CN114312774A

CN114312774A - Automatic driving lane keeping method, device, terminal device and storage medium

Info

Publication number: CN114312774A
Application number: CN202011050935.2A
Authority: CN
Inventors: 申琳; 粘凤菊; 张友朋; 方越
Original assignee: Schaeffler Technologies AG and Co KG
Current assignee: Schaeffler Technologies AG and Co KG
Priority date: 2020-09-29
Filing date: 2020-09-29
Publication date: 2022-04-12

Abstract

The present disclosure relates to the field of vehicle technologies, and in particular, to an automatic driving lane keeping method, an automatic driving lane keeping apparatus, a terminal device, and a storage medium. The method comprises the following steps: acquiring lane information of a vehicle to be steered in a lane keeping state, wherein the lane information is used for indicating the vehicle road deviation condition of the vehicle; obtaining a steering control parameter of the vehicle, wherein the steering control parameter is obtained by self-learning based on sample lane information and sample steering information of the vehicle; and determining the steering information of the vehicle according to the lane information and the steering control parameters, wherein the steering information is used for controlling the vehicle to keep a lane in the steering process. According to the steering control information obtained by self-learning and the lane information used for indicating the deviation of the vehicle road, the steering information of the vehicle can be accurately determined, so that the lane keeping function is improved, and the accuracy of vehicle control and the driving safety are improved.

Description

Automatic driving lane keeping method, device, terminal device and storage medium

Technical Field

The present disclosure relates to the field of vehicle technologies, and in particular, to an automatic driving lane keeping method, an automatic driving lane keeping apparatus, a terminal device, and a storage medium.

Background

The lane keeping method includes a method in which the lane keeping system can correct the traveling track of the vehicle so that the vehicle keeps traveling in the original lane when the vehicle abnormally deviates from the lane.

In the related art, a lane keeping system recognizes a sign line of a driving lane using a forward-looking camera, calculates a relative position of a vehicle and the lane, and when the vehicle deviates from the driving lane, the lane keeping system generates an active correction torque to correct the vehicle back to the driving lane or keep the vehicle driving in the middle of the driving lane.

With the development of vehicle control systems, the performance and functional requirements of vehicle control systems are continuously improved, and how to improve the lane keeping function, a reasonable and effective scheme is not provided in the related art.

Disclosure of Invention

In view of the above, the present disclosure provides an autonomous driving lane keeping method, an autonomous driving lane keeping apparatus, a terminal device, and a storage medium. The technical scheme comprises the following steps:

according to an aspect of the present disclosure, there is provided an autonomous driving lane keeping method, the method including:

acquiring lane information of a vehicle to be steered in a lane keeping state, wherein the lane information is used for indicating the vehicle road deviation condition of the vehicle;

obtaining a steering control parameter of the vehicle, wherein the steering control parameter is a parameter obtained by self-learning based on sample lane information and sample steering information of the vehicle;

and determining steering information of the vehicle according to the lane information and the steering control parameters, wherein the steering information is used for controlling the vehicle to keep a lane in the steering process.

In a possible implementation manner, the lane information includes a first deviation parameter and a second deviation parameter, the first deviation parameter includes a lateral deviation amount of a planned lane center line of the driving lane from the vehicle center, and the second deviation parameter includes a tangent value of an included angle of the planned lane center line of the driving lane and the vehicle body direction.

In another possible implementation manner, the obtaining the steering control parameter of the vehicle includes:

determining a vehicle speed section to which the vehicle speed of the vehicle belongs, wherein the vehicle speed section is used for indicating a vehicle speed range;

and acquiring the steering control parameters corresponding to the vehicle speed sections according to a preset corresponding relation, wherein the preset corresponding relation comprises the corresponding relation between a plurality of vehicle speed sections and a plurality of groups of steering control parameters.

In another possible implementation manner, the obtaining of the steering control parameter of the vehicle comprises:

acquiring a plurality of groups of sample data groups of the vehicle in a lane keeping state in the manual driving process, wherein each group of sample data groups comprises the sample lane information and the sample steering information of the vehicle;

and self-learning to obtain the steering control parameters of the vehicle according to the multiple groups of sample data sets of the vehicle.

