Vehicle trajectory tracking method, system, device and computer readable storage medium

Abstract

A vehicle trajectory tracking method, system, device and computer readable storage medium belong to the field of automatic driving, including collecting multiple sets of vehicle historical driving records, including rear axle axis distance difference, yaw angle difference, rear axle axis speed and yaw angle angular velocity between the front and rear time; after dividing all rear axle axis distance differences, yaw angle differences, rear axle axis speeds and yaw angle angular velocities into multiple levels according to numerical value, multiple distance difference levels, angle difference levels, speed levels and angular velocity levels are obtained; a vehicle trajectory tracking model is trained based on the foregoing levels, and vehicle trajectory tracking is performed. When controlling the rear axle axis speed and the yaw angle angular velocity according to the rear axle axis distance difference and the yaw angle difference, the application divides each data according to the level, trains the control model, thereby realizing fuzzy control, avoiding frequent changes of the control quantity caused by frequent changes of the state quantity, and improving driving safety and comfort.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving, and specifically to a vehicle trajectory tracking method, system, device, and computer-readable storage medium. Background Art

[0002] The most widely used autonomous driving control algorithms are the Proportional Integral Derivative (PID) algorithm, the Linear Quadratic Regulator (LQR) algorithm, the Model Predictive Control (MPC) algorithm, and the Sliding Mode Control (SMC) algorithm.

[0003] PID control method is widely used, but the selection of traditional PID controller parameters mainly depends on repeated experiments and experience, and it is impossible to change the control parameters in time according to the changes in state.

[0004] The LQR control method is a stable control method that has high requirements on system dynamics and is sensitive to system noise.

[0005] The MPC control method considers the nonlinear dynamic model of the control system and predicts the output behavior of the system in the future, which requires high computing power.

[0006] The SMC control method is a variable structure control method that has high requirements on system stability and noise interference suppression capability, and is prone to chattering problems.

[0007] Existing autonomous driving control algorithms each present various problems. Generally speaking, commonly used trajectory tracking algorithms all pre-establish precise mathematical models. These state variables are collected during vehicle movement, and then fed into the mathematical model for solution to obtain control variables, which are then used to control the vehicle's movement in real time. Because the mathematical models controlling the vehicle have pre-set parameters, and the model parameters do not change according to different driving conditions during driving, each set of state variable data collected generates a set of control variable data. This requires a large amount of computation and high computing power. Furthermore, changes in state variables continuously and in real time affect the control variables. Constant changes in the control variables force the vehicle to constantly adjust, affecting the stability of the vehicle's trajectory tracking and, in turn, the ride comfort. If the state variables fluctuate, especially abnormally, the vehicle's state can suddenly change, seriously impacting vehicle safety. Summary of the Invention

[0008] The present application provides a vehicle trajectory tracking method, system, device and computer-readable storage medium, which can solve the technical problems of poor stability and safety of vehicle trajectory tracking in the prior art.

[0009] In a second aspect, an embodiment of the present application provides a vehicle trajectory tracking method, the method comprising:

[0010] Collecting multiple sets of vehicle historical driving records, each set of vehicle historical driving records includes vehicle historical input and vehicle historical output, the vehicle historical input is the rear axle axis center distance difference and yaw angle difference between two adjacent moments, and the vehicle historical output is the rear axle axis center speed and yaw angle speed between two adjacent moments;

[0011] According to the numerical level classification strategy, the rear axle axis distance difference, yaw angle difference, rear axle axis speed, and yaw angle angular velocity in all vehicle historical driving records are divided into multiple levels according to the numerical value, thereby obtaining multiple distance difference levels, angle difference levels, speed levels, and angular velocity levels;

[0012] The distance difference level and the angle difference level are used as input quantities, and the speed level and the angular velocity level are used as output quantities for model training to obtain a vehicle trajectory tracking model for vehicle trajectory tracking.

[0013] In conjunction with the second aspect, in one embodiment, the plurality of distance difference levels include no difference, relatively close, very close, far, and very far;

[0014] The upper limit of the numerical range of the no-difference level is smaller than the lower limit of the numerical range of the relatively close level, the upper limit of the numerical range of the relatively close level is smaller than the lower limit of the numerical range of the very close level, the upper limit of the numerical range of the very close level is smaller than the lower limit of the numerical range of the far level, and the upper limit of the numerical range of the far level is smaller than the lower limit of the numerical range of the very far level.

[0015] In conjunction with the second aspect, in one embodiment, the plurality of angle difference levels include a first negative large level, a first negative small level, a first zero level, a first positive small level, and a first positive large level;

[0016] The upper limit of the numerical range of the first negative major level is smaller than the lower limit of the numerical range of the first negative minor level, the upper limit of the numerical range of the first negative minor level is smaller than the lower limit of the numerical range of the first zero level, the upper limit of the numerical range of the first zero level is smaller than the lower limit of the numerical range of the first positive minor level, and the upper limit of the numerical range of the first positive minor level is smaller than the lower limit of the numerical range of the first positive major level.

