Adaptive cruise control method, system and device based on target driving range

Abstract

The application discloses a kind of adaptive cruise control method, system and equipment based on target driving mileage, the method includes: after entering navigation auxiliary driving mode is driven, target driving mileage information is determined, the current residual driving range information of vehicle is acquired in real time;According to target driving mileage information and current residual driving range information, determine energy-saving coefficient;According to energy-saving coefficient, the weight matrix in adaptive cruise LQR controller is dynamically selected by fuzzy controller;According to the expected acceleration of current time, according to the expected acceleration of current time, adaptive cruise control is carried out when energy-saving coefficient is in the preset threshold range.This application calculates energy-saving coefficient in real time according to target driving mileage information and current residual driving range information required by navigation, adjusts the following vehicle control mode of adaptive cruise system according to energy-saving coefficient, realizes the coordination of safety and economy of vehicle adaptive cruise system.

Description

Technical Field

[0001] This invention relates to the field of adaptive cruise technology, and in particular to an adaptive cruise control method, system and device based on a target driving distance. Background Technology

[0002] Adaptive cruise control, as a crucial component of advanced driver assistance systems (ADAS) in automobiles, is fundamental to achieving autonomous driving. Domestic and international automakers are increasingly emphasizing research into the control theory of adaptive cruise control systems. Current research on adaptive cruise control systems largely focuses on improving driving safety and passenger comfort, developing both standard cruise control controllers and adaptive cruise control controllers.

[0003] In existing technologies, when a vehicle enters energy-saving control mode, multiple controls are applied to the vehicle, including maximum speed limits and torque characteristic limits. This not only significantly impacts the vehicle's performance but also fails to adjust the control effect of the energy-saving mode according to the vehicle's real-time energy-saving needs. Therefore, achieving a balance between safety and fuel economy in adaptive cruise control systems has become a pressing issue.

[0004] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this invention is to provide an adaptive cruise control method, system, and device based on a target driving mileage, aiming to solve the technical problem of how to achieve a balance between safety and economy in vehicle adaptive cruise control systems.

[0006] To achieve the above objectives, the present invention provides an adaptive cruise control method based on a target driving distance, the adaptive cruise control method based on a target driving distance comprising:

[0007] After the driver enters the navigation destination in the navigation-assisted driving mode, the system determines the target driving distance between the current location and the destination location in real time, and obtains the vehicle's current remaining driving range information.

[0008] The energy-saving coefficient is determined based on the target driving range information and the current remaining driving range information;

[0009] Based on the energy-saving coefficient, the weight matrix in the adaptive cruise LQR controller is dynamically selected by a fuzzy controller.

[0010] The desired acceleration at the current moment is determined based on the weight matrix.

[0011] When the energy-saving coefficient is within a preset threshold range, adaptive cruise control is performed based on the desired acceleration at the current moment.

[0012] Optionally, before the step of dynamically selecting the weight matrix in the adaptive cruise LQR controller based on the energy-saving coefficient using a fuzzy controller, the following steps are included:

[0013] Obtain the vehicle's fuel efficiency coefficient after the driver inputs the navigation destination in navigation-assisted driving mode;

[0014] A fuzzy controller is constructed by pre-setting fuzzy control rules;

[0015] The vehicle energy-saving coefficient is input into the fuzzy controller to obtain the weight coefficients in the state weight matrix and the control weight matrix corresponding to different energy-saving requirements.

[0016] Optionally, before the step of determining the desired acceleration at the current moment based on the weight matrix, the method further includes:

[0017] Obtain the status information of your own vehicle and the vehicle in front;

[0018] A vehicle following model is established based on the vehicle's status information and the preceding vehicle's status information.

[0019] Optionally, after the step of establishing a vehicle following model based on the self-vehicle state information and the preceding vehicle state information, the method further includes:

[0020] Based on the vehicle following model, the system state variables, system control variables, and system disturbance variables are determined, and the following state space equations corresponding to the adaptive cruise system are established according to the system state variables, system control variables, and system disturbance variables.

[0021] Optionally, the step of determining the expected acceleration at the current moment based on the weight matrix includes:

[0022] Based on the energy-saving coefficient, the desired acceleration at the current moment is calculated by the adaptive cruise LQR controller according to the weight matrix.

[0023] Optionally, the step of calculating the desired acceleration at the current moment based on the energy-saving coefficient and the weight matrix using the adaptive cruise LQR controller includes:

[0024] Obtain vehicle speed information, vehicle speed information, vehicle acceleration information, vehicle acceleration information, relative distance between vehicle and vehicle in front information, and safe distance between vehicle and vehicle in front information;

[0025] Based on the energy-saving coefficient, the desired acceleration at the current moment is calculated by the adaptive cruise control (LQR) controller according to the weight matrix, the vehicle speed information, the speed information of the vehicle in front, the acceleration information of the vehicle in front, the relative distance information between the vehicle in front and the vehicle in front, and the safe distance information between the vehicle in front and the vehicle in front.