In another possible implementation manner, the obtaining the steering control parameter of the vehicle by self-learning according to the multiple groups of sample data sets of the vehicle includes:

dividing the multiple groups of sample data groups into training sample sets corresponding to multiple vehicle speed sections according to the sample vehicle speed in each group of sample data groups, wherein the training sample sets comprise at least two groups of sample data groups;

and for each vehicle speed section in the plurality of vehicle speed sections, self-learning to obtain the steering control parameter corresponding to the vehicle speed section according to the training sample set corresponding to the vehicle speed section.

According to another aspect of the present disclosure, there is provided an autonomous driving lane keeping apparatus, the apparatus including:

the system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring lane information of a vehicle to be steered in a lane keeping state, and the lane information is used for indicating the vehicle road deviation condition of the vehicle;

the second acquisition module is used for acquiring steering control parameters of the vehicle, and the steering control parameters are parameters obtained by self-learning based on sample lane information and sample steering information of the vehicle;

and the determining module is used for determining the steering information of the vehicle according to the lane information and the steering control parameters, and the steering information is used for controlling the vehicle to keep a lane in the steering process.

In another possible implementation manner, the lane information includes a vehicle speed of the vehicle, and the second obtaining module is configured to:

In another possible implementation manner, the apparatus further includes: the third acquisition module and the self-learning module;

the third acquisition module is configured to acquire multiple groups of sample data sets in a lane keeping state of the vehicle during manual driving, where each group of sample data sets includes the sample lane information and the sample steering information of the vehicle;

the self-learning module is used for self-learning to obtain the steering control parameters of the vehicle according to the multiple groups of sample data sets of the vehicle.

In another possible implementation manner, the sample lane information includes a sample vehicle speed of the vehicle, and the self-learning module is further configured to:

According to another aspect of the present disclosure, there is provided a terminal device including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to perform the method described above.

According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the above-described method.

The lane information of the vehicle to be steered in the lane keeping state is obtained, and the lane information is used for indicating the vehicle road deviation condition of the vehicle; obtaining a steering control parameter of the vehicle, wherein the steering control parameter is obtained by self-learning based on sample lane information and sample steering information of the vehicle; determining steering information of the vehicle according to the lane information and the steering control parameters, wherein the steering information is used for controlling the vehicle to keep a lane in the steering process; according to the steering control information obtained by self-learning and the lane information used for indicating the deviation of the vehicle road, the steering information of the vehicle can be accurately determined, so that the lane keeping function is improved, and the accuracy of vehicle control and the driving safety are improved.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.

FIG. 1 illustrates a schematic block diagram of a system provided by an exemplary embodiment of the present disclosure;

FIG. 2 illustrates a flow chart of an autonomous driving lane keeping method provided by an exemplary embodiment of the present disclosure;

FIG. 3 illustrates a schematic diagram of a lane keeping method involved in manual driving provided by an exemplary embodiment of the present disclosure;

FIG. 4 illustrates a schematic diagram of a lane keeping method involved in autonomous driving provided by an exemplary embodiment of the present disclosure;

FIG. 5 illustrates a flow chart of an autonomous driving lane keeping method provided by an exemplary embodiment of the present disclosure;

FIG. 6 is a schematic diagram illustrating an autonomous lane keeping method according to an exemplary embodiment of the present disclosure with respect to vehicle road information;

FIG. 7 illustrates a schematic structural diagram of an autonomous driving lane keeping apparatus provided by an exemplary embodiment of the present disclosure;

fig. 8 is a block diagram illustrating a terminal device according to an example embodiment.

Detailed Description

Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.

Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.

First, an application scenario to which the present disclosure relates will be described.

Referring to fig. 1, a schematic structural diagram of a system provided in an exemplary embodiment of the present disclosure is shown.

