Self-adaptive cruise control method and device and computer readable storage medium

Abstract

The invention is suitable for the technical field of automation control, and provides a self-adaptive cruise control method and device, terminal equipment and a computer readable storage medium, and the method comprises the steps: obtaining the driving operation information of a driver under a current road condition and a preset acceleration curve corresponding to the current road condition; generating an updated acceleration curve corresponding to the current road condition according to a preset acceleration curve and the driving operation information of the driver under the current road condition, and replacing the preset acceleration curve with the updated acceleration curve so as to control the vehicle by using the updated acceleration curve when the vehicle is in the same road condition next time. According to the adaptive cruise control method and the adaptive cruise control system, the individual requirements of the driver are considered, the adaptive cruise system is optimized according to the driving habit of the driver, the adaptive cruise system better fits the driving habit of the user, vehicle control better conforms to the psychological expectation of the driver, the utilization rate and the applicability of the adaptive cruise system are improved, and the user experience is improved. And the use experience of the user is improved.

Description

Technical Field

[0001] The present application belongs to the field of automation control technology, and in particular, relates to an adaptive cruise control method, device, terminal equipment and computer-readable storage medium. Background Art

[0002] The current adaptive cruise control system is relatively mature, and more and more vehicles on the market are equipped with adaptive cruise control systems. However, the driver's driving habits, as a complex factor, often determine the driver's acceptance and recognition of the adaptive cruise control system. Drivers with different personalities have different requirements for the control system. For example, aggressive drivers focus on efficiency, like to drive fast, brake less, brake later, and keep a smaller distance from the car in front; while conservative drivers focus on safety, keep a larger distance from the car in front, and brake as early as possible for safety. Therefore, in actual use, the adaptive cruise control system cannot flexibly adapt to each driver's personal driving habits, and the fit with user needs is poor, which makes the driver feel too mechanical, resulting in a reduced user experience. Summary of the invention

[0003] The embodiments of the present application provide an adaptive cruise control method, apparatus, terminal device and computer-readable storage medium, which can solve the problem that the adaptive cruise system cannot flexibly adapt to each driver's individual driving habits and has a poor fit with user needs.

[0004] In a first aspect, an embodiment of the present application provides an adaptive cruise control method, comprising:

[0005] Acquire the driver's driving operation information under the current road condition and the preset acceleration curve corresponding to the current road condition;

[0006] An updated acceleration curve corresponding to the current road condition is generated based on the preset acceleration curve and the driving operation information of the driver under the current road condition, and the preset acceleration curve is replaced by the updated acceleration curve, so that the updated acceleration curve is used to control the vehicle next time the vehicle is in the same road condition.

[0007] In a possible implementation, before obtaining the driving operation information of the driver under the current road condition and the preset acceleration curve corresponding to the current road condition, the method further includes:

[0008] Acquiring the driver's driving state information, and determining the driver's driving concentration level according to the driver's driving state information;

[0009] If the concentration level of the driver's driving behavior is higher than a preset threshold, the driving operation information of the driver under the current road condition is obtained.

[0010] In a possible implementation, the driver status information includes: a facial image of the driver, and the acquiring of the driver's driving status information and determining the concentration of the driver's driving behavior according to the driver status information include:

[0011] capturing at least one facial image of the driver;

[0012] Based on the at least one facial image, the driver's attentiveness is determined.

[0013] In a possible implementation, determining the driver's concentration level according to the at least one facial image includes:

[0014] Determine the driver's concentration level corresponding to each facial image according to the driver's sight angle in each facial image collected and the corresponding relationship between the driver's sight angle and the concentration level;

[0015] According to the driver's concentration level corresponding to each facial image, a final driver's concentration level is generated.

[0016] In a possible implementation, determining the driver's concentration level corresponding to each facial image according to the driver's sight angle in each collected facial image and the correspondence between the driver's sight angle and the concentration level includes:

[0017] Extracting key point positions in the facial image;

[0018] According to the key point positions, obtaining the driver's eye information and head angle information;

[0019] Determining the driver's sight angle according to the driver's eye information and head angle information;

[0020] The driver's concentration level corresponding to each facial image is determined according to the driver's sight angle and the corresponding relationship between the driver's sight angle and the concentration level.

