Self-adaptive cruise target selection method and device, computer equipment and medium

Abstract

The embodiment of the invention provides a self-adaptive cruise target selection method and device, computer equipment and a medium, and relates to the technical field of self-adaptive cruise, and the method comprises the steps: obtaining historical tracks of a plurality of targets under a self-vehicle coordinate system; self-vehicle traveling parameters are obtained, and a self-vehicle predicted traveling track is calculated according to the self-vehicle traveling parameters; according to the historical trajectory of the target and the predicted driving trajectory of the vehicle, whether the vehicle is located on the historical trajectory of the target is judged, and different target screening trajectories are obtained according to the judgment result. Under the condition of not increasing any software and hardware cost, the method is suitable for a lane-line-free scene, and the accuracy and stability of ACC target selection in the lane-line-free scene are improved.

Description

Technical Field

[0001] The present invention relates to the field of adaptive cruise technology, and in particular to an adaptive cruise target selection method, device, computer equipment and medium. Background Art

[0002] The Adaptive Cruise Control (ACC) function is a basic function of the Advanced Driving Assistance System (ADAS). It can identify and analyze the traffic conditions in the driving direction through a single camera or a camera and radar fusion method, and automatically control the acceleration and deceleration of the vehicle in the longitudinal direction according to the traffic conditions in the driving direction of the vehicle, thereby reducing the driver's operating burden. When the target selection module determines that there is no main target or the main target is far away, the ACC function can drive at the cruising speed set by the driver; when the target selection module determines that there is a main target and the main target is close, the ACC function can automatically maintain the relative distance with the vehicle in front according to the following time interval set by the driver.

[0003] The existing target selection technical solutions (such as the technical solutions disclosed in patent CN114643988A) are usually as follows when there are no lane lines: Figure 4 As shown, 1. A cubic curve is fitted as the lane centerline based on the curvature of the ego vehicle, the rate of change of curvature and the heading angle of the obstacle vehicle; 2. The lane width is taken as a preset value; 3. The lateral offset between the obstacle vehicle and the fitted lane centerline is calculated; 4. The obstacle vehicle to be tracked is determined based on the lateral offset and the obstacle screening boundary. However, this scheme has the following problems: 1. Since the trajectory prediction of the ego vehicle based on the driving state of the ego vehicle usually fluctuates greatly, especially the stability of the far-end trajectory is insufficient, relying solely on the ego vehicle's driving trajectory for target selection will result in insufficient stability of target selection; 2. Relying solely on the ego vehicle's driving trajectory for target selection will result in the loss of the originally followed target in some special scenarios, such as avoiding the scenario of vehicles invading the ego vehicle's driving path (such as Figure 6 As shown in the figure, during the ego vehicle avoidance process, the predicted trajectory will deviate to one side. At this time, the following target is not on the ego vehicle trajectory, and the target will be lost temporarily until the avoidance action is completed.

[0004] The existing target selection technical solutions (such as the technical solutions disclosed in patent CN115973162A) are usually as follows when there are no lane lines: Figure 5As shown, 1. determine a set of historical driving trajectories corresponding to each of the multiple front vehicles, and a set of historical driving trajectories includes multiple historical trajectory points; 2. determine a set of radius hypotheses associated with the target driving trajectory of the vehicle based on one or more driving parameters associated with the driving trajectory of the vehicle, and each radius hypothesis in a set of radius hypotheses corresponds to a corresponding radius hypothesis circular trajectory; 3. select the optimal radius hypothesis from a set of radius hypotheses based on the distance difference between adjacent points in the multiple historical trajectory points and the corresponding radius hypothesis circular trajectory; 4. and determine at least one of the local optimal radius and the global optimal radius associated with the target driving trajectory according to the selected optimal radius hypothesis. However, the disadvantage of this scheme is that in a curvature radius space of the self-vehicle driving trajectory, a curvature radius that best matches all the front target historical trajectory points is searched. This method works better when there are many targets and most of the targets are consistent with the driving trend of the self-vehicle. However, when there are few targets or most of the targets are inconsistent with the driving trend of the self-vehicle, the prediction of the self-vehicle driving trajectory is easily disturbed by the target historical trajectory, resulting in a decrease in accuracy and stability. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides an adaptive cruise target selection method to solve the problem of insufficient stability and low accuracy of target selection in the prior art, including:

[0006] Obtain the historical trajectories of multiple targets in the ego-vehicle coordinate system;

[0007] Acquiring a vehicle travel parameter, and calculating a predicted vehicle travel trajectory according to the vehicle travel parameter;

[0008] According to the historical trajectory of the target and the predicted driving trajectory of the vehicle, it is judged whether the vehicle is on the historical trajectory of the target, and different target screening trajectories are obtained according to the judgment result.