The system includes a vehicle system 120 and a control system 140. The vehicle system 120 includes a sensing system 122 and an actuating system 124. Control system 140 establishes communication connections with sensing system 122 and actuating system 124, respectively.

Optionally, the control system 140 is a Lane Keeping System (LKS) control system.

Optionally, the control system 140 includes a terminal device provided on the vehicle. The sensing system 122 includes a camera and sensors disposed on the vehicle, such as sensors including wheel angle sensors. The implement system 124 includes a steering actuator of the vehicle. The embodiments of the present disclosure do not limit this.

The sensing system 122 is configured to collect lane information of a vehicle to be steered, where the lane information is used to indicate a lane deviation condition of the vehicle, and send the collected lane information to the control system 140. The control system 140 is configured to determine steering information of the vehicle according to the lane information collected by the sensing system 122 and the steering control parameters obtained by self-learning. The control system 140 is configured to send a control instruction carrying the steering information to the execution system 124, and the execution system 124 is configured to steer according to the control instruction sent by the control system 140.

In the following, several exemplary embodiments are used to describe the automatic driving lane keeping method provided by the disclosed embodiments.

Referring to fig. 2, a flowchart of an automatic driving lane keeping method according to an exemplary embodiment of the present disclosure is shown, and the present embodiment is illustrated by using the method in the control system. The method comprises the following steps.

Step 201, obtaining lane information of a vehicle to be steered in a lane keeping state, wherein the lane information is used for indicating the vehicle road deviation condition of the vehicle.

During automatic driving of a vehicle, a control system acquires lane information of the vehicle to be steered in a lane-keeping state.

Wherein the lane keeping state is used for indicating to control the vehicle to keep running on the planned driving lane.

Optionally, during the automatic driving of the vehicle, the control system obtains a state flag of the vehicle, the state flag indicating whether the vehicle is in a lane keeping state. Illustratively, the status flag is a first value to indicate that the vehicle is in a lane-keeping state and a second value to indicate that the vehicle is not in a lane-keeping state. For example, the first value is 1 and the second value is 0. The embodiment of the present disclosure does not impose limitations on the determination conditions of the lane keeping state during autonomous driving.

Optionally, in the automatic driving process, under the condition that the vehicle is in the lane keeping state, the sensing system of the vehicle collects lane information of the vehicle in real time and sends the collected lane information to the control system. Correspondingly, the control system acquires lane information of the vehicle.

The lane information is used for indicating the vehicle road deviation condition of the vehicle, namely the lane information is used for indicating the existence or nonexistence of the vehicle road deviation. The lane departure is the deviation in position between the vehicle and the planned lane of travel. The planned driving lane is the current lane where the vehicle is located.

Optionally, the lane information is used to indicate a deviation between the vehicle and a lane center line of the planned driving lane.

Optionally, the lane information includes a first deviation parameter and a second deviation parameter, the first deviation parameter includes a lateral deviation amount of a lane center line of the planned driving lane from a vehicle center, and the second deviation parameter includes a tangent value of an included angle of the lane center line of the planned driving lane and a vehicle body direction.

Optionally, the lane information further includes at least one of a vehicle speed, a wheel base, and a road curvature radius of the driving lane of the vehicle. The embodiments of the present disclosure do not limit this.

Step 202, obtaining a steering control parameter of the vehicle, wherein the steering control parameter is a parameter obtained by self-learning based on the sample lane information and the sample steering information of the vehicle.

Optionally, the control system performs self-learning in advance based on the sample lane information and the sample steering information of the vehicle to obtain the steering control parameter, and stores the steering control parameter of the vehicle. When the lane information of the vehicle to be steered is used for indicating that the lane deviation exists, the control system acquires the stored steering control parameters.

The steering control parameters include parameters obtained by self-learning based on sample lane information and sample steering information of the vehicle. Optionally, the sample lane information and the sample steering information are information collected during manual driving of the vehicle.

Optionally, the steering control parameter is a proportional-integral-derivative (PID) controller parameter.

