Vehicle control method, device, equipment and medium

Abstract

The embodiments of the present invention invent a vehicle control method, device, equipment and medium. The method includes: creating a corresponding two-vehicle relative motion constraint optimization model based on the driving trajectory prediction model of the front vehicle, the kinematic model and dynamic characteristics of the target vehicle, determining the target vehicle's automatic avoidance path based on the predetermined two-vehicle non-collision condition and the two-vehicle relative motion constraint optimization model; and controlling the target vehicle to travel along the automatic avoidance path based on a pre-created path tracking error model and a handling stability control model. In the embodiments of the present invention, the automatic avoidance path of the target vehicle is determined based on the predetermined two-vehicle non-collision condition and the two-vehicle relative motion constraint optimization model; and controlling the target vehicle to travel along the automatic avoidance path based on a pre-created path tracking error model and a handling stability control model, thereby realizing the automatic emergency lane change avoidance operation of the vehicle driving condition and ensuring the vehicle's handling stability during the automatic emergency lane change avoidance operation.

Description

Technical Field

[0001] The present invention relates to the field of vehicle technology, and in particular to a vehicle control method, device, equipment and medium. Background Art

[0002] Guided by the technological advancements of electrification, intelligence, and connectivity, lidar, forward-facing cameras, active braking subsystems, and active steering subsystems have become standard features of autonomous vehicles. Currently, domestic and international OEMs, research institutes, and high-tech companies primarily utilize the fused information generated by these sensors and by-wire control subsystems to achieve autonomous driving in conventional driving conditions. However, the safety of vehicles operating under extreme high-speed driving conditions has long been a research focus in the automotive industry. Using the fused information generated by these sensors and by-wire control subsystems to address active safety issues in these conditions has become a new research direction. Therefore, in-depth research on automatic avoidance systems for vehicles operating under extreme high-speed driving conditions holds significant theoretical and practical value for fully leveraging the newly installed lidar, forward-facing cameras, active braking subsystems, and active steering subsystems to address the automotive industry's ongoing concern: active safety in these extreme high-speed driving conditions.

[0003] At present, domestic and foreign OEMs use automatic emergency braking avoidance systems to improve the active safety of vehicles when encountering obstacles during high-speed driving. However, a large number of research results show that compared with automatic emergency braking avoidance systems, automatic emergency lane change avoidance systems can reduce the probability of obstacle collisions during high-speed driving. However, automatic emergency lane change avoidance systems are prone to cause understeering or oversteering of the vehicle, thereby causing the vehicle to lose stability. Summary of the Invention

[0004] In view of this, the present invention provides a vehicle control method, device, equipment and medium to realize automatic emergency lane change avoidance operation of the vehicle under high-speed extreme driving conditions, avoid understeering or oversteering of the vehicle during the automatic emergency lane change avoidance process, and ensure the vehicle's handling stability during the automatic emergency lane change avoidance operation.

[0005] According to one aspect of the present invention, an embodiment of the present invention provides a vehicle control method, the method comprising:

[0006] Creating a corresponding relative motion constraint optimization model for the two vehicles based on a trajectory prediction model of the leading vehicle and a kinematic model and dynamic characteristics of a target vehicle, wherein the leading vehicle and the target vehicle are located in the same lane;

[0007] Determining an automatic avoidance path for the target vehicle based on a predetermined non-collision condition between the two vehicles and an optimization model of relative motion constraints between the two vehicles;

[0008] The target vehicle is controlled to travel along the automatic avoidance path based on a pre-created path tracking error model and a handling stability control model.

[0009] According to another aspect of the present invention, an embodiment of the present invention further provides a vehicle control device, the device comprising:

[0010] a model creation module for creating a corresponding relative motion constraint optimization model for the two vehicles based on a trajectory prediction model of the preceding vehicle and a kinematic model and dynamic characteristics of a target vehicle, wherein the preceding vehicle and the target vehicle are located in the same lane;

[0011] a path determination module, configured to determine an automatic avoidance path for the target vehicle based on a predetermined non-collision condition between the two vehicles and an optimization model of relative motion constraints between the two vehicles;

[0012] A path driving module is used to control the target vehicle to drive along the automatic avoidance path based on a pre-created path tracking error model and a handling stability control model.

[0013] According to another aspect of the present invention, an embodiment of the present invention further provides an electronic device, comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor. The computer program is executed by the at least one processor to enable the at least one processor to perform the vehicle control method described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the vehicle control method described in any embodiment of the present invention when executed.

[0018] The technical solution of the embodiment of the present invention creates a corresponding two-vehicle relative motion constraint optimization model based on the driving trajectory prediction model of the front vehicle, the kinematic model and dynamic characteristics of the target vehicle, and determines the automatic avoidance path of the target vehicle based on the predetermined two-vehicle non-collision condition and the two-vehicle relative motion constraint optimization model; based on the pre-established path tracking error model and handling stability control model, the target vehicle is controlled to travel along the automatic avoidance path, realizing the automatic emergency lane change avoidance operation of the vehicle under high-speed extreme driving conditions, avoiding understeering or oversteering of the vehicle during the automatic emergency lane change avoidance process, thereby ensuring the handling stability of the vehicle during the automatic emergency lane change avoidance operation.

[0019] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 A flow chart of a vehicle control method provided by one embodiment of the present invention;

[0022] Figure 2 A flow chart of another vehicle control method provided by an embodiment of the present invention;

[0023] Figure 3 A schematic structural diagram of an automatic emergency lane change avoidance system for a vehicle provided by one embodiment of the present invention;

[0024] Figure 4 A schematic diagram of a car circle envelope provided by one embodiment of the present invention;

[0025] Figure 5 A schematic diagram of a path tracking error of an automatic emergency lane change avoidance system for a vehicle provided by one embodiment of the present invention;

[0026] Figure 6 A schematic diagram of vehicle handling stability control provided by one embodiment of the present invention;

[0027] Figure 7 This is a structural block diagram of a vehicle control device provided by one embodiment of the present invention;

[0028] Figure 8A schematic structural diagram of an electronic device that can be used to implement an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0029] To help those skilled in the art better understand the present invention, the following clearly and completely describes the technical solutions in the embodiments of the present invention, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "object" and "target" and the like in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, or apparatus.

