Vehicle longitudinal control method, device, storage medium and autonomous driving vehicle

Abstract

The present disclosure provides a method, device, storage medium and autonomous driving vehicle for longitudinal control of a vehicle, which relates to the field of computer technology, and more specifically to the field of artificial intelligence technology such as intelligent transportation and autonomous driving. The specific implementation scheme is as follows: when performing longitudinal control of the vehicle for autonomous driving, the predicted longitudinal speed of the vehicle at each predicted time point in the predicted time domain after the current moment is combined to determine the predicted longitudinal control parameters of the vehicle's model predictive controller at each predicted time point in the predicted time domain, and the longitudinal control amount at each predicted time point in the predicted time domain is determined in combination with the longitudinal control parameter information, the longitudinal model in the model predictive controller and the expected trajectory of the vehicle, and the longitudinal control amount at each predicted time point in the predicted time domain is also determined, and the longitudinal control of the vehicle is performed based on the longitudinal control amount at the first predicted time point in the predicted time domain. In this way, the accuracy and stability of the longitudinal control of the vehicle are improved, thereby improving the safety of the vehicle's autonomous driving.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, specifically to the field of artificial intelligence technology such as intelligent transportation and autonomous driving, and more particularly to a longitudinal control method, device, storage medium, and autonomous driving vehicle for a vehicle. Background Art

[0002] With the development of science and technology, self-driving vehicles have become an important development direction of future automobiles. Self-driving vehicles can not only help improve people's travel convenience and travel experience, but also greatly improve people's travel efficiency.

[0003] In the process of longitudinal control of an autonomous vehicle, related technologies generally use a longitudinal control variable determined by the autonomous driving system to control the vehicle's longitudinal direction. The accuracy of the longitudinal control variable is crucial to the safe operation of the autonomous vehicle. Summary of the Invention

[0004] The present disclosure provides a longitudinal control method, device, storage medium and autonomous driving vehicle for a vehicle.

[0005] According to one aspect of the present disclosure, a method for longitudinal control of a vehicle is provided, the method comprising: determining a predicted longitudinal velocity sequence of the vehicle in a predicted time domain after a current moment, wherein the predicted longitudinal velocity sequence comprises: predicted longitudinal velocities at each predicted time point; for each predicted time point, determining longitudinal control parameter information of a model predictive controller of the vehicle at the predicted time point based on the predicted longitudinal velocity at the predicted time point; determining a longitudinal control quantity sequence in the predicted time domain based on the longitudinal control parameter information, a longitudinal model in the model predictive controller, and an expected trajectory of the vehicle, wherein the longitudinal control quantity sequence comprises: longitudinal control quantities at each predicted time point; and performing autonomous driving longitudinal control on the vehicle based on the longitudinal control quantity at the first predicted time point in the predicted time domain.

[0006] According to another aspect of the present disclosure, a longitudinal control device for a vehicle is provided, the device comprising: a first determination module for determining a predicted longitudinal velocity sequence of the vehicle in a prediction domain after a current moment, wherein the predicted longitudinal velocity sequence comprises: predicted longitudinal velocities at each prediction time point; a second determination module for determining, for each prediction time point, longitudinal control parameter information of a model predictive controller of the vehicle at the prediction time point based on the predicted longitudinal velocity at the prediction time point; a third determination module for determining a longitudinal control quantity sequence in the prediction domain based on the longitudinal control parameter information, a longitudinal model in the model predictive controller, and an expected trajectory of the vehicle, wherein the longitudinal control quantity sequence comprises: longitudinal control quantities at each prediction time point; and a control module for performing autonomous driving longitudinal control of the vehicle based on the longitudinal control quantity at the first prediction time point in the prediction domain.

[0007] According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the longitudinal control method of a vehicle of the present disclosure.

[0008] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the longitudinal control method of a vehicle disclosed in an embodiment of the present disclosure.

[0009] According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the longitudinal control method of a vehicle of the present disclosure is implemented.

[0010] According to another aspect of the present disclosure, an autonomous driving vehicle is provided, which includes the electronic device disclosed in the embodiment of the present disclosure.

[0011] One embodiment of the above application has the following advantages or beneficial effects:

[0012] When performing longitudinal control of a vehicle for autonomous driving, the predicted longitudinal speed of the vehicle at each prediction time point in the prediction time domain after the current moment is combined to determine the predicted longitudinal control parameters of the vehicle's model predictive controller at each prediction time point in the prediction time domain. Furthermore, the longitudinal control amount at each prediction time point in the prediction time domain is determined by combining the longitudinal control parameter information, the longitudinal model in the model predictive controller, and the desired trajectory of the vehicle. The longitudinal control amount is then used to perform longitudinal control of the vehicle based on the longitudinal control amount at the first prediction time point in the prediction time domain. Thus, by combining the predicted longitudinal speed at each prediction time point in the prediction time domain, the longitudinal control amount for controlling the vehicle's longitudinal direction is accurately determined, improving the accuracy and stability of the vehicle's longitudinal control and, in turn, enhancing the safety of the vehicle's autonomous driving.