Optionally, a plurality of sets of steering control parameters are stored in the control system, each set of steering control parameters includes a plurality of parameters, and the control system obtains one set of steering control parameters from the plurality of sets of steering control parameters according to a preset selection strategy.

Illustratively, the plurality of sets of steering control parameters are steering control parameters corresponding to the plurality of driving styles. The embodiments of the present disclosure do not limit this.

And step 203, determining the steering information of the vehicle according to the lane information and the steering control parameters, wherein the steering information is used for controlling the vehicle to keep a lane in the steering process.

The control system determines steering information of the vehicle according to the lane information and the steering control parameters of the vehicle. And controlling the vehicle to perform lane keeping during steering according to the steering information of the vehicle.

Optionally, the steering information includes a wheel angle or a steering wheel angle. For convenience of explanation, the following description will be given only taking an example in which the steering information includes the wheel rotation angle.

Alternatively, the control system controls the vehicle to perform lane keeping during steering, i.e. the control system controls the vehicle to keep driving on the planned driving lane during steering.

In summary, the embodiment of the present disclosure obtains the lane information of the vehicle to be steered in the lane keeping state, where the lane information is used to indicate the lane deviation condition of the vehicle; obtaining a steering control parameter of the vehicle, wherein the steering control parameter is obtained by self-learning based on sample lane information and sample steering information of the vehicle; determining steering information of the vehicle according to the lane information and the steering control parameters, wherein the steering information is used for controlling the vehicle to keep a lane in the steering process; based on the steering control information obtained by self-learning and the lane information used for indicating the deviation of the vehicle road, the steering information of the vehicle can be accurately determined, so that the lane keeping function is improved, and the accuracy of vehicle control and the driving safety are improved.

The control system further stores a plurality of groups of steering control parameters, the steering control parameters are corresponding to a plurality of driving styles, and the control system obtains one group of steering control parameters from the steering control parameters according to a preset selection strategy, so that the automatic driving personalized customization is realized.

It should be noted that, in the automatic driving lane keeping method provided by the embodiment of the present disclosure, the self-learning process of the steering control parameter includes: in the manual driving process, the control system obtains sample lane information and sample steering information of the vehicle, and self-learning is carried out according to the sample lane information and the sample steering information of the vehicle to obtain steering control parameters. The use process of the steering control parameters comprises the following steps: in the automatic driving process, the control system acquires lane information of a vehicle to be steered in a lane keeping state, and determines steering information of the vehicle according to the lane information and steering control parameters obtained by self learning. Fig. 3 and 4 show schematic diagrams relating to the automated driving lane keeping method in the manual driving process and the automated driving process, respectively.

Referring to fig. 3, a schematic diagram of a lane keeping method in a manual driving process according to an exemplary embodiment of the disclosure is shown.

Optionally, control system 140 is an LKS control system, and control system 140 includes an LKS steering control parameter self-learning system 142 and an LKS steering control system 144.

During manual driving, control system 140 determines in real time whether the vehicle is in a lane keeping state.

Optionally, in the manual driving process, the control system 140 collects lane information of the vehicle in real time, determines whether the lane information of the vehicle meets a preset condition, and determines that the vehicle is in a lane keeping state if the lane information of the vehicle meets the preset condition; and if the lane information of the vehicle does not meet the preset condition, determining that the vehicle is not in a lane keeping state.

In one possible implementation, the lane information includes a lane line quality parameter indicating a degree of clarity of a lane line of a driving lane in which the vehicle is located, a first deviation parameter, a second deviation parameter, and a vehicle driving parameter indicating whether the vehicle is in a forward driving state, for example, the vehicle driving parameter includes a vehicle speed and a gear of the vehicle. When the lane line quality parameter meets a preset quality condition, the first deviation parameter and the second deviation parameter do not change suddenly, and the vehicle is in a forward driving state, determining that the vehicle is in a lane keeping state; otherwise, the vehicle is determined not to be in the lane keeping state. For example, the preset quality condition includes that the lane line quality parameter is greater than a preset quality threshold.