[0031] In one embodiment, Figure 1 This is a flow chart of a vehicle control method provided by one embodiment of the present invention. This embodiment is applicable to the situation when the vehicle automatically performs an emergency lane change avoidance operation. The method can be executed by a vehicle control device, which can be implemented in the form of hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0032] S110 , creating a corresponding two-vehicle relative motion constraint optimization model based on the driving trajectory prediction model of the front vehicle and the kinematic model and dynamic characteristics of the target vehicle, wherein the front vehicle and the target vehicle are located in the same lane.

[0033] The term "front vehicle" can be understood as the vehicle ahead of the target vehicle in the same lane detected by an image detection device, which can be a radar detection device, a forward-looking camera device, or the like. The target vehicle is equipped with an image detection device to detect the vehicle ahead. It should be noted that the front vehicle and the target vehicle are located in the same lane. It can be understood that the front vehicle refers to the vehicle that is in the same lane as the target vehicle and is closest to the target vehicle.

[0034] Among them, dynamic characteristics refer to the relationship between various parameters of the power system during the dynamic process. In this embodiment, the driving trajectory prediction model can be understood as the driving trajectory prediction model of the vehicle in front of the lane detected by the image detection device. The driving trajectory prediction model is related to the coordinates of the rear axle midpoint, azimuth angle, vehicle speed, acceleration and other information of the front vehicle. For example, when the lidar and the front-view camera detect that the vehicle in front of the lane is traveling at a fixed deceleration, the prediction model of the front vehicle can be described by the formula: Among them, (x1, y1) and θ1 represent the coordinates and azimuth of the rear axle midpoint of the front vehicle, respectively. Indicates the deceleration of the vehicle ahead.

[0035] It should be noted that each parameter with a dot above it refers to the derivative of that parameter, but the meanings are the same. Similarly, the parameters with a dot in the following text all represent the derivative of that parameter and will not be explained one by one.

[0036] In this embodiment, the kinematic model can be understood as the kinematic model of the target vehicle, and the kinematic model of the target vehicle can be related to information such as the rear axle midpoint coordinates, azimuth angle, trajectory curvature, deceleration, front wheel steering angle, and front wheel steering angular velocity of the target vehicle. For example, the rear axle midpoint coordinates, azimuth angle, trajectory curvature, deceleration, front wheel steering angle, and front wheel steering angular velocity of the target vehicle are represented by (x2, y2), θ2, ρ2, a respectively. x2 , δ f and ω f Indicates that, at this time, the kinematic model of the target vehicle can be described by the formula: Where L represents the wheelbase of the target vehicle.

[0037] In this embodiment, a two-vehicle relative motion constraint optimization model can be created using a preceding vehicle's trajectory prediction model and the target vehicle's kinematic model and dynamic characteristics. The two-vehicle relative motion constraint optimization model can be understood as an optimization equation for the relative motion constraints between the target vehicle and the preceding vehicle. The two-vehicle relative motion constraint optimization model is dependent on both the target vehicle's related information and the preceding vehicle's related information. For example, the two-vehicle relative motion constraint optimization model can be expressed as: Among them, χ=[x1 y1 θ1 v1 x2 y2 θ2 v2 ρ2] T and u=[a x2 ω f ] Tare the system state vector and control vector respectively, (x1, y1) and θ1 represent the coordinates and azimuth of the rear axle midpoint of the front vehicle, v1 represents the deceleration of the front vehicle, (x2, y2) represents the coordinates of the rear axle midpoint of the target vehicle, θ2, ρ2, a x2 , δ f and ω f They represent the target vehicle's azimuth, trajectory curvature, deceleration, front wheel steering angle, and front wheel steering angular velocity respectively.

[0038] S120 : Determine an automatic avoidance path for the target vehicle based on a predetermined non-collision condition between the two vehicles and an optimization model of relative motion constraints between the two vehicles.

[0039] Among them, the predetermined non-collision condition between the two vehicles can be understood as the non-collision condition between the target vehicle and the vehicle in front. It can be determined by the circular envelope of the vehicle in front and the target vehicle, or by the elliptical envelope of the vehicle in front and the target vehicle. This embodiment does not limit this.

[0040] In this embodiment, the automatic avoidance path of the target vehicle can be determined based on a predetermined non-collision condition between the target vehicle and the preceding vehicle, as well as an optimization model of the relative motion constraints between the target vehicle and the preceding vehicle. Specifically, the non-collision condition between the target vehicle and the preceding vehicle can be determined based on the radius and coordinates of the center of the circle encompassing the target vehicle and the preceding vehicle. When the non-collision condition between the target vehicle and the preceding vehicle is satisfied, the optimization model of the relative motion constraints between the target vehicle and the preceding vehicle is solved accordingly, using the minimization of the control vector as the optimization constraint, to determine the automatic avoidance path of the target vehicle.

[0041] S130 : Control the target vehicle to travel along the automatic avoidance path based on the pre-created path tracking error model and the handling stability control model.

[0042] In this embodiment, the pre-created path tracking error model may include information related to the path tracking error model and the control variables, as well as nominal values ​​and perturbations of the system matrix and the control matrix. The nominal values ​​may also be referred to as reference values, and the combination of the nominal values ​​and perturbations may be expressed as the true value. For example, the pre-created path tracking error model may be expressed as:

[0043] e(k+1)=(A+ΔA)e(k)+(B+ΔB)u(k), where u=δ fis the control quantity, e(k) is the discretized system error; A and B are the nominal values ​​of the system matrix and the control matrix, respectively; ΔA and ΔB are the perturbations of the system matrix and the control matrix, respectively. A, B, ΔA and ΔB can be expressed as formulas (1), (2) and (3).

[0044]

[0045]

[0046] [ΔA ΔB]=DF[E1 E2] (3)

[0047] Among them, C f 、C r 、C fe and C re Respectively represent the nominal value of the front wheel cornering stiffness, the nominal value of the rear wheel cornering stiffness, the front wheel perturbation and the rear wheel perturbation, M, I z , L f and L r Denote vehicle mass, moment of inertia, front axle wheelbase and rear axle wheelbase respectively, and F denotes normalization, which is the normalization of the [E12]E result. D, E1 and E2 in formula (3) can be expressed as formula (4), formula (5) and formula (6) respectively.