[0013] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The accompanying drawings are provided to facilitate a better understanding of the present invention and do not constitute a limitation of the present disclosure.

[0015] Figure 1 is a schematic diagram according to a first embodiment of the present disclosure;

[0016] Figure 2 is a schematic diagram according to a second embodiment of the present disclosure;

[0017] Figure 3 is a schematic diagram according to a third embodiment of the present disclosure;

[0018] Figure 4 is a schematic diagram according to a fourth embodiment of the present disclosure;

[0019] Figure 5 is a schematic diagram according to a fifth embodiment of the present disclosure;

[0020] Figure 6 is a schematic diagram according to a sixth embodiment of the present disclosure;

[0021] Figure 7 is a schematic diagram according to a seventh embodiment of the present disclosure;

[0022] Figure 8 It is a block diagram of an electronic device for implementing the longitudinal control method of a vehicle according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0023] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0024] The following describes the longitudinal control method, device, storage medium and autonomous driving vehicle of the vehicle according to the embodiments of the present disclosure with reference to the accompanying drawings.

[0025] Figure 1 is a schematic diagram according to a first embodiment of the present disclosure, which provides a longitudinal control method for a vehicle.

[0026] like Figure 1 As shown, the longitudinal control method of the vehicle may include:

[0027] Step 101 : determining a predicted longitudinal velocity sequence of the vehicle in a predicted time domain after a current moment, wherein the predicted longitudinal velocity sequence includes: predicted longitudinal velocities at various predicted time points.

[0028] Among them, the executor of the vehicle longitudinal control method implemented in this embodiment is the vehicle's longitudinal control device, which can be implemented by software and / or hardware. The vehicle's longitudinal control device can be an electronic device, or can be configured in an electronic device to achieve automatic driving control of the vehicle.

[0029] In some exemplary implementations, the electronic device may be a terminal device or a server, etc., which is not specifically limited in this embodiment.

[0030] In some exemplary embodiments, the terminal device may be a device that can be installed in any vehicle, such as an onboard controller or a vehicle-mounted computer. In some examples, the vehicle's longitudinal control device may be an onboard computer. It should be noted that the onboard computer in this example has an autonomous driving function, capable of path planning and autonomous driving control of the vehicle. In other examples, the vehicle's longitudinal control device may be a server that can communicate with the vehicle and perform autonomous driving control of the vehicle.

[0031] In some embodiments of the present disclosure, while a vehicle is in motion, in order to accurately perform longitudinal control of the vehicle for autonomous driving and improve the safety and stability of vehicle driving, the speed of the vehicle in a predicted time domain after the current moment can be predicted based on the current vehicle state of the vehicle at the current moment, so as to obtain the predicted longitudinal speed of the vehicle at each predicted time point in the predicted time domain after the current moment, and the predicted longitudinal speeds can be sorted according to the chronological order of the predicted time points to obtain a predicted longitudinal speed sequence of the vehicle in the predicted time domain.

[0032] The prediction time domain refers to a period of time after the current moment, which may include N prediction time points, and there is a preset time interval between two adjacent prediction time points.

[0033] The preset time interval is pre-set and can be set based on actual needs. For example, the prediction time domain may include 25 prediction time points, and the time interval between two adjacent prediction time points may be 1 second. For another example, the prediction time domain may include 10 prediction time points, and the time interval between two adjacent prediction time points may be 2 seconds.

[0034] In some exemplary embodiments, in order to accurately control the longitudinal direction of the vehicle, the current speed of the vehicle at the current moment may be combined to determine the length of the prediction time domain and the time interval between prediction time points.

[0035] Step 102 : For each prediction time point, longitudinal control parameter information of a model predictive controller of the vehicle at the prediction time point is determined according to the predicted longitudinal speed at the prediction time point.

[0036] In other exemplary embodiments, after determining the predicted longitudinal speed at each prediction time point, for each prediction time point, based on the pre-saved correspondence between the longitudinal speed and the longitudinal control parameter information, the longitudinal control parameter information corresponding to the predicted longitudinal speed at the prediction time point is determined, and the determined longitudinal control parameter information is used as the longitudinal control parameter information at the prediction time point.

[0037] The longitudinal control parameter information may include a first penalty weight applied to a state quantity error, a second penalty weight applied to a control quantity, and a second penalty weight applied to a control quantity increment.

[0038] Step 103 : determining a longitudinal control amount sequence in a prediction time domain according to the longitudinal control parameter information, the longitudinal model in the model predictive controller, and the desired trajectory of the vehicle, wherein the longitudinal control amount sequence includes the longitudinal control amount at each prediction time point.