It should be noted that the embodiment of the present disclosure does not limit the manner of determining the lane keeping state during manual driving.

When the vehicle is in a lane keeping state, the sensing system 122 of the vehicle collects sample lane information and sample steering information in real time and sends the sample lane information and the sample steering information to the LKS steering control parameter self-learning system 142, and the LKS steering control parameter self-learning system 142 is used for carrying out self-learning on the basis of the sample lane information and the sample steering information of the vehicle to obtain steering control parameters of the vehicle, and sending the steering control parameters obtained by the self-learning to the LKS steering control system 144. By means of self-learning of the steering control parameters, the steering control parameters can be continuously updated according to habits of drivers, so that the behavior of automatic lane keeping is more comfortable and closer to the behavior of lane keeping in manual driving. In the self-learning of the steering control parameters, a plurality of sets of steering control parameters with different driving styles can be summarized for the selection of passengers.

Referring to fig. 4, a schematic diagram of a lane keeping method in an automatic driving process according to an exemplary embodiment of the present disclosure is shown based on the embodiment provided in fig. 3.

During autonomous driving, when the vehicle is in a lane keeping state, the sensing system 122 of the vehicle collects lane information in real time and sends the collected lane information to the LKS steering control system 144. The LKS steering control system 144 is configured to determine steering information of the vehicle according to the lane information and the steering control parameter obtained by self-learning, and send a control instruction carrying the steering information to the execution system 124, where the execution system 124 is configured to steer according to the control instruction sent by the control system 140.

It should be noted that the division of the above systems is only exemplary, for example, the LKS steering control parameter self-learning system 142 and the LKS steering control system 144 may be implemented as two systems, or may be combined into one system. The embodiments of the present disclosure do not limit this.

Referring to fig. 5, a flowchart of an automatic driving lane keeping method according to an exemplary embodiment of the present disclosure is shown, which is illustrated in the present embodiment as being used in the control system. The method comprises the following steps.

Step 501, obtaining multiple groups of sample data groups of a vehicle in a lane keeping state in the process of manual driving.

When the vehicle is in a lane keeping state in the manual driving process, the control system obtains multiple groups of sample data sets in the lane keeping state of the vehicle in the manual driving process through the sensing system, and each group of sample data sets comprises sample lane information and sample steering information of the vehicle.

The sample lane information is lane information acquired in the manual driving process. The meaning of the sample lane information can be described in analogy with the related description of the reference lane information, and is not described herein again.

The sample steering information is steering information acquired in the manual driving process, and comprises a sample wheel corner or a sample steering wheel corner. The meaning of the sample steering information can be analogized to the related description of the reference steering information, and is not described herein again.

And 502, self-learning to obtain the steering control parameters of the vehicle according to the multiple groups of sample data sets of the vehicle.

Optionally, the self-learning by the control system of the steering control parameter of the vehicle according to a plurality of sets of sample data of the vehicle includes: for each group of sample data groups in the multiple groups of sample data groups, determining predicted steering information according to the sample lane information and the initial steering control parameters; comparing the predicted steering information with the sample steering information to obtain a calculation loss, wherein the calculation loss is used for indicating an error between the predicted steering information and the sample steering information; and training to obtain the steering control parameters of the vehicle according to the respective corresponding calculation loss of the multiple groups of sample data sets.

Wherein, the initial steering control parameter is the steering control parameter which is initially set.

It should be noted that, the control system determines the predicted steering information according to the sample lane information and the initial steering control parameter, and may determine the steering information according to the lane information and the steering control parameter by analogy with the reference control system, which is not described herein first.

In step 503, in the automatic driving process, under the condition that the vehicle to be steered is in the lane keeping state, lane information of the vehicle is acquired.

Optionally, the lane information further comprises road parameters comprising the inverse of twice the radius of curvature of the road. The road curvature radius is the road curvature radius of a planned driving lane, and is also called the turning radius of a vehicle to be steered.