[0021] In a possible implementation, generating an updated acceleration curve corresponding to the current road condition according to the preset acceleration curve and the driving operation information of the driver under the current road condition includes:

[0022] According to the initial acceleration and initial covariance of the current system, combined with a plurality of preset Kalman parameter matrices, an updated acceleration at each time point in the preset acceleration curve is generated;

[0023] The updated accelerations at all time points are summarized to generate the updated acceleration curve.

[0024] In a possible implementation, based on the acceleration initial value and the covariance initial value of the current system, combined with a plurality of preset Kalman parameter matrices, an updated acceleration at each time point in the preset acceleration curve is generated, including:

[0025] At a starting time point of a preset acceleration curve, based on the initial acceleration and initial covariance corresponding to the starting time point, combined with a plurality of preset Kalman parameter matrices, an updated acceleration and an updated covariance corresponding to the starting time point are generated;

[0026] The updated covariance and updated acceleration are used as the initial covariance and initial acceleration of the next time point adjacent to the current time point, and at the next adjacent time point, based on the initial acceleration and initial covariance corresponding to the next time point, combined with the multiple Kalman parameter matrices, an updated acceleration and updated covariance corresponding to the next time point are generated until each time point is traversed.

[0027] In a possible implementation, the multiple Kalman parameter matrices include: a discrete state transition matrix, a system error matrix, an observation matrix, a measurement error matrix, and a control matrix. According to the initial acceleration and initial covariance corresponding to a time point, combined with the preset multiple Kalman parameter matrices, an updated acceleration and updated covariance corresponding to the time point are generated, including:

[0028] Generate an intermediate acceleration and an intermediate covariance according to a discrete state transfer matrix, a system error matrix, a control matrix, an initial acceleration, an initial covariance, and a preset acceleration at a corresponding time point in a preset acceleration curve;

[0029] The updated acceleration and the updated covariance are generated according to the discrete state transfer matrix, the system error matrix, the intermediate acceleration, the intermediate covariance and the collected operation acceleration of the driver at the corresponding time point.

[0030] In a second aspect, an embodiment of the present application provides an adaptive cruise control device, comprising:

[0031] Information acquisition module: obtains the driver's driving operation information under the current road conditions and the preset acceleration curve corresponding to the current road conditions;

[0032] Update control module: Generate an updated acceleration curve corresponding to the current road condition based on the preset acceleration curve and the driving operation information of the driver under the current road condition, and replace the preset acceleration curve with the updated acceleration curve, so that the updated acceleration curve can be used to control the vehicle next time the vehicle is in the same road condition.

[0033] In a third aspect, an embodiment of the present application provides a terminal device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described above when executing the computer program.

[0034] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method as described above is implemented.

[0035] Beneficial effects of this application

[0036] The present application provides an adaptive cruise control system method, device, terminal equipment and storage medium, wherein the method comprises: obtaining driving operation information of a driver under current road conditions and a preset acceleration curve corresponding to the current road conditions, and generating an updated acceleration curve corresponding to the current road conditions according to the preset acceleration curve and the driving operation information of the driver under the current road conditions, replacing the preset acceleration curve with the updated acceleration curve, so as to control the vehicle using the updated acceleration curve when the vehicle is in the same road conditions next time.

[0037] Through the method provided by this application, the originally solidified adaptive cycle system parameters can be adjusted in combination with the driver's driving operating habits, and the vehicle can be controlled with the adjusted parameters. Therefore, this application takes into account the personalized needs of the driver, optimizes the adaptive cruise system according to the driver's driving habits, makes the adaptive cruise system more in line with the user's driving habits, and controls the vehicle more in line with the driver's psychological expectations, which can improve the utilization and applicability of the adaptive cruise system and improve the user's experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0039] Figure 1 is a flow chart of an adaptive cruise control method provided by an embodiment of the present application;

[0040] Figure 2 is a flow chart of an adaptive cruise system control method provided by another embodiment of the present application;

[0041] Figure 3 is a flow chart for determining the driver's concentration level provided by an embodiment of the present application;

[0042] Figure 4 is a flow chart for determining the driver's concentration level according to the driver's sight angle provided by an embodiment of the present application;

[0043] Figure 5 is a flow chart of generating and updating an acceleration curve provided by an embodiment of the present application;

[0044] Figure 6 is a flow chart of generating update acceleration provided by an embodiment of the present application;

[0045] Figure 7 is a flow chart of generating updated acceleration and updated covariance provided by an embodiment of the present application;

[0046] Figure 8 is a schematic diagram of the structure of an adaptive cruise control system provided in an embodiment of the present application;

