration of the vehicle; determining a planning control model based on the actual state quantity, the current planning path and the adaptive parameter; wherein the determination of the planning control model based on the actual state quantity, the current planning path and the adaptive parameter comprises: acquiring a control sequence, the control sequence comprising N predicted acceleration control quantities; determining N-1 predicted state quantities based on the control sequence and the actual state quantity; and determining the planning control model based on the actual state quantity, N-1 predicted state quantities, N first planning state quantities, N planning acceleration control quantities, the control sequence and the adaptive parameter; performing longitudinal control on the vehicle based on the planning control model.

2. The method of claim 1, wherein, The current planning path comprises N first planning state quantities, and the historical planning path comprises N second planning state quantities; the determination of the adaptive parameter based on the current planning path and the historical planning path comprises: acquiring a difference between the first planning state quantities and the second planning state quantities; determining the adaptive parameter based on the difference between the first planning state quantities and the second planning state quantities; wherein the adaptive parameter and the difference are in a positive correlation.

3. The method of claim 2, wherein, Each of the first planning state quantities comprises a first planning position, a first planning speed and a first planning acceleration; Each of the second planning state quantities comprises a second planning position, a second planning speed and a second planning acceleration; the acquisition of the difference between the first planning state quantities and the second planning state quantities comprises: acquiring a first difference between the first planning position and the second planning position; acquiring a second difference between the first planning speed and the second planning speed; acquiring a third difference between the first planning acceleration and the second planning acceleration; determining the sum of the first difference, the second difference and the third difference as the difference between the first planning state quantities and the second planning state quantities.

4. The method of claim 1, wherein, The determination of the planning control model based on the actual state quantity, N-1 predicted state quantities, N first planning state quantities, N planning acceleration control quantities, the control sequence and the adaptive parameter comprises: determine a first planning item based on the actual state quantity, N-1 prediction state quantities and N first planning state quantities; determine a second planning item based on N planning acceleration control quantities and the control sequence; determine a third planning item based on a difference between adjacent prediction accelerations in the control sequence; determine a fourth planning item as a ratio between the actual state quantity and N-1 prediction state quantities and the adaptive parameter; determine a fifth planning item as a product between a difference between adjacent state quantities in the actual state quantity and N-1 prediction state quantities and the adaptive parameter; determine a sum of the first planning item, the second planning item, the third planning item, the fourth planning item and the fifth planning item as the planning control model.

5. The method of claim 4, wherein, The determination of the first planning item based on the actual state quantity, N-1 prediction state quantities and N first planning state quantities comprises: determining a first error between the actual state quantity and N-1 prediction state quantities and corresponding N first planning state quantities; determining a product between the first error and a first proportional factor as the first planning item; The determination of the second planning item based on N planning acceleration control quantities and the control sequence comprises: determining a second error between N planning acceleration control quantities and corresponding N prediction acceleration control quantities in the control sequence; determining a product between the second error and a second proportional factor as the second planning item; The determination of the third planning item based on a difference between adjacent prediction accelerations in the control sequence comprises: determining a product between the difference between adjacent prediction accelerations in the control sequence and the second proportional factor as the third planning item.

6. The method of claim 1, wherein, The N prediction accelerations included in the control sequence are arranged in a sequential order according to their corresponding time, the planning control model is characterized by a cost function, and the longitudinal control of the vehicle based on the planning control model comprises: optimizing and solving the control sequence and the adaptive parameter in the planning control model to determine a target control sequence and a target adaptive parameter; determining a first value in the target control sequence as a target acceleration control quantity in a case where the target adaptive parameter is less than or equal to a first preset value and a cost function value corresponding to the cost function is less than or equal to a second preset value; performing longitudinal control of the vehicle based on the target acceleration control quantity.

7. A control device of a vehicle characterized by comprising: The device comprises: a first obtaining module configured to obtain a current planning path and a historical planning path, the current planning path representing a planning path determined when the vehicle is at an actual position, and the historical planning path representing a planning path determined when the vehicle is at a historical position, the time when the vehicle travels to the historical position being earlier than the time when the vehicle travels to the actual position; wherein the current planning path comprises N first planning state quantities and corresponding N planning acceleration control quantities; The first determining module is configured to determine an adaptive parameter based on the current planning path and the historical planning path, the adaptive parameter representing a deviation between the current planning path and the historical planning path. The second obtaining module is configured to obtain an actual state quantity, the actual state quantity including at least one of an actual position, an actual speed, and an actual acceleration of the vehicle. The second determining module is configured to determine a planning control model based on the actual state quantity, the current planning path, and the adaptive parameter, wherein the second determining module is specifically configured to obtain a control sequence, the control sequence including N predicted acceleration control quantities; determine N-1 predicted state quantities based on the control sequence and the actual state quantity; and determine the planning control model based on the actual state quantity, N-1 predicted state quantities, N first planning state quantities, N planning acceleration control quantities, the control sequence, and the adaptive parameter. The control module is configured to perform longitudinal control on the vehicle based on the planning control model.

8. A vehicle characterized by comprising: The computer readable storage medium stores computer program instructions, and the computer program instructions can be invoked by a processor to execute the method according to any one of claims 1-6. The computer readable storage medium stores computer program instructions, and the computer program instructions can be invoked by a processor to execute the method according to any one of claims 1-6. ​ ​ 9. A computer-readable storage medium, characterized in that, ​

Images