An intelligent driving vehicle lane changing and lane keeping integrated decision control method

Abstract

The application relates to a kind of intelligent driving vehicle lane changing and lane keeping integrated decision control methods, the method comprises the following steps: step 1, the lane line information and other traffic participants in the real-time driving environment of intelligent driving vehicle are identified by visual sensor;Step 2, the safe and feasible lane driving behavior instruction of intelligent driving vehicle is determined by decision;Step 3, according to the lane driving behavior instruction, the target driving lane of lane driving control is determined;Step 4, the transverse and longitudinal motion control instruction of making intelligent driving vehicle drive to target lane is determined by decision;Step 5, the longitudinal and transverse motion control instruction of lane driving issued by intelligent driving vehicle is executed.Compared with prior art, the application can realize the continuous decision and control of different lane driving behaviors such as lane keeping and lane changing, improve the system function integration, reduce the system complexity, and be easier to realize.

Description

Technical Field

[0001] This invention relates to the field of motion control technology for intelligent driving vehicles, and in particular to an integrated decision-making and control method for lane changing and lane keeping in intelligent driving vehicles. Background Technology

[0002] Structured road scenarios are common for autonomous vehicles. Because structured roads have clear road markings and distinct geometric features, autonomous vehicles can determine and control their movement within the appropriate lane based on the surrounding environment and road information. Typical lane control functions include lane keeping assist and automatic lane changing, which are fundamental to autonomous driving. Therefore, accurately and quickly controlling these lane-keeping behaviors is crucial for the driving safety of autonomous vehicles.

[0003] There is currently considerable research on lane control for intelligent driving vehicles. For example, Chinese patent application CN114655202 A discloses a lane keeping control method based on yaw rate control, using yaw rate as the control variable to achieve lane keeping control. Chinese patent application CN112141110A discloses a vehicle lane changing method that automatically determines lane changing scenarios and achieves autonomous lane changing based on automated driving guidance navigation. However, most intelligent driving vehicles configure independent control modules or employ different planning and control methods for different lane driving functions, switching between functions according to the driving scenario. Such technical solutions will lead to increased complexity in intelligent driving control systems as functions increase. In actual driving scenarios, some lane driving behaviors are continuous, such as lane keeping and lane changing; using the same method for control could improve control system efficiency. Furthermore, most current intelligent driving vehicle lane changing control technologies rely on high-precision vehicle positioning information, resulting in high implementation costs.

[0004] In summary, existing lane control methods for intelligent driving vehicles are designed independently for different functions, resulting in low functional integration and complex systems.

[0005] Therefore, there is an urgent need to design an integrated decision-making and control method that can realize various lane driving behaviors such as lane keeping and lane changing, which can provide a reference solution for solving the lane driving control technology problem of intelligent driving vehicles. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the existing technology and provide an integrated decision-making and control method for lane changing and lane keeping of intelligent driving vehicles. This method can realize integrated decision-making and control of various lane driving behaviors such as lane keeping and lane changing, improve the system's functional integration, reduce system complexity, and is easier to implement.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] According to a first aspect of the present invention, an integrated decision-making and control method for lane changing and lane keeping of an intelligent driving vehicle is provided, the method comprising the following steps:

[0009] Step 1: Identify lane line information and other traffic participants in the real-time driving environment of the intelligent driving vehicle through visual sensors, and process the information to obtain the relative motion state information of each lane and its matching lane line, lane center line and traffic participants.

[0010] Step 2: Based on lane lines, relative motion information of traffic participants, and lane change requests, determine safe and feasible lane driving behavior instructions for the intelligent driving vehicle. The lane driving behavior instructions include lane keeping, left lane change, right lane change, and lane change suppression.

[0011] Step 3: Determine the target driving lane for lane driving control based on the lane driving behavior instruction;

[0012] Step 4: Decision-making regarding the lateral and longitudinal motion control commands to guide the intelligent driving vehicle to the target lane, wherein...