[0047] Fig. 9 It is a schematic diagram of the structure of the terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0048] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0049] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0050] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0051] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0052] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0053] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0054] The current adaptive cruise control system is relatively mature, and more and more vehicles on the market are equipped with adaptive cruise control systems. However, the driver's driving habits, as a complex factor, often determine the driver's acceptance and recognition of the adaptive cruise control system. Drivers with different personalities have different requirements for the control system. For example, aggressive drivers focus on efficiency, like to drive fast, brake less, brake later, and keep a small distance from the car in front; while conservative drivers focus on safety, keep a large distance from the car in front, and brake as early as possible for safety. Therefore, in actual use, the adaptive cruise control system cannot flexibly adapt to each driver's personal driving habits, and the fit with user needs is poor, which makes the driver feel too mechanical, resulting in a reduced user experience.

[0055] The present application provides an adaptive cruise control method, device, terminal device and storage medium, wherein the method comprises: obtaining the driving operation information of the driver under the current road condition and the preset acceleration curve corresponding to the current road condition, and generating an updated acceleration curve corresponding to the current road condition according to the preset acceleration curve and the driving operation information of the driver under the current road condition, and replacing the preset acceleration curve with the updated acceleration curve, so as to control the vehicle with the updated acceleration curve when the vehicle is in the same road condition next time. Through the method provided by the present application, the originally solidified adaptive cycle system parameters can be adjusted in combination with the driving operation habits of the driver, and the vehicle can be controlled with the adjusted parameters. Therefore, the present application takes into account the personalized needs of the driver, optimizes the adaptive cruise system according to the driving habits of the driver, makes the adaptive cruise system more in line with the user's driving habits, and controls the vehicle more in line with the driver's psychological expectations, which can improve the utilization and applicability of the adaptive cruise system and improve the user's experience.

[0056] In order to illustrate the technical solution of the present application, specific embodiments are provided below.

[0057] See also Figure 1 The process of an embodiment of the adaptive cruise system control method shown, as an example but not a limitation, includes the following steps:

[0058] Step S1: obtaining the driving operation information of the driver under the current road condition and the preset acceleration curve corresponding to the current road condition;

[0059] Step S2: generating an updated acceleration curve corresponding to the current road condition based on the preset acceleration curve and the driving operation information of the driver under the current road condition, and replacing the preset acceleration curve with the updated acceleration curve, so that the updated acceleration curve can be used to control the vehicle next time the vehicle is in the same road condition.

[0060] In order to better understand the present invention, first of all, a brief introduction to the adaptive cruise system is given. The adaptive cruise control system (Adaptive Cruise Control, ACC for short), also known as an intelligent cruise control system or an active cruise system, has a radar distance sensor, a wheel speed sensor and an ACC electronic control unit. Through the interaction of this system, the vehicle speed is actively intervened, which can not only allow the vehicle to maintain a certain driving speed, but also automatically adjust the speed according to the distance from the vehicle in front to ensure the best safety distance from the vehicle in front, so that the vehicle can maintain a relatively safe range. ACC automatically controls the speed and distance based on the accurate judgment of the real-time road conditions, thereby reducing the driver's operation of the accelerator and brake. The main purpose of ACC is to improve the driver's comfort, reduce the workload, and ensure driving safety. It can relieve the fatigue of stepping on the accelerator or braking for a long time, making driving easier and improving comfort.

[0061] It should be noted that the driving operation information mentioned in step S1 and step S2 includes: the acceleration value collected after the driver steps on the brake and accelerator. Since the acceleration value is more in line with the driver's driving habits, the present application merges it with the preset acceleration curve to obtain an updated acceleration curve, and uses the updated acceleration curve to control the vehicle so that the vehicle's driving process is closer to the driver's expectations.

[0062] Through the above method, the originally fixed adaptive cycle system parameters can be adjusted in combination with the driver's driving operation habits, and the vehicle can be controlled with the adjusted parameters. This method takes into account the driver's personalized needs and optimizes the adaptive cruise system according to the driver's driving habits, so that the adaptive cruise system is more in line with the user's driving habits, and the control of the vehicle is more in line with the driver's psychological expectations, which can improve the utilization and applicability of the adaptive cruise system and improve the user's experience.

