CN114604255A

CN114604255A - Vehicle control method, vehicle control device, computer device, and storage medium

Info

Publication number: CN114604255A
Application number: CN202210418418.9A
Authority: CN
Inventors: 杜小甫
Original assignee: Mufeng Electronic Technology Guangzhou Co ltd
Current assignee: Mufeng Electronic Technology Guangzhou Co ltd
Priority date: 2022-04-20
Filing date: 2022-04-20
Publication date: 2022-06-10

Abstract

The invention discloses a vehicle control method, a vehicle control device, a computer device and a computer storage medium. The vehicle control method adjusts the vehicle control parameters in time according to the psychological data of the driver and controls the vehicle to run according to the adjusted control parameters. The vehicle control method includes: determining first psychological data of a driver corresponding to a target vehicle, wherein the first psychological data comprises emotion data and/or attention data; inputting the first psychological data into a control parameter adjustment model to obtain a first calculation control parameter; adjusting the target vehicle based on the first calculated control parameter.

Description

Vehicle control method, vehicle control device, computer device, and storage medium

Technical Field

The invention relates to the field of automatic driving, in particular to a control method of an automatic driving vehicle and related equipment.

Background

In recent years, the development of automatic driving reduces the labor intensity of a driver for driving a vehicle to a certain extent, and effectively reduces the fatigue of the driver caused by controlling the vehicle for a long time.

The attention of the prior art automatic driving vehicle Control is mainly that the vehicle itself does not use the feeling or emotion brought to the user by the system as a Control factor, and the vehicle is input to perform closed-loop Control, for example, once the following distance of an Adaptive Cruise Control (ACC) is set, the system follows the vehicle according to the set parameters, and if the user feels that the following distance is short in the following process, panic is generated, and the system does not know or uses the panic as the system Control input to adjust the system parameters, so that discomfort or distrust of the user for the automatic driving system can be brought.

Disclosure of Invention

The invention provides a method and related equipment for actively identifying the emotion of a driver based on physiological parameters of the driver and adjusting control parameters of an automatic driving vehicle based on the emotion of the driver, aiming at solving the technical problem that in the prior art, closed-loop control is carried out by taking the feeling or emotion brought to the system as control input.

A first aspect of the invention provides a vehicle control method including the steps of;

determining first psychological data of a driver corresponding to a target vehicle, wherein the first psychological data comprises emotion data and/or attention data;

inputting the first psychological data into a control parameter adjustment model to obtain a first calculation control parameter, wherein the control parameter adjustment model is obtained by training a control parameter training sample, and the control parameter training sample comprises training psychological data corresponding to at least one driver and a control parameter corresponding to the training psychological data;

adjusting the target vehicle based on the first calculated control parameter.

Optionally, the determining the first psychological data of the driver corresponding to the target vehicle includes the following steps:

collecting a first body characteristic parameter of the driver;

inputting the first body feature parameter into a driver recognition model to obtain the first psychological data.

Optionally, the method further comprises the following steps:

acquiring physical characteristic parameters corresponding to at least one driver and actual psychological data corresponding to the physical characteristic parameters;

preprocessing the physical characteristic parameters corresponding to the at least one driver and the actual psychological data corresponding to the physical characteristic parameters;

performing iterative operation according to the preprocessed data and the initial driver identification model until a preset iteration termination condition is reached;

determining the initial driver identification model when the preset iteration termination condition is reached as the driver identification model.

Optionally, the method further comprises the following steps:

judging whether the iteration times reach a preset value or not, and if so, determining that the preset iteration termination condition is met;

or the like, or, alternatively,

and judging whether the model parameters corresponding to the initial driver identification model are converged, if so, determining that the preset iteration termination condition is met.

Optionally, the method further comprises the following steps:

determining second psychological data of the driver after adjustment of the target vehicle based on the first calculated control parameter;

if the second psychological data does not reach a preset adjusting threshold value, determining a second calculation control parameter based on the second psychological data and the control parameter adjusting model;

adjusting the target vehicle based on the second calculated control parameter.