[0013] The lateral motion control command is the desired steering wheel angle, which is a weighted function of the lateral distance deviation, heading angle deviation, and road curve radius at the pre-aiming point on the center line of the target lane.

[0014] The longitudinal motion control command is the desired acceleration, which is determined by the vehicle following control strategy based on the relative motion state of the traffic participants closest to the vehicle in the lane where the vehicle is located and in the target lane. If there are no traffic participants within the safe distance in front of the vehicle, the desired acceleration is determined by the cruise control strategy based on the deviation between the desired cruise speed and the actual speed.

[0015] Step 5: The intelligent driving vehicle executes the issued longitudinal and lateral movement control commands for lane driving.

[0016] Preferably, the information regarding each lane and its corresponding lane lines, lane center lines, and traffic participants mentioned in step 1 includes the following steps:

[0017] Step 1.1: Remove data that does not belong to the lane boundary lines from the identified lane line data according to the line type;

[0018] Step 1.2: Fit the parametric equations for the remaining lane lines. The parametric equations are expressed by the cubic polynomial shown in equation (1):

[0019] y i (x)=c 0i +c 1i x+c 2i x 2 +c 3i x 3 (1)

[0020] In the formula, i is the number of lane lines; x and y are the longitudinal and lateral distances in the vehicle coordinate system, respectively; c0, c1, c2, and c3 are the polynomial coefficients, and c0 is defined as the lateral position of the lane line relative to the origin of the vehicle in the vehicle coordinate system, with positive on the left and negative on the right.

[0021] Step 1.3: Sort the lane lines in descending order of c0, and perform difference on the sorted c0 sequence to obtain the width of each lane;

[0022] Step 1.4: Remove lane lines that do not meet the lane width requirements to obtain lane lines that meet the control method requirements;

[0023] Step 1.5: Define the number of lanes as the number of lane lines after filtering minus 1, and set the lane IDs from right to left as 1, 2, 3, ... n; where n is the number of lanes.

[0024] Step 1.6: Determine the current lane ID of the vehicle based on c0. Specifically, if c0 < 0, it means that the lane line is located to the right of the vehicle. The number of lane lines with c0 < 0 is defined as the current lane ID of the vehicle.

[0025] Step 1.7: Match lane lines and traffic participant information for each lane. The lane line information includes the lateral distance between the lane lines on both sides of the lane and the lane center line and the vehicle, the heading angle and curvature information, and the color and lane line type information of the lane lines on both sides of the lane, which are represented by a cubic polynomial.

[0026] Preferably, the formula for calculating the lateral distance between the lane centerline and the vehicle in step 1.7 is as follows:

[0027]

[0028] In the formula, y cj This represents the lateral distance between the center line of the j-th lane and the origin of the vehicle's coordinate system, where i = j represents the i-th lane line;

[0029] The formula for calculating the curvature of the lane centerline is as follows:

[0030]

[0031] In the formula, ρ cj Let y' be the curvature of the centerline of the j-th lane, and y' and y'' be the first and second derivatives of y, respectively.

[0032] The relative motion status information of traffic participants includes the relative position and relative speed information of traffic participants in each lane relative to their own vehicle.

[0033] Preferably, the lane change suppression in step 2 specifically refers to: the lane driving behavior instruction issued by the control system behavior decision module when the vehicle determines that it cannot continue to safely change lanes based on the relative motion state information of surrounding traffic participants during the lane change process.

[0034] Preferably, the lane driving behavior instruction in step 3 corresponds to different target lanes, specifically including:

[0035] When the lane driving behavior instruction is lane keeping or lane change suppression, the target driving lane is the lane where the vehicle is located.

[0036] When the lane driving behavior instruction is to change lanes to the left, the target driving lane is the first lane to the left of the lane where the vehicle is located;

[0037] When the lane driving behavior instruction is to change lanes to the right, the target driving lane is the first lane to the right of the lane where the vehicle is located.

