CN111204348A

CN111204348A - Method and device for adjusting vehicle running parameters, vehicle and storage medium

Info

Publication number: CN111204348A
Application number: CN202010071430.8A
Authority: CN
Inventors: 胡太群
Original assignee: Tencent Cloud Computing Beijing Co Ltd
Current assignee: Tencent Cloud Computing Beijing Co Ltd
Priority date: 2020-01-21
Filing date: 2020-01-21
Publication date: 2020-05-29

Abstract

The application discloses a method and a device for adjusting vehicle running parameters, a vehicle and a storage medium, wherein the method comprises the following steps: acquiring current running information of a vehicle and current state information of one or more passengers in the vehicle; determining a control parameter of the vehicle based on the state information and the travel information; and adjusting the current running parameters of the vehicle based on the control parameters to obtain target running parameters, so that the vehicle runs according to the target running parameters. According to the embodiment of the application, the current state of the passenger is considered, the control parameter in the vehicle running process is predicted, the current running parameter of the vehicle is adjusted according to the control parameter, the running parameter of the vehicle is intelligently adjusted according to the state of the passenger, the comfort requirements of different passengers are met, and the comfort of the passenger is improved.

Description

Method and device for adjusting vehicle running parameters, vehicle and storage medium

Technical Field

The present application relates generally to the field of automatic driving technology, and more particularly, to a method, an apparatus, a vehicle, and a storage medium for adjusting a driving parameter of a vehicle.

Background

With the development of artificial intelligence, the automatic driving technology of automobiles is gradually used. In other words, in the running process of the automatic driving vehicle, the vehicle running task can be guided and decided without the need of physical driving operation performed by the driver, and the driving operation and control behavior of the driver can be replaced.

Currently, in the course of executing a driving task, an autonomous vehicle is generally controlled based on a control mode that is set in advance and matches with an external environment, according to a change in the external environment.

For the control of the automatic driving automobile, different passengers have different requirements under the same environment, so that the preset driving mode matched with the external environment cannot meet the real-time requirements of the different passengers, and the passenger experience is influenced.

Disclosure of Invention

In view of the above-mentioned drawbacks and deficiencies of the prior art, it is desirable to provide a method, an apparatus, a vehicle and a storage medium for intelligently adjusting vehicle driving parameters to improve passenger comfort.

In a first aspect, an embodiment of the present application provides a method for intelligently adjusting a vehicle driving parameter, where the method includes:

acquiring current running information of a vehicle and current state information of one or more passengers in the vehicle;

determining a control parameter of the vehicle based on the state information and the travel information;

and adjusting the current running parameters of the vehicle based on the control parameters to obtain target running parameters, so that the vehicle runs according to the target running parameters.

In a second aspect, the present application provides, by way of example, an apparatus for intelligently adjusting a driving parameter of a vehicle, the apparatus comprising:

the system comprises an acquisition module, a judgment module and a control module, wherein the acquisition module is used for acquiring the current running information of a vehicle and the current state information of one or more passengers in the vehicle;

a determination module for determining a control parameter of the vehicle based on the state information and the travel information;

and the adjusting module is used for adjusting the current running parameters of the vehicle based on the control parameters to obtain target running parameters, so that the vehicle runs according to the target running parameters.

In a third aspect, an embodiment of the present application provides a vehicle, including a first acquisition device, a second acquisition device, a memory, a processor, and a computer program stored on the memory and executable on the processor;

the first acquisition device is used for acquiring the current state information of one or more passengers in the vehicle;

the second acquisition device is used for acquiring the current running information of the vehicle;

the processor is configured to implement the method of adjusting vehicle driving parameters as described in the first aspect when executing the program.

In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, the computer program being used for implementing the method for adjusting the vehicle driving parameters as described in the first aspect above.

The method, the device, the vehicle and the storage medium for intelligently adjusting the vehicle running parameters, provided by the embodiment of the application, can be used for predicting the control parameters in the vehicle running process by monitoring the current state information of passengers in the vehicle and the current running information of the vehicle and further considering the current state of the passengers, so that the current running parameters of the vehicle can be adjusted according to the control parameters, the vehicle can run according to the adjusted running parameters, the intelligent adjustment of the running parameters of the vehicle according to the state of the passengers is realized, the comfort requirements of different passengers are met, and the comfort of the passengers is improved.