Optionally, the method further comprises the following steps:

determining an expected return function corresponding to the control parameter adjustment model;

evaluating the control parameters output by the control parameter adjusting model through the expected return function;

and updating the control parameter adjustment model according to the evaluation result.

Optionally, the method further comprises the following steps:

judging whether the second calculation control parameter reaches a preset parameter threshold value;

if the second calculation control parameter reaches a preset parameter threshold value, sending a prompt message;

adjusting control parameters according to corresponding feedback information sent by the driver aiming at the prompt information;

and adjusting the target vehicle according to the adjusted control parameters.

A second aspect of the invention provides a vehicle control apparatus comprising:

the vehicle-mounted device comprises a determining unit, a judging unit and a display unit, wherein the determining unit is used for determining first psychological data of a driver corresponding to a target vehicle, and the first psychological data comprises emotion data and/or attention data;

the calculation unit is used for inputting the first psychological data into a control parameter adjustment model to obtain a first calculation control parameter, wherein the control parameter adjustment model is obtained by training a training sample, and the training sample comprises training psychological data corresponding to at least one driver and a control parameter corresponding to the training psychological data;

an adjustment unit configured to adjust the target vehicle based on the first calculated control parameter.

Optionally, the determining unit is specifically configured to:

collecting first body characteristic parameters of the driver;

Optionally, the determination unit determines the driver recognition model by:

Optionally, the determining unit determines the iteration termination condition by:

or the like, or a combination thereof,

Optionally, the determining unit is further configured to:

adjusting the target vehicle based on the second calculated control parameter.

Optionally, the vehicle control apparatus further includes:

an update unit configured to:

Optionally, the adjusting unit is further configured to:

and adjusting the target vehicle according to the adjusted control parameters.

A third aspect of the present invention provides a computer apparatus comprising: at least one connected processor, memory, and transceiver; wherein the memory is configured to store program codes, and the processor is configured to call the program codes in the memory to execute the steps of the control method of the vehicle according to the first aspect.

A fourth aspect of embodiments of the present invention provides a computer storage medium that includes instructions that, when executed on a computer, cause the computer to perform the steps of the control method for a vehicle according to the first aspect described above.

Compared with the related art, in the embodiment provided by the invention, the vehicle control device determines the first psychological data of the target vehicle driver, determines the first calculation control parameter according to the first psychological data, adjusts the running state of the target vehicle according to the first calculation control parameter, can timely adjust the vehicle control parameter according to the psychological data of the driver, controls the vehicle to run according to the adjusted control parameter, and provides better automatic driving experience for a user.

Drawings

Fig. 1 is a system architecture diagram of a vehicle control apparatus according to an embodiment of the present invention;

FIG. 2 is a schematic flow chart of a vehicle control method provided by an embodiment of the present invention;

FIG. 3 is a schematic flow chart diagram of a training method for a driver recognition model according to an embodiment of the present invention;

FIG. 4 is a schematic diagram of a training process of training a driver recognition model according to an embodiment of the present invention;

fig. 5 is a schematic view of a virtual structure of a vehicle control device according to an embodiment of the present invention;

fig. 6 is a schematic hardware structure diagram of a server according to an embodiment of the present invention; and

FIG. 7 is a schematic diagram of dynamic optimization of a control parameter adjustment model.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.

The invention provides a vehicle control method based on active human-computer interaction, which takes psychological data of a driver as input, takes control parameters of a vehicle as output and adjusts the vehicle based on the output control parameters. The method can feed back subjective factors of the driver to the input automatic driving system and change vehicle control parameters so as to improve the driving feeling of the driver.

Fig. 1 is a system architecture diagram of a vehicle control device according to an embodiment of the present invention.

In the present embodiment, the system architecture of the vehicle control apparatus includes a driver detection sensor 101, a vehicle state sensor 102, a vehicle control apparatus 103, and a vehicle 104.