[0063] It should be noted that the setting of this scheme is of practical significance. In specific road conditions, such as when the vehicle in front slows down, the system will send an execution signal to the engine or brake system to reduce the speed so that the vehicle and the vehicle in front maintain a safe driving distance. In the process of reducing the speed, the deceleration time or the acceleration during the deceleration may not be consistent with the driver's expectations. For example, aggressive drivers are accustomed to braking when they are close to the vehicle in front, while conservative drivers may be accustomed to slowing down and braking in advance; or if there is no vehicle in front of the vehicle, the driver can use the expected speed set by the driver to drive, and in the process of accelerating the vehicle to the expected speed, aggressive drivers may step on the accelerator more urgently, hoping to accelerate to the expected speed in a shorter time, while conservative drivers may accelerate slowly to the expected speed for safety considerations. Therefore, it is necessary to propose a method for optimizing the adaptive cruise system according to the user's driving habits, provide users with an exclusive adaptive cruise control method, make the adaptive cruise system more in line with the user's driving habits, and improve the user's experience.

[0064] By way of example and not limitation, the following steps are included:

[0065] In one possible implementation, see Figure 2 , before obtaining the driving operation information of the driver under the current road condition and the preset acceleration curve corresponding to the current road condition, it also includes:

[0066] Step A1: obtaining the driver's driving state information, and determining the driver's driving concentration level according to the driver's driving state information;

[0067] Step A2: If the driver's driving concentration level is higher than a preset threshold, the driver's driving operation information under the current road condition is obtained.

[0068] In order to better understand this solution, here is a brief introduction to distracted driving behavior: Driving a car is a complex task that requires full concentration. Distracted driving refers to any activity that diverts the driver's attention from the road. Several studies have identified three main types of distractions: visual distractions (the driver's eyes are not on the road), manual distractions (the driver's hands are removed from the steering wheel), and cognitive distractions (the driver's attention is on the driving task). For example, distracted behaviors include: smoking, talking on the phone, yawning, drinking water in the car, eating in the car, looking down at something, looking at the scenery outside the window, and taking both or one hand off the steering wheel.

[0069] It should be noted that distracted driving is one of the main causes of traffic accidents and it can happen to anyone, no matter how experienced the driver is. Therefore, in this embodiment, in order to ensure that the collected driving operation information of the driver is obtained when the driver is concentrating on driving, before obtaining the driving operation information of the driver under the current road conditions, it is necessary to judge the driving state of the driver. In this embodiment, the concentration of the driver's driving behavior is judged according to the driving state information of the driver. When the concentration of the driver does not reach the preset value, the driving operation information of the driver is not collected. Only when the concentration of the driver is higher than the preset threshold, the driving operation information of the driver is collected, thereby ensuring the reliability of the driving operation information.

[0070] In a possible implementation, the driver status information includes: a facial image of the driver, and the acquiring of the driver's driving status information and determining the concentration of the driver's driving behavior according to the driver status information include:

[0071] Step A101: collecting at least one facial image of the driver;

[0072] Step A102: Determine the driver's concentration level based on the at least one facial image.

[0073] It should be noted that, in this embodiment, the concentration level is mainly judged based on facial image information, because facial images contain more effective information, such as head posture, sight angle, and eye state (eyes open or closed).

[0074] In other possible implementations, the driver's manual interference behavior, such as the driver's hand leaving the steering wheel, can also be detected to judge the driver's concentration in driving behavior. Specifically, a grip force sensor installed on the steering wheel can be used. When the driver holds the steering wheel and applies enough pressure to the steering wheel, the steering wheel will detect that the driver's hand has not left the steering wheel; if the driver does not hold the steering wheel with enough force, the system will detect that the driver's hand is out of the steering wheel.

[0075] It should be noted that the above-mentioned methods for detecting manual interference behavior are all existing technologies and are not the main points of the invention of this application, so they will not be elaborated here. In addition, the above-mentioned methods for detecting manual interference behavior are only used as exemplary descriptions, and this application includes but is not limited to them.

[0076] In other possible implementations, the driver's cognitive interference behaviors (such as drinking water, eating, smoking, etc. in the car) can also be detected to determine the driver's concentration on driving. Specifically, there are two commonly used cognitive interference behavior detection methods: the first is to regard such action recognition as a target detection problem. For example, for phone call recognition, the mobile phone is regarded as the target to be detected, and for in-car drinking water detection, the water cup is regarded as the target to be detected. Another method is to regard action recognition as a single-frame image classification task, collect pictures under various action behaviors, manually annotate supervision signals for training, and use a classifier to determine the final action classification.