Drawings

Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:

FIG. 1 is a schematic diagram of a prediction system according to an embodiment of the present application;

FIG. 2 is a schematic flow chart illustrating a method for adjusting vehicle driving parameters according to an embodiment of the present application;

FIG. 3 is a schematic flow chart illustrating a method for adjusting vehicle driving parameters according to yet another embodiment of the present application;

FIG. 4 is a schematic flow chart illustrating evaluation model construction according to an embodiment of the present application;

FIG. 5 is a schematic structural diagram of an apparatus for adjusting vehicle driving parameters according to an embodiment of the present application;

FIG. 6 is a schematic structural diagram of an apparatus for adjusting vehicle driving parameters according to yet another embodiment of the present application;

fig. 7 is a schematic structural diagram of a computer system of a terminal device according to another embodiment of the present application.

Detailed Description

The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant disclosure and are not limiting of the disclosure. It should be further noted that, for the convenience of description, only the portions relevant to the disclosure are shown in the drawings.

It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.

Artificial Intelligence (AI) is a theory, method, technique and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend and expand human Intelligence, perceive the environment, acquire knowledge and use the knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technique of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a manner similar to human intelligence. Artificial intelligence is the research of the design principle and the realization method of various intelligent machines, so that the machines have the functions of perception, reasoning and decision making.

The artificial intelligence technology is a comprehensive subject and relates to the field of extensive technology, namely the technology of a hardware level and the technology of a software level. The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.

Machine Learning (ML) involves multiple disciplines such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory, and the like. The special research on how a computer simulates or realizes the learning behavior of human beings so as to acquire new knowledge or skills and reorganize the existing knowledge structure to continuously improve the performance of the computer. Machine learning is the core of artificial intelligence, is the fundamental approach for computers to have intelligence, and is applied to all fields of artificial intelligence. Machine learning and deep learning generally include techniques such as artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and formal education learning.

The automatic driving technology generally comprises technologies such as high-precision maps, environment perception, behavior decision, path planning, motion control and the like, and has wide application prospects. During the driving process of the automatic driving vehicle, different individuals have different requirements on the driving state of the vehicle. For example, special groups, such as old, weak, sick, pregnant, young, prefer a relatively smooth and safe driving status; a robust young person likes a more aggressive driving state. Similarly, for the same passenger, under different states, such as on-duty time, fast rhythm requirement, off-duty or rest time, slow rhythm requirement, and in addition, the driving mode requirements for different road conditions are different, such as that the psychology of the passenger is dynamically changed along with the road conditions in the riding process.

Based on the different requirements of the passengers on the vehicle running states in different states, the method for intelligently adjusting the vehicle running parameters provided by the embodiment of the application estimates the comfort level of the passengers in the current running state by acquiring the current state information of the passengers and the vehicle running information in real time and utilizing an estimation model constructed by a machine learning algorithm in the running process of the automatic driving vehicle, and then predicts the control parameters of the vehicle running states by utilizing the adjustment model according to the estimation result so as to intelligently adjust the vehicle running parameters and meet the requirements of the passengers.

It can be understood that, in the method for intelligently adjusting vehicle driving parameters in the embodiment of the present application, in order to implement real-time measurement of state information of a passenger and driving information of a vehicle, a first acquisition device and a second acquisition device are configured on the vehicle, such as an automatic driving automobile, and the first acquisition device may include a plurality of monitors, such as a camera, a heartbeat/pulse measurement instrument, a blood pressure measurement instrument, and the like, for acquiring facial expressions, heartbeats, pulses, blood pressures, and the like of the passenger. The second collecting device may include a camera, a millimeter wave radar, an ultrasonic radar, a laser radar, a GPS/IMU, etc. for collecting driving information such as a driving track, driving parameters, e.g., a lateral speed and a longitudinal speed, etc. of the vehicle, surrounding environment information such as a driving speed, a driving track, lateral and longitudinal distances to the vehicle, distances between surrounding pedestrians and the vehicle, a driving track, a driving speed, etc. of other vehicles, surrounding buildings, traffic facilities, etc.