[0039] Among them, the Model Predictive Control (MPC) is used to predict the longitudinal model, the expected trajectory and the longitudinal vehicle state parameters of the vehicle at the current moment in the MPC to obtain the longitudinal control amount at each prediction time point in the prediction time domain, so as to perform automatic driving control processing on the vehicle based on the longitudinal control amount at the first prediction time point in the prediction time domain.

[0040] The model predictive controller uses an advanced process control strategy for prediction. For example, it first randomly determines a control variable sequence, then determines a predicted trajectory based on this control variable sequence. Based on the predicted trajectory and the desired trajectory, it adjusts the control variable sequence. This process is repeated until the difference between the predicted and desired trajectories meets a specified condition.

[0041] It should be noted that the aforementioned longitudinal model, which can also be referred to as a longitudinal state equation model, is used to predict the longitudinal vehicle state parameters of the vehicle. Specifically, after a first longitudinal vehicle state parameter and a corresponding longitudinal control variable are input into the longitudinal model, the longitudinal model can predict a second longitudinal vehicle state parameter that the vehicle will enter after the longitudinal control variable is applied to the vehicle under the first longitudinal vehicle state parameter.

[0042] The longitudinal vehicle state parameters in this embodiment may include longitudinal displacement, longitudinal velocity, longitudinal torque, etc.

[0043] In this embodiment, the longitudinal control amount may include a driving control amount and / or a braking control amount.

[0044] The state quantity error mentioned above refers to the error between the expected state quantity and the predicted state quantity at the prediction time point.

[0045] The expected trajectory refers to the trajectory that the vehicle is expected to achieve in the predicted time domain when planning the vehicle.

[0046] Among them, the predicted trajectory refers to the prediction of the vehicle's operating status at each predicted time point within the prediction time domain based on the vehicle's actual operating conditions at the current moment, and the trajectory determined based on the predicted operating status of the vehicle at each predicted time point.

[0047] Step 104 , performing autonomous driving longitudinal control on the vehicle based on the longitudinal control amount at the first prediction time point within the prediction time domain.

[0048] In some exemplary embodiments of the present disclosure, after determining a sequence of longitudinal control quantities of a vehicle in a prediction time domain, the longitudinal control quantity at the first prediction time point in the prediction time domain may be used as the longitudinal control quantity of the vehicle at the next moment, and based on the longitudinal control quantity, the vehicle may be longitudinally controlled at the next moment.

[0049] In an exemplary embodiment of the present disclosure, the predicted time points within the prediction time domain can be sorted in chronological order to obtain a sorted result, wherein the first predicted time point is the time point ranked first in the sorted result. For example, the sorted result is t1, t2, t3, t4, t5, where t1 is the first predicted time point.

[0050] In some exemplary embodiments, the time interval between the current moment and the next moment is the same as the time interval between adjacent predicted time points. When performing longitudinal control at the next moment, since the next moment is the first predicted time point in the prediction time domain, the longitudinal control variable at the first predicted time point in the prediction time domain can be used as the longitudinal control variable for performing longitudinal control of the vehicle at the next moment.

[0051] The longitudinal control method for a vehicle of an embodiment of the present disclosure, when performing longitudinal control of the vehicle for autonomous driving, combines the predicted longitudinal speed of the vehicle at each predicted time point in the predicted time domain after the current moment to determine the predicted longitudinal control parameters of the vehicle's model predictive controller at each predicted time point in the predicted time domain, and combines the longitudinal control parameter information, the longitudinal model in the model predictive controller, and the desired trajectory of the vehicle to determine the longitudinal control amount at each predicted time point in the predicted time domain, and performs longitudinal control of the vehicle based on the longitudinal control amount at the first predicted time point in the predicted time domain. Thus, the longitudinal control amount for longitudinal control of the vehicle is accurately determined in combination with the predicted longitudinal speed at each predicted time point in the predicted time domain, thereby improving the accuracy and stability of the vehicle's longitudinal control, and thereby improving the safety of the vehicle's autonomous driving.

[0052] In some embodiments, in order to accurately determine the longitudinal control parameter information at each prediction time point in the prediction time domain, a possible implementation of the above step 102 is as follows: Figure 2 As shown, this may include:

[0053] Step 201 : determining a first penalty weight of a state quantity error of a model predictive controller of a vehicle at a prediction time point based on the predicted longitudinal speed at the prediction time point.

[0054] In some exemplary embodiments, the longitudinal control parameter may include a first penalty weight, where the first penalty weight refers to a penalty weight applied by the vehicle's model predictive controller to the longitudinal model when performing model predictive control on the longitudinal model, for a state quantity error at the prediction time point.