[0077] It should be noted that the above-mentioned methods for detecting cognitive interference behaviors are all existing technologies and are not the main points of the invention of this application, so they will not be elaborated here. In addition, the above-mentioned methods for detecting cognitive interference behaviors are only used as exemplary descriptions, and this application includes but is not limited to them.

[0078] In one possible implementation, see Figure 3 A flow chart of determining the driver's concentration level according to the at least one facial image provided by an embodiment of the present application is shown, including:

[0079] Step A111: determining the driver's concentration level corresponding to each facial image according to the driver's sight angle in each collected facial image and the corresponding relationship between the driver's sight angle and the concentration level;

[0080] Step A112: Generate a final driver concentration level according to the driver concentration level corresponding to each facial image.

[0081] It should be noted that this embodiment provides a method for determining the driver's concentration level based on facial images, specifically through the driver's line of sight angle information. Because in the driver's actual driving process, once the line of sight angle deviates too much, it means that the driver is not concentrating on driving behavior. Possible situations include the driver lowering his head to look for something or turning back to talk to the rear passengers. When a similar line of sight angle deviates too much, the driver's driving operation information will no longer be collected, thereby ensuring the reliability of the collected data.

[0082] It should be noted that if only one facial image is collected, the driver's line of sight angle determined from the facial image is combined with the correspondence between the line of sight angle and the degree of concentration to directly generate the final driver's concentration. If multiple facial images are collected, the driver's line of sight angle determined from each facial image is combined with the correspondence between the line of sight angle and the degree of concentration to determine the driver's concentration corresponding to each facial image, and the average of the concentration of all images is taken as the final driver's concentration. The driver's concentration determined by collecting multiple facial images can reflect the driver's driving status over a period of time, and the result is more accurate.

[0083] In one possible implementation, see Figure 4 The step of determining the driver's concentration level corresponding to each facial image based on the driver's sight angle in each facial image collected and the corresponding relationship between the driver's sight angle and the concentration level includes:

[0084] Step A121: extracting key point positions in the facial image;

[0085] Step A122: acquiring the driver's eye information and head angle information according to the key point positions;

[0086] Step A123: determining the driver's sight angle according to the driver's eye information and head angle information;

[0087] Step A124: Determine the driver's concentration level corresponding to each facial image based on the driver's sight angle and the correspondence between the driver's sight angle and the concentration level.

[0088] In this embodiment, a specific method for determining the driver's concentration level based on the driver's sight angle in each collected facial image is provided.

[0089] Among them, in step A121, a deep learning algorithm can be used to train a deep learning model, and the facial image can be input into the deep learning model. The deep learning model outputs 2D key point position information in the facial image, including the position information of key points such as the facial contour, left and right eyes, nose, and mouth.

[0090] It should be noted that in step A122, obtaining the driver's eye information includes: based on the 2D key point position information, for example, based on the position information of the 2D key points at the left and right eyes in the 2D key point position information, cropping the face area image to obtain corresponding left eye image information and right eye image information; obtaining the head angle information includes: based on the 2D key point position information and the 3D key point position information, solving the conversion relationship between the 2D key points represented by the 2D key point position information and the 3D key points represented by the 3D key point position information to obtain the rotation matrix of the camera, and then converting according to the rotation matrix of the camera to obtain the Euler angle, that is, the posture of the head relative to the camera, thereby obtaining the head angle information.

[0091] It should be noted that in step A123, based on the original face image and the left eye image, right eye image and head angle information corresponding to the original face image, the driver's line of sight angle corresponding to the original face image is obtained, that is, the angle at which the current driver's line of sight deviates from the front.

[0092] In one possible implementation, see Figure 5 , generating an updated acceleration curve corresponding to the current road condition according to the preset acceleration curve and the driving operation information of the driver under the current road condition, comprising:

[0093] Step S201: generating an updated acceleration at each time point in the preset acceleration curve according to the initial acceleration and initial covariance of the current system in combination with a plurality of preset Kalman parameter matrices;

[0094] Step S202: Summarize the updated accelerations at all time points to generate the updated acceleration curve.

[0095] In order to better understand this solution, we first briefly explain Kalman filtering. Kalman filtering is an algorithm that uses the linear system state equation to optimally estimate the system state through the system input and output observation data. Since the observation data includes the influence of noise and interference in the system, the optimal estimation can also be regarded as a filtering process.