It can also be understood that the vehicle may be an autonomous vehicle or a general vehicle, and the method for intelligently adjusting the vehicle driving parameters according to the embodiment of the present application may be executed by a terminal device configured on the vehicle. The predictive control system shown in fig. 1 is loaded on the terminal device, and the predictive control system can be implemented based on an MPC control algorithm. The predictive control system may include a predictive model configured with an evaluation model and a tuning model constructed based on machine learning, a first acquisition device, and a second acquisition device. The evaluation model is used for evaluating the current comfort level of the passenger according to the data acquired by the first acquisition device and the second acquisition device. The adjusting model is used for controlling the vehicle according to the current comfort level and the current collected data, and realizing the real-time dynamic adjustment of the vehicle driving parameters according to the passenger states so as to meet the requirements of all passengers.

The method for adjusting the vehicle driving parameters provided by the embodiment of the application relates to the technologies of automatic driving of artificial intelligence and the like, and is specifically explained by the following embodiment. The method, the device, the terminal device and the storage medium for adjusting the vehicle driving parameters according to the embodiment of the present application are described in detail with reference to fig. 2 to 7.

Fig. 2 is a schematic flowchart of a method for adjusting vehicle driving parameters according to an embodiment of the present application, where the method is performed by a terminal device configured on a vehicle, and includes:

s210, the terminal device acquires the current running information of the vehicle and the current state information of one or more passengers in the vehicle.

S220, the terminal equipment determines the control parameters of the vehicle based on the state information and the running information;

and S230, the terminal equipment adjusts the current running parameters of the vehicle based on the control parameters to obtain target running parameters, so that the vehicle runs according to the target running parameters.

Specifically, in the embodiment of the present application, during a vehicle driving process, such as an automatic driving vehicle, a predictive control system configured on a vehicle terminal acquires current state information of a passenger and current driving information of the vehicle, which are acquired by a first acquisition device and a second acquisition device, at a certain period, that is, the first acquisition device and the second acquisition device store the acquired information in a memory of a terminal device, so that a processor of the terminal device reads the current state information of the passenger and the driving information of the vehicle from the memory at a preset period, such as 1 minute, as one period.

For example, the current status information of each passenger collected by the first collecting device is read, such as the image information of the current facial expression of the passenger collected by the camera, the current heartbeat and pulse of the passenger collected by the heartbeat/pulse meter, the current blood pressure of the passenger collected by the blood pressure meter, and the like.

Reading the current driving information of the automatically-driven automobile collected by the camera in the second collecting device, such as reading the environmental information and driving parameters around the automobile collected by the camera and the millimeter wave radar, the ultrasonic radar and the laser radar, specifically, information such as the driving track, the driving speed, the transverse distance and the longitudinal distance with the automobile, the track, the traveling speed and the distance with the automobile of pedestrians, surrounding buildings and the like of other automobiles; reading the transverse speed, the transverse acceleration, the longitudinal acceleration and the longitudinal speed of the vehicle; the current driving track of the vehicle acquired by the GPS or IMU can also be read.

It is understood that when the vehicle starts to travel, information such as the age and sex of the passenger may be input to the terminal device by the passenger or the trainee, and the time information of the passenger's ride may be input. This information is entered, for example, through the touch panel of the terminal or through the client of the passenger's mobile terminal.

It will also be appreciated that when there are multiple passengers in the vehicle, the collected status information may include the current status of all passengers.

After the current driving information of the vehicle and the current state information of the passengers are acquired, the control parameters of the vehicle can be calculated by using the acquired state information and the driving information. And then, the current running parameters of the vehicle can be adjusted by using the control parameters to obtain new running parameters, namely target running parameters, so that the vehicle runs according to the adjusted running parameters.

The control parameter represents the amplitude of the current driving parameter to be adjusted, for example, the percentage of the current driving parameter, that is, the percentage corresponding to the control parameter is used as the target to adjust the current driving parameter of the vehicle, so that the current driving parameter is increased or decreased by a certain amplitude.

The driving parameter may be an attitude parameter during the driving of the automobile, such as a current lateral speed, a longitudinal speed, a steering angle, a lateral acceleration, a longitudinal acceleration, or the like of the vehicle. This is not limited by the present application.