[0038] Preferably, step 4, generating the lateral movement control command for the intelligent driving vehicle, includes the following steps:

[0039] Step 4.1: Based on the vehicle speed and target lane information, obtain the lateral position y at two pre-aimed distance points on the center line of the target lane using an index. pm and road curvature ρ pm The information and calculation formula are as follows:

[0040]

[0041] In the formula, d pm =t pm *v represents the aiming distance, t pm Let t be the aiming time, v be the vehicle's speed, and m = 1, 2 represent the first and second aiming points, satisfying t p1 <t p2 ;

[0042] Step 4.2: Based on the vehicle speed information, calculate the lateral position deviation e for lane driving control. y Heading angle deviation and road radius R:

[0043]

[0044]

[0045]

[0046] In the formula, e ymax =max{y p1 ,y p2};

[0047] Step 4.3: Calculate the desired steering wheel angle control command for lane driving control lateral movement:

[0048]

[0049] In the formula, k1, k2, and k3 are the weighting coefficients for lateral error control, heading error control, and cornering compensation control, respectively. The weighting coefficient values ​​can be reasonably determined through actual vehicle testing and calibration under different working conditions.

[0050] Step 4.4: Output the desired steering wheel angle after limiting it within the constraint conditions.

[0051] Preferably, the desired steering wheel angle and desired acceleration should respectively satisfy the following constraints:

[0052]

[0053]

[0054] In the formula, δ is the desired steering wheel angle, δ min and δ max These are the minimum and maximum values ​​of the desired steering wheel angle, respectively. The desired rate of change of steering wheel angle, and Let be the minimum and maximum values ​​of the desired rate of change of steering wheel angle, respectively, and let 'a' be the desired acceleration. min and a max These are the minimum and maximum values ​​of the desired acceleration, respectively. For the desired rate of change of acceleration, and These are the minimum and maximum values ​​of the expected rate of change of acceleration, respectively.

[0055] The minimum and maximum values ​​of the constraints on longitudinal and lateral motion control commands can be determined comprehensively based on vehicle driving safety, stability, comfort, and physical limitations.

[0056] Preferably, step 5 specifically involves: inputting the desired acceleration control command to the longitudinal motion controller of the intelligent driving vehicle, and inputting the desired steering wheel angle control command to the electronic steering controller, so as to realize the longitudinal and lateral motion control of the intelligent driving vehicle.

[0057] According to a second aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement any of the methods described above.

[0058] According to a third aspect of the invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements any of the methods described herein.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] 1) The integrated decision-making and control method is adopted to realize continuous decision-making and control of different lane driving behaviors such as lane keeping and lane changing, which improves the system's functional integration, reduces system complexity, and is easier to implement;

[0061] 2) Lane keeping and lane changing functions can be directly decided and controlled based on lane information obtained by vision sensors, without relying on high-precision positioning equipment and high-precision maps for path planning, which can improve the efficiency of autonomous driving system and reduce system cost. Attached Figure Description

[0062] Figure 1 This is a flowchart of the integrated decision-making and control method for lane changing and lane keeping of intelligent driving vehicles in an embodiment of the present invention;

[0063] Figure 2 This is a flowchart of the lane information processing method in an embodiment of the present invention;

[0064] Figure 3 This is a schematic diagram of the target lane pre-aiming point for left lane change control in an embodiment of the present invention. Detailed Implementation

[0065] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0066] Example

[0067] This embodiment presents an integrated decision-making and control method for lane changing and lane keeping in intelligent driving vehicles. Figure 1This is a schematic flowchart of an integrated decision-making and control method for lane changing and lane keeping of an intelligent driving vehicle according to an embodiment of the present invention. Figure 1 As shown, the decision control method may include the following steps:

[0068] Step 1: Using visual sensors, such as intelligent driving vehicle perception cameras, identify lane lines and other traffic participants in the real-time driving environment of the intelligent driving vehicle, and further process the lane line information to obtain information on the relative motion state of each lane and its matching lane lines, lane center lines, and traffic participants.