[0026] Furthermore, to achieve the above objectives, the present invention also proposes an adaptive cruise control system based on a target driving mileage, the adaptive cruise control system based on the target driving mileage comprising:

[0027] The acquisition module is used to determine the target driving distance information between the current location and the destination location in real time after the driver enters the navigation destination in the navigation-assisted driving mode, and to obtain the current remaining driving distance information of the vehicle.

[0028] The determining module is used to determine the energy-saving coefficient based on the target driving mileage information and the current remaining driving mileage information;

[0029] The determining module is further configured to determine the weight matrix corresponding to the adaptive cruise LQR controller based on the energy-saving coefficient using a fuzzy controller;

[0030] The determining module is further configured to determine the expected acceleration at the current moment based on the weight matrix;

[0031] The control module is used to perform adaptive cruise control based on the desired acceleration at the current moment when the energy-saving coefficient is within a preset threshold range.

[0032] Furthermore, to achieve the above objectives, the present invention also proposes an adaptive cruise control device based on a target driving mileage. The device includes: a memory, a processor, and an adaptive cruise control program based on a target driving mileage stored in the memory and executable on the processor. The adaptive cruise control program based on a target driving mileage is configured to implement the steps of the adaptive cruise control method based on a target driving mileage as described above.

[0033] Furthermore, to achieve the above objectives, the present invention also proposes a storage medium storing an adaptive cruise control program based on a target driving mileage, wherein when the adaptive cruise control program based on the target driving mileage is executed by a processor, it implements the steps of the adaptive cruise control method based on the target driving mileage described above.

[0034] This invention first determines the target mileage information between the current location and the destination location in real time after the driver inputs the navigation destination in navigation-assisted driving mode, and obtains the vehicle's current remaining driving range information. Then, it determines an energy-saving coefficient based on the target mileage information and the current remaining driving range information. Next, based on the energy-saving coefficient, a weight matrix in the adaptive cruise control (LQR) controller is dynamically selected through a fuzzy controller. Finally, the desired acceleration at the current moment is determined based on the weight matrix. When the energy-saving coefficient is within a preset threshold range, adaptive cruise control is performed based on the desired acceleration at the current moment. Compared to existing technologies that perform multiple controls on the vehicle after entering energy-saving control mode, including maximum speed limits and torque external characteristic limits, which not only significantly affect the vehicle's performance but also fail to adjust the control effect of the energy-saving mode according to the vehicle's real-time energy-saving needs, this invention calculates the current energy-saving coefficient in real time based on the target mileage information required for navigation and the current remaining driving range information, and adjusts the following control mode of the adaptive cruise system according to the energy-saving coefficient, achieving a balance between the safety and economy of the vehicle's adaptive cruise system. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the structure of an adaptive cruise control device based on a target driving mileage in the hardware operating environment involved in the embodiments of the present invention;

[0036] Figure 2 This is a flowchart illustrating the first embodiment of the adaptive cruise control method based on target driving mileage of the present invention.

[0037] Figure 3 This is the overall structure of the adaptive cruise control system in the first embodiment of the adaptive cruise control method based on target driving mileage of the present invention;

[0038] Figure 4 This is a flowchart of the energy-saving adaptive cruise control method of the first embodiment of the adaptive cruise control method based on target driving mileage of the present invention;

[0039] Figure 5 This is a structural block diagram of the first embodiment of the adaptive cruise control system based on target driving mileage of the present invention.

[0040] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0041] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0042] Reference Figure 1 , Figure 1This is a schematic diagram of the adaptive cruise control device structure based on target driving mileage in the hardware operating environment involved in the embodiments of the present invention.

[0043] like Figure 1 As shown, the adaptive cruise control device based on target driving mileage may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wireless-Fidelity (Wi-Fi) interface). The memory 1005 may be high-speed random access memory (RAM) or stable non-volatile memory (NVM), such as a disk storage device. Optionally, the memory 1005 may also be a storage system independent of the aforementioned processor 1001.

[0044] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on adaptive cruise control devices based on target driving distance and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0045] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and an adaptive cruise control program based on the target driving mileage.

[0046] exist Figure 1 In the adaptive cruise control device based on target driving mileage shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and memory 1005 in the adaptive cruise control device based on target driving mileage of the present invention can be set in the adaptive cruise control device based on target driving mileage. The adaptive cruise control device based on target driving mileage calls the adaptive cruise control program based on target driving mileage stored in the memory 1005 through the processor 1001 and executes the adaptive cruise control method based on target driving mileage provided in the embodiment of the present invention.

[0047] This invention provides an adaptive cruise control method based on a target driving distance, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the adaptive cruise control method based on target driving mileage of the present invention.

[0048] In this embodiment, the adaptive cruise control method based on target driving mileage includes the following steps:

[0049] Step S10: After the driver enters the navigation destination in the navigation-assisted driving mode, the target driving distance information between the current location and the destination location is determined in real time, and the current remaining driving distance information of the vehicle is obtained.