For example, when the determined control parameter indicates that a ten percent reduction in current longitudinal speed is required. The current longitudinal speed of the vehicle may be adjusted in accordance with the control parameter such that the longitudinal speed is reduced by ten percent, thereby slowing the vehicle.

For another example, when the determined control parameter indicates that a five percent reduction in the current longitudinal acceleration is required, then the current longitudinal acceleration of the vehicle may be adjusted based on the control parameter such that the longitudinal acceleration is reduced by five percent, thereby slowing the vehicle up.

It can be understood that, in the adjustment of the driving parameters according to the control parameters, for example, the current longitudinal speed is adjusted according to the control parameters, in the specific implementation process, the bottom layer control system, that is, the bottom layer system of the terminal device, is called, so that the bottom layer control system adjusts the accelerator pedal to directly adjust the longitudinal acceleration of the vehicle, thereby implementing the adjustment of the current longitudinal speed of the vehicle. The method for intelligently adjusting the vehicle running parameters comprises the steps of monitoring the current state information of passengers in a vehicle, predicting the control parameters in the vehicle running process by considering the current state of the passengers, adjusting the current running parameters of the vehicle according to the control parameters, enabling the vehicle to run according to the adjusted running parameters, achieving intelligent adjustment of the running parameters of the vehicle according to the state of the passengers, meeting the requirements of comfort and safety of different passengers, and improving the comfort of the passengers.

For better understanding and description of the method for adjusting the vehicle driving parameters according to the embodiment of the present application, the prediction process of the prediction model is explained in detail below with reference to fig. 3.

Fig. 3 is a schematic flow chart of a method for adjusting vehicle driving parameters according to another embodiment of the present application, as shown in fig. 3, the method is performed by a terminal device configured on a vehicle, and the method includes:

s310, the terminal device obtains current running information of the vehicle and current state information of at least one or more passengers in the vehicle.

S320, the terminal device calculates a current comfort level corresponding to each passenger based on the state information and the travel information corresponding to each passenger.

Specifically, the state information and the driving information are obtained, which are similar to S210 in the above embodiment and are not described herein again.

After the state information and the driving information are acquired, the current comfort level of each passenger can be calculated by using the acquired current state information of each passenger and the driving information of the vehicle.

For example, when a machine learning model is preloaded in the terminal device, i.e. a pre-built evaluation model, the current comfort level of one or more passengers can be evaluated using the evaluation model. The obtained state information and the driving information are preprocessed, digitized and normalized, and then input into a pre-constructed evaluation model, so that the evaluation model outputs the current comfort level of each passenger. This comfort level indicates the comfort level of the occupant in the current driving state of the vehicle. Such as the intuitive feel of the occupant at the current forward speed, or reverse speed, or left or right turn speed.

The comfort level may be defined as a value within a range of values, such as [0,10 ]. Each value represents a different comfort level. For example, a larger value of the evaluation model output indicates that the passenger is currently comfortable, e.g., 0 is output, indicating that the passenger is currently experiencing very bad conditions, and may need to slow down; the output is 10, which indicates that the passengers feel the best, and the current driving state can be maintained.

It is understood that the pre-constructed evaluation model may be a mapping relationship between the state information of the passenger sample and the driving information of the vehicle sample, and the comfort level corresponding to the state information of the passenger sample and the driving information of the vehicle sample. Specifically, the Convolutional Neural Network (CNN) shown in fig. 3 may be used for construction, and the specific process is as follows:

first, training data including state information of the passenger samples and travel information of the vehicle samples, and comfort levels corresponding to the state information of the passenger samples and the travel information of the vehicle samples may be acquired.

For example, a plurality of passengers and vehicles are taken as samples, and a plurality of tests are carried out to complete sample data acquisition. Namely, a plurality of passengers are placed in an automatic driving vehicle, different driving states of the driving vehicle, namely different driving speeds and driving environments are set, and then the facial expression, heartbeat, pulse, blood pressure, age and gender of each passenger in each driving state are collected. And after finishing each driving state, collecting the current visual feeling of each passenger, such as taking the [0,10] as the range, and collecting the comfort level of each passenger in each driving state as the output value of the evaluation model.