[0017] In conjunction with the second aspect, in one embodiment, the plurality of speed levels include a slow level, a very slow level, a fast level, a very fast level, and a very fast level;

[0018] The upper limit of the numerical range of the slow level is less than the lower limit of the numerical range of the very slow level, the upper limit of the numerical range of the very slow level is less than the lower limit of the numerical range of the fast level, the upper limit of the numerical range of the fast level is less than the lower limit of the numerical range of the very fast level, and the upper limit of the numerical range of the very fast level is less than the lower limit of the numerical range of the very fast level.

[0019] In conjunction with the second aspect, in one embodiment, the plurality of angular velocity levels include a second negative large level, a second negative small level, a second zero level, a second positive small level, and a second positive large level;

[0020] The upper limit of the numerical range of the second negative major level is smaller than the lower limit of the numerical range of the second negative minor level, the upper limit of the numerical range of the second negative minor level is smaller than the lower limit of the numerical range of the second zero level, the upper limit of the numerical range of the second zero level is smaller than the lower limit of the numerical range of the second positive minor level, and the upper limit of the numerical range of the second positive minor level is smaller than the lower limit of the numerical range of the second positive major level.

[0021] In conjunction with the second aspect, in one embodiment, the rear axle axis center distance difference, yaw angle difference, rear axle axis center speed, and yaw angle angular velocity are all based on a geodetic coordinate system;

[0022] When the vehicle's driving route is along the positive direction of the X-axis, if the vehicle's real-time position at the current moment is to the left of the vehicle's target position at the next moment, the yaw angle difference and the yaw angle velocity are both positive.

[0023] In conjunction with the second aspect, in one embodiment, the performing vehicle trajectory tracking according to the vehicle trajectory tracking model specifically includes the following steps:

[0024] Collect the current vehicle real-time location information and the next vehicle target location information;

[0025] Calculate the rear axle center distance difference and yaw angle difference at the next moment relative to the current moment based on the vehicle's real-time position information and the vehicle's target position information;

[0026] The distance difference level corresponding to the rear axle center distance difference and the angle difference level corresponding to the yaw angle difference are input into the vehicle trajectory tracking model to obtain the speed level and angular velocity level;

[0027] Determine whether the current speed level and angular velocity level are the same as the speed level and angular velocity level obtained according to the model. If so, continue to control the vehicle according to the current rear axle axis speed and yaw angular velocity. If not, switch the current rear axle axis speed and / or yaw angular velocity to the speed level and / or angular velocity level obtained according to the model and continue to control the vehicle.

[0028] In a second aspect, an embodiment of the present application provides a vehicle trajectory tracking system, the vehicle trajectory tracking system comprising:

[0029] a data acquisition module for collecting multiple sets of historical vehicle driving records, each set of historical vehicle driving records including vehicle historical inputs and vehicle historical outputs, wherein the vehicle historical inputs are the rear axle axis center distance difference and the yaw angle difference between two adjacent moments, and the vehicle historical outputs are the rear axle axis center velocity and the yaw angle velocity between two adjacent moments;

[0030] a data processing module configured to classify rear axle axis distance difference, yaw angle difference, rear axle axis speed, and yaw angle angular velocity in all historical vehicle driving records into multiple levels according to numerical values, thereby obtaining multiple distance difference levels, angle difference levels, speed levels, and angular velocity levels;

[0031] The tracking control module is used to perform model training with the distance difference level and the angle difference level as input and the speed level and the angular velocity level as output to obtain a vehicle trajectory tracking model for vehicle trajectory tracking.

[0032] In a third aspect, an embodiment of the present application provides a vehicle trajectory tracking device, which includes a processor, a memory, and a vehicle trajectory tracking program stored in the memory and executable by the processor, wherein when the vehicle trajectory tracking program is executed by the processor, the steps of the vehicle trajectory tracking method are implemented.

[0033] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a vehicle trajectory tracking program is stored, wherein when the vehicle trajectory tracking program is executed by a processor, the steps of the vehicle trajectory tracking method are implemented.