[0055] In the related art, the penalty weight imposed by the model predictive controller for the state quantity error at each prediction time point is typically fixed. However, since the longitudinal model is typically linear, the longitudinal velocity corresponding to the prediction time point during the optimization process may be negative. In reality, no matter how large the longitudinal control variable (e.g., braking force) of the vehicle is, the longitudinal velocity of the vehicle cannot be negative. This is a limitation of the linear model. If the vehicle's model predictive controller strictly limits the longitudinal velocity to a non-negative number during the optimization solution, it may cause the solution to fail. In order to accurately determine the longitudinal control variable later and improve the safety of autonomous driving control, in some embodiments, for each prediction time point, it can be determined whether the predicted longitudinal velocity of the vehicle at the prediction time point is greater than zero. If the predicted longitudinal velocity at the prediction time point is greater than or equal to zero, the penalty weight for the state quantity error at the prediction time point is greater than zero. In some examples, the penalty weight for the state quantity error at the prediction time point can be set to a preset value greater than zero, where the preset value is an empirical value obtained based on multiple experiments.

[0056] In other embodiments, when the predicted longitudinal speed at the prediction time point is less than zero, the penalty weight of the state quantity error at the prediction time point is equal to zero.

[0057] Step 202 : obtaining a second penalty weight preset for the longitudinal control amount at the prediction time point, wherein the second penalty weights at different prediction time points are the same.

[0058] Step 203: Obtain a third penalty weight preset for the control amount increment at the prediction time point, wherein the third penalty weight at different prediction time points is the same.

[0059] Step 204 : Generate longitudinal control parameter information of the vehicle's model predictive controller at the prediction time point based on the first penalty weight, the second penalty weight, and the third penalty weight.

[0060] In some exemplary embodiments, in order to accurately determine the predicted longitudinal velocity sequence of the vehicle in the prediction time domain, a possible implementation of the above step 101 is as follows: Figure 3 As shown, this may include:

[0061] Step 301: Obtain the current longitudinal speed of the vehicle at the current moment.

[0062] Step 302 : Acquire an expected longitudinal acceleration sequence of the vehicle in the prediction time domain, wherein the expected longitudinal acceleration sequence includes: expected longitudinal acceleration at each prediction time point.

[0063] Step 303 : Determine a predicted longitudinal velocity sequence of the vehicle in the prediction time domain based on the current longitudinal velocity and the expected longitudinal acceleration sequence.

[0064] In some exemplary embodiments, the current longitudinal speed and the expected longitudinal speed sequence may be input into a longitudinal speed prediction model to obtain a predicted longitudinal speed sequence of the vehicle in a prediction time domain through the longitudinal speed prediction model.

[0065] In some exemplary embodiments, in order to accurately determine the predicted longitudinal velocity sequence within the prediction time domain, another possible implementation method for determining the predicted longitudinal velocity sequence of the vehicle within the prediction time domain based on the current longitudinal velocity and the expected longitudinal acceleration sequence is as follows: Figure 4 As shown, this may include:

[0066] Step 401 : for the i-th prediction time point, determine the predicted longitudinal speed at the i-th prediction time point according to the expected longitudinal acceleration and the current longitudinal speed at the i-th prediction time point, where the initial value of i is 1.

[0067] The i-th prediction time point refers to the i-th prediction time point among multiple prediction time points arranged in chronological order in the prediction time domain.

[0068] Step 402: add 1 to i.

[0069] Step 403, when i is less than or equal to N, determine the predicted longitudinal velocity at the i-th prediction time point based on the expected longitudinal acceleration at the i-th prediction time point and the predicted longitudinal velocity at the (i-1)-th prediction time point, and jump to step 402, where N is the total number of prediction time points in the prediction time domain.

[0070] In some exemplary embodiments, the above calculation of the predicted longitudinal speed V at the i-th prediction time i The calculation formula is as follows:

[0071]

[0072] Among them, a in the formula r represents the expected acceleration at the rth prediction time point, where r ranges from 1 to i, and v in the formula cur represents the current longitudinal velocity, where t in the formula represents the time interval between prediction time points.

[0073] For example, when the time interval t between prediction time points is 1 second, the calculation formula for calculating the predicted longitudinal speed at the i-th prediction time can be expressed as:

[0074]

[0075] The predicted longitudinal speed at the i-th prediction time refers to the longitudinal speed obtained by the vehicle when traveling according to the expected longitudinal speed corresponding to each of the previous i prediction time points at the current longitudinal speed.

[0076] It can be understood that when i is greater than N, the process ends directly.

[0077] In some embodiments of the present disclosure, in order to accurately determine the longitudinal control amount sequence in the prediction time domain, a possible implementation of the above step 104 is as follows: Figure 5 As shown, this may include:

[0078] Step 501 : Determine the longitudinal vehicle state parameters of the vehicle at the current moment, and determine the initial longitudinal control amount of the vehicle at each predicted time point.