[0069] The information processing method for obtaining each lane and its matching lane lines and lane center lines is as follows: Figure 2 As shown, the specific steps include:

[0070] Step 1.1: Remove data that does not belong to the lane boundary line from the lane line data identified by the visual sensor according to the line type, such as data that is mistakenly identified as lane line by the road edge;

[0071] Step 1.2: Fit the parametric equations of the remaining lane lines. The parametric equations can be expressed by the cubic polynomial shown in equation (1):

[0072] y i (x)=c 0i +c 1i x+c 2i x 2 +c 3i x 3 (1)

[0073] In the formula, i is the number of lane lines; x and y are the longitudinal and lateral distances in the vehicle coordinate system, respectively; c0, c1, c2, and c3 are the polynomial coefficients, and c0 is defined as the lateral position of the lane line relative to the origin of the vehicle in the vehicle coordinate system (positive on the left and negative on the right).

[0074] Step 1.3: Sort the lane lines in descending order of c0, and perform difference on the sorted c0 sequence to obtain the width of each lane;

[0075] Step 1.4: Remove lane lines that do not meet the lane width requirements, such as lane lines with a lane width of less than 2.5m, to obtain lane lines that meet the requirements of the control method.

[0076] Step 1.5: Define the number of lanes as the number of lane lines after filtering minus 1, and set the lane IDs from right to left as 1, 2, 3, ... n;

[0077] Step 1.6: Determine the current lane ID of the vehicle based on c0. Specifically, if c0 < 0, it means that the lane line is located to the right of the vehicle. The number of lane lines with c0 < 0 is defined as the current lane ID of the vehicle.

[0078] Step 1.7: Match lane markings and traffic participant information for each lane.

[0079] The lane line information includes the lateral distance between the lane lines on both sides of the lane and the lane center line and the vehicle, the heading angle and curvature information, and the color and lane line type information of the lane lines on both sides of the lane, which are represented by a cubic polynomial.

[0080] The formula for calculating the lateral distance between the lane centerline and the vehicle is as follows:

[0081]

[0082] In the formula, y cj This represents the lateral distance between the center line of the j-th lane and the origin of the vehicle's coordinate system, where i = j represents the i-th lane line;

[0083] The formula for calculating the curvature of the lane centerline is as follows:

[0084]

[0085] In the formula, ρ cj Let y' be the curvature of the centerline of the j-th lane, and y' and y'' be the first and second derivatives of y, respectively.

[0086] The traffic participant information includes the relative position and relative speed information of traffic participants and their own vehicles in each lane.

[0087] Step 2: Based on lane markings, traffic participant information, and lane change request instructions, determine the lane driving behavior instructions for the intelligent driving vehicle.

[0088] The lane driving behavior instructions include any one of lane keeping, left lane changing, right lane changing, and lane change suppression. Lane change suppression refers to the lane driving behavior instruction issued by the control system decision module when it determines, based on the relative motion information of surrounding traffic participants, that the vehicle cannot safely continue the lane change during the lane-changing process. For example, if the intelligent driving vehicle detects that the longitudinal or lateral distance between itself and vehicles in the target lane is too small, resulting in insufficient safe lane-changing space, it may decide to issue a lane-change suppression instruction and wait for an opportunity to re-execute the lane-changing instruction.

[0089] Step 3: Determine the target lane for lane driving control based on the lane driving behavior instructions of the intelligent driving vehicle; and the lane driving behavior instructions correspond to different target lanes, including:

[0090] When the lane driving behavior instruction is lane keeping or lane change suppression, the target driving lane is the lane where the vehicle is located.

[0091] When the lane driving behavior instruction is to change lanes to the left, the target driving lane is the first lane to the left of the lane where the vehicle is located;

[0092] When the lane driving behavior instruction is to change lanes to the right, the target driving lane is the first lane to the right of the lane where the vehicle is located.