[0050] It is easy to understand that the execution subject of this embodiment can be an adaptive cruise control system with functions such as data processing, network communication and program execution, or other computer devices with similar functions. This embodiment does not limit it.

[0051] It should be noted that the adaptive cruise control system includes a vehicle following model and an adaptive cruise LQR controller. The vehicle following model requires the adaptive cruise LQR controller to calculate the desired acceleration, but the adaptive cruise LQR controller needs a fuzzy logic unit to determine the weight coefficients in the weight matrices Q and R, where Q is the state weight matrix and R is the control weight matrix. (Reference) Figure 3 , Figure 3 This is the overall structure of the adaptive cruise control system in the first embodiment of the adaptive cruise control method based on target driving mileage of the present invention.

[0052] In practice, after the driver enters the navigation destination in navigation-assisted driving mode, the system comprehensively calculates the vehicle's current remaining driving range information in real time. The navigation information indicates the target driving distance required to reach the destination. .

[0053] It should also be noted that an energy-saving coefficient is constructed based on the target driving range information required for navigation and the current remaining driving range to represent different energy-saving needs, and the current energy-saving coefficient is calculated in real time.

[0054] Step S20: Determine the energy-saving coefficient based on the target driving mileage information and the current remaining driving mileage information.

[0055] In this embodiment, based on the vehicle's current remaining driving range information Navigation information indicates the target driving distance required to reach the destination. The real-time energy-saving coefficient is obtained through comparative calculation. .

[0056] It should be noted that a higher energy efficiency coefficient indicates a higher energy-saving requirement for the vehicle. When the vehicle does not enter energy-saving mode and exits navigation-assisted driving mode, it will simultaneously notify the driver through the human-machine interface that the vehicle's remaining driving range is insufficient to reach the destination; when When the vehicle dynamically selects a linear quadratic optimal controller (i.e., an adaptive cruise control LQR controller) with weighted coefficients based on the energy-saving coefficient, the range of the energy-saving coefficient is: .

[0057] Step S30: Based on the energy-saving coefficient, dynamically select the weight matrix corresponding to the adaptive cruise LQR controller using a fuzzy controller.

[0058] Step S40: Determine the expected acceleration at the current moment based on the weight matrix.

[0059] In the actual implementation, it is also necessary to obtain the status information of the vehicle itself and the vehicle in front, and to establish a vehicle following model based on the status information of the vehicle itself and the vehicle in front.

[0060] It should be noted that the vehicle status information includes the vehicle speed. Vehicle acceleration The preceding vehicle's status information includes its speed. acceleration of the vehicle in front .

[0061] In this embodiment, the vehicle following model in the adaptive cruise control system is as follows: In following mode, the speeds of the vehicle and the vehicle in front are respectively... and The accelerations of the vehicle and the vehicle in front are respectively and The relative distance between the vehicle and the vehicle in front is The safe distance between your vehicle and the vehicle in front is Then the speed difference between the car and the car in front is The following distance error between the vehicle and the vehicle in front is Following distance is The safe following distance varies with the speed of the vehicle and the following distance. , The adaptive cruise control algorithm calculates and outputs the minimum distance to be maintained when parking to the upper-level controller. The controller execution has a delay, therefore the actual acceleration and expected acceleration There is a hysteresis, which is characterized by a first-order inertial hysteresis element:

[0062] In the formula, It is a time constant. The constant coefficients were obtained through real vehicle experiments.

[0063] Furthermore, based on the vehicle following model, the system state variables, system control variables, and system disturbance variables are determined, and the following state space equation corresponding to the adaptive cruise system is established.

[0064] In its implementation, a linear quadratic controller is used for closed-loop optimal control to find the optimal solution that satisfies both performance requirements (vehicle following performance) and reduces the input (desired acceleration), thereby achieving energy savings during adaptive cruise control. The state of the adaptive cruise control system is the following distance error. Relative speed difference acceleration The optimal control input is the desired acceleration. Therefore, the state-space equation for the adaptive cruise control system when following another vehicle is established:

[0065] in, For system state variables, It is a system control input. This represents the system disturbance.

[0066] Furthermore, based on the energy-saving coefficient, the desired acceleration at the current moment is calculated by the adaptive cruise LQR controller according to the weight matrix.

[0067] In this embodiment, the desired acceleration The desired acceleration under different energy-saving requirements can be obtained by using different weight matrices. This enables an energy-efficient adaptive cruise control system.

[0068] Furthermore, before determining the weight matrix corresponding to the adaptive cruise LQR controller based on the energy-saving coefficient using a fuzzy controller, it is necessary to obtain the vehicle's energy-saving coefficient after the driver inputs the navigation destination in the navigation-assisted driving mode. Then, a fuzzy controller is constructed using preset fuzzy control rules. After that, the vehicle's energy-saving coefficient is input into the fuzzy controller to obtain the weight coefficients in the state weight matrix and the control weight matrix corresponding to different energy-saving requirements.