[0048]

[0049]

[0050]

[0051] It should be noted that relevant new energy indicators can be defined for the pre-created path tracking error model, and the relevant new energy indicators can be transformed accordingly through relevant linear matrix inequalities to solve the convex optimization problem with relevant constraints of the linear matrix inequality group.

[0052] In this embodiment, the handling stability control model may include the vehicle's center of mass slip angle, yaw rate, wheel longitudinal force, and average lateral force. Based on the handling stability control model, a vehicle handling stability control law can be established to prevent understeering or oversteering during vehicle path tracking. For example, the vehicle handling stability control model can be expressed as: Where β and γ represent the vehicle's sideslip angle and yaw rate, respectively; F x1 、F x2 、F x3 、F x4 Expressed as the wheel longitudinal force, and Expressed as the mean lateral force, l f , δ f and t f They are respectively represented as the distance from the vehicle's front axle to the center of mass, the front wheel steering angle, and the front wheel track.

[0053] It should be noted that the vehicle handling stability control model can be modified. Based on the above vehicle handling stability control model, it can be modified as follows: Where d = [d1 d2] T and θ is the system uncertainty, M z 、M u Indicates the mass of the car, F y , γ, and I z are expressed as the mean lateral force, actual yaw rate, and moment of inertia, respectively. It should be noted that for the revised vehicle handling stability control model, relevant vehicle control performance indicators can also be defined, and based on relevant stability theories, understeering or oversteering can be avoided during vehicle path tracking.

[0054] In this embodiment, the target vehicle can be controlled to travel along the automatic avoidance path based on a pre-established path tracking error model and a handling stability control model. Specifically, the path tracking error control amount for the target vehicle can be determined based on the pre-established path tracking error model; the braking control amount and steering control amount for the target vehicle can be determined based on the pre-established handling stability control model. The target vehicle can then be controlled to travel along the automatic avoidance path based on the path tracking error control amount, braking control amount, and steering control amount determined by the two models, respectively.

[0055] The technical solution of the embodiment of the present invention creates a corresponding two-vehicle relative motion constraint optimization model based on the driving trajectory prediction model of the leading vehicle, the kinematic model and dynamic characteristics of the target vehicle, and determines the automatic avoidance path of the target vehicle based on the predetermined two-vehicle non-collision condition and the two-vehicle relative motion constraint optimization model; based on the pre-established path tracking error model and handling stability control model, the target vehicle is controlled to travel along the automatic avoidance path, realizing the automatic emergency lane change avoidance operation of the vehicle under high-speed extreme driving conditions, avoiding understeering or oversteering of the vehicle during the automatic emergency lane change avoidance process, thereby ensuring the handling stability of the vehicle during the automatic emergency lane change avoidance operation.

[0056] In one embodiment, the vehicle control method further includes:

[0057] Create a system matrix based on the nominal values ​​of cornering stiffness, vehicle mass, moment of inertia, front axle wheelbase, and rear axle wheelbase;

[0058] Create a control matrix based on the nominal values ​​of cornering stiffness, vehicle mass, moment of inertia, and front axle wheelbase;

[0059] Create a corresponding path tracking error model based on the system matrix, control matrix and perturbation.

[0060] The nominal cornering stiffness value can be understood as a reference value for tire cornering stiffness. This includes both the front and rear wheel nominal cornering stiffness values, expressed as a reference value for the ratio of tire cornering force to slip angle. The moment of inertia can be understood as a measure of the inertia of a rigid body rotating about its axis. It should be noted that the sum of the nominal value of a relevant parameter and the corresponding perturbation is the actual value of that parameter.

[0061] In this embodiment, a system matrix can be created based on the nominal value of the tire cornering stiffness, vehicle mass, moment of inertia, front axle wheelbase, and rear axle wheelbase. A control matrix can be created based on the nominal value of the tire cornering stiffness, vehicle mass, moment of inertia, and front axle wheelbase. Then, a corresponding path tracking error model can be created based on the obtained system matrix, control matrix, and perturbation. Specifically, the lateral position deviation, azimuth deviation, and yaw rate deviation can be used to represent the path tracking error of the vehicle active avoidance system. The path tracking error can be expressed as e=[y c -y d θ-θ d γ-γ d ], where y c 、y d ,θ,θ d , γ, and γ d They are respectively represented as the actual lateral position, desired lateral position, actual heading angle, desired heading angle, actual yaw rate and desired yaw rate of the vehicle.

[0062] In one embodiment, the vehicle control method further includes:

[0063] Obtain the mean values ​​of the target vehicle's center of mass sideslip angle, yaw rate, wheel longitudinal force, and lateral force;

[0064] The corresponding handling stability control model is constructed based on the average values ​​of the sideslip angle, yaw angular velocity, wheel longitudinal force and lateral force.

[0065] The slip angle can be understood as the angle between the velocity of the vehicle's center of mass and the direction of the vehicle's front. The yaw rate can be understood as the heading angle minus the slip angle, where the heading angle refers to the angle between the velocity of the vehicle's center of mass and the horizontal axis in the ground coordinate system.

[0066] In this embodiment, a corresponding handling stability control model can be constructed based on the acquired target vehicle's center of mass slip angle, yaw rate, wheel longitudinal force, and average lateral force. Of course, the parameters for constructing the corresponding handling stability control model are not limited to these parameters and may also include the distance from the vehicle's front axle to the center of mass, the front wheel steering angle, and the front wheel distance, among others.

[0067] In one embodiment, Figure 2 This is a flow chart of another vehicle control method provided by an embodiment of the present invention. Based on the above embodiments, this embodiment further refines the method of determining the automatic avoidance path of the target vehicle based on the predetermined non-collision condition between the two vehicles and the optimization model of the relative motion constraints between the two vehicles, and controlling the target vehicle to travel along the automatic avoidance path based on the pre-established path tracking error model and the handling stability control model. Figure 2 As shown, the vehicle control method in this embodiment may specifically include the following steps:

[0068] S210: Creating a corresponding two-vehicle relative motion constraint optimization model based on the driving trajectory prediction model of the front vehicle and the kinematic model and dynamic characteristics of the target vehicle, wherein the front vehicle and the target vehicle are located in the same lane.