[0009] The adaptive cruise target selection method provided by the present invention also has the following feature: in calculating the predicted driving trajectory of the self-vehicle according to the self-vehicle travel parameters, the predicted driving trajectory of the self-vehicle is obtained by calculating the longitudinal distance x in the self-vehicle coordinate system, the sideslip angle λ of the front center point of the self-vehicle, and the vehicle turning radius R.

[0010] The adaptive cruise target selection method provided by the present invention also has the following characteristics: the vehicle travel parameters include: the vehicle predicted driving trajectory includes the vehicle predicted driving trajectory equation:

[0011] y=D1*x+D2*x 2

[0012] Where y is the lateral distance in the ego vehicle coordinate system, x is the longitudinal distance in the ego vehicle coordinate system, D1 is the first-order coefficient of the ego vehicle prediction trajectory equation, and D2 is the second-order coefficient of the ego vehicle prediction trajectory equation. D1 = tanλ,

[0013] The adaptive cruise target selection method provided by the present invention also has the following feature: the vehicle turning radius R is calculated by the transmission ratio from the steering wheel to the front wheels, the wheelbase, the front axle lateral stiffness coefficient, the rear axle lateral stiffness coefficient, the vehicle speed and the steering wheel angle.

[0014] The adaptive cruise target selection method provided by the present invention also has the following feature: the self-vehicle front protection center point sideslip angle λ is obtained by calculating the distance from the rear axle to the front protection center point and the rear wheel sideslip angle.

[0015] The adaptive cruise target selection method provided by the present invention also has the following feature: the determining whether the vehicle is on the target historical track includes determining whether the vehicle is on the target historical track by the following formula:

[0016]

[0017] Among them, A0 is the zero-order coefficient of the historical trajectory equation of the ego vehicle following the target, W obj The target vehicle width, W ego is the width of the vehicle, f(k) is the result of judging whether the vehicle is on the historical trajectory of the target in the current frame, and f(k-1) is the result of judging whether the vehicle is on the historical trajectory of the target in the previous frame.

[0018] When f(k) = 1, the vehicle is on the target historical trajectory; when f(k) = 0, the vehicle is not on the target historical trajectory.

[0019] The adaptive cruise target selection method provided by the present invention also has the following characteristics: obtaining different target screening trajectories according to the judgment results includes:

[0020] If the vehicle is judged to be on the target historical trajectory, the target screening trajectory equation for the target is: y = A1*x + A2*x 2 ,

[0021] If the vehicle is judged not to be on the historical trajectory of the target, the target screening trajectory equation for the target is: y = D1*x + D2*x 2 ,

[0022] Among them, y is the lateral distance in the ego-vehicle coordinate system, x is the longitudinal distance in the ego-vehicle coordinate system, A1 is the linear coefficient of the historical trajectory equation of the ego-vehicle following the target, A2 is the quadratic coefficient of the historical trajectory equation of the ego-vehicle following the target, D1 is the linear coefficient of the ego-vehicle predicted trajectory equation, and D2 is the quadratic coefficient of the ego-vehicle predicted trajectory equation.

[0023] A second object of the present invention is to provide an adaptive cruise target selection device, comprising:

[0024] The target history track acquisition module is used to obtain the target history track and target vehicle width;

[0025] A self-vehicle traveling parameter acquisition module, used to obtain the self-vehicle traveling parameters;

[0026] A calculation module, used for calculating the predicted driving trajectory of the vehicle through the vehicle's travel parameters;

[0027] The judgment module is used to judge whether the vehicle is on the current target historical trajectory through the predicted driving trajectory of the vehicle and the data obtained by the target historical trajectory acquisition module, and obtain different target screening trajectories.