[0069] In practice, the two objectives of controlling the state error and controlling energy consumption in an adaptive cruise control system are often contradictory and mutually restrictive. Requiring a small vehicle control state error inevitably leads to increased energy consumption, while achieving energy conservation necessitates relaxing the vehicle's control performance. For an adaptive cruise control LQR controller, to meet the control requirements with the minimum control input, i.e., to satisfy the following performance of the adaptive cruise control system with the minimum acceleration, a linear quadratic optimization performance index function is introduced. .

[0070] Matrix Q and R are the weight matrices for LQR optimal control:

[0071]

[0072]

[0073] The parameter values ​​in matrices Q and R represent the corresponding system state and control variables across the entire performance index. The larger the weight coefficient, the more important the corresponding state error or control quantity is in the performance function.

[0074] Furthermore, using the vehicle's energy-saving coefficient in navigation-assisted driving mode as the input of the fuzzy controller, the parameters in the weight matrices Q and R corresponding to different energy-saving requirements are dynamically selected to establish an adaptive cruise system LQR controller that automatically adjusts the weight matrix according to energy-saving requirements.

[0075] Fuzzy rule: Q is the state weight matrix, and R is the control weight matrix. The larger the weight coefficients related to vehicle following performance in matrix Q, the smaller the error between the actual vehicle driving state and the desired safe driving state during the entire control period, and the faster the system's state decays, meaning better vehicle following control performance of the adaptive cruise system. The elements in the weight matrix R represent the system's input acceleration. In the overall performance metrics The weights of acceleration reflect the amount of energy consumed throughout the control range; that is, the greater the acceleration, the worse the vehicle's energy economy. Larger values ​​in matrix R reduce the corresponding control input, slowing down the system's state decay. This requires less energy consumption throughout the control range, resulting in better vehicle energy economy. In summary, under low driving range conditions, the requirements for system control level are relatively low, while the requirements for system energy economy are relatively high, demanding that the adaptive cruise control system be more energy-efficient when following other vehicles. Conversely, under high driving range conditions, the requirements for adaptive cruise control system are relatively low in terms of energy economy, but relatively high in terms of system control level, requiring the vehicle to have better following performance.

[0076] In the specific implementation, the energy-saving coefficient in navigation-assisted driving mode is used as the input, and the elements in the state weight matrix Q and control weight matrix R are used as the system output. A single-input, four-output fuzzy controller is designed based on fuzzy logic rules. The energy-saving coefficient is set within its range. The system is divided into segments of 20%, and parameters are tuned using test data. The element values ​​of the weight matrix for the energy-saving coefficient in the lowest and highest segments are obtained, representing the energy-saving coefficient itself. The domain of discourse is , The domain of discourse is , The domain of discourse is , The domain of discourse is The domain of r is Each of the five variables is divided into five fuzzy subsets, and corresponding preset fuzzy control rules are established. Finally, the weight coefficients in the weight matrices Q and R are dynamically selected based on the established fuzzy controller.

[0077] In this embodiment, the obtained weighting coefficients are passed to the adaptive cruise LQR controller to optimize the performance index function. Achieving the optimal state feedback matrix The constant matrix P can be obtained by solving the Riccati matrix algebraic equation; establish the Riccati matrix algebraic equation. Where A is the state matrix and B is the control matrix, the constant matrix P is solved, and then K is solved; finally, the optimal control is obtained. .

[0078] Step S50: When the energy-saving coefficient is within the preset threshold range, perform adaptive cruise control based on the desired acceleration at the current moment.

[0079] To avoid excessively high expected acceleration that exceeds the normal range or affects comfort, the expected acceleration value of the control variable is... Use a saturation function for restriction.

[0080] Minimum acceleration value The maximum acceleration value is However, when the expected acceleration is large, that is When a preset threshold is set, the acceleration amplitude is further limited using an energy-saving coefficient.

[0081]

[0082] By constructing an energy-saving coefficient in the navigation-assisted driving mode and using the energy-saving coefficient as the input of the fuzzy controller, the weight matrices Q and R corresponding to different energy-saving coefficients can be obtained based on the established fuzzy controller. The weight coefficients in the weight matrices Q and R are dynamically selected, the linear quadratic optimal control algorithm is improved, and a variable weight coefficient LQR controller is established. By solving the linear quadratic optimal control algorithm, the expected acceleration of the adaptive cruise control system when following the vehicle is obtained, so that the expected acceleration of the control quantity can be adaptively adjusted according to different energy-saving demand scenarios, so as to achieve the purpose of saving energy by adjusting the acceleration when the driving range is insufficient.