[0069] S220 : Construct circular envelopes of the preceding vehicle and the target vehicle in sequence based on the pre-acquired length, width, and rear overhang length of the corresponding vehicle.

[0070] The rear overhang can be understood as the horizontal distance from the rear end of the vehicle to the center of the rear axle. The rear overhang accommodates the impact-absorbing structure of the vehicle frame. It should be noted that the vehicle's length is equal to the front overhang + rear overhang + wheelbase.

[0071] In this embodiment, the circular envelope can be understood as a circular envelope formed by using multiple circles of the same radius to include the outer contour of the vehicle. It should be noted that the target vehicle and the vehicle in front have corresponding circular envelopes. The composition of the vehicle circular envelope can be based on the vehicle length, width and rear overhang length of the target vehicle and the vehicle in front, and can also include other parameter information, which is not limited in this embodiment.

[0072] In this embodiment, the circular envelope of the vehicle in front can be constructed based on the pre-acquired length, width and rear overhang length of the vehicle in front, and the circular envelope of the target vehicle can be constructed based on the pre-acquired length, width and rear overhang length of the target vehicle. It should be noted that each circular envelope has a corresponding circle radius and center radius. Of course, there is no limit to the number of circles containing the outer contours of the target vehicle and the vehicle in front. For example, the number of circles containing the outer contour of the target vehicle can be 3 or 4; the number of circles containing the outer contour of the vehicle in front can be 3 or 4; and the number of circles containing the outer contour of the target vehicle and the number of circles containing the outer contour of the vehicle in front can be the same or different, and this embodiment does not impose any restrictions on this.

[0073] S230: Determine a no-collision condition between the two vehicles based on the radius and center coordinates of the circle envelope corresponding to the front vehicle and the radius and center coordinates of the circle envelope corresponding to the target vehicle.

[0074] In this embodiment, the non-collision condition between the front vehicle and the target vehicle can be determined based on the radius and center coordinates of the circular envelope corresponding to the front vehicle, as well as the radius and center coordinates of the circular envelope corresponding to the target vehicle, and rapid obstacle avoidance detection of vehicles can be achieved through the center distance, thereby improving the obstacle avoidance detection efficiency.

[0075] For example, the circular envelopes of the front vehicle and the target vehicle are composed of three circles with the same radius, where the length, width and rear overhang length of the front vehicle are represented by L and L respectively. 1c , W1 and L 1r , then we can know that the three circle radii and center coordinates of the front vehicle can be expressed as formula (7), formula (8), formula (9) and formula (10) respectively,

[0076]

[0077]

[0078]

[0079]

[0080] Among them, the rotation transformation matrix T1 can be expressed as The length, width and rear overhang length of the target vehicle are L 2c , W2 and L 2r , then the three circle radii and center coordinates of the vehicle in front can be expressed as formula (11), formula (12), formula (13) and formula (14), respectively.

[0081]

[0082]

[0083]

[0084]

[0085] Among them, in formula (8), formula (9) and formula (10), T2 is represented by the rotation transformation matrix, which can be expressed as

[0086] Therefore, from the three circle radii and center coordinates of the front vehicle (Equations (7), (8), (9), and (10) and the three circle radii and center coordinates of the target vehicle (Equations (11), (12), (13), and (14), it can be seen that the condition for no collision between the front vehicle and the target vehicle can be expressed as

[0087] S240: When a non-collision condition between the two vehicles is satisfied and the relative motion constraint optimization model of the two vehicles is solved based on minimizing the control vector, an automatic avoidance path for the target vehicle is determined.

[0088] The minimized control vector can be understood as a transposed matrix composed of acceleration and front wheel steering angular velocity. For example, the minimized control vector can be expressed as u=[a x2 ω f ] T , where a x2 and ω f represent acceleration and front wheel steering angular velocity respectively.

[0089] In this embodiment, under the premise of ensuring that the front vehicle and the target vehicle do not collide, the optimization goal is to minimize the system control amount, simplify the optimization model of the relative motion constraints of the two vehicles, and use the Gaussian pseudo-spectral method to solve the optimization problem of the constraints to obtain the optimal automatic emergency lane change avoidance path for the vehicle's high-speed extreme driving conditions.

[0090] S250 : Determine a path tracking error control amount of the target vehicle according to a pre-created path tracking error model.

[0091] Among them, the path tracking error control amount can be understood as the path tracking control output of the vehicle's automatic emergency lane change avoidance system. According to the path tracking error control amount, fast, accurate and stable tracking control of the path planning vehicle's automatic emergency lane change avoidance path can be achieved.

[0092] In this embodiment, a path tracking error control variable for the target vehicle is determined based on a pre-created path tracking error model. Specifically, a corresponding performance index can be determined based on the pre-created path tracking error model. The performance index can then be converted into a corresponding linear matrix inequality constraint using a corresponding inequality conversion method. Finally, the corresponding path tracking control variable can be determined based on the resulting linear matrix inequality constraint.

[0093] In one embodiment, determining the path tracking error control amount of the target vehicle according to a pre-created path tracking error model includes:

[0094] Determine corresponding performance indicators based on the path tracking error model;

[0095] The performance indicators are converted into corresponding linear matrix inequality constraints through linear matrix inequality;

[0096] The corresponding path tracking control quantity is determined according to the linear matrix inequality constraints.

[0097] The performance index can be understood as the relevant performance index corresponding to the pre-created path tracking error model. For example, the quadratic performance index is defined as Among them, e T (k), Q, e(k), u T (k) and R represent the transposition of the deviation amount, the weight coefficient, the deviation amount, the transposition of the control amount, and the weight coefficient, respectively.

[0098] In this embodiment, the corresponding performance index can be determined based on the path tracking error model. The minimization of the quadratic performance index can be converted into a convex optimization problem constrained by a linear matrix inequality group through linear matrix inequality. The optimal solution of the convex optimization problem constrained by the linear matrix inequality group is used to determine the system tracking amount output by the path tracking control of the vehicle automatic emergency lane change avoidance system, so as to achieve fast, accurate and stable tracking control of the automatic emergency lane change avoidance path of the vehicle under high-speed extreme driving conditions output by the path planning.