The driver detection sensor 101 is configured to collect a physical characteristic parameter of the driver and transmit the physical characteristic parameter of the driver to the vehicle control device 103. The driver detection sensor 101 may include an in-vehicle camera, or may include an in-vehicle millimeter wave radar and other devices installed in the vehicle, or may further include a bracelet connected to the bluetooth of the vehicle 104 or other devices that are not fixedly installed in the vehicle 104, or may also include a plurality of devices simultaneously, for example, an in-vehicle camera and a wearable device connected to the bluetooth of the vehicle, which is not limited specifically. The physical characteristic parameters collected by the driver detection sensor 101 include facial data (such as facial expressions, eye spatial positions, blinking frequency, pupil focusing position, and the like, without limitation), head data (such as breathing frequency, and the like, without limitation), hand data (such as hand posture and grip strength, and the like, without limitation), heartbeat data, and the like, and certainly, leg postures (such as a pre-braking posture or not) may also be included, without limitation.

The vehicle sensor 102 collects vehicle state data and surrounding environment data, and transmits the vehicle state data and surrounding environment data of the mobile phone to the vehicle control device 103. The vehicle state data includes vehicle speed, vehicle following distance, sensitivity parameters of Lane Departure Warning (LDW), and the like, and is not limited specifically; the environment data includes the number of lanes on the road on which the vehicle 104 travels, the lane attribute of the vehicle 104, and other vehicle information around the vehicle 104 (for example, in a four-lane road, the vehicle 104 is located on the second lane on the left side of the road, the vehicle speed is 90km/h, the lane speed limit is 100km/h, a car with a vehicle speed of 80km/h is located in front of the vehicle 104, a car with a vehicle speed of 120km/h is located 20m in front of the left lane of the vehicle 104, and a truck with a vehicle speed of 120km/h is located 30m in front of the right lane), although it is not limited thereto, and environment information around the road (for example, cliffs or lakes are located on both sides of the road).

The vehicle control device 103 receives the physiological characteristic parameters of the driver collected by the driver sensor 101 and the vehicle state data and the surrounding environment data collected by the vehicle state sensor, and the vehicle control device 103 inputs the received physical characteristic parameters into the driver recognition model and outputs psychological data corresponding to the physical characteristic parameters, wherein the psychological data are attention data and/or emotion data. The vehicle control device 103 inputs the psychological data into the control parameter adjustment model, outputs the vehicle control parameters corresponding to the psychological data, and the vehicle control device 103 issues a control command to adjust the vehicle 104 according to the output vehicle control parameters. The adjustment of the vehicle 104 includes, but is not limited to, adjusting a vehicle speed, adjusting a following distance of the vehicle, adjusting an alarm threshold of a lane departure warning, and the like.

Referring to fig. 2, a schematic flow chart of a vehicle control method according to an embodiment of the present invention includes:

201. first psychological data of a driver corresponding to the target vehicle are determined.

In this embodiment, the vehicle control apparatus may collect, by the driver detection sensor, a first body characteristic parameter of a driver corresponding to the target vehicle, input the first body characteristic parameter into the driver recognition model, and output first psychological data corresponding to the first body characteristic, where the first psychological data includes emotion data and/or attention data.

202. And inputting the first psychological data into the control parameter adjustment model to obtain a first calculation control parameter.

In this embodiment, the control parameter adjustment model is obtained by training a control parameter training sample, where the control parameter training sample includes training psychological data corresponding to at least one driver and a control parameter corresponding to the training psychological data. The psychological data includes emotion data and/or attention data, and the control parameters include ACC following distance, vehicle speed, LDW alarm threshold and the like, which are not limited specifically.

It should be noted that the control parameter adjustment model may be an ACC following distance parameter adjustment model, an LDW alarm threshold parameter adjustment model, or another parameter adjustment model (for example, a constant-speed cruise vehicle speed adjustment model), or of course, may also include multiple parameter adjustment models, and inputs one psychological data, so as to output multiple control parameters such as an ACC following distance parameter and an LDW alarm threshold parameter at the same time.

It can be understood that the control parameters corresponding to the training psychological data may be acquired based on a subjective questionnaire method, for example, if the driver considers that the vehicle following distance is 100m in the adaptive cruise and belongs to the relaxed state of emotion, the vehicle following distance 100m is the control parameter, and the relaxed state of emotion is the psychological data. The control parameter adjustment model takes the psychological data of the driver as input and takes the control parameters as output, and the off-line training mode of the control parameter adjustment model is the same as that of the driver identification model, which is not repeated herein.