[0083] refer to Figure 4 , Figure 4This is a flowchart of the energy-saving adaptive cruise control method of the first embodiment of the adaptive cruise control method based on target driving mileage of the present invention. After the driver inputs the navigation destination through the human-machine interaction unit in the navigation assisted driving mode, the system calculates the energy-saving coefficient based on the target driving mileage required to reach the navigation destination and the vehicle's remaining driving mileage. The vehicle uses a linear quadratic optimal control (LQR) algorithm that dynamically selects weight coefficients based on the energy-saving coefficient for adaptive cruise control. A fuzzy controller is constructed based on the energy-saving coefficient, and the weight matrix in the linear quadratic optimal controller is dynamically selected. The expected acceleration at the current moment is calculated based on the weight matrix output by the fuzzy controller. When the expected acceleration value is greater than a preset value, the expected acceleration is further limited. The system calculates the energy-saving coefficient in real time for feedback control, and the execution unit controls the vehicle to follow the vehicle in front based on the expected acceleration, ultimately realizing an energy-saving adaptive cruise system based on the remaining driving mileage of the navigation system.

[0084] In this embodiment, after the driver enters the navigation destination in the navigation-assisted driving mode, the target driving mileage information between the current location and the destination location is determined, and the current remaining driving range information of the vehicle is obtained. Then, the energy-saving coefficient is determined based on the target driving mileage information and the current remaining driving range information. After that, the weight matrix corresponding to the adaptive cruise LQR controller is determined by the fuzzy controller based on the energy-saving coefficient. Finally, the expected acceleration at the current moment is determined based on the weight matrix. When the expected acceleration value is greater than a preset threshold, the expected acceleration value is further restricted. Compared to existing technologies that impose multiple controls on the vehicle when it enters energy-saving control mode, including maximum speed limits and torque external characteristic limits, which not only significantly impact vehicle performance but also fail to adjust the control effect of the energy-saving mode according to the vehicle's real-time energy-saving needs, this embodiment constructs an energy-saving coefficient based on the vehicle's current remaining driving range and the target driving range required to reach the destination. This coefficient characterizes different energy-saving needs of the vehicle. An improved linear quadratic optimal control algorithm is proposed, and a fuzzy controller is established. By dynamically selecting the weight coefficients in the linear quadratic controller through the energy-saving coefficient, an energy-saving adaptive cruise following control algorithm is designed. This algorithm uses a linear quadratic controller to control the vehicle's following behavior, achieving energy-saving goals while ensuring driving safety. Thus, in scenarios with high energy-saving needs, energy economy is prioritized to ensure reaching the destination, while in scenarios with low energy-saving needs, the control performance of the system is prioritized.

[0085] Reference Figure 5 , Figure 5 This is a structural block diagram of the first embodiment of the adaptive cruise control system based on target driving mileage of the present invention.

[0086] like Figure 5As shown, the adaptive cruise control system based on target driving mileage proposed in this embodiment of the invention includes:

[0087] The acquisition module 5001 is used to determine the target driving mileage information between the current location and the destination location in real time after the driver enters the navigation destination in the navigation-assisted driving mode, and to obtain the current remaining driving mileage information of the vehicle.

[0088] It should be noted that the adaptive cruise control system based on target driving distance includes a vehicle following model and an adaptive cruise LQR controller. The vehicle following model requires the adaptive cruise LQR controller to calculate the desired acceleration, but the adaptive cruise LQR controller needs a fuzzy logic unit to determine the weight coefficients in the weight matrices Q and R, where Q is the state weight matrix and R is the control weight matrix. (Reference) Figure 3 , Figure 3 This is the overall structure of the adaptive cruise control system in the first embodiment of the adaptive cruise control method based on target driving mileage of the present invention.

[0089] In practice, after the driver enters the navigation destination in navigation-assisted driving mode, the system comprehensively calculates the vehicle's current remaining driving range information in real time. The navigation information indicates the target driving distance required to reach the destination. .

[0090] It should also be noted that an energy-saving coefficient is constructed based on the target driving range information required for navigation and the current remaining driving range to represent different energy-saving needs, and the current energy-saving coefficient is calculated in real time.

[0091] The determining module 5002 is used to determine the energy-saving coefficient based on the target driving mileage information and the current remaining driving mileage information.

[0092] In this embodiment, based on the vehicle's current remaining driving range information Navigation information indicates the target driving distance required to reach the destination. The real-time energy-saving coefficient is obtained through comparative calculation. .

[0093] It should be noted that a higher energy efficiency coefficient indicates a higher energy-saving requirement for the vehicle. When the vehicle does not enter energy-saving mode and exits navigation-assisted driving mode, it will simultaneously notify the driver through the human-machine interface that the vehicle's remaining driving range is insufficient to reach the destination; when When the vehicle dynamically selects a linear quadratic optimal controller (i.e., an adaptive cruise control LQR controller) with weighted coefficients based on the energy-saving coefficient, the range of the energy-saving coefficient is: .

[0094] The determining module 5002 is further configured to determine the weight matrix corresponding to the adaptive cruise LQR controller based on the energy-saving coefficient using a fuzzy controller.

[0095] The determining module 5002 is further configured to determine the expected acceleration at the current moment based on the weight matrix.