Further, after sample data collection is completed, the evaluation model can be constructed by using a machine learning algorithm and the training data, namely, the state information of the passenger sample, the driving information of the vehicle sample and the mapping relation between the state information of the passenger sample and the comfort level corresponding to the driving information of the vehicle sample are established.

For example, the collected data is input into a convolutional neural network algorithm to learn the characteristics of each sample data, so that training of an evaluation model for passenger comfort evaluation is completed, and a mapping relationship is obtained.

In a specific training process, as shown in fig. 4, the acquired data may be subjected to weighted normalization processing, and converted into an N × N input matrix, such as a 32 × 32 input matrix, to perform sampling and convolution for multiple times, that is, feature screening and combination are performed, such as gradually reducing from 32 × 32 to a 5 × 5 matrix, and finally 1024 learning results are obtained. Furthermore, the learning results are counted, 10 results, namely 10 results from poor comfort are output, different input data are represented, and output results corresponding to different comfort levels are output.

S330, the terminal device determines a minimum comfort level among the current comfort levels of the plurality of passengers.

Specifically, the method for adjusting the vehicle driving parameters in the embodiment of the application can be input into the prediction model after the model to be evaluated outputs the current comfort level of the passenger.

It is understood that when a plurality of passengers are seated on the vehicle, the evaluation model outputs the current comfort level of each passenger, i.e., outputs a plurality of comfort levels. The terminal device needs to determine the minimum comfort level from the plurality of comfort levels to determine the reference before making the prediction.

It is understood that when there is only one passenger in the vehicle, S340 may be directly performed without performing S330.

S340, it is determined whether the current comfort level or the minimum comfort level is less than a comfort level threshold.

And S350, when the current comfort level or the minimum comfort level is smaller than the comfort level threshold value, the terminal equipment determines the control parameter based on the current comfort level or the minimum comfort level and the driving information.

And S360, the terminal equipment adjusts the attitude parameter of the vehicle based on the control parameter until the comfort level of the passenger is greater than or equal to the comfort level threshold value.

Specifically, after the current comfort level of one or more passengers is calculated by using the above method and the current comfort level of one passenger is determined, or the minimum comfort level of the current comfort levels of a plurality of passengers is determined to be less than a comfort level threshold, the current comfort level or the minimum comfort level and the driving information can be used to calculate the control parameters for adjusting the driving parameters of the vehicle.

It can be understood that in the embodiment of the present application, in the calculation process, aiming at ensuring that all passengers feel comfortable, a comfort threshold is preset, and the comfort threshold represents the lowest value that the comfort of all passengers needs to meet during the driving process of the vehicle, namely, the comfort of all passengers is ensured to be greater than the preset threshold. If set to 5, this means that all passengers are required to have a comfort level greater than or equal to 5 during the running of the vehicle. The method can be determined according to actual conditions, and is not limited in this respect.

Alternatively, the control parameters may be determined by using a pre-established regulation model, that is, when the calculated current comfort level is less than the comfort level threshold value, which indicates that the comfort level of the passenger is not good enough, the comfort level is taken as a reference, so that the prediction model predicts the control parameters of the vehicle according to the comfort level and the current driving speed. That is, the comfort level or the minimum comfort level, and the travel information may be input into a pre-constructed regulation model, a magnitude that the travel speed of the vehicle needs to be regulated when the comfort level is raised to a comfort level threshold value is predicted, and the magnitude value, that is, the control parameter of the travel speed of the vehicle is output.

For example, when the calculated current lowest comfort level is 3, it is found by comparison to be less than the comfort level threshold 5. The adjustment model calculates the magnitude of the reduction or increase in the driving speed of the vehicle when the comfort level is increased to 5, such as an output of-0.1, which indicates that the degree of travel of the vehicle needs to be reduced by 10% of the current lateral or longitudinal speed, or an output of 0.1, which indicates that the degree of travel of the vehicle needs to be increased by 10% of the current lateral or longitudinal speed, based on the current driving speed of the vehicle, the surrounding environment, and the current comfort level 3.