[0099] S260: Determine a braking control amount and a steering control amount of the target vehicle according to a pre-created handling stability control model.

[0100] The braking control variable can be understood as the output of the vehicle's handling stability control. The steering control variable can also be understood as the output of the vehicle's handling stability control. The target vehicle's braking and steering control variables can help prevent understeering and oversteering during vehicle path tracking.

[0101] In this embodiment, the braking control amount and the steering control amount of the target vehicle can be determined based on a pre-created handling stability control model. Specifically, the pre-created handling stability control model can be used to determine corresponding control performance indicators, and the braking control amount and the steering control amount of the target vehicle can be determined based on the corresponding control performance indicators.

[0102] In one embodiment, determining the braking control amount and the steering control amount of the target vehicle according to a pre-created handling stability control model includes:

[0103] Determine the corresponding control performance index based on the pre-created handling stability control model;

[0104] Determine the corresponding correction yaw moment control amount according to the control performance index;

[0105] The braking control amount and the steering control amount of the target vehicle are determined according to the corrected yaw moment control amount, the wheel longitudinal force and the lateral force.

[0106] Among them, the relevant control performance indicators corresponding to the pre-created handling stability control model of the control performance indicators may include relevant parameter information such as the center of mass sideslip angle error, yaw angle error, and sliding mode plane. It should be noted that the sliding mode plane can allow the two errors to converge to zero in a fixed manner. The center of mass sideslip angle error and yaw angle error can reflect the stability and handling of the vehicle. The sliding mode plane can determine the convergence method of the center of mass sideslip angle error and yaw angle error. For example, the control performance indicators corresponding to the pre-created handling stability control model can be expressed as: Among them, β d =0 and where z1 represents the sideslip angle error, z2 represents the yaw angle error, and σ represents the sliding plane.

[0107] In this embodiment, the corresponding control performance index can be determined based on the pre-created handling stability control model, and the corresponding correction yaw moment control amount can be determined based on the obtained control performance index. The braking control amount and steering control amount of the target vehicle can be determined based on the obtained correction yaw moment control amount, as well as the wheel longitudinal force and lateral force.

[0108] Specifically, the corresponding control performance index can be determined based on the pre-created handling stability control model, and then the Lyapunov stability theory can be used to obtain the vehicle correction yaw moment control quantity. Since the correction yaw moment control quantity can be directly mapped to the wheel longitudinal force and lateral force, the tire characteristics can be used to establish the relationship between the tire lateral force increment and the front wheel angle, and the tire characteristics can be used to establish the relationship between the tire longitudinal force increment and the wheel braking torque. The mapping relationship between the vehicle correction yaw moment control quantity and the wheel longitudinal force and lateral force is corrected to obtain the target vehicle's braking control quantity and steering control quantity based on the defined objective function as the optimization target, thereby avoiding understeering or oversteering during the vehicle path tracking process.

[0109] S270: Control the target vehicle to travel along the automatic avoidance path according to the path tracking error control amount, the braking control amount, and the steering control amount.

[0110] In this embodiment, after determining the path tracking error control amount, the braking control amount, and the steering control amount, the target vehicle can be controlled to travel along the automatic avoidance path according to the determined path tracking error control amount, the braking control amount, and the steering control amount.

[0111] The above technical solution of the embodiment of the present invention sequentially constructs the circular envelopes of the front vehicle and the target vehicle based on the pre-acquired length, width and rear overhang length of the corresponding vehicle; determines the non-collision condition of the two vehicles according to the circle radius and center coordinates of the circular envelope corresponding to the front vehicle, as well as the circle radius and center coordinates of the circular envelope corresponding to the target vehicle; when the non-collision condition of the two vehicles is met, the relative motion constraint optimization model of the two vehicles is solved based on the goal of minimizing the control vector, and the automatic avoidance path of the target vehicle is determined, thereby improving the obstacle avoidance detection efficiency and realizing the automatic emergency lane change avoidance operation of the vehicle under high-speed extreme driving conditions. The tracking error model determines the path tracking error control amount of the target vehicle; the braking control amount and the steering control amount of the target vehicle are determined according to the pre-created handling stability control model; the target vehicle is controlled to travel along the automatic avoidance path according to the path tracking error control amount, the braking control amount and the steering control amount, thereby achieving fast, accurate and stable tracking control of the automatic emergency lane change avoidance path, further realizing the automatic emergency lane change avoidance operation of the vehicle under high-speed extreme driving conditions, avoiding understeering or oversteering of the vehicle during the automatic emergency lane change avoidance process, and ensuring the handling stability of the vehicle during the automatic emergency lane change avoidance operation.

[0112] In one embodiment, Figure 3 This is a schematic diagram of the structure of an automatic emergency lane change avoidance system for a vehicle provided by one embodiment of the present invention. Figure 3As shown, the system includes: a laser radar, a front-view camera, a steering wheel angle sensor, a wheel speed sensor, a yaw angular velocity sensor, a vehicle longitudinal acceleration sensor, a vehicle lateral acceleration sensor, a control unit 310, an active braking subsystem 320 and an active steering subsystem 330. It should be noted that the control unit 310 includes a path planning module 311, a path tracking control module 312 and a vehicle handling stability control module 313. Among them, the control module in the embodiment of the present invention is equivalent to the control unit, the active braking subsystem and the active steering subsystem in this embodiment. This vehicle represents the target vehicle in the embodiment of the present invention. When the laser radar and the front-view camera detect that the vehicle in front of this lane is traveling at a fixed deceleration, the front vehicle prediction model can be described by formula (15):

[0113]

[0114] Among them, (x1, y1) and θ1 are the coordinates and azimuth of the rear axle midpoint of the front vehicle, respectively.

[0115] Using (x2, y2), θ2, ρ2, a x2 , δ f and ω f are the coordinates of the rear axle midpoint, azimuth, trajectory curvature, deceleration, front wheel steering angle, and front wheel steering angular velocity of the vehicle. The kinematic model of the vehicle can be described by formula (16):

[0116]

[0117] Among them, L is the wheelbase of the vehicle.