[0096] In the actual implementation, it is also necessary to obtain the status information of the vehicle itself and the status information of the vehicle in front of it, and to establish a vehicle following model based on the status information of the vehicle itself and the vehicle in front of it.

[0097] It should be noted that the vehicle status information includes the vehicle speed. Vehicle acceleration The preceding vehicle's status information includes its speed. acceleration of the vehicle in front .

[0098] In this embodiment, the vehicle following model in the adaptive cruise control system is as follows: In following mode, the speeds of the vehicle and the vehicle in front are respectively... and The accelerations of the vehicle and the vehicle in front are respectively and The relative distance between the vehicle and the vehicle in front is The safe distance between your vehicle and the vehicle in front is Then the speed difference between the car and the car in front is The following distance error between the vehicle and the vehicle in front is Following distance is The safe following distance varies with the speed of the vehicle and the following distance. , The adaptive cruise control algorithm calculates and outputs the minimum distance to be maintained when parking to the upper-level controller. The controller execution has a delay, therefore the actual acceleration and expected acceleration There is a hysteresis, which is characterized by a first-order inertial hysteresis element:

[0099] In the formula, It is a time constant. The constant coefficients were obtained through real vehicle experiments.

[0100] Furthermore, based on the vehicle following model, the system state variables, system control variables, and system disturbance variables are determined, and the following state space equation corresponding to the adaptive cruise system is established.

[0101] In its implementation, a linear quadratic controller is used for closed-loop optimal control to find the optimal solution that satisfies both performance requirements (vehicle following performance) and reduces the input (desired acceleration), thereby achieving energy savings during adaptive cruise control. The state of the adaptive cruise control system is the following distance error. Relative speed difference acceleration The optimal control input is the desired acceleration. Therefore, the state-space equation for the adaptive cruise control system when following another vehicle is established:

[0102] in, For system state variables, It is a system control input. This represents the system disturbance.

[0103] Furthermore, based on the energy-saving coefficient, the desired acceleration at the current moment is calculated by the adaptive cruise LQR controller according to the weight matrix.

[0104] In the specific implementation, the vehicle speed information and the speed information of the vehicle in front are obtained. Then, based on the vehicle following model, the expected acceleration at the current moment is calculated by the adaptive cruise LQR controller according to the weight matrix, the vehicle speed information and the speed information of the vehicle in front.

[0105] In this embodiment, the desired acceleration The desired acceleration under different energy-saving requirements can be obtained by using different weight matrices. This enables an energy-efficient adaptive cruise control system.

[0106] Furthermore, before determining the weight matrix corresponding to the adaptive cruise LQR controller based on the energy-saving coefficient using a fuzzy controller, it is necessary to obtain the vehicle's energy-saving coefficient after the driver inputs the navigation destination in the navigation-assisted driving mode. Then, a fuzzy controller is constructed using preset fuzzy control rules. After that, the vehicle's energy-saving coefficient is input into the fuzzy controller to obtain the weight coefficients in the state weight matrix and the control weight matrix corresponding to different energy-saving requirements.

[0107] In practice, the two objectives of controlling the state error and controlling energy consumption in an adaptive cruise control system are often contradictory and mutually restrictive. Requiring a small vehicle control state error inevitably leads to increased energy consumption, while achieving energy conservation necessitates relaxing the vehicle's control performance. For an adaptive cruise control LQR controller, to meet the control requirements with the minimum control input, i.e., to satisfy the following performance of the adaptive cruise control system with the minimum acceleration, a linear quadratic optimization performance index function is introduced. .

[0108] Matrix Q and R are the weight matrices for LQR optimal control:

[0109]

[0110]

[0111] The parameter values ​​in matrices Q and R represent the corresponding system state and control variables across the entire performance index. The larger the weight coefficient, the more important the corresponding state error or control quantity is in the performance function.

[0112] Furthermore, using the vehicle's energy-saving coefficient in navigation-assisted driving mode as the input of the fuzzy controller, the parameters in the weight matrices Q and R corresponding to different energy-saving requirements are dynamically selected to establish an adaptive cruise system LQR controller that automatically adjusts the weight matrix according to energy-saving requirements.

[0113] Fuzzy rule: Q is the state weight matrix, and R is the control weight matrix. The larger the weight coefficients related to vehicle following performance in matrix Q, the smaller the error between the actual vehicle driving state and the desired safe driving state during the entire control period, and the faster the system's state decays, meaning better vehicle following control performance of the adaptive cruise system. The elements in the weight matrix R represent the system's input acceleration. In the overall performance metrics The weights of acceleration reflect the amount of energy consumed throughout the control range; that is, the greater the acceleration, the worse the vehicle's energy economy. Larger values ​​in matrix R reduce the corresponding control input, slowing down the system's state decay. This requires less energy consumption throughout the control range, resulting in better vehicle energy economy. In summary, under low driving range conditions, the requirements for system control level are relatively low, while the requirements for system energy economy are relatively high, demanding that the adaptive cruise control system be more energy-efficient when following other vehicles. Conversely, under high driving range conditions, the requirements for adaptive cruise control system are relatively low in terms of energy economy, but relatively high in terms of system control level, requiring the vehicle to have better following performance.