[0079] In some exemplary embodiments, the initial longitudinal control amount of the vehicle at each predicted time point may be determined based on the longitudinal vehicle state parameters of the vehicle at the current moment.

[0080] In other exemplary embodiments, an initial longitudinal control amount preset for the vehicle at each predicted time point may be obtained.

[0081] It is understandable that the initial longitudinal control amounts corresponding to the various prediction time points may be the same, or may be different, or may be partially the same. This embodiment does not impose any specific limitation on this, and the actual situation shall prevail.

[0082] Step 502 : Determine the predicted longitudinal vehicle state parameters at each prediction time point based on the longitudinal vehicle state parameters, the initial longitudinal control amount at each prediction time point, and the longitudinal model.

[0083] In some exemplary embodiments, a possible implementation method for determining the predicted longitudinal vehicle state parameters at each prediction time point based on the longitudinal vehicle state parameters, the initial longitudinal control amount at each prediction time point, and the longitudinal model may be: for the i-th prediction time point, based on the longitudinal model, using the initial longitudinal control amount at the i-th prediction time point and the current longitudinal vehicle state parameters, determine the predicted longitudinal vehicle state parameters at the i-th prediction time point, where the initial value of i is 1; add 1 to i; when i is less than or equal to N, determine the predicted longitudinal vehicle state parameters at the i-th prediction time point based on the expected longitudinal acceleration at the i-th prediction time point and the predicted longitudinal vehicle state parameters at the i-1-th prediction time point, and jump to the step of adding 1 to i, where N is the total number of prediction time points in the prediction time domain.

[0084] Step 503 : constructing an objective function based on the predicted longitudinal vehicle state parameters at each predicted time point, the expected longitudinal vehicle state parameters at each predicted time point in the expected trajectory, and the longitudinal control parameter information.

[0085] The formula of the objective function J is as follows:

[0086]

[0087] Among them, Y in the formula i represents the predicted longitudinal vehicle state parameter at the i-th prediction time point; Y in the formula ri represents the expected longitudinal vehicle state parameter at the i-th prediction time point; Q i represents the first penalty weight corresponding to the state error at the i-th prediction time point; U i represents the initial longitudinal control amount at the i-th prediction time point; ΔU i Represents the control amount increment at the i-th prediction time point; R1 represents the second penalty weight; R2 represents the third penalty weight; T represents transpose; N represents the total number of prediction time points in the prediction domain.

[0088] Step 504 : adjusting the initial longitudinal control amount at each prediction time point according to the value of the objective function to obtain the longitudinal control amount at each prediction time point.

[0089] That is to say, after the objective function is determined, the optimal solution of the objective function can be solved through rolling optimization to obtain the longitudinal control amount at each prediction time point.

[0090] Step 505: Determine a longitudinal control amount sequence according to the longitudinal control amount at each predicted time point.

[0091] In one embodiment of the present disclosure, after obtaining the longitudinal control amount at each predicted time point, the longitudinal control amount may be sorted according to the chronological order of the predicted time points to obtain a longitudinal control amount sequence.

[0092] In order to implement the above embodiment, the embodiment of the present disclosure further provides a longitudinal control device for a vehicle.

[0093] Figure 6 is a schematic diagram of a sixth embodiment of the present disclosure, which provides a longitudinal control device for a vehicle.

[0094] like Figure 6 As shown, the longitudinal control device 600 of the vehicle may include a first determination module 601, a second determination module 602, a third determination module 603 and a control module 604, wherein:

[0095] The first determining module 601 is configured to determine a predicted longitudinal velocity sequence of the vehicle in a predicted time domain after a current moment, wherein the predicted longitudinal velocity sequence includes: predicted longitudinal velocities at each predicted time point.

[0096] The second determining module 602 is configured to determine, for each prediction time point, longitudinal control parameter information of the vehicle's model predictive controller at the prediction time point based on the predicted longitudinal speed at the prediction time point.

[0097] The third determination module 603 is configured to determine a longitudinal control amount sequence in a prediction time domain based on the longitudinal control parameter information, the longitudinal model in the model predictive controller, and the desired trajectory of the vehicle, wherein the longitudinal control amount sequence includes the longitudinal control amount at each prediction time point.

[0098] The control module 604 is configured to perform longitudinal control of the vehicle for automatic driving according to the longitudinal control amount at the first prediction time point within the prediction time domain.

[0099] The longitudinal control method for a vehicle of an embodiment of the present disclosure, when performing longitudinal control of the vehicle for autonomous driving, combines the predicted longitudinal speed of the vehicle at each predicted time point in the predicted time domain after the current moment to determine the predicted longitudinal control parameters of the vehicle's model predictive controller at each predicted time point in the predicted time domain, and combines the longitudinal control parameter information, the longitudinal model in the model predictive controller, and the desired trajectory of the vehicle to determine the longitudinal control amount at each predicted time point in the predicted time domain, and performs longitudinal control of the vehicle based on the longitudinal control amount at the first predicted time point in the predicted time domain. Thus, the longitudinal control amount for longitudinal control of the vehicle is accurately determined in combination with the predicted longitudinal speed at each predicted time point in the predicted time domain, thereby improving the accuracy and stability of the vehicle's longitudinal control, and thereby improving the safety of the vehicle's autonomous driving.