[0093] Step 4: Decision-making for lateral and longitudinal motion control commands to move the intelligent driving vehicle to the target lane;

[0094] The lateral motion control command for the intelligent driving vehicle is the desired steering wheel angle, which is a weighted function of the lateral distance deviation, heading angle deviation, and road curve radius at the pre-aiming point on the center line of the target lane. It can be calculated according to the following steps:

[0095] Step 4.1: Based on the vehicle speed and target lane information, obtain the lateral position y at two pre-aimed distance points on the center line of the target lane using an index. pm and road curvature ρ pm The information and calculation formula are as follows:

[0096]

[0097] In the formula, d pm =t pm *v represents the aiming distance, t pm Let t be the aiming time, v be the vehicle's speed, and m = 1, 2 represent the first and second aiming points, satisfying t p1 <t p2 ;

[0098] Step 4.2: Combine the vehicle speed information and calculate the lateral position deviation e for lane driving control according to equations (5)-(7). y Heading angle deviation and road radius R:

[0099]

[0100]

[0101]

[0102] In the formula, e ymax =max{y p1 ,y p2};

[0103] Figure 3The following is given: when the lane driving behavior is a left lane change, the lateral position deviation e is given based on the two aiming points on the center line of the vehicle and the target lane. y Heading angle deviation A schematic diagram of road curvature;

[0104] Step 4.3: Calculate the desired steering wheel angle control command for lane driving control lateral movement according to equation (8):

[0105]

[0106] In the formula, k1, k2, and k3 are the weighting coefficients for lateral error control, heading error control, and curve compensation control, respectively. The weighting coefficient values ​​can be reasonably determined through actual vehicle testing and calibration under different working conditions. For example, different coefficient values ​​can be calibrated under different vehicle speeds and different road adhesion coefficients.

[0107] Step 4.4: After limiting the desired steering wheel angle to the range of constraints shown in equation (9), output the result.

[0108]

[0109] In the formula, δ is the desired steering wheel angle, δ min and δ max These are the minimum and maximum values ​​of the desired steering wheel angle, respectively. The desired rate of change of steering wheel angle, and These are the minimum and maximum values ​​of the expected rate of change of steering wheel angle, respectively.

[0110] The longitudinal motion control command for lane driving control of the intelligent driving vehicle is the desired acceleration, which is determined based on the relative motion state of the traffic participants closest to the vehicle in the lane where the vehicle is located and in the target lane, according to the vehicle following control strategy; if there are no traffic participants within the safe distance range in front of the vehicle, the desired acceleration is determined based on the deviation between the desired cruise speed and the actual speed according to the cruise control strategy; and the desired acceleration should satisfy the constraint conditions shown in equation (10):

[0111]

[0112] In the formula, a is the desired acceleration, a min and a max These are the minimum and maximum values ​​of the desired acceleration, respectively. For the desired rate of change of acceleration, and These are the minimum and maximum values ​​of the expected rate of change of acceleration, respectively.

[0113] The minimum and maximum values ​​of the two constraints on longitudinal and lateral motion control commands mentioned above can be determined comprehensively based on factors such as safety, stability, comfort, and physical limitations.

[0114] Step 5: The intelligent driving vehicle executes longitudinal and lateral movement control commands for lane driving, specifically including:

[0115] The desired acceleration control command is input to the longitudinal motion controller of the intelligent driving vehicle to achieve longitudinal and lateral motion control of the intelligent driving vehicle. The longitudinal motion controller executing the acceleration control command varies depending on the vehicle model. For example, when the intelligent driving vehicle is a traditional gasoline-powered vehicle, the longitudinal motion controller can be the engine electronic control unit (ECU), which coordinates the transmission control unit (TCU) and the electronic brake control unit (BCU) to control the vehicle's drive or braking to achieve the desired acceleration. The BCU is specific to the vehicle's braking system configuration. When the intelligent driving vehicle is an electric vehicle, the longitudinal motion controller can be the vehicle controller, which coordinates the motor controller (MCU) and the BCU to control the vehicle's drive or braking.