In one illustrative example, as shown in FIG. 6, a₀The lateral offset of the lane centre line from the vehicle centre, i.e. a, for the planned driving lane₀Is a first deviation parameter; theta₀Is the included angle between the lane central line and the vehicle body direction. a is₁＝tan(θ₀)，a₁(not shown) is the tangent of the angle between the lane center line and the vehicle body direction, i.e. a₁Is a second deviation parameter;

a₂(not shown in the figure) is the inverse of twice the radius of curvature of the road. a is₀And a₁Together may be used to indicate a lane departure condition.

Optionally, the lane information further includes at least one of a vehicle speed, a wheel base, and a road curvature radius of the planned driving lane of the vehicle.

Step 504, obtaining a steering control parameter of the vehicle, wherein the steering control parameter is a parameter obtained by self-learning based on the sample lane information and the sample steering information of the vehicle.

And the control system acquires the steering control parameters obtained by self learning. Alternatively, the control system obtains the self-learned steering control parameter when the lane information of the vehicle is used to indicate the presence of the lane departure.

It should be noted that, reference may be made to the relevant description in the above embodiments for the process of the control system obtaining the steering control parameter obtained by self-learning, and details are not described herein again.

And 505, determining the steering information of the vehicle according to the lane information and the steering control parameters.

The control system determines steering information of the vehicle according to the lane information and the steering control parameters, wherein the steering information comprises wheel turning angles or steering wheel turning angles.

Taking the example that the lane information comprises a first deviation parameter and a second deviation parameter, the vehicle speed, the wheel base and the road curvature radius of a driving lane, the control system determines the steering information of the vehicle according to the lane information and the steering control parameters, and the method comprises the following steps: the wheel rotation angle δ of the vehicle is calculated by the following formula:

wherein the content of the first and second substances,

wherein, a₀Is a first deviation parameter, a₁Is a second deviation parameter, k₁、k₂、k₃、k₄、k₅、k₆Are steering control parameters, K is a preset stability factor, u is the speed of the vehicle, R_{Road surface}Is the radius of curvature of the road, L is the wheelbase of the vehicle, a₂Which is the inverse of twice the radius of curvature of the road.

The steering control parameter is also called PID controller parameter, and the steering control parameter α ═ k₁,k₂,k₃,k₄,k₅,k₆]。

After the control system determines the steering information of the vehicle according to the lane information and the steering control parameters, the control system sends the control command carrying the wheel turning angle to the execution system, and the execution system performs steering according to the control command sent by the control system.

To sum up, in the automatic driving lane keeping method provided by the embodiment of the disclosure, when a vehicle is in a lane keeping state in the manual driving process, multiple sets of sample data sets of the vehicle in the lane keeping state in the manual driving process are acquired, a steering control parameter of the vehicle is obtained according to self-learning of the multiple sets of sample data sets of the vehicle in the lane keeping state in the manual driving process, lane information of the vehicle is acquired under the condition that the vehicle to be steered is in the lane keeping state in the automatic driving process, the steering control parameter of the vehicle is acquired, and steering information of the vehicle is determined according to the lane information and the steering control parameter; the characteristics of manual driving are fused, and the vehicle control effect is further improved while the lane keeping function is ensured.

It should be noted that, depending on the vehicle characteristics, the wheel angles of different vehicle speeds should be different even with the same lane offset, and therefore, in the steering control parameter self-learning, the steering controller parameters at different vehicle speeds should be determined separately.

In the manual driving process, when the vehicle is in a lane keeping state, the vehicle collects sample lane information and sample wheel corners in real time and sends the sample lane information and the sample wheel corners to a control system, wherein the sample lane information comprises sample vehicle speed and sample lane departure information of the wheels. The control system divides a plurality of groups of sample data groups into training sample sets corresponding to a plurality of vehicle speed sections according to the sample vehicle speed in each group of sample data groups, wherein each training sample set comprises at least two groups of sample data groups; and for each vehicle speed section in the plurality of vehicle speed sections, self-learning to obtain the steering control parameter corresponding to the vehicle speed section according to the training sample set corresponding to the vehicle speed section.