[0096] In layman's terms, when there is a system that contains some unknown information and we want to predict some future actions of the system, the Kalman filter is a relatively effective tool. A certain system may contain many different state information. For example, a car driving system includes state information such as displacement, speed, thrust, etc. The more state information we obtain, the more favorable it is for us to estimate the information we want to predict, because most of the state information in a system is interrelated. For the Kalman filter, the correlation between these different states is represented by the covariance matrix.

[0097] A typical example of Kalman filtering is to predict the coordinates and velocity of an object's position from a finite, noisy sequence of observations of the object's position (which may be biased). It can be found in many engineering applications (such as radar and computer vision). For example, for radar, people are interested in its ability to track targets. However, the measurements of the position, velocity, and acceleration of the target are often noisy at all times. Kalman filtering uses the dynamic information of the target to try to remove the influence of noise and obtain a good estimate of the target's position. This estimate can be an estimate of the current target position (filtering), an estimate of the future position (prediction), or an estimate of the past position (interpolation or smoothing).

[0098] The principle of Kalman filtering is to simply say that the best estimate at the current moment is a linear correction based on the best estimate at the previous moment and the observed value at the current moment. Mathematically, Kalman filtering is a linear minimum variance statistical estimation method that obtains the best estimate of physical parameters by processing a series of actual measurement data with errors. The problem it essentially solves is to find the estimated value under the minimum mean square error. Its characteristics are that it can be calculated by recursive methods, and the required data storage volume is small, which is convenient for real-time processing. Specifically, Kalman filtering is to use prediction equations and measurement equations to estimate the system state.

[0099] It should be noted that, in this embodiment, by means of Kalman filtering, the optimal acceleration curve for the current road condition can be obtained according to the preset acceleration curve and the driving operation information of the driver under the current road condition.

[0100] In one possible implementation, see Figure 6 , based on the initial acceleration value and the initial covariance value of the current system, combined with a plurality of preset Kalman parameter matrices, generating an updated acceleration at each time point in the preset acceleration curve, including:

[0101] Step S211: at the starting time point of the preset acceleration curve, based on the initial acceleration and initial covariance corresponding to the starting time point, combined with a plurality of preset Kalman parameter matrices, generating an updated acceleration and an updated covariance corresponding to the starting time point;

[0102] Step S212: Use the updated covariance and updated acceleration as the initial covariance and initial acceleration of the next time point adjacent to the current time point, and at the adjacent next time point, generate the updated acceleration and updated covariance corresponding to the next time point according to the initial acceleration and initial covariance corresponding to the next time point in combination with the multiple Kalman parameter matrices until each time point is traversed.

[0103] It should be noted that, since the meaning of Kalman filtering is that the best estimate at the current moment is a linear correction based on the best estimate at the previous moment according to the observed value at the current moment, in this embodiment, the updated acceleration and updated covariance calculated at the current moment are used as the initial covariance and initial acceleration at the next time point, and the updated acceleration and updated covariance corresponding to the next time point are continuously calculated. Therefore, Kalman filtering not only filters out the noise of the measurement signal, but also combines the previous estimates. Its characteristics are that it can be calculated by recursive method, and the required data storage volume is small, which is convenient for real-time processing.

[0104] In one possible implementation, see Figure 7 , the multiple Kalman parameter matrices include: discrete state transfer matrix (A), system error matrix (Q), observation matrix (H), measurement error matrix (R) and control matrix (B), according to the initial acceleration and initial covariance corresponding to a time point, combined with the preset multiple Kalman parameter matrices, generate the updated acceleration and updated covariance corresponding to the time point, including:

[0105] Step S221: Generate an intermediate acceleration and an intermediate covariance according to the discrete state transfer matrix (A), the system error matrix (Q), the control matrix (B), the initial acceleration, the initial covariance, and the preset acceleration at the corresponding time point in the preset acceleration curve;

[0106] Step S222: Generate the updated acceleration and the updated covariance according to the discrete state transfer matrix (A), the system error matrix (Q), the intermediate acceleration, the intermediate covariance and the collected operating acceleration of the driver at the corresponding time point.