[0114] In the specific implementation, the energy-saving coefficient in navigation-assisted driving mode is used as the input, and the elements in the state weight matrix Q and control weight matrix R are used as the system output. A single-input, four-output fuzzy controller is designed based on fuzzy logic rules. The energy-saving coefficient is set within its range. The system is divided into segments of 20%, and parameters are tuned using test data. The element values ​​of the weight matrix for the energy-saving coefficient in the lowest and highest segments are obtained, representing the energy-saving coefficient itself. The domain of discourse is , The domain of discourse is , The domain of discourse is , The domain of discourse is The domain of r is Each of the five variables is divided into five fuzzy subsets, and corresponding preset fuzzy control rules are established. Finally, the weight coefficients in the weight matrices Q and R are dynamically selected based on the established fuzzy controller.

[0115] The process of determining the weight matrix corresponding to the adaptive cruise LQR controller based on the energy-saving coefficient using a fuzzy controller is as follows: the weight coefficient corresponding to the energy-saving coefficient is determined using a fuzzy controller, and then the weight matrix corresponding to the adaptive cruise LQR controller is determined.

[0116] In this embodiment, the obtained weighting coefficients are passed to the adaptive cruise LQR controller to optimize the performance index function. Achieving the optimal state feedback matrix The constant matrix P can be obtained by solving the Riccati matrix algebraic equation; establish the Riccati matrix algebraic equation. Where A is the state matrix and B is the control matrix, the constant matrix P is solved, and then K is solved; finally, the optimal control is obtained. .

[0117] The control module 5003 is used to perform adaptive cruise control based on the desired acceleration at the current moment when the energy-saving coefficient is within a preset threshold range.

[0118] To avoid excessively high expected acceleration that exceeds the normal range or affects comfort, the expected acceleration value of the control variable is... Use a saturation function for restriction.

[0119] Minimum acceleration value The maximum acceleration value is However, when the expected acceleration is large, that is When a preset threshold is set, the acceleration amplitude is further limited using an energy-saving coefficient.

[0120]

[0121] By constructing an energy-saving coefficient in the navigation-assisted driving mode and using the energy-saving coefficient as the input of the fuzzy controller, the weight matrices Q and R corresponding to different energy-saving coefficients can be obtained based on the established fuzzy controller. The weight coefficients in the weight matrices Q and R are dynamically selected, the linear quadratic optimal control algorithm is improved, and a variable weight coefficient LQR controller is established. By solving the linear quadratic optimal control algorithm, the desired acceleration of the adaptive cruise control system when following the vehicle is obtained, so that the desired acceleration of the control quantity can be adaptively adjusted according to different energy-saving demand scenarios, so as to achieve the purpose of saving energy by adjusting the acceleration when the driving range is insufficient.

[0122] refer to Figure 4 , Figure 4 This is a flowchart of the energy-saving adaptive cruise control method of the first embodiment of the adaptive cruise control method based on target driving mileage of the present invention. The driver inputs the navigation destination or enters the navigation assisted driving mode through the human-machine interaction unit. The system calculates the energy-saving coefficient based on the target driving mileage required to reach the navigation destination and the vehicle's remaining driving mileage. The vehicle uses a linear quadratic optimal control (LQR) algorithm that dynamically selects weight coefficients based on the energy-saving coefficient for adaptive cruise control. A fuzzy controller is constructed based on the energy-saving coefficient, and the weight matrix in the linear quadratic optimal controller is dynamically selected. The expected acceleration at the current moment is calculated based on the weight matrix output by the fuzzy controller. When the expected acceleration value is greater than a preset value, the expected acceleration is further limited. The system calculates the energy-saving coefficient in real time for feedback control, and the execution unit controls the vehicle to follow the vehicle in front based on the expected acceleration, ultimately realizing an energy-saving adaptive cruise system based on the navigation target driving mileage and the vehicle's current remaining driving mileage.

[0123] In this embodiment, after the driver enters the navigation destination in the navigation-assisted driving mode, the target driving mileage information between the current location and the destination location is determined, and the current remaining driving range information of the vehicle is obtained. Then, the energy-saving coefficient is determined based on the target driving mileage information and the current remaining driving range information. After that, the weight matrix corresponding to the adaptive cruise LQR controller is determined by the fuzzy controller based on the energy-saving coefficient. Finally, the expected acceleration at the current moment is determined based on the weight matrix. When the expected acceleration value is greater than a preset threshold, the expected acceleration is further restricted. Compared to existing technologies that impose multiple controls on the vehicle when it enters energy-saving control mode, including maximum speed limits and torque external characteristic limits, which not only significantly impact vehicle performance but also fail to adjust the control effect of the energy-saving mode according to the vehicle's real-time energy-saving needs, this embodiment constructs an energy-saving coefficient based on the vehicle's current remaining driving range and the target driving range required to reach the destination. This coefficient characterizes different energy-saving needs of the vehicle. An improved linear quadratic optimal control algorithm is proposed, and a fuzzy controller is established. By dynamically selecting the weight coefficients in the linear quadratic controller through the energy-saving coefficient, an energy-saving adaptive cruise following control algorithm is designed. This algorithm uses a linear quadratic controller to control the vehicle's following behavior, achieving energy-saving goals while ensuring driving safety. Thus, in scenarios with high energy-saving needs, energy economy is prioritized to ensure reaching the destination, while in scenarios with low energy-saving needs, the control performance of the system is prioritized.