[0034] The beneficial effects of the technical solutions provided in the embodiments of the present application include:

[0035] When controlling the rear axle axis speed and yaw angle velocity based on the rear axle axis distance difference and yaw angle difference, each data is divided into levels and the control model is trained to achieve fuzzy control, avoid frequent changes in control quantity due to frequent changes in state quantity, and improve driving safety and comfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A schematic diagram of establishing a vehicle kinematic model based on active front wheel steering in this application;

[0037] Figure 2 This is a flow chart of an embodiment of a vehicle trajectory tracking method of the present application;

[0038] Figure 3This is a schematic diagram of the functional modules of an embodiment of the vehicle trajectory tracking system of the present application;

[0039] Figure 4 This is a schematic diagram of the hardware structure of the vehicle trajectory tracking device involved in the embodiment of the present application. DETAILED DESCRIPTION

[0040] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe 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 the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0041] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0042] In a second aspect, an embodiment of the present application provides a vehicle trajectory tracking method.

[0043] In one embodiment, referring to Figure 2 , Figure 2 This is a flow chart of the second embodiment of the vehicle trajectory tracking method of this application. Figure 2 As shown, the vehicle trajectory tracking method includes:

[0044] Step S1: collecting multiple sets of vehicle historical driving records, each set of vehicle historical driving records includes vehicle historical input and vehicle historical output, the vehicle historical input is the rear axle axis center distance difference and yaw angle difference between two adjacent moments, and the vehicle historical output is the rear axle axis center velocity and yaw angle velocity between two adjacent moments;

[0045] Step S2: According to the numerical classification strategy, the rear axle axis distance difference, yaw angle difference, rear axle axis speed, and yaw angle angular velocity in all historical vehicle driving records are classified into multiple levels according to their numerical values, thereby obtaining multiple distance difference levels, angle difference levels, speed levels, and angular velocity levels;

[0046] Step S3: Using the distance difference level and the angle difference level as input quantities and the speed level and the angular velocity level as output quantities to perform model training, thereby obtaining a vehicle trajectory tracking model for vehicle trajectory tracking.

[0047] In this embodiment, considering that the vehicle is in a low-speed driving condition, it is not necessary to consider too much about dynamic issues such as vehicle stability control, so the following can be established: Figure 1The vehicle kinematic model is established based on the active steering of the vehicle front wheels. The x-axis is the horizontal coordinate of the geodetic coordinate system, and the y-axis is the vertical coordinate of the geodetic coordinate system. Figure 1 The vehicle's driving path is along the positive direction of the X axis. Where O is the coordinate origin, φ is the vehicle's yaw angle, is the front wheel deflection angle of the vehicle, and The points N and M are the axis centers of the front and rear axles of the vehicle, respectively. The kinematic constraints of the front and rear axles of the vehicle are as follows:

[0048] (1)

[0049] The calculation formula for the speed at the center of the vehicle's rear axle is the following formula (2):

[0050] (2)

[0051] The geometric relationship between the front and rear wheels is shown in the following formula (3):

[0052] (3)

[0053] The calculation formula of the vehicle's yaw rate is as follows (4):

[0054] (4)

[0055] Combining the above formulas (1) to (4), the kinematic model of the vehicle can be transformed into the following formula (5):

[0056]

[0057] Through this model, it can be seen that by adjusting the vehicle rear axle axis speed By controlling the yaw angle and angular velocity w, the vehicle can move in different positions in the geodetic coordinate system.

[0058] Assume that there is a planned trajectory in the geodetic coordinate system, and the coordinates of the vehicle at the current moment are point E ( , ), the target position coordinates at the next moment are point F ( , ), the yaw angle difference between the current angle and the ideal angle is , the distance between the rear axle center and the rear axle center at the two moments is d, so in the design of the fuzzy controller, and d are the input of the controller, and the rear axle speed and yaw angular velocity w are used as the output of the controller.

[0059] Preferably, the multiple distance difference levels include no difference, relatively close, very close, far, and very far. Accordingly, all rear axle axis distance differences d are divided into five fuzzy sets: no difference (ZO), relatively close (BJJ), very close (HJ), far (Y), and very far (HY). The membership function of each variable is defined using the above fuzzification.

[0060] The upper limit of the numerical range of the no-difference level is smaller than the lower limit of the numerical range of the relatively close level, the upper limit of the numerical range of the relatively close level is smaller than the lower limit of the numerical range of the very close level, the upper limit of the numerical range of the very close level is smaller than the lower limit of the numerical range of the far level, and the upper limit of the numerical range of the far level is smaller than the lower limit of the numerical range of the very far level.

[0061] Preferably, the multiple angle difference levels include a first negative large level, a first negative small level, a first zero level, a first positive small level, and a first positive large level. Accordingly, all yaw angle differences are also divided into five fuzzy sets: first negative large (NB), first negative small (NS), first zero (ZO), first positive small (PS), and first positive large (PB). The membership function of each variable is defined using this fuzzification.