[0116] The desired steering wheel angle control command is input to the electronic steering controller; wherein the electronic steering controller is configured according to the steering system, such as an electronic power steering system or an active steering system.

[0117] The electronic device of this invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM may also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0118] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0119] The processing unit executes the various methods and processes described above, such as methods S1 to S5. For example, in some embodiments, methods S1 to S5 may be implemented as computer software programs tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of methods S1 to S5 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute methods S1 to S5 by any other suitable means (e.g., by means of firmware).

[0120] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload programmable logic devices (CPLDs), and so on.

[0121] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0122] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0123] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for integrated decision-making and control of lane changing and lane keeping in intelligent driving vehicles, characterized in that, The method includes the following steps: Step 1: Identify lane line information and other traffic participant information in the real-time driving environment of the intelligent driving vehicle through visual sensors, and process the information to obtain information on the relative motion state of each lane, its matching lane line, lane center line, and traffic participants. Step 2: Based on lane lines, relative motion information of traffic participants, and lane change requests, determine safe and feasible lane driving behavior instructions for the intelligent driving vehicle. The lane driving behavior instructions include lane keeping, left lane change, right lane change, and lane change suppression. Step 3: Determine the target driving lane for lane driving control based on the lane driving behavior instruction; Step 4: Decision-making regarding the lateral and longitudinal motion control commands to guide the intelligent driving vehicle to the target lane, wherein... The lateral motion control command is the desired steering wheel angle, which is a weighted function of the lateral distance deviation, heading angle deviation, and road curve radius at the pre-aiming point on the center line of the target lane. The longitudinal motion control command is the desired acceleration, which is determined by the vehicle following control strategy based on the relative motion state of the traffic participants closest to the vehicle in the lane where the vehicle is located and in the target lane. If there are no traffic participants within the safe distance in front of the vehicle, the desired acceleration is determined by the cruise control strategy based on the deviation between the desired cruise speed and the actual speed. Step 5: The intelligent driving vehicle executes the issued longitudinal and lateral movement control commands for lane driving.

2. The integrated decision-making and control method for lane changing and lane keeping of an intelligent driving vehicle according to claim 1, characterized in that, The information regarding each lane and its corresponding lane lines, lane center lines, and traffic participants mentioned in step 1 includes the following steps: Step 1.1: Remove data that does not belong to the lane boundary lines from the identified lane line data according to the line type; Step 1.2: Fit the parametric equations for the remaining lane lines. The parametric equations are expressed by the cubic polynomial shown in equation (1): y i (x)=c 0i +c 1i x+c 2i x 2 +c 3i x 3 (1) In the formula, i is the number of lane lines; x and y are the longitudinal and lateral distances in the vehicle coordinate system, respectively; c0, c1, c2, and c3 are the polynomial coefficients, and c0 is defined as the lateral position of the lane line relative to the origin of the vehicle in the vehicle coordinate system, with positive on the left and negative on the right. Step 1.3: Sort the lane lines in descending order of c0, and perform difference on the sorted c0 sequence to obtain the width of each lane; Step 1.4: Remove lane lines that do not meet the lane width requirements to obtain lane lines that meet the control method requirements; Step 1.5: Define the number of lanes as the number of lane lines after filtering minus 1, and set the lane IDs from right to left as 1, 2, 3, ... n; where n is the number of lanes. Step 1.6: Determine the current lane ID of the vehicle based on c0. Specifically, if c0 < 0, it means that the lane line is located to the right of the vehicle. The number of lane lines with c0 < 0 is defined as the current lane ID of the vehicle. Step 1.7: Match lane lines and traffic participant information for each lane. The lane line information includes the lateral distance between the lane lines on both sides of the lane and the lane center line and the vehicle, the heading angle and curvature information, and the color and lane line type information of the lane lines on both sides of the lane, which are represented by a cubic polynomial.