[0100] In one embodiment of the present disclosure, Figure 7 As shown, the longitudinal control device 700 of the vehicle may include: a first determination module 701, a second determination module 702, a third determination module 703 and a control module 704, wherein the first determination module 701 may include: a first acquisition unit 7011, a second acquisition unit 7012 and a determination unit 7013.

[0101] It should be noted that the detailed description of the control module 704 can be found in the above Figure 6 The description of the control module 604 in will not be described again here.

[0102] In one embodiment of the present disclosure, the second determination module 702 is specifically used to: determine a first penalty weight for a state quantity error of a model predictive controller of the vehicle at a prediction time point based on a predicted longitudinal speed at the prediction time point; obtain a second penalty weight pre-set for a longitudinal control quantity at a prediction time point, wherein the second penalty weight at different prediction time points is the same; obtain a third penalty weight pre-set for a control quantity increment at a prediction time point, wherein the third penalty weight at different prediction time points is the same; and generate longitudinal control parameter information of the model predictive controller of the vehicle at the prediction time point based on the first penalty weight, the second penalty weight and the third penalty weight.

[0103] In one embodiment of the present disclosure, when the predicted longitudinal speed at the prediction time point is greater than or equal to zero, the penalty weight of the state quantity error at the prediction time point is greater than zero; when the predicted longitudinal speed at the prediction time point is less than zero, the penalty weight of the state quantity error at the prediction time point is equal to zero.

[0104] In one implementation of the present disclosure, the first determining module 701 includes:

[0105] The first acquisition unit 7011 is used to obtain the current longitudinal speed of the vehicle at the current moment.

[0106] The second acquiring unit 7012 is configured to acquire an expected longitudinal acceleration sequence of the vehicle in the prediction time domain, wherein the expected longitudinal acceleration sequence includes: an expected longitudinal acceleration at each prediction time point.

[0107] The determination unit 7013 is used to determine the predicted longitudinal velocity sequence of the vehicle in the prediction time domain based on the current longitudinal velocity and the expected longitudinal acceleration sequence.

[0108] In one embodiment of the present disclosure, the above-mentioned determination unit 7013 is specifically used to: for the i-th prediction time point, determine the predicted longitudinal speed at the i-th prediction time point based on the expected longitudinal acceleration and the current longitudinal speed at the i-th prediction time point, where the initial value of i is 1; add 1 to i; when i is less than or equal to N, determine the predicted longitudinal speed at the i-th prediction time point based on the expected longitudinal acceleration at the i-th prediction time point and the predicted longitudinal speed at the i-1-th prediction time point, and go to the step of adding 1 to i, where N is the total number of prediction time points in the prediction time domain.

[0109] In one embodiment of the present disclosure, the third determination module 703 is specifically configured to: determine the longitudinal vehicle state parameters of the vehicle at the current moment, and determine the initial longitudinal control amount of the vehicle at each predicted time point; determine the predicted longitudinal vehicle state parameters at each predicted time point based on the longitudinal vehicle state parameters, the initial longitudinal control amount at each predicted time point, and the longitudinal model; construct an objective function based on the predicted longitudinal vehicle state parameters at each predicted time point, the expected longitudinal vehicle state parameters at each predicted time point in the expected trajectory, and longitudinal control parameter information; adjust the initial longitudinal control amount at each predicted time point based on the value of the objective function to obtain the longitudinal control amount at each predicted time point; and determine a longitudinal control amount sequence based on the longitudinal control amount at each predicted time point.

[0110] It should be noted that the above explanation of the longitudinal control method of the vehicle is also applicable to the longitudinal control device of the vehicle in this embodiment, and this embodiment will not elaborate on this.

[0111] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0112] Figure 8 A schematic block diagram of an example electronic device 800 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided as examples only and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0113] like Figure 8As shown, the electronic device 800 may include a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. Various programs and data required for the operation of the device 800 may also be stored in the RAM 803. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0114] Various components in device 800 are connected to I / O interface 805, including an input unit 806, such as a keyboard, mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a magnetic disk, optical disk, etc.; and a communication unit 809, such as a network card, modem, wireless communication transceiver, etc. The communication unit 809 allows device 800 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0115] The computing unit 801 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as the longitudinal control method for a vehicle. For example, in some embodiments, the longitudinal control method for a vehicle can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the longitudinal control method for a vehicle described above can be performed. Alternatively, in other embodiments, the computing unit 801 can be configured to perform the longitudinal control method for a vehicle by any other suitable means (e.g., via firmware).