[0107] It should be noted that, in step S221, the specific calculation process includes:

[0108] (1) Generate the acceleration estimate (x_) based on the discrete state transfer matrix (A), initial acceleration (x), control matrix (B), and control vector u. The specific formula is: x_ = A*x + Bu;

[0109] (2) Generate the estimated covariance (Pk_) based on the discrete state transfer matrix (A), the initial covariance (Pk) and the system error matrix (Q). The specific formula is: Pk_ = A*Pk*A'+Q;

[0110] (3) Generate the intermediate Kalman gain (Kk1) based on the estimated covariance (Pk_), the observation matrix (H) and the measurement error matrix (R). The specific formula is: Kk1 = (Pk_*H') / (H*Pk_*H'+R(1,1));

[0111] (4) Generate the intermediate acceleration (x1) according to the estimated acceleration value (x_), the intermediate Kalman gain (Kk1), the preset acceleration at the corresponding time point in the preset acceleration curve and the observation matrix (H). The specific formula is: x1 = x_ + Kk1*(temp_var1(i)-H*x_);

[0112] (5) Generate the intermediate covariance (Pk1) based on the intermediate Kalman gain (Kk1), the observation matrix (H) and the estimated covariance (Pk_). The specific formula is: Pk1 = (diag([1 1 1])-Kk1*H)*Pk_.

[0113] It should be noted that, in step S222, the specific calculation process includes:

[0114] (6) Generate the Kalman gain (Kk) based on the intermediate covariance (Pk1), the observation matrix (H) and the measurement error matrix (R). The specific formula is: Kk = (Pk1*H') / (H*Pk1*H'+R(2,2));

[0115] (7) Generate updated acceleration (x2) based on the intermediate acceleration (x1), the intermediate Kalman gain (Kk1), the driver's operating acceleration at the corresponding time point and the observation matrix (H). The specific formula is: x2 = x1 + Kk1 * (temp_var2 (i) - H * x1);

[0116] (8) Generate the updated covariance (Pk) based on the Kalman gain (Kk), the observation matrix (H) and the intermediate covariance (Pk1). The specific formula is: Pk = (diag([1 1 1])-Kk*H)*Pk1.

[0117] An adaptive cruise control device provided in an embodiment of the present application, referring to Figure 8 ,include:

[0118] Information acquisition module 301: acquiring the driver's driving operation information under the current road condition and a preset acceleration curve corresponding to the current road condition;

[0119] Update control module 302: Generate an updated acceleration curve corresponding to the current road condition based on the preset acceleration curve and the driving operation information of the driver under the current road condition, and replace the preset acceleration curve with the updated acceleration curve, so that the updated acceleration curve can be used to control the vehicle next time the vehicle is in the same road condition.

[0120] By using the above two modules together, the original fixed adaptive cycle system parameters can be adjusted in combination with the driver's driving habits, and the vehicle can be controlled with the adjusted parameters. This device takes into account the driver's personalized needs and optimizes the adaptive cruise system according to the driver's driving habits, making the adaptive cruise system more in line with the user's driving habits and the control of the vehicle more in line with the driver's psychological expectations, which can improve the utilization and applicability of the adaptive cruise system and improve the user experience.

[0121] Fig. 9 The terminal device 400 includes: at least one processor 401 ( Figure 4 Only one is shown in the figure) a processor, a memory 402, and a computer program 403 stored in the memory 402 and executable on the at least one processor 401, and when the processor 401 executes the computer program 403, the steps in the above-mentioned adaptive cruise control method embodiment are implemented.

[0122] The terminal device 400 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal device may include, but is not limited to, a processor 401 and a memory 402. Those skilled in the art will appreciate that Fig. 9 It is only an example of the terminal device 400 and does not constitute a limitation on the terminal device 400. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, for example, it may also include input and output devices, network access devices, etc.

[0123] The processor 401 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0124] In some embodiments, the memory 402 may be an internal storage unit of the terminal device 400, such as a hard disk or memory of the terminal device 400. In other embodiments, the memory 402 may also be an external storage device of the terminal device 400, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the terminal device 400. Further, the memory 402 may also include both an internal storage unit of the terminal device 400 and an external storage device. The memory 402 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program, etc. The memory 402 may also be used to temporarily store data that has been output or is to be output.

[0125] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0126] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0127] An embodiment of the present application provides a computer program product. When the computer program product runs on a mobile terminal, the mobile terminal can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0128] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0129] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0130] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0131] In the embodiments provided in the present application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0132] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0133] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. An adaptive cruise control method, characterized in that: include: Obtaining the driver's driving operation information under the current road condition and the preset acceleration curve corresponding to the current road condition; An updated acceleration curve corresponding to the current road condition is generated based on the preset acceleration curve and the driving operation information of the driver under the current road condition, and the preset acceleration curve is replaced by the updated acceleration curve, so that the updated acceleration curve is used to control the vehicle next time the vehicle is in the same road condition.