It should be noted that in conventional control systems, the control target is generally given directly in a given amount, whereas in intelligent control systems, the control target is sometimes not explicit or directly available. In the invention, after the driver emotion data information is calculated and output by the driver recognition model, the driver emotion data is recognized by the control parameter adjustment model. The following describes in detail a specific manner of emotion recognition of the driver:

1. identifying system parameters:

the state of the description system is determined according to the input and the output of the system, and the complex nonlinear dynamic system formed by the emotion, the motion, the vehicle and the surrounding environment of the driver is difficult to describe by using a linear function or according to a priori knowledge. In the invention, the deep neural network (namely, the control parameter adjustment model) is used for system identification, so that the system identification has the capability of fitting complex nonlinear input and is converted into parameter optimization of the deep neural network.

2. Optimization of control objectives:

in the invention, a deep neural network fitting system dynamic model is used, namely, a control parameter adjusting model is trained by utilizing data of a past period, wherein the data comprises the emotion, the motion and the motion state of a driver, and the position relation of a vehicle and the surrounding environment (such as the distance change between the vehicle and a front vehicle, the deviation distance between the vehicle and a lane and the like).

The emotion change trend of the driver at the future time is predicted through the trained control parameter adjustment model, meanwhile, the emotion, the action, the motion state of the vehicle and the position relation between the vehicle and the surrounding environment (such as the distance change between the vehicle and the front vehicle, the deviation distance between the vehicle and a lane and the like) of the driver are comprehensively considered, so that the overall system state (including the emotion change of the driver, the change of the action and the action amplitude, the motion state change of the vehicle caused by the emotion change, the change of the action and the change of the motion state of the vehicle and the like) in the future can be predicted, and the adjustment parameters (such as the following distance, the vehicle speed and other vehicle states are determined by using the state change value between the current state and the predicted next state, the following distance is taken as an example, the current following distance is 50m, the following distance is taken as an example, and the adjustment parameters are increased by 30m according to the distance at the next moment being 80 m.

After the above system dynamic prediction, an expected return function is introduced into the control parameter adjustment model, so that the control parameter adjustment model can evaluate the adjustment parameter predicted each time through the expected return function, and optimize the control parameter adjustment model according to the evaluation result, which is described below with reference to fig. 7:

please refer to fig. 7, which is a schematic diagram of dynamic optimization of a control parameter adjustment model, wherein S0 represents a state of a system at a current time, S1 represents a predicted state of the system at a next time, V0 represents an action taken by the system at the current time, and G represents an expected reward function when the system executes action V0 at the current state S0.

Thus, each step performs an optimal action and the deep neural network is trained on the rewards produced by each step, resulting in a calculation that identifies and optimizes vehicle motion control parameters based on driver emotion, behavior as inputs.

203. The target vehicle is adjusted based on the first calculated control parameter.

In this embodiment, the vehicle control device instructs the vehicle according to the first calculation control parameter to adjust the running state of the target vehicle, for example, the following distance when the target vehicle runs is 50m, the following distance in the first calculation control parameter is 70m, and the vehicle control device instructs the vehicle according to the first calculation control parameter to adjust the following distance of the target vehicle to 70 m.

The vehicle control device performs the following operations after adjusting the target vehicle based on the first calculated control parameter:

determining second psychological data of the driver after the target vehicle is adjusted based on the first calculated control parameter;

if the second psychological data does not reach the preset adjusting threshold value, determining a second calculation control parameter based on the second psychological data and the control parameter adjusting model;

the target vehicle is adjusted based on the second calculated control parameter.

In this embodiment, after adjusting the target vehicle based on the first calculated control parameter, the vehicle control apparatus may further determine second psychological data of the driver, where the second psychological data includes mood data and/or attention data, and then determine whether the second psychological data reaches a preset adjustment threshold, for example, determine whether the driver is in a relaxed state, and may classify a stressed state corresponding to the driver, such as a first-level stressed state, a second-level stressed state, a relaxed state, and the like, where the classification has an association relationship with the preset adjustment threshold; if the second psychological data is determined not to reach the preset adjustment threshold (for example, the preset adjustment threshold is set to be in a relaxed state, and the second psychological data is still in a tensed state), determining a second calculation control parameter based on the second psychological data and the control parameter adjustment model; the target vehicle is adjusted based on the second calculated control parameter.