[0028] The third object of the present invention is to provide a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the aforementioned adaptive cruise target selection methods when executing the computer program.

[0029] A fourth object of the present invention is to provide a computer-readable storage medium, wherein the computer-readable storage medium stores a method for executing any one of the aforementioned adaptive cruise target selection methods.

[0030] Compared with the prior art, the beneficial effects achieved by at least one of the above technical solutions adopted in the embodiments of this specification include at least: obtaining the historical trajectories of multiple targets in the ego vehicle coordinate system; obtaining the ego vehicle travel parameters, and calculating the ego vehicle predicted driving trajectory according to the ego vehicle travel parameters; judging whether the ego vehicle is on the target historical trajectory according to the target historical trajectory and the ego vehicle predicted driving trajectory, and obtaining different target screening trajectories according to the judgment results. This application is applicable to the scene without lane lines without increasing any software and hardware costs, and improves the accuracy and stability of ACC target selection in the scene without lane lines. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 is a flow chart of an adaptive cruise target selection method provided by an embodiment of the present invention;

[0033] Figure 2 is a structural block diagram of a computer device provided by an embodiment of the present invention;

[0034] Figure 3 is a structural block diagram of an adaptive cruise target selection device provided by an embodiment of the present invention;

[0035] Figure 4 It is a flow chart of the target selection method provided by the prior art 1;

[0036] Figure 5 It is a flow chart of the target selection method provided by the second prior art;

[0037] Figure 6 This is a schematic diagram of the use of a target selection method provided by prior art 1 in some scenarios. DETAILED DESCRIPTION

[0038] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0039] The following describes the implementation methods of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The present application can also be implemented or applied through other different specific implementation methods, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, in the absence of conflict, the following embodiments and the features in the embodiments can be combined with each other. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work belong to the scope of protection of the present application.

[0040] In an embodiment of the present invention, an adaptive cruise target selection method is provided, such as Figure 1 As shown, including:

[0041] S1: Obtain the historical trajectories of multiple targets in the ego-vehicle coordinate system;

[0042] S2: Obtain the vehicle's travel parameters;

[0043] S3: Calculating the predicted driving trajectory of the vehicle according to the vehicle travel parameters;

[0044] S4: judging whether the vehicle is on the historical trajectory of the target according to the historical trajectory of the target and the predicted driving trajectory of the vehicle;

[0045] S5: Obtain different target screening trajectories according to the judgment result of S4.

[0046] In some embodiments, the historical trajectory parameter equations of up to ten targets in the vehicle coordinate system are obtained through the camera, and each historical trajectory parameter equation can be expressed as:

[0047] y=A0+A1*x+A2*x 2

[0048] Where: x is the longitudinal distance in the ego-vehicle coordinate system, y is the lateral distance in the ego-vehicle coordinate system, A0 is the zero-order coefficient of the historical trajectory equation of the ego-vehicle following the target, A1 is the first-order coefficient of the historical trajectory equation of the ego-vehicle following the target, and A2 is the second-order coefficient of the historical trajectory equation of the ego-vehicle following the target.

[0049] In some embodiments, in calculating the predicted driving trajectory of the vehicle according to the vehicle travel parameters, the predicted driving trajectory of the vehicle is obtained by calculating the longitudinal distance x, the sideslip angle λ of the front center point of the vehicle and the turning radius R of the vehicle in the vehicle coordinate system.

[0050] In some embodiments, the vehicle turning radius R is calculated by the transmission ratio from the steering wheel to the front wheels, the wheelbase, the front axle lateral stiffness coefficient, the rear axle lateral stiffness coefficient, the vehicle speed and the steering wheel angle.

[0051] In some embodiments, the sideslip angle λ from the front protection center of the vehicle is calculated by the distance from the rear axle to the front protection center and the rear wheel sideslip angle.