Further, after the adjusting model outputs the control parameter, the control parameter may be sent to a bottom layer control system of the vehicle, that is, a bottom layer system of the terminal device, so that the bottom layer control system may adjust the attitude parameter of the vehicle according to the control parameter. And if the current attitude parameter of the vehicle is adjusted according to the obtained control parameter, adjusting an accelerator pedal, a brake pedal or a steering wheel.

For example, when the adjusted posture parameter is longitudinal acceleration or lateral acceleration, the current lateral acceleration or longitudinal acceleration of the vehicle can be adjusted by adjusting a throttle pedal, a brake pedal or a steering wheel according to the obtained control parameter to increase or decrease the lateral speed or longitudinal speed of the vehicle at the next moment, so as to reach the target driving parameter, that is, until the comfort level of the passenger is greater than or equal to the comfort level threshold.

It can be understood that, when the comparison shows that the current comfort level of one passenger or the lowest comfort level value in the current comfort levels of a plurality of passengers is greater than or equal to the comfort level threshold value, which indicates that the current driving state of the vehicle meets the requirements of the passengers, and no adjustment is needed, the method may output an end instruction, for example, output 0, which indicates that the current driving speed of the vehicle does not need to be adjusted, and this adjustment is ended. I.e., after the execution of S340, the method ends.

It can also be understood that, due to the error of the bottom layer regulation system in the regulation process of the vehicle running speed, after the running speed is regulated according to the control parameters, the running parameters of the vehicle do not reach the determined target running parameters, that is, the running state of the vehicle still cannot meet the requirements of passengers.

On the basis, the intelligent adjustment algorithm can also perform multiple prediction adjustments until the driving state of the vehicle meets the requirements of all passengers, that is, the method can return to S310 after executing S360 a certain time, and after the driving state of the vehicle is adjusted, the current state information of the passengers and the driving information of the vehicle are obtained again, so as to reevaluate the comfort level of the passengers after the driving state is adjusted. And then judging whether the comfort level reaches a comfort level threshold value or not so as to readjust until the lowest comfort level value is smaller than a preset threshold value, so that the vehicle runs according to the target running parameters, and ending the method.

It is understood that the pre-constructed adjustment model in the embodiment of the present application may be a mapping relationship between the state information of the passenger sample and the driving information of the vehicle sample, a comfort level corresponding to the state information of the passenger sample and the driving information of the vehicle sample, and a control amount corresponding to the comfort level. The training may be performed using a PID algorithm or an MPC machine learning algorithm, or may be implemented using a CNN algorithm as well.

Specifically, the construction process of the adjustment model is as follows:

first, training data is collected, and the training data may also include state information of the passenger samples and driving information of the vehicle samples, comfort levels corresponding to the state information of the passenger samples and the driving information of the vehicle samples, and control parameters corresponding to the comfort levels.

For example, the intuitive comfort of passengers in the sample is acquired, and the running speed of the vehicle, the safe longitudinal acceleration of the vehicle, the lateral acceleration of the vehicle and the surrounding environment information in the sample, such as the moving speed of surrounding obstacles, the distance between the surrounding obstacles and the vehicle, are taken as sample data of the model.

Further, intuitive comfort of the passenger sample is set, and a comfort threshold (namely a reference standard of the model) is set, and the lateral speed and the longitudinal speed of the vehicle are dynamically controlled according to the comfort threshold.

And finally, learning the characteristics of the sample data by adopting a machine learning algorithm, such as a PID algorithm or an MPC algorithm, completing the training of the PID algorithm or the MPC algorithm, and determining the amount of adjustment of the transverse speed or the longitudinal speed of the vehicle when the current comfort level is adjusted to the comfort level above a comfort level threshold value under different comfort levels, namely establishing the mapping relation among the state information of the passenger sample, the driving information of the vehicle sample, the comfort level corresponding to the state information of the passenger sample and the driving information of the vehicle sample, and the control amount corresponding to the comfort level.

In another aspect, the present embodiment provides an apparatus for adjusting vehicle driving parameters, as shown in fig. 5, the apparatus 500 includes:

an obtaining module 510, configured to obtain current driving information of a vehicle and current status information of one or more passengers in the vehicle;

a determining module 520 for determining a control parameter of the vehicle based on the state information and the driving information;

an adjusting module 530, configured to adjust the current driving parameter of the vehicle based on the control parameter, so as to obtain a target driving parameter, so that the vehicle drives according to the target driving parameter at the next time.