3. The integrated decision-making and control method for lane changing and lane keeping of an intelligent driving vehicle according to claim 2, characterized in that, The formula for calculating the lateral distance between the lane centerline and the vehicle in step 1.7 is as follows: In the formula, y cj This represents the lateral distance between the center line of the j-th lane and the origin of the vehicle's coordinate system, where i = j represents the i-th lane line; The formula for calculating the curvature of the lane centerline is as follows: In the formula, ρ cj Let be the curvature of the centerline of the j-th lane, and y′ and y″ be the first and second derivatives of y, respectively; The relative motion status information of traffic participants includes the relative position and relative speed information of traffic participants in each lane relative to their own vehicle.

4. The integrated decision-making and control method for lane changing and lane keeping of an intelligent driving vehicle according to claim 1, characterized in that, The lane change suppression mentioned in step 2 specifically refers to the lane driving behavior instruction issued by the control system behavior decision module when the vehicle determines that it cannot continue to safely change lanes based on the relative motion status information of surrounding traffic participants during the lane change process.

5. The integrated decision-making and control method for lane changing and lane keeping of an intelligent driving vehicle according to claim 1, characterized in that, The lane driving behavior instruction in step 3 corresponds to different target lanes, specifically including: When the lane driving behavior instruction is lane keeping or lane change suppression, the target driving lane is the lane where the vehicle is located. When the lane driving behavior instruction is to change lanes to the left, the target driving lane is the first lane to the left of the lane where the vehicle is located; When the lane driving behavior instruction is to change lanes to the right, the target driving lane is the first lane to the right of the lane where the vehicle is located.

6. The integrated decision-making and control method for lane changing and lane keeping of an intelligent driving vehicle according to claim 1, characterized in that, Step 4, generating lateral movement control commands for the intelligent driving vehicle, includes the following steps: Step 4.1: Based on the vehicle speed and target lane information, obtain the lateral position y at two pre-aimed distance points on the center line of the target lane using an index. pm and road curvature ρ pm The information and calculation formula are as follows: In the formula, d pm =t pm *v represents the aiming distance, t pm Let t be the aiming time, v be the vehicle's speed, and m = 1, 2 represent the first and second aiming points, satisfying t p1 <t p2 ; Step 4.2: Based on the vehicle speed information, calculate the lateral position deviation e for lane driving control. y , heading angle deviation and road radius R: In the formula, e ymax = max{y p1 ,y p2 }; Step 4.3: Calculate the desired steering wheel angle control command for lane driving control lateral movement: In the formula, k1, k2, and k3 are the weighting coefficients for lateral error control, heading error control, and cornering compensation control, respectively. The weighting coefficient values ​​can be reasonably determined through actual vehicle testing and calibration under different working conditions. Step 4.4: Output the desired steering wheel angle after limiting it within the constraint conditions.

7. The integrated decision-making and control method for lane changing and lane keeping of an intelligent driving vehicle according to claim 6, characterized in that, The desired steering wheel angle and desired acceleration should respectively satisfy the following constraints: In the formula, δ is the desired steering wheel angle, δ min and δ max These are the minimum and maximum values ​​of the desired steering wheel angle, respectively. The desired rate of change of steering wheel angle, and Let be the minimum and maximum values ​​of the desired rate of change of steering wheel angle, respectively, and let 'a' be the desired acceleration. min and a max These are the minimum and maximum values ​​of the desired acceleration, respectively. For the desired rate of change of acceleration, and These are the minimum and maximum values ​​of the expected rate of change of acceleration, respectively. The minimum and maximum values ​​of the constraints on longitudinal and lateral motion control commands can be determined comprehensively based on vehicle driving safety, stability, comfort, and physical limitations.

8. The integrated decision-making and control method for lane changing and lane keeping of an intelligent driving vehicle according to claim 1, characterized in that, Step 5 specifically involves inputting the desired acceleration control command into the longitudinal motion controller of the intelligent driving vehicle and inputting the desired steering wheel angle control command into the electronic steering controller, so as to realize the longitudinal and lateral motion control of the intelligent driving vehicle.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 8.

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