[0052] The details are as follows:

[0053] The vehicle's travel parameters are obtained through the vehicle's CAN. The vehicle's travel parameters include the vehicle's yaw angle and speed, the vehicle's speed v, the steering wheel angle θ and other signals. The side slip angle λ of the vehicle's front center is calculated as follows:

[0054]

[0055] Among them, θ is the steering wheel angle, K is the transmission ratio from the steering wheel to the front wheels, L is the wheelbase, k f is the front axle cornering stiffness coefficient, k r is the rear axle cornering stiffness coefficient, v is the vehicle speed, M is the vehicle mass, L f is the distance from the front axle to the center of mass, Cr is the rear axle cornering stiffness, L 0 is the distance from the rear axle to the front center point, R is the turning radius of the vehicle, α r is the rear wheel slip angle.

[0056] The vehicle trajectory equation is defined in the vehicle coordinate system as follows:

[0057] y=D0+D1*x+D2*x 2 +D3*x 3

[0058] Among them, D0 is the zero-order coefficient of the self-vehicle prediction trajectory equation, D1 is the first-order coefficient of the self-vehicle prediction trajectory equation, D2 is the second-order coefficient of the self-vehicle prediction trajectory equation, and D3 is the third-order coefficient of the self-vehicle prediction trajectory equation. The self-vehicle can be equivalent to circular motion when turning in a steady state. The curvature of the self-vehicle is a constant, so D3 = 0; the front center point of the self-vehicle is the origin of the self-vehicle coordinate system, so D0 = 0. Therefore, the self-vehicle trajectory equation can be simplified as follows:

[0059] y=D1*x+D2*x 2

[0060] Where D1 = tanλ,

[0061] In some embodiments, determining whether the vehicle is on the target historical trajectory includes determining whether the vehicle is on the target historical trajectory by the following formula:

[0062]

[0063] Among them, W obj The target vehicle width, W ego is the vehicle width, f(k) is the judgment result of whether the vehicle is on the target historical trajectory in the current frame, and f(k-1) is the judgment result of whether the vehicle is on the target historical trajectory in the previous frame. When f(k) = 1, the vehicle is on the target historical trajectory; when f(k) = 0, the vehicle is not on the target historical trajectory.

[0064] In some embodiments, obtaining different target screening trajectories according to the judgment results includes:

[0065] If the vehicle is judged to be on the target historical trajectory, the target screening trajectory equation for the target is: y = A1*x + A2*x 2 ,

[0066] If the vehicle is judged not to be on the historical trajectory of the target, the target screening trajectory equation for the target is: y = D1*x + D2*x 2 .

[0067] In some embodiments, a computer device is provided, such as Figure 2 As shown, it includes a memory 201, a processor 202, and a computer program stored in the memory 201 and executable on the processor 202. When the processor 202 executes the computer program, the adaptive cruise target selection method of any of the above embodiments is implemented.

[0068] Specifically, the computer device may be a computer terminal, a server or a similar computing device.

[0069] In some embodiments, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a method for executing the adaptive cruise target selection method described in any of the above embodiments.

[0070] Specifically, computer-readable storage media include permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer-readable storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable storage media does not include transitory media such as modulated data signals and carrier waves.

[0071] Based on the same inventive concept, an adaptive cruise target selection device is also provided in an embodiment of the present invention, as described in the following embodiments. Since the principle of solving the problem by an adaptive cruise target selection device is similar to that of an adaptive cruise target selection method, the implementation of an adaptive cruise target selection device can refer to the implementation of the adaptive cruise target selection method, and the repeated parts will not be repeated. As used below, the term "unit" or "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0072] An adaptive cruise target selection device, the structural block diagram is as follows Figure 3 As shown, it includes: a target historical trajectory acquisition module, a vehicle travel parameter acquisition module, a calculation module and a judgment module.

[0073] The target history track acquisition module 301 is used to acquire the target history track and the target vehicle width;

[0074] The vehicle travel parameter acquisition module 302 is used to acquire the vehicle travel parameters;

[0075] A calculation module 303 is used to calculate the predicted driving trajectory of the vehicle according to the vehicle travel parameters;

[0076] The judgment module 304 is used to judge whether the vehicle is on the current target historical trajectory through the predicted driving trajectory of the vehicle and the data obtained by the target historical trajectory acquisition module, and obtain different target selection trajectories.