In one illustrative example, the control system divides the travelable speed of the vehicle into m vehicle speed segments, i.e. [ v ]₁,…,v_m]And m is a positive integer greater than 1. And then classifying the multiple groups of sample data groups into the vehicle speed sections according to the sample vehicle speeds in the multiple groups of collected sample data groups. The measured value corresponding to each vehicle speed section comprises n groups of data, each group of data comprises 6 values, and the measured values can be represented as:

the control system calculates the measured values by interpolation or least squaresTo the optimal controller parameter corresponding to each vehicle speed section, namely the steering control parameter,

after the steering control parameters are obtained through self-learning, the control system stores preset corresponding relations between a plurality of vehicle speed sections and a plurality of groups of steering control parameters. In the automatic driving process, under the condition that a vehicle to be steered is in a lane keeping state, lane information of the vehicle is obtained, wherein the lane information comprises the speed of the vehicle; determining a vehicle speed section to which the vehicle speed of the vehicle belongs, wherein the vehicle speed section is used for indicating a vehicle speed range; and acquiring steering control parameters corresponding to the vehicle speed sections according to a preset corresponding relation, wherein the preset corresponding relation comprises the corresponding relation between a plurality of vehicle speed sections and a plurality of groups of steering control parameters.

In summary, the embodiment of the present disclosure further determines the steering controller parameters at different vehicle speeds in the self-learning of the steering control parameters, so that the system can obtain the steering control parameters corresponding to the vehicle speed section to which the vehicle speed of the vehicle belongs from multiple sets of steering control parameters in the subsequent automatic driving process, thereby realizing the lane guarantee function and simultaneously considering the vehicle characteristics, and further improving the accuracy of vehicle control and the driving safety.

The following are embodiments of the apparatus of the embodiments of the present disclosure, and for portions of the embodiments of the apparatus not described in detail, reference may be made to technical details disclosed in the above-mentioned method embodiments.

Referring to fig. 7, a schematic structural diagram of an automatic driving lane keeping apparatus according to an exemplary embodiment of the present disclosure is shown. The autonomous lane keeping apparatus may be implemented as all or a part of a terminal device by software, hardware, or a combination of both. The device includes: a first obtaining module 710, a second obtaining module 720, and a determining module 730.

A first obtaining module 710, configured to obtain lane information of a vehicle to be steered in a lane keeping state, where the lane information is used to indicate a lane deviation condition of the vehicle;

the second obtaining module 720 is configured to obtain a steering control parameter of the vehicle, where the steering control parameter is a parameter obtained by self-learning based on sample lane information and sample steering information of the vehicle;

the determining module 730 is configured to determine steering information of the vehicle according to the lane information and the steering control parameter, where the steering information is used to control the vehicle to perform lane keeping during steering.

In one possible implementation manner, the lane information includes a first deviation parameter and a second deviation parameter, the first deviation parameter includes a lateral deviation amount of a lane center line of the planned driving lane from a vehicle center, and the second deviation parameter includes a tangent value of an included angle between the lane center line of the planned driving lane and a vehicle body direction.

In another possible implementation manner, the lane information includes a vehicle speed of the vehicle, and the second obtaining module 720 is configured to:

and acquiring steering control parameters corresponding to the vehicle speed sections according to a preset corresponding relation, wherein the preset corresponding relation comprises the corresponding relation between a plurality of vehicle speed sections and a plurality of groups of steering control parameters.

the third acquisition module is used for acquiring a plurality of groups of sample data groups in a lane keeping state of the vehicle in the manual driving process, wherein each group of sample data groups comprises sample lane information and sample steering information of the vehicle;

and the self-learning module is used for self-learning to obtain the steering control parameters of the vehicle according to the multiple groups of sample data groups of the vehicle.