2. The adaptive cruise control method according to claim 1, characterized in that: Before obtaining the driving operation information of the driver under the current road condition and the preset acceleration curve corresponding to the current road condition, the method further includes: Acquiring the driver's driving state information, and determining the driver's driving concentration level according to the driver's driving state information; If the concentration level of the driver's driving behavior is higher than a preset threshold, the driving operation information of the driver under the current road condition is obtained.

3. The adaptive cruise control method according to claim 2, characterized in that: The driver status information includes: a facial image of the driver. The acquiring of the driver driving status information and determining the concentration of the driver's driving behavior according to the driver status information include: capturing at least one facial image of the driver; Based on the at least one facial image, the driver's attentiveness is determined.

4. The adaptive cruise control method according to claim 3, characterized in that: Determining the driver's concentration level based on the at least one facial image includes: Determine the driver's concentration level corresponding to each facial image according to the driver's sight angle in each facial image collected and the corresponding relationship between the driver's sight angle and the concentration level; According to the driver's concentration level corresponding to each facial image, a final driver's concentration level is generated.

5. The adaptive cruise control method according to claim 4, characterized in that: Determining the driver's concentration level corresponding to each facial image according to the driver's sight angle in each facial image collected and the corresponding relationship between the driver's sight angle and the concentration level includes: Extracting key point positions in the facial image; According to the key point positions, obtaining the driver's eye information and head angle information; Determining the driver's sight angle according to the driver's eye information and head angle information; The driver's concentration level corresponding to each facial image is determined according to the driver's sight angle and the corresponding relationship between the driver's sight angle and the concentration level.

6. The adaptive cruise control method according to claim 1, characterized in that: The step of generating an updated acceleration curve corresponding to the current road condition according to the preset acceleration curve and the driving operation information of the driver under the current road condition comprises: According to the initial acceleration and initial covariance of the current system, combined with a plurality of preset Kalman parameter matrices, an updated acceleration at each time point in the preset acceleration curve is generated; The updated accelerations at all time points are summarized to generate the updated acceleration curve.

7. The adaptive cruise control method according to claim 6, characterized in that: According to the initial acceleration value and the initial covariance value of the current system, combined with a plurality of preset Kalman parameter matrices, an updated acceleration at each time point in the preset acceleration curve is generated, including: At a starting time point of a preset acceleration curve, based on the initial acceleration and initial covariance corresponding to the starting time point, combined with a plurality of preset Kalman parameter matrices, an updated acceleration and an updated covariance corresponding to the starting time point are generated; The updated covariance and updated acceleration are used as the initial covariance and initial acceleration of the next time point adjacent to the current time point, and at the next adjacent time point, based on the initial acceleration and initial covariance corresponding to the next time point, combined with the multiple Kalman parameter matrices, an updated acceleration and updated covariance corresponding to the next time point are generated until each time point is traversed.

8. The adaptive cruise control method according to claim 7, characterized in that: The multiple Kalman parameter matrices include: a discrete state transfer matrix, a system error matrix, an observation matrix, a measurement error matrix, and a control matrix. According to the initial acceleration and initial covariance corresponding to a time point, combined with the preset multiple Kalman parameter matrices, an updated acceleration and updated covariance corresponding to the time point are generated, including: Generate an intermediate acceleration and an intermediate covariance according to a discrete state transfer matrix, a system error matrix, a control matrix, an initial acceleration, an initial covariance, and a preset acceleration at a corresponding time point in a preset acceleration curve; The updated acceleration and the updated covariance are generated according to the discrete state transfer matrix, the system error matrix, the intermediate acceleration, the intermediate covariance and the collected operation acceleration of the driver at the corresponding time point.

9. An adaptive cruise control device, characterized in that: include: Information acquisition module: obtains the driver's driving operation information under the current road conditions and the preset acceleration curve corresponding to the current road conditions; Update control module: Generate an updated acceleration curve corresponding to the current road condition based on the preset acceleration curve and the driving operation information of the driver under the current road condition, and replace the preset acceleration curve with the updated acceleration curve, so that the updated acceleration curve can be used to control the vehicle next time the vehicle is in the same road condition.

10. A terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

11. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

Images