[0118] In this embodiment, the preceding vehicle prediction model and the host vehicle kinematic model are combined to establish the equation for the relative motion between the host vehicle and the preceding vehicle, which can be described by the following formula: Among them, χ=[x1 y1 θ1 v1 x2 y2 θ2 v2 ρ2] T and u=[a x2 ω f ] T are the system state vector and control vector respectively.

[0119] In this embodiment, it is assumed that the control variable of the relative motion equation of the two vehicles is zero at the initial time t0, and the state vector of the relative motion equation of the two vehicles at the initial time t0 is expressed by the formula:

[0120]

[0121] In this embodiment, assuming that the road width is l0, the state vector of the car after emergency lane change to avoid obstacles is at the end time t f Should satisfy formula (18)

[0122]

[0123] In this embodiment, considering the vehicle mechanical constraints, the constraints on the vehicle longitudinal deceleration and front wheel steering angular velocity can be expressed by formula (19):

[0124]

[0125] Among them, ω fmax is the maximum steering angular velocity of the front wheels of the vehicle.

[0126] In this embodiment, considering the tire friction ellipse constraint, the vehicle longitudinal deceleration and lateral acceleration are constrained and can be expressed as formula (20):

[0127]

[0128] Among them, η is an adjustable parameter; g is the acceleration of gravity; a y is the lateral acceleration of the car, which can be expressed as

[0129] In one embodiment, Figure 4 A schematic diagram of a car circle envelope provided by an embodiment of the present invention. Figure 4 As shown in the figure, three circles with the same radius are used to contain the outer contour of the car, and the distance between the centers of the circles is used to achieve rapid detection of car obstacles, thereby improving the efficiency of obstacle avoidance detection. It is known that the length, width and rear overhang length of the car are L respectively. 2c , W2 and L 2r , then the three circle radii and center coordinates can be expressed as follows:

[0130]

[0131]

[0132]

[0133]

[0134] Among them, in formula (21), formula (22), formula (23) and formula (24), T2 is the rotation transformation matrix, which can be expressed as formula (25),

[0135]

[0136] Similarly, if the length, width and rear overhang of the front vehicle are L respectively 1c , W1 and L 1r , then the radii of the three circles are expressed as: The coordinates of the circle center can be expressed as

[0137]

[0138]

[0139]

[0140] Among them, the rotation transformation matrix T1 can be expressed as

[0141] Therefore, the condition for no collision between the two vehicles can be expressed as:

[0142]

[0143] In this embodiment, under the premise of ensuring that the vehicle in front does not collide with the vehicle, the path planning model constraint optimization solution model is established with minimizing the system control amount as the optimization goal. The Gaussian pseudo-spectral method is used to solve the constrained optimization problem to obtain the optimal automatic emergency lane change avoidance path for the vehicle's high-speed extreme driving conditions.

[0144] In one embodiment, Figure 5 This is a schematic diagram of a path tracking error model for an automatic emergency lane change avoidance system for an automobile provided by an embodiment of the present invention. Based on this model, a path tracking control module that satisfies stability is established to achieve rapid, accurate, and stable tracking control of the automatic emergency lane change avoidance path output by the path planning module for automobiles in high-speed extreme driving conditions. Specifically, the lateral position deviation, azimuth angle deviation, and yaw angular velocity deviation are used to represent the error. Figure 5 The path tracking error of the active avoidance system for automobiles in the high-speed extreme driving condition of the present invention is shown as follows: e = [y c -y d θ-θ d γ-γ d ] T , using C f 、C r 、C fe and C re Indicates the nominal value of the cornering stiffness and the perturbation, using M, I z , L f and L r Denotes the vehicle mass, moment of inertia, and wheelbases of the front and rear axles. The path tracking error model of the automatic emergency lane change avoidance system for vehicles facing high-speed extreme driving conditions of the present invention can be expressed as:

[0145] e(k+1)=(A+ΔA)e(k)+(B+ΔB)u(k) (30)

[0146] Among them, u=δ fis the control quantity; A, B, ΔA and ΔB are the nominal values ​​and perturbations of the system matrix and control matrix respectively, which can be expressed as

[0147]

[0148]

[0149] [ΔAΔB]=DF[E1 E2] (33)

[0150] Among them, matrices D, E1 and E2 can be expressed as

[0151]

[0152]

[0153]

[0154] Define the quadratic performance index as And through linear matrix inequalities, the minimization of quadratic performance indicators is transformed into a convex optimization problem constrained by the following linear matrix inequality group.

[0155]

[0156] In this embodiment, if the optimal solution of the convex optimization problem constrained by the above linear matrix inequality group is (ε, W, X, H).

[0157] Then the output of the path tracking control module of the automatic emergency lane change avoidance system for automobiles in high-speed extreme driving conditions of the present invention can be expressed as u * (k) = WXe(k).

[0158] In one embodiment, Figure 6 This is a schematic diagram of a vehicle handling stability control module provided by an embodiment of the present invention. Based on this module, a vehicle handling stability control law is established to avoid understeering or oversteering during the vehicle path tracking process. Figure 6 As shown, the vehicle handling stability control model can be expressed as

[0159]

[0160] Among them, β and γ are the sideslip angle and yaw rate of the vehicle's center of mass; F x1 、F x2 、F x3 、F x4 、 and is the mean of the longitudinal and lateral forces on the wheel.

[0161] It should be noted that the vehicle handling stability control model can be modified to

[0162]

[0163] Where d = [d1 d2] T and θ are system uncertainties.