In another possible implementation, the sample lane information includes a sample vehicle speed of the vehicle, and the self-learning module is further configured to:

dividing a plurality of groups of sample data groups into training sample sets corresponding to a plurality of vehicle speed sections according to the sample vehicle speed in each group of sample data groups, wherein each training sample set comprises at least two groups of sample data groups;

It should be noted that, when the apparatus provided in the foregoing embodiment implements the functions thereof, only the division of the above functional modules is illustrated, and in practical applications, the above functions may be distributed by different functional modules according to actual needs, that is, the content structure of the device is divided into different functional modules, so as to complete all or part of the functions described above.

With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.

The embodiment of the present disclosure further provides a terminal device, where the terminal device includes: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to perform the method described above.

Embodiments of the present disclosure also provide a non-transitory computer-readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the above-described method.

Fig. 8 is a block diagram illustrating a terminal device 800 according to an example embodiment. Referring to fig. 8, terminal device 800 may include one or more of the following components: processing component 802, memory 804, power component 806, multimedia component 808, audio component 810, input/output (I/O) interface 812, sensor component 814, and communication component 816.

The processing component 802 generally controls overall operation of the terminal device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.

The memory 804 is configured to store various types of data to support operations at the terminal device 800. Examples of such data include instructions for any application or method operating on terminal device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.

Power components 806 provide power to the various components of terminal device 800. Power components 806 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for terminal device 800.

The multimedia component 808 comprises a screen providing an output interface between the terminal device 800 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. When the terminal device 800 is in an operation mode, such as a shooting mode or a video mode, the front camera and/or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive an external audio signal when the terminal device 800 is in an operation mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.

Sensor component 814 includes one or more sensors for providing various aspects of state assessment for terminal device 800. For example, sensor assembly 814 may detect an open/closed status of terminal device 800, the relative positioning of components, such as a display and keypad of terminal device 800, sensor assembly 814 may also detect a change in the position of terminal device 800 or a component of terminal device 800, the presence or absence of user contact with terminal device 800, orientation or acceleration/deceleration of terminal device 800, and a change in the temperature of terminal device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

Communication component 816 is configured to facilitate communications between terminal device 800 and other devices in a wired or wireless manner. The terminal device 800 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

In an exemplary embodiment, the terminal device 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.

In an exemplary embodiment, a non-transitory computer-readable storage medium, such as the memory 804, is also provided that includes computer program instructions executable by the processor 820 of the terminal device 800 to perform the above-described method.

The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.

The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: 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), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.

The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.

The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).

Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

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 disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). 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.

Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein were chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the techniques in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. An autonomous driving lane keeping method, the method comprising:

2. The method of claim 1, wherein the lane information comprises a first deviation parameter comprising a lateral offset of a planned lane center line of the driving lane from the vehicle center and a second deviation parameter comprising a tangent of an angle of the planned lane center line of the driving lane from the vehicle body direction.

3. The method of claim 1, wherein the lane information includes a vehicle speed of the vehicle, and the obtaining steering control parameters of the vehicle includes:

4. The method of claim 1, wherein prior to obtaining the steering control parameter of the vehicle, comprising:

5. The method of claim 4, wherein the sample lane information comprises a sample vehicle speed of the vehicle, and the self-learning from the multiple sets of sample data sets of the vehicle to derive the steering control parameter of the vehicle comprises:

6. An autonomous driving lane keeping apparatus, characterized in that the apparatus comprises:

7. The apparatus of claim 6, wherein the lane information comprises a first deviation parameter and a second deviation parameter, the first deviation parameter comprises a lateral offset of a planned lane center line of the driving lane from the vehicle center, and the second deviation parameter comprises a tangent of an angle of the planned lane center line of the driving lane from the vehicle body direction.

8. The apparatus of claim 6, wherein the lane information comprises a vehicle speed of the vehicle, the second obtaining module to:

9. The apparatus of claim 6, further comprising: the third acquisition module and the self-learning module;

10. The apparatus of claim 9, wherein the sample lane information comprises a sample vehicle speed of the vehicle, the self-learning module further to:

11. A terminal device, characterized in that the terminal device comprises: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to perform the method of any one of claims 1 to 5.

12. A non-transitory computer readable storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement the method of any of claims 1 to 5.