It can be understood that the vehicle control device judges whether the second calculation control parameter reaches the preset parameter threshold value after continuously adjusting the vehicle; if so, sending prompt information, making feedback by the driver according to the prompt information, and adjusting control parameters according to corresponding feedback information sent by the driver aiming at the prompt information; and adjusting the target vehicle according to the adjusted control parameters. That is, if the driver is still in a stressed state after adjusting the control parameters of the vehicle for many times, for example, after increasing the following distance of the vehicle for many times, the voice prompt message may be sent out to interact with the driver, for example, to inquire whether the driver continues to increase the following distance, or to inquire whether the driver stops for a rest.

In addition, after the second psychological data and the first calculated control parameter are determined, the control parameter adjustment model can be updated through the second psychological data and the first calculated control parameter.

How to adjust the target vehicle according to the calculated control parameters is described in detail below by taking adaptive cruise (ACC) following distance parameter adjustment and Lane Departure Warning (LDW) warning threshold parameter adjustment as examples:

firstly, adjusting the following distance of an ACC:

firstly, a driver recognition model and an ACC following distance parameter adjustment model are trained off line. The ACC following distance parameter adjustment model is one of control parameter adjustment models, the emotion state in driver psychological data is used as input, and the following distance is used as output.

Secondly, the vehicle control device collects body characteristic data of the driver through a driver sensor, inputs the body characteristic data into a driver recognition model and outputs the first emotion of the driver.

And thirdly, inputting the first emotion of the driver into the ACC vehicle following distance parameter adjustment model, outputting the first vehicle following distance parameter, adjusting the vehicle following distance of the vehicle to be consistent with the first vehicle following distance parameter, and if the first emotion is in a tension state, increasing the vehicle following distance of the vehicle.

And finally, after the following distance of the vehicle is adjusted to be consistent with the first following distance parameter, determining the second emotion of the driver again, and updating the ACC following distance parameter adjustment model according to the second emotion and the first following distance parameter. And if the first emotion is a nervous state, determining whether the second emotion reaches a relaxed state, if not, judging whether the following distance reaches a preset parameter threshold value, such as 250m, if so, sending a prompt message to inquire a driver whether to increase the following distance, and if so, adjusting the vehicle according to feedback of the driver on the prompt message to increase the following distance.

Adjusting LDW alarm threshold parameters:

firstly, training a driver recognition model and an LDW alarm threshold parameter adjustment model off line. The LDW alarm threshold parameter adjustment model is one of control parameter adjustment models, takes the attention of a driver as input, and takes the LDW alarm threshold parameter as output.

Secondly, the vehicle control system collects body characteristic data of the driver through a driver sensor, inputs the body characteristic data into a driver recognition model and outputs first attention of the driver.

Thirdly, inputting the first attention of the driver into the LDW alarm threshold parameter adjustment model, outputting a first alarm parameter, adjusting the LDW alarm threshold of the vehicle to be consistent with the first alarm parameter, and if the first attention is attention concentration, increasing the LDW alarm threshold so that the system alarm is not sensitive and the normal driving of the driver is not interfered; if the first attention is distraction or distraction, the LDW alarm threshold value is adjusted to be small, so that the driver is reminded of deviating from the lane in time.

And finally, after the LDW alarm threshold value of the vehicle is adjusted to be consistent with the first alarm parameter, interacting with a driver through voice prompt to inquire whether the LDW alarm threshold value of the driver is proper or not, if so, acquiring second attention of the driver, and updating the LDW alarm threshold value parameter adjustment model according to the second attention and the first alarm parameter.

In summary, in the embodiment provided by the present invention, by determining the first psychological data of the driver of the target vehicle, determining the first calculation control parameter according to the first psychological data, and adjusting the operating state of the target vehicle according to the first calculation control parameter, the vehicle control parameter can be adjusted in time according to the psychological data of the driver, and the vehicle is controlled to operate according to the adjusted control parameter, so as to provide better automatic driving experience for the user.