[0164] In this embodiment, for the above-mentioned modified vehicle handling stability control model, the control performance index is defined as

[0165]

[0166] Among them, β d =0 and

[0167] Therefore, using Lyapunov stability theory, we can get the following vehicle correction yaw moment control quantity to avoid understeering or oversteering during vehicle path tracking:

[0168]

[0169] in, The real-time update is performed in the following way, which can be expressed as follows:

[0170]

[0171] Considering that the vehicle correction yaw moment control quantity can be directly mapped to the wheel longitudinal force and lateral force, we have M u =HΔu, where H and Δu are expressed as

[0172]

[0173] Δu=[ΔF yf ΔF x1 ΔF x2 ΔF x3 ΔF x4 ] T (44)

[0174] Using tire characteristics, the relationship between tire lateral force increment and front wheel turning angle is established as

[0175] ΔF yf =-2C f Δα (45)

[0176]

[0177] Δα=-Δδ f (47)

[0178] The relationship between the tire longitudinal force increment and the wheel braking torque is established using tire characteristics:

[0179] ΔF xi =ΔT bi / R w i=1,…,4 (48)

[0180] It should be noted that the mapping relationship between the vehicle correction yaw moment control amount and the wheel longitudinal force and lateral force is modified to

[0181] M u =H1Δu1 (49)

[0182] Among them, H1 and Δu1 can be expressed as

[0183]

[0184] Δu1=[Δδ f ΔT b1 ΔT b2 ΔT b3 ΔT b4 ] T (51)

[0185] In this embodiment, the objective function is defined as With minimization of the objective function as the optimization goal, a stepwise quadratic programming method is used to obtain the output of the vehicle handling stability control module, namely: the active braking subsystem control variable and the active steering subsystem control variable, which are expressed as the braking control variable and the steering control variable of the target vehicle in the embodiment of the present invention.

[0186] In this embodiment, the trajectory of the preceding vehicle is predicted using fused information from a LiDAR and forward-looking camera. A constrained optimization model for the relative motion of the two vehicles is then established. This constrained optimization problem is solved using the Gaussian pseudospectral method to obtain the vehicle's automatic emergency lane change avoidance path. Subsequently, a path tracking control module is constructed using measurement information from the steering wheel angle sensor, wheel speed sensor, yaw rate sensor, longitudinal acceleration sensor, and lateral acceleration sensor. This path tracking control is achieved by controlling the vehicle's active steering subsystem. Finally, a vehicle handling stability control module is designed using measurements from the steering wheel angle sensor, wheel speed sensor, yaw rate sensor, longitudinal acceleration sensor, and lateral acceleration sensor. This module controls the vehicle's active braking and steering subsystems to ensure the vehicle's handling stability during the automatic emergency lane change avoidance maneuver.

[0187] In one embodiment, Figure 7This is a structural block diagram of a vehicle control device provided by an embodiment of the present invention. The device is suitable for controlling the automatic emergency lane change avoidance operation of the vehicle. The device can be implemented by hardware / software. It can be configured in an electronic device to implement a vehicle control method in an embodiment of the present invention. Figure 7 As shown, the device includes: a model creation module 710 , a path planning module 720 and a control module 730 .

[0188] The model creation module 710 is configured to create a corresponding relative motion constraint optimization model for the two vehicles based on a trajectory prediction model of the preceding vehicle and a kinematic model and dynamic characteristics of a target vehicle, wherein the preceding vehicle and the target vehicle are located in the same lane.

[0189] A path planning module 720 is configured to determine an automatic avoidance path for the target vehicle based on a predetermined non-collision condition between the two vehicles and an optimization model of relative motion constraints between the two vehicles;

[0190] The control module 730 is configured to control the target vehicle to travel along the automatic avoidance path based on a pre-created path tracking error model and a handling stability control model.

[0191] The technical solution of the embodiment of the present invention is to create a corresponding two-vehicle relative motion constraint optimization model according to the driving trajectory prediction model of the front vehicle, the kinematic model and dynamic characteristics of the target vehicle through a model creation module; to determine the automatic avoidance path of the target vehicle according to the predetermined two-vehicle non-collision condition and the two-vehicle relative motion constraint optimization model through a path planning module; and to control the target vehicle to travel along the automatic avoidance path based on the pre-created path tracking error model and handling stability control model through a control module, thereby realizing the automatic emergency lane change avoidance operation of the vehicle under high-speed extreme driving conditions, avoiding understeering or oversteering of the vehicle during the automatic emergency lane change avoidance process, and thus ensuring the handling stability of the vehicle during the automatic emergency lane change avoidance operation.

[0192] In one embodiment, the path planning module 720 includes:

[0193] a circular envelope construction unit, configured to sequentially construct circular envelopes of the preceding vehicle and the target vehicle based on the pre-acquired length, width, and rear overhang length of the corresponding vehicle;

[0194] a condition determination unit, configured to determine a non-collision condition between the two vehicles based on the radius and center coordinates of the circle envelope corresponding to the front vehicle and the radius and center coordinates of the circle envelope corresponding to the target vehicle;

[0195] The path determination unit is used to solve the relative motion constraint optimization model of the two vehicles based on the goal of minimizing the control vector when the non-collision condition between the two vehicles is met, and determine the automatic avoidance path of the target vehicle.

[0196] In one embodiment, the vehicle control method further includes:

[0197] A first matrix creation module is used to create a system matrix based on the nominal value of the cornering stiffness, the vehicle mass, the moment of inertia, the front axle wheelbase, and the rear axle wheelbase;

[0198] A second matrix creation module is used to create a control matrix based on the nominal value of the cornering stiffness, the vehicle mass, the moment of inertia and the front axle wheelbase;

[0199] The error model creation module is used to create a corresponding path tracking error model according to the system matrix, the control matrix and the perturbation.

[0200] In one embodiment, the vehicle control method further includes:

[0201] An information acquisition module is used to obtain the target vehicle's center of mass sideslip angle, yaw rate, wheel longitudinal force and lateral force average;

[0202] The control model construction module is used to construct a corresponding handling stability control model based on the sideslip angle of the center of mass, the yaw angular velocity, the longitudinal force of the wheel and the average value of the lateral force.

[0203] In one embodiment, the control module 730 includes:

[0204] a first control amount determination unit, configured to determine a path tracking error control amount of the target vehicle according to a pre-created path tracking error model;

[0205] a second control amount determination unit, configured to determine a braking control amount and a steering control amount of the target vehicle according to a pre-created handling stability control model;

[0206] A path driving unit is used to control the target vehicle to travel along the automatic avoidance path according to the path tracking error control amount, the braking control amount and the steering control amount.