Further, as shown in fig. 6, in the apparatus 600 for adjusting vehicle driving parameters in another embodiment, the determining module 520 specifically includes:

a first determination unit 521 for calculating a current comfort level corresponding to each passenger based on the state information and the travel information corresponding to each passenger;

a second determining unit 522, configured to determine the control parameter based on the comfort level and the driving information when the current comfort level is less than a comfort level threshold.

Optionally, in the apparatus for adjusting vehicle driving parameters according to the embodiment of the present application, when there are a plurality of passengers, the determining module 520 further includes:

a third determining unit 523 configured to determine a minimum comfort level of the current comfort levels of the plurality of passengers.

The second determining unit 522 is specifically configured to:

when the minimum comfort level is less than the comfort level threshold, the control parameter is determined based on the minimum comfort level and the driving information.

Optionally, in the apparatus for adjusting vehicle driving parameters provided in the embodiment of the present application, the determining module 520 further includes:

a fourth determining unit 524, configured to determine, when the current comfort level or the minimum comfort level is greater than or equal to the comfort level threshold, that the driving parameter in the current driving information is the target driving parameter of the vehicle.

Optionally, in the apparatus for adjusting a vehicle driving parameter provided in the embodiment of the present application, the current driving parameter is a current attitude parameter of the vehicle, and the adjusting module is specifically configured to:

based on the control parameter, adjusting the current attitude parameter of the vehicle until the comfort level of the passenger is greater than or equal to the comfort level threshold.

Optionally, in the apparatus for adjusting vehicle driving parameters provided in the embodiment of the present application, the first determining unit 521 is specifically configured to:

and inputting the driving information and the state information into a pre-established evaluation model, and outputting the current comfort level corresponding to each passenger.

Wherein, the device still includes:

a first training module 540, configured to obtain training data, where the training data includes state information of a passenger sample and driving information of a vehicle sample, and a comfort level corresponding to the state information of the passenger sample and the driving information of the vehicle sample;

and constructing the evaluation model by using a machine learning algorithm and the training data.

Optionally, in the apparatus for adjusting a vehicle driving parameter provided in the embodiment of the present application, the second determining unit 522 is specifically configured to:

and inputting the comfort level and the driving information into a pre-constructed regulation model, and outputting the control parameters.

Wherein, the device still includes:

a second training module 550, configured to obtain training data, where the training data includes state information of the passenger sample and driving information of the vehicle sample, a comfort level corresponding to the state information of the passenger sample and the driving information of the vehicle sample, and a control amount corresponding to the comfort level;

and constructing the adjusting model by utilizing a machine learning algorithm and the training data.

In another aspect, embodiments of the present application provide a vehicle including a first acquisition device, a second acquisition device, a memory, a processor, and a computer program stored on the memory and executable on the processor.

The first acquisition device is used for acquiring the current state information of one or more passengers in the vehicle.

The second acquisition device is used for acquiring the current running information of the vehicle.

The processor is used for implementing the method for adjusting the vehicle running parameters as the embodiment when executing the program.

Referring now to fig. 7, fig. 7 is a schematic diagram of a computer system of a vehicle according to an embodiment of the present disclosure.

As shown in fig. 7, the computer system 300 includes a Central Processing Unit (CPU)301 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)302 or a program loaded from a storage section 303 into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data necessary for the operation of the system 300 are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.

The following components are connected to the I/O interface 305: an input portion 306 including a keyboard, a mouse, and the like; an output section 307 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 308 including a hard disk and the like; and a communication section 309 including a network interface card such as a LAN card, a modem, or the like. The communication section 309 performs communication processing via a network such as the internet. A drive 310 is also connected to the I/O interface 305 as needed. A removable medium 311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 310 as necessary, so that a computer program read out therefrom is mounted into the storage section 308 as necessary.

In particular, according to embodiments of the application, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a machine-readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 303, and/or installed from the removable medium 311. The above-described functions defined in the system of the present application are executed when the computer program is executed by the Central Processing Unit (CPU) 301.