[0116] Various embodiments of the devices and techniques described above herein can be implemented in digital electronic circuit devices, integrated circuit devices, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), devices on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable device that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage device, at least one input device, and at least one output device, and transmit data and instructions to the storage device, the at least one input device, and the at least one output device.

[0117] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0118] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use with an instruction execution device, device or equipment or used in combination with an instruction execution device, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared or semiconductor devices, devices or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0119] To provide interaction with a user, the devices and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0120] The apparatus and techniques described herein can be implemented in a computing device that includes backend components (e.g., as a data server), or a computing device that includes middleware components (e.g., an application server), or a computing device that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the apparatus and techniques described herein), or a computing device that includes any combination of such backend components, middleware components, or front-end components. The components of the apparatus can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain network.

[0121] A computer device may include a client and a server. The client and server are generally remote from each other and typically interact via a communication network. The client-server relationship is established by computer programs running on the respective computers and establishing a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, a host product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosts and VPS services ("Virtual Private Servers" or "VPS"). The server may be a cloud server, a distributed device server, or a server integrated with blockchain.

[0122] In one embodiment of the present disclosure, the present disclosure also provides an autonomous driving vehicle, comprising Figure 8 The exemplary electronic device.

[0123] It should be noted that the electronic device is used to implement the longitudinal control method for a vehicle according to an embodiment of the present disclosure. It should be understood that the various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution disclosed in this disclosure can be achieved. This is not a limitation herein.

[0124] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A method for longitudinal control of a vehicle, comprising: Determining a predicted longitudinal velocity sequence of the vehicle in a predicted time domain after a current moment, wherein the predicted longitudinal velocity sequence includes: predicted longitudinal velocities at each predicted time point; For each of the predicted time points, determining longitudinal control parameter information of the model predictive controller of the vehicle at the predicted time point according to the predicted longitudinal speed at the predicted time point; Determining a longitudinal control amount sequence in the prediction time domain based on the longitudinal control parameter information, the longitudinal model in the model predictive controller, and the expected trajectory of the vehicle, wherein the longitudinal control amount sequence includes: longitudinal control amounts at each prediction time point; performing autonomous driving longitudinal control of the vehicle according to the longitudinal control amount at a first prediction time point within the prediction time domain; The step of determining a predicted longitudinal velocity sequence of the vehicle in a predicted time domain after a current moment includes: Obtaining the current longitudinal speed of the vehicle at the current moment; Acquiring an expected longitudinal acceleration sequence of the vehicle in the prediction time domain, wherein the expected longitudinal acceleration sequence includes: the expected longitudinal acceleration at each of the prediction time points; A predicted longitudinal velocity sequence of the vehicle in the prediction time domain is determined according to the current longitudinal velocity and the expected longitudinal acceleration sequence.

2. The method according to claim 1, wherein Determining longitudinal control parameter information of the model predictive controller of the vehicle at the prediction time point based on the predicted longitudinal speed at the prediction time point includes: determining a first penalty weight for a state quantity error of a model predictive controller of the vehicle at the prediction time point based on the predicted longitudinal speed at the prediction time point; Obtaining a second penalty weight preset for the longitudinal control amount at the prediction time point, wherein the second penalty weights at different prediction time points are the same; Obtaining a third penalty weight preset for the control amount increment at the prediction time point, wherein the third penalty weight at different prediction time points is the same; Longitudinal control parameter information of the vehicle's model predictive controller at the prediction time point is generated according to the first penalty weight, the second penalty weight, and the third penalty weight.

3. The method according to claim 2, wherein: When the predicted longitudinal speed at the prediction time point is greater than or equal to zero, the penalty weight of the state quantity error at the prediction time point is greater than zero; when the predicted longitudinal speed at the prediction time point is less than zero, the penalty weight of the state quantity error at the prediction time point is equal to zero.

4. The method according to claim 1, wherein Determining the predicted longitudinal velocity sequence of the vehicle in the prediction time domain according to the current longitudinal velocity and the expected longitudinal acceleration sequence includes: For an i-th prediction time point, determining a predicted longitudinal velocity at the i-th prediction time point based on the expected longitudinal acceleration at the i-th prediction time point and the current longitudinal velocity, where an initial value of i is 1; Add 1 to the i; When i is less than or equal to N, the predicted longitudinal velocity at the i-th prediction time point is determined based on the expected longitudinal acceleration at the i-th prediction time point and the predicted longitudinal velocity at the (i-1)-th prediction time point, and the process proceeds to the step of adding 1 to i, where N is the total number of prediction time points in the prediction time domain.