[0124] Other embodiments or specific implementations of the adaptive cruise control system based on target driving mileage of the present invention can be referred to the above-described method embodiments, and will not be repeated here.

[0125] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0126] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0127] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0128] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. An adaptive cruise control method based on a target driving distance, characterized in that, The adaptive cruise control method based on target driving distance includes the following steps: After the driver enters the navigation destination in the navigation-assisted driving mode, the system determines the target driving distance between the current location and the destination location in real time, and obtains the vehicle's current remaining driving range information. The energy-saving coefficient is determined based on the target driving range information and the current remaining driving range information; Based on the energy-saving coefficient, the weight matrix in the adaptive cruise LQR controller is dynamically selected by a fuzzy controller. The desired acceleration at the current moment is determined based on the weight matrix. When the energy-saving coefficient is within a preset threshold range, adaptive cruise control is performed based on the desired acceleration at the current moment. Before the step of dynamically selecting the weight matrix in the adaptive cruise LQR controller based on the energy-saving coefficient using a fuzzy controller, the following steps are included: Obtain the vehicle's fuel efficiency coefficient after the driver inputs the navigation destination in navigation-assisted driving mode; A fuzzy controller is constructed by pre-setting fuzzy control rules; The vehicle energy-saving coefficient is input into the fuzzy controller to obtain the weight coefficients in the state weight matrix and the control weight matrix corresponding to different energy-saving requirements.

2. The method as described in claim 1, characterized in that, Before the step of determining the expected acceleration at the current moment based on the weight matrix, the method further includes: Obtain the status information of your own vehicle and the vehicle in front; A vehicle following model is established based on the vehicle's status information and the preceding vehicle's status information.

3. The method as described in claim 2, characterized in that, After the step of establishing a vehicle following model based on the vehicle's state information and the preceding vehicle's state information, the method further includes: Based on the vehicle following model, the system state variables, system control variables, and system disturbance variables are determined, and the following state space equations corresponding to the adaptive cruise system are established according to the system state variables, system control variables, and system disturbance variables.

4. The method as described in claim 2, characterized in that, The step of determining the expected acceleration at the current moment based on the weight matrix includes: Based on the energy-saving coefficient, the desired acceleration at the current moment is calculated by the adaptive cruise LQR controller according to the weight matrix.

5. The method as described in claim 4, characterized in that, The step of calculating the desired acceleration at the current moment based on the energy-saving coefficient and the weight matrix using the adaptive cruise LQR controller includes: Obtain vehicle speed information, vehicle speed information, vehicle acceleration information, vehicle acceleration information, relative distance between vehicle and vehicle in front information, and safe distance between vehicle and vehicle in front information; Based on the energy-saving coefficient, the desired acceleration at the current moment is calculated by the adaptive cruise control (LQR) controller according to the weight matrix, the vehicle speed information, the speed information of the vehicle in front, the acceleration information of the vehicle in front, the relative distance information between the vehicle in front and the vehicle in front, and the safe distance information between the vehicle in front and the vehicle in front.

6. An adaptive cruise control system based on a target driving distance, characterized in that, The adaptive cruise control system based on target driving distance includes: The acquisition module is used to determine the target driving distance information between the current location and the destination location in real time after the driver enters the navigation destination in the navigation-assisted driving mode, and to obtain the current remaining driving distance information of the vehicle. The determining module is used to determine the energy-saving coefficient based on the target driving range information and the current remaining driving range information; The determining module is further configured to determine the weight matrix corresponding to the adaptive cruise LQR controller based on the energy-saving coefficient using a fuzzy controller; The determining module is further configured to determine the expected acceleration at the current moment based on the weight matrix; The control module is used to perform adaptive cruise control based on the desired acceleration at the current moment when the energy-saving coefficient is within a preset threshold range. The target-mileage-based adaptive cruise control system comprises the steps of implementing the target-mileage-based adaptive cruise control method as described in any one of claims 1 to 5.

7. An adaptive cruise control device based on a target driving mileage, characterized in that, The device includes: a memory, a processor, and an adaptive cruise control program based on a target mileage stored in the memory and executable on the processor, the adaptive cruise control program based on the target mileage being configured to implement the steps of the adaptive cruise control method based on a target mileage as described in any one of claims 1 to 5.

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