It should be noted that the computer readable medium shown in the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units or modules described in the embodiments of the present application may be implemented by software or hardware. The described units or modules may also be provided in a processor, and may be described as: a processor, comprising: the device comprises an acquisition module, a determination module and an adjustment module. The names of these units or modules do not in some cases form a limitation on the units or modules themselves, for example, the adjusting module may also be described as "adjusting the current driving parameters of the vehicle based on the control parameters to obtain the target driving parameters so that the vehicle drives according to the target driving parameters".

As another aspect, the present application also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments; or may be separate and not incorporated into the electronic device. The computer readable storage medium stores one or more programs which, when executed by one or more processors, perform the method for adjusting vehicle driving parameters described herein:

To sum up, the method, the device, the vehicle and the storage medium for adjusting the vehicle driving parameters provided by the embodiment of the application predict the control parameters in the vehicle driving process by monitoring the current state information of the passengers in the vehicle and the current driving information of the vehicle and further considering the current state of the passengers, so as to adjust the current driving parameters of the vehicle according to the control parameters, so that the vehicle drives according to the adjusted driving parameters, the driving parameters of the vehicle are intelligently adjusted according to the states of the passengers, the comfort and safety requirements of different passengers are met, and the comfort of the passengers is improved.

The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the disclosure. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.

Claims

1. A method of adjusting a vehicle driving parameter, the method comprising:

2. The method of adjusting vehicle driving parameters of claim 1, wherein the determining control parameters of the vehicle based on the state information and the driving information comprises:

calculating a current comfort level corresponding to each passenger based on the state information and the travel information corresponding to each passenger;

when the current comfort level is less than a comfort level threshold, determining the control parameter based on the current comfort level and the driving information.

3. The method of adjusting vehicle driving parameters of claim 1, wherein when the number of passengers is multiple, the determining control parameters of the vehicle based on the state information and the driving information comprises:

calculating the current comfort level of each passenger based on the state information and the driving information corresponding to each passenger;

determining a minimum comfort level of the comfort levels;

when the minimum comfort level is less than the comfort level threshold, determining the control parameter based on the minimum comfort level and the driving information.

4. The method of adjusting vehicle driving parameters according to claim 2 or 3, wherein the current driving parameter is a current attitude parameter of a vehicle, and the adjusting the current driving parameter of the vehicle based on the control parameter comprises:

adjusting the current attitude parameter of the vehicle based on the control parameter until the comfort level of the occupant is greater than or equal to the comfort level threshold.

5. The method of adjusting vehicle driving parameters according to claim 2 or 3, wherein the calculating a current comfort level corresponding to each passenger based on the state information and the driving information corresponding to each passenger comprises:

and inputting the running information and the state information into a pre-established evaluation model, and outputting the current comfort level corresponding to each passenger, wherein the evaluation model is the state information of the passenger sample and the running information of the vehicle sample, and the mapping relation between the comfort levels corresponding to the state information of the passenger sample and the running information of the vehicle sample.

6. The method of adjusting vehicle driving parameters according to claim 2 or 3, wherein the determining the control parameters based on the current comfort level and the driving information comprises:

and inputting the comfort level and the running information into a pre-constructed regulation model, and outputting the control parameters, wherein the regulation model is a mapping relation between the state information of the passenger sample and the running information of the vehicle sample, the comfort level corresponding to the state information of the passenger sample and the running information of the vehicle sample, and the control quantity corresponding to the comfort level.

7. The method of adjusting vehicle driving parameters of claim 3, further comprising:

and when the current comfort level is greater than or equal to the comfort level threshold value, determining the running parameters in the current running information as the target running parameters of the vehicle.

8. An apparatus for adjusting vehicle driving parameters, the apparatus comprising:

9. A vehicle comprising a first acquisition device, a second acquisition device, a memory, a processor, and a computer program stored on the memory and executable on the processor;

the processor is configured to implement the method of adjusting vehicle driving parameters according to any one of claims 1-7 when executing the program.

10. A computer-readable storage medium, on which a computer program for implementing the method of adjusting a vehicle driving parameter according to any one of claims 1-7 is stored.