Referring to fig. 3, a detailed description is given below of a training method of a driver recognition model with reference to fig. 3, which is a schematic flow chart of a training method of a driver recognition model according to an embodiment of the present invention, and the training method includes:

301. and acquiring physical characteristic parameters corresponding to at least one driver and actual psychological data corresponding to the physical characteristic parameters.

In this embodiment, the vehicle control device may obtain the physical characteristic parameter corresponding to at least one driver and the actual psychological data corresponding to the physical characteristic parameter from a sample database in which sample data corresponding to a plurality of drivers is stored.

302. And preprocessing the physical characteristic parameters corresponding to at least one driver and the actual psychological data corresponding to the physical characteristic parameters.

In this embodiment, after acquiring the physical characteristic parameter corresponding to at least one driver and the actual psychological data corresponding to the physical characteristic parameter, the vehicle control device may perform preprocessing on the physical characteristic parameter and the actual psychological data, where the preprocessing includes, but is not limited to, removing a unique attribute, processing a missing value, encoding a feature, normalizing data, and selecting a feature.

303. And performing iterative operation according to the preprocessed data and the initial driver identification model until a preset iteration termination condition is reached.

304. And determining the initial driver identification model when a preset iteration termination condition is reached as the driver identification model.

In this embodiment, the vehicle control device may perform iterative training on the preprocessed data through the neural network until a preset iteration termination condition is reached, and determine the initial driver recognition model when the preset iteration termination condition is reached as the driver recognition model.

It should be noted that the iteration termination condition may be determined by the following steps:

judging whether the iteration times reach a preset value or not, and if so, determining that a preset iteration termination condition is met;

or the like, or, alternatively,

and judging whether the model parameters corresponding to the initial driver identification model are converged, and if so, determining that a preset iteration termination condition is met.

The following describes a driver recognition model offline training process in detail with reference to fig. 4, and please refer to fig. 4, which is a schematic diagram of a training process for training a driver recognition model according to an embodiment of the present invention.

The training computer device 401 obtains a training sample set from the database 402, where the training sample set includes at least one body characteristic parameter corresponding to the driver and actual psychological data corresponding to the body characteristic parameter. It can be understood that the physical characteristic parameters corresponding to at least one driver include data of face, head, hand, heartbeat, and the like of the driver, and the psychological data corresponding to the physical characteristic parameters include at least one of emotion data and attention data, and may be collected by means of a subjective evaluation questionnaire.

An initial training model is configured on the training computer device 401, the initial training model is an initial driver identification model, and the training computer device 401 can perform iterative training on the initial training model through a training sample set to determine a reference model parameter for iteratively updating the initial training model until a preset iteration termination condition is reached; model parameters are input into the initial training model to determine a target training model. After each iteration is finished, judging whether the iteration number reaches a preset value (for example, the preset iteration number is 1000), if so, determining that a preset iteration termination condition is met, and determining an initial training model when the iteration is terminated as a driver identification model; or after each iteration is finished, judging whether the model parameters corresponding to the initial training model are converged, if so, determining that a preset iteration termination condition is met, and determining the initial training model when the iteration is terminated as the driver identification model.

It should be noted that the initial training model may be understood as a function of a deep neural network, in which coefficients are in an unknown state, the coefficients in the unknown state may be understood as model parameters of the initial training model, each body feature information may be understood as a plurality of input parameters, the corresponding psychological data may be understood as corresponding output parameters, and a relationship between the input parameters and the output parameters may be represented as y ═ f (x1, x2, …, xN). And inputting a plurality of groups of input parameters and output parameters into the initial training model to determine the unknown model parameters, thereby completing the training of the model and obtaining the target training model. And determining the target training model as a driver recognition model, so that after the body characteristic parameters of the driver corresponding to the target vehicle are input into the driver image recognition model, the corresponding output parameters y can be output by extracting the first body characteristic parameter information (x1, x2, …, xN) of the driver corresponding to the target vehicle, so as to obtain first psychological data corresponding to the first body characteristic parameters. The present invention is explained above from the perspective of a vehicle control method, and is explained below from the perspective of a vehicle control device.