5. The method according to claim 1, wherein The determining of the longitudinal control amount sequence in the prediction time domain according to the longitudinal control parameter information, the longitudinal model in the model predictive controller, and the expected trajectory of the vehicle includes: determining a longitudinal vehicle state parameter of the vehicle at the current moment, and determining an initial longitudinal control amount of the vehicle at each of the predicted time points; determining a predicted longitudinal vehicle state parameter at each of the prediction time points based on the longitudinal vehicle state parameter, the initial longitudinal control amount at each of the prediction time points, and the longitudinal model; constructing an objective function based on the predicted longitudinal vehicle state parameters at each of the predicted time points, the expected longitudinal vehicle state parameters at each of the predicted time points in the expected trajectory, and the longitudinal control parameter information; adjusting the initial longitudinal control amount at each of the predicted time points according to the value of the objective function to obtain the longitudinal control amount at each of the predicted time points; The longitudinal control amount sequence is determined according to the longitudinal control amount at each of the predicted time points.

6. A longitudinal control device for a vehicle, comprising: a first determining module, configured to determine a predicted longitudinal velocity sequence of the vehicle in a predicted time domain after a current moment, wherein the predicted longitudinal velocity sequence includes: predicted longitudinal velocities at respective predicted time points; a second determining module, configured to determine, for each of the predicted time points, longitudinal control parameter information of the model predictive controller of the vehicle at the predicted time point based on the predicted longitudinal speed at the predicted time point; a third determining module, configured to determine a longitudinal control amount sequence in the prediction time domain based on the longitudinal control parameter information, the longitudinal model in the model predictive controller, and the expected trajectory of the vehicle, wherein the longitudinal control amount sequence includes: longitudinal control amounts at each prediction time point; a control module, configured to perform longitudinal control of the vehicle for automatic driving according to the longitudinal control amount at a first predicted time point within the predicted time domain; The first determining module includes: a first acquiring unit, configured to acquire a current longitudinal speed of the vehicle at the current moment; a second acquiring unit, configured to acquire an expected longitudinal acceleration sequence of the vehicle in the prediction time domain, wherein the expected longitudinal acceleration sequence includes: an expected longitudinal acceleration at each of the prediction time points; A determination unit is configured to determine a predicted longitudinal velocity sequence of the vehicle in the prediction time domain according to the current longitudinal velocity and the expected longitudinal acceleration sequence.

7. The device according to claim 6, wherein The second determining module is specifically configured to: determining a first penalty weight for a state quantity error of a model predictive controller of the vehicle at the prediction time point based on the predicted longitudinal speed at the prediction time point; Obtaining a second penalty weight preset for the longitudinal control amount at the prediction time point, wherein the second penalty weights at different prediction time points are the same; Obtaining a third penalty weight preset for the control amount increment at the prediction time point, wherein the third penalty weight at different prediction time points is the same; Longitudinal control parameter information of the vehicle's model predictive controller at the prediction time point is generated according to the first penalty weight, the second penalty weight, and the third penalty weight.

8. The device according to claim 7, wherein When the predicted longitudinal speed at the prediction time point is greater than or equal to zero, the penalty weight of the state quantity error at the prediction time point is greater than zero; when the predicted longitudinal speed at the prediction time point is less than zero, the penalty weight of the state quantity error at the prediction time point is equal to zero.

9. The device according to claim 6, wherein The determining unit is specifically configured to: For an i-th prediction time point, determining a predicted longitudinal velocity at the i-th prediction time point based on the expected longitudinal acceleration at the i-th prediction time point and the current longitudinal velocity, where an initial value of i is 1; Add 1 to the i; When i is less than or equal to N, the predicted longitudinal velocity at the i-th prediction time point is determined based on the expected longitudinal acceleration at the i-th prediction time point and the predicted longitudinal velocity at the (i-1)-th prediction time point, and the process proceeds to the step of adding 1 to i, where N is the total number of prediction time points in the prediction time domain.

10. The device according to claim 6, wherein The third determining module is specifically configured to: determining a longitudinal vehicle state parameter of the vehicle at the current moment, and determining an initial longitudinal control amount of the vehicle at each of the predicted time points; determining a predicted longitudinal vehicle state parameter at each of the prediction time points based on the longitudinal vehicle state parameter, the initial longitudinal control amount at each of the prediction time points, and the longitudinal model; constructing an objective function based on the predicted longitudinal vehicle state parameters at each of the predicted time points, the expected longitudinal vehicle state parameters at each of the predicted time points in the expected trajectory, and the longitudinal control parameter information; adjusting the initial longitudinal control amount at each of the predicted time points according to the value of the objective function to obtain the longitudinal control amount at each of the predicted time points; The longitudinal control amount sequence is determined according to the longitudinal control amount at each of the predicted time points.

11. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 5.

12. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 5.

13. A computer program product comprising a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 5.

14. An autonomous driving vehicle comprising: The electronic device according to claim 11.

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