[0207] In one embodiment, the first control amount determination unit includes:

[0208] a performance indicator determination subunit, configured to determine a corresponding performance indicator based on a path tracking error model;

[0209] An indicator conversion subunit, configured to convert the performance indicator into a corresponding linear matrix inequality constraint condition through a linear matrix inequality;

[0210] The control quantity determination subunit is used to determine the corresponding path tracking control quantity according to the linear matrix inequality constraint condition.

[0211] In one embodiment, the second control amount determination unit includes:

[0212] a performance index determination subunit, configured to determine corresponding control performance indexes based on a pre-created handling stability control model;

[0213] a first control variable determination subunit, configured to determine a corresponding correction yaw moment control variable according to the control performance index;

[0214] The second control variable determination subunit is used to determine the braking control variable and the steering control variable of the target vehicle according to the corrected yaw moment control variable, the wheel longitudinal force and the lateral force.

[0215] The vehicle control device provided in the embodiment of the present invention can execute the vehicle control method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0216] In one embodiment, Figure 8 1 shows a schematic diagram of the structure of an electronic device that can be used to implement an embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0217] like Figure 8 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0218] Multiple components in electronic device 10 are connected to I / O interface 15, including an input unit 16, such as a keyboard and mouse; an output unit 17, such as various types of displays and speakers; a storage unit 18, such as a magnetic disk and optical disk; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0219] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors that run machine learning model algorithms, a digital signal processor (DSP), and any other suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the vehicle data processing method.

[0220] In some embodiments, the vehicle data processing method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the vehicle data processing method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute the vehicle data processing method in any other suitable manner (e.g., via firmware).

[0221] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system comprising at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0222] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0223] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0224] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device that has: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0225] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0226] A computing system may include a client and a server. The client and server are generally remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers and establishing a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, a hosting product within a cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0227] It should be understood that the various forms of the processes shown above can be used, and steps can be reordered, added, or deleted. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0228] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A vehicle control method, characterized in that: include: Creating a corresponding relative motion constraint optimization model for the two vehicles based on a trajectory prediction model of the leading vehicle and a kinematic model and dynamic characteristics of a target vehicle, wherein the leading vehicle and the target vehicle are located in the same lane; Determining an automatic avoidance path for the target vehicle based on a predetermined non-collision condition between the two vehicles and an optimization model of relative motion constraints between the two vehicles; Controlling the target vehicle to travel along the automatic avoidance path based on a pre-created path tracking error model and a handling stability control model; The controlling the target vehicle to travel along the automatic avoidance path based on the pre-created path tracking error model and the handling stability control model includes: Determining a path tracking error control amount for a target vehicle based on a pre-created path tracking error model; Determining the braking control amount and the steering control amount of the target vehicle according to a pre-created handling stability control model; The target vehicle is controlled to travel along the automatic avoidance path according to the path tracking error control amount, the braking control amount, and the steering control amount.

2. The method according to claim 1, characterized in that The step of determining the automatic avoidance path of the target vehicle based on the predetermined non-collision condition between the two vehicles and the relative motion constraint optimization model between the two vehicles includes: Constructing circular envelopes of the preceding vehicle and the target vehicle in sequence based on the length, width, and rear overhang length of the corresponding vehicle acquired in advance; Determine the no-collision condition between the two vehicles based on the radius and center coordinates of the circle envelope corresponding to the front vehicle and the radius and center coordinates of the circle envelope corresponding to the target vehicle; Under the condition that the two vehicles do not collide, the relative motion constraint optimization model of the two vehicles is solved based on the goal of minimizing the control vector to determine the automatic avoidance path of the target vehicle.

3. The method according to claim 1, characterized in that The method further comprises: Create a system matrix based on the nominal values ​​of cornering stiffness, vehicle mass, moment of inertia, front axle wheelbase, and rear axle wheelbase; Create a control matrix based on the nominal values ​​of cornering stiffness, vehicle mass, moment of inertia, and front axle wheelbase; A corresponding path tracking error model is created according to the system matrix, the control matrix and the perturbation amount.

4. The method according to claim 1, wherein The method further comprises: Obtain the mean values ​​of the target vehicle's center of mass sideslip angle, yaw rate, wheel longitudinal force, and lateral force; The corresponding handling stability control model is constructed based on the average values ​​of the sideslip angle, yaw angular velocity, wheel longitudinal force and lateral force.

5. The method according to claim 1, wherein The determining of the target vehicle's path tracking error control amount based on the pre-created path tracking error model includes: Determine corresponding performance indicators based on the path tracking error model; Converting the performance indicator into a corresponding linear matrix inequality constraint condition through a linear matrix inequality; The corresponding path tracking control amount is determined according to the linear matrix inequality constraint condition.

6. The method according to claim 1, characterized in that The step of determining the braking control amount and the steering control amount of the target vehicle according to the pre-created handling stability control model includes: Determine the corresponding control performance index based on the pre-created handling stability control model; Determining a corresponding correction yaw moment control amount according to the control performance index; The braking control amount and the steering control amount of the target vehicle are determined according to the corrected yaw moment control amount, the wheel longitudinal force and the lateral force.

7. A vehicle control device, characterized in that: include: a model creation module for creating a corresponding two-vehicle relative motion constraint optimization model based on a driving trajectory prediction model of a leading vehicle and a kinematic model and dynamic characteristics of a target vehicle, wherein the leading vehicle and the target vehicle are located in the same lane; A path planning module, configured to determine an automatic avoidance path for the target vehicle based on a predetermined non-collision condition between the two vehicles and an optimization model of relative motion constraints between the two vehicles; a control module, configured to control the target vehicle to travel along the automatic avoidance path based on a pre-established path tracking error model and a handling stability control model; The control module includes: a first control amount determination unit, configured to determine a path tracking error control amount of the target vehicle according to a pre-created path tracking error model; a second control amount determination unit, configured to determine a braking control amount and a steering control amount of the target vehicle according to a pre-created handling stability control model; A path driving unit is used to control the target vehicle to travel along the automatic avoidance path according to the path tracking error control amount, the braking control amount and the steering control amount.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor. The computer program is executed by the at least one processor to enable the at least one processor to perform the vehicle control method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the vehicle control method according to any one of claims 1 to 6 when executed.

Images