[0077] Obviously, those skilled in the art should understand that the modules or steps of the above-mentioned embodiments of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. In this way, the embodiments of the present invention are not limited to any specific combination of hardware and software.

[0078] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the embodiments of the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An adaptive cruise target selection method, characterized in that: include: Obtain the historical trajectories of multiple targets in the ego-vehicle coordinate system; Acquiring a vehicle travel parameter, and calculating a predicted vehicle travel trajectory according to the vehicle travel parameter; According to the historical trajectory of the target and the predicted driving trajectory of the vehicle, it is judged whether the vehicle is on the historical trajectory of the target, and different target screening trajectories are obtained according to the judgment result.

2. The adaptive cruise target selection method according to claim 1, characterized in that: In the calculation of the predicted driving trajectory of the self-vehicle according to the self-vehicle traveling parameters, the predicted driving trajectory of the self-vehicle is obtained by calculating the longitudinal distance x, the sideslip angle λ of the front center point of the self-vehicle and the turning radius R of the vehicle in the self-vehicle coordinate system.

3. The adaptive cruise target selection method according to claim 2, characterized in that: The predicted driving trajectory of the vehicle includes the predicted driving trajectory equation of the vehicle: y=D1*x+D2*x 2 Where y is the lateral distance in the ego vehicle coordinate system, x is the longitudinal distance in the ego vehicle coordinate system, D1 is the first-order coefficient of the ego vehicle prediction trajectory equation, and D2 is the second-order coefficient of the ego vehicle prediction trajectory equation. D1 = tanλ, 4. The adaptive cruise target selection method according to claim 2, characterized in that: The vehicle turning radius R is calculated by the transmission ratio from the steering wheel to the front wheels, the wheelbase, the front axle cornering stiffness coefficient, the rear axle cornering stiffness coefficient, the vehicle speed and the steering wheel angle.

5. The adaptive cruise target selection method according to claim 2, characterized in that: The sideslip angle λ from the front protection center of the vehicle is obtained by calculating the distance from the rear axle to the front protection center and the rear wheel sideslip angle.

6. The adaptive cruise target selection method according to claim 1, characterized in that: The determining whether the vehicle is on the target historical track includes determining whether the vehicle is on the target historical track by the following formula: Among them, A0 is the zero-order coefficient of the historical trajectory equation of the ego vehicle following the target, W onj The target vehicle width, W ego is the width of the vehicle, f(k) is the result of judging whether the vehicle is on the historical trajectory of the target in the current frame, and f(k-1) is the result of judging whether the vehicle is on the historical trajectory of the target in the previous frame. When f(k) = 1, the vehicle is on the target historical trajectory; when f(k) = 0, the vehicle is not on the target historical trajectory.

7. The adaptive cruise target selection method according to claim 1, characterized in that: The obtaining of different target screening trajectories according to the judgment results includes: If the vehicle is judged to be on the target historical trajectory, the target screening trajectory equation for the target is: y = A1*x + A2*x 2 , If the vehicle is judged not to be on the historical trajectory of the target, the target screening trajectory equation for the target is: y = D1*x + D2*x 2 , Among them, y is the lateral distance in the ego-vehicle coordinate system, x is the longitudinal distance in the ego-vehicle coordinate system, A1 is the linear coefficient of the historical trajectory equation of the ego-vehicle following the target, A2 is the quadratic coefficient of the historical trajectory equation of the ego-vehicle following the target, D1 is the linear coefficient of the ego-vehicle predicted trajectory equation, and D2 is the quadratic coefficient of the ego-vehicle predicted trajectory equation.

8. An adaptive cruise target selection device, characterized in that: include: The target history track acquisition module is used to obtain the target history track and target vehicle width; A self-vehicle traveling parameter acquisition module, used to obtain the self-vehicle traveling parameters; A calculation module, used for calculating the predicted driving trajectory of the vehicle through the vehicle's travel parameters; The judgment module is used to judge whether the vehicle is on the current target historical trajectory through the predicted driving trajectory of the vehicle and the data obtained by the target historical trajectory acquisition module, and obtain different target screening trajectories.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the adaptive cruise target selection method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores the adaptive cruise target selection method according to any one of claims 1 to 7.

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