Referring to fig. 5, a schematic view of a virtual structure of a vehicle control device according to an embodiment of the present invention is shown, where the vehicle control device 500 includes:

a determining unit 501, configured to determine first psychological data of a driver corresponding to a target vehicle, where the first psychological data includes emotion data and/or attention data;

a calculating unit 502, configured to input the first psychological data into a control parameter adjustment model to obtain a first calculated control parameter, where the control parameter adjustment model is obtained by training a training sample, and the training sample includes training psychological data corresponding to at least one driver and a control parameter corresponding to the training psychological data;

an adjusting unit 503, configured to adjust the target vehicle based on the first calculated control parameter.

Optionally, the determining unit 501 is specifically configured to:

collecting first body characteristic parameters of the driver;

inputting the first physical characteristic parameter into a driver recognition model to obtain the first psychological data.

Alternatively, the determination unit 501 determines the driver recognition model by:

Optionally, the determining unit 501 determines the iteration termination condition by:

or the like, or, alternatively,

Optionally, the determining unit 501 is further configured to:

adjusting the target vehicle based on the second calculated control parameter.

Optionally, the vehicle control device 500 further includes:

an updating unit 504, configured to:

Optionally, the adjusting unit 503 is further configured to:

and adjusting the target vehicle according to the adjusted control parameters.

Fig. 6 is a schematic structural diagram of a server according to the present invention. The server 600 of the present embodiment includes at least one processor 601, at least one network interface 604 or other user interface 603, memory 605, and at least one communication bus 602. The server 600 optionally contains a user interface 603 including a display, keyboard or pointing device. Memory 605 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory. The memory 605 stores execution instructions, and when the server 600 operates, the processor 601 communicates with the memory 605, and the processor 601 calls the instructions stored in the memory 605 to execute the control method of the vehicle. The operating system 606, which contains various programs for implementing various basic services and for handling hardware-dependent tasks.

The server provided by the embodiment of the invention can execute the technical scheme of the embodiment of the vehicle control method, the implementation principle and the technical effect are similar, and the details are not repeated here.

The embodiment of the present invention further provides a computer-readable medium, which includes a computer executable instruction, where the computer executable instruction can enable a server to execute the vehicle control method described in the foregoing embodiment, and the implementation principle and the technical effect are similar, and are not described herein again.

Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and these modifications or substitutions do not depart from the spirit of the corresponding technical solutions of the embodiments of the present invention.

Claims

1. A vehicle control method characterized by comprising the steps of:

adjusting the target vehicle based on the first calculated control parameter.

2. The method of claim 1, wherein determining the first psychological data of the driver corresponding to the target vehicle comprises:

collecting first body characteristic parameters of the driver;

3. The method according to claim 2, characterized in that the method further comprises the steps of:

4. A method according to claim 3, characterized in that the method further comprises the steps of:

or the like, or, alternatively,

5. The method according to any one of claims 1 to 4, characterized in that the method further comprises the steps of:

adjusting the target vehicle based on the second calculated control parameter.

6. The method of claim 5, further comprising the steps of:

7. The method of claim 5, further comprising the steps of:

if the second calculation control parameter reaches a preset parameter threshold value, sending out prompt information;

and adjusting the target vehicle according to the adjusted control parameters.

8. A vehicle control apparatus characterized by comprising:

the calculation unit is used for inputting the first psychological data into a control parameter adjustment model to obtain a first calculation control parameter, wherein the control parameter adjustment model is obtained by training a control parameter training sample, and the control parameter training sample comprises training psychological data corresponding to at least one driver and a control parameter corresponding to the training psychological data;

9. A computer device, comprising:

at least one connected processor, memory, and transceiver;

wherein the memory is configured to store program code and the processor is configured to invoke the program code in the memory to perform the steps of the control method of the vehicle of any one of claims 1 to 7.

10. A computer storage medium, comprising:

instructions that, when executed on a computer, cause the computer to perform the method of controlling a vehicle of any one of claims 1 to 7.