[0062] The upper limit of the numerical range of the first negative major level is smaller than the lower limit of the numerical range of the first negative minor level, the upper limit of the numerical range of the first negative minor level is smaller than the lower limit of the numerical range of the first zero level, the upper limit of the numerical range of the first zero level is smaller than the lower limit of the numerical range of the first positive minor level, and the upper limit of the numerical range of the first positive minor level is smaller than the lower limit of the numerical range of the first positive major level.

[0063] Preferably, the multiple speed levels include slow, very slow, fast, very fast, and very fast. Accordingly, all rear axle center speeds are divided into five fuzzy sets: slow (M), very slow (HM), fast (K), very fast (HK), and very fast (FCK). The fuzzification described above is used to define the membership function of each variable.

[0064] The upper limit of the numerical range of the slow level is less than the lower limit of the numerical range of the very slow level, the upper limit of the numerical range of the very slow level is less than the lower limit of the numerical range of the fast level, the upper limit of the numerical range of the fast level is less than the lower limit of the numerical range of the very fast level, and the upper limit of the numerical range of the very fast level is less than the lower limit of the numerical range of the very fast level.

[0065] Preferably, the multiple angular velocity levels include a second negative large level, a second negative small level, a second zero level, a second positive small level, and a second positive large level. Accordingly, all yaw angular velocities are also divided into five fuzzy sets: second negative large (NB'), second negative small (NS'), second zero (ZO'), second positive small (PS'), and second positive large (PB'). The membership functions of each variable are defined using this fuzzification.

[0066] The upper limit of the numerical range of the second negative major level is smaller than the lower limit of the numerical range of the second negative minor level, the upper limit of the numerical range of the second negative minor level is smaller than the lower limit of the numerical range of the second zero level, the upper limit of the numerical range of the second zero level is smaller than the lower limit of the numerical range of the second positive minor level, and the upper limit of the numerical range of the second positive minor level is smaller than the lower limit of the numerical range of the second positive major level.

[0067] Furthermore, in one embodiment, the rear axle axis center distance difference, yaw angle difference, rear axle axis center speed, and yaw angle speed are all based on a geodetic coordinate system;

[0068] When the vehicle's driving route is along the positive direction of the X-axis, if the vehicle's real-time position at the current moment is to the left of the vehicle's target position at the next moment, the yaw angle difference and the yaw angle velocity are both positive.

[0069] The vehicle trajectory tracking is performed according to the vehicle trajectory tracking model, and specifically includes the following steps:

[0070] Collect the current vehicle real-time location information and the next vehicle target location information;

[0071] Calculate the rear axle center distance difference and yaw angle difference at the next moment relative to the current moment based on the vehicle's real-time position information and the vehicle's target position information;

[0072] The distance difference level corresponding to the rear axle center distance difference and the angle difference level corresponding to the yaw angle difference are input into the vehicle trajectory tracking model to obtain the speed level and angular velocity level;

[0073] Determine whether the current speed level and angular velocity level are the same as the speed level and angular velocity level obtained according to the model. If so, continue to control the vehicle according to the current rear axle axis speed and yaw angular velocity. If not, switch the current rear axle axis speed and / or yaw angular velocity to the speed level and / or angular velocity level obtained according to the model and continue to control the vehicle.

[0074] In this embodiment, the rear axle axis distance difference and yaw angle difference obtained based on the actual collected vehicle real-time position information and vehicle target position information are divided into different levels to achieve input fuzzification. After the level rather than the specific value is input into the model, the model makes a fuzzy decision and the output is also a level. If the value of any one of the rear axle axis speed and yaw angle angular velocity at the current moment is not within the level range of the model output, the control target is to adjust it to within the level range of the model output. If both are within the level range of the model output, the rear axle axis speed and yaw angle angular velocity at the current moment can be maintained.

[0075] In a specific embodiment, the input and output are divided into 5 subsets, so 25 fuzzy control rules as shown in Table 1 below can be formulated.

[0076] Table 1 25 fuzzy control rules

[0077]

[0078] When the input d and The fluctuation is small, that is, the value after the change and the value before the change are still within the same level range. Then, when the target output and the current output take several levels as input and output, the model calculation amount is reduced, the control stability is improved, and frequent changes in output caused by frequent small changes in input are avoided, thereby achieving precise adjustment of the steering angle and speed, ensuring that the vehicle travels stably along the planned path, while balancing the calculation speed and the stability of the tracking process, ensuring the vehicle's driving safety and ride comfort.

[0079] In a second aspect, an embodiment of the present application also provides a vehicle trajectory tracking system.

[0080] In one embodiment, referring to Figure 3 , Figure 3 This is a functional module diagram of an embodiment of the vehicle trajectory tracking system of this application. Figure 3 As shown, the vehicle trajectory tracking device includes:

[0081] A data acquisition module 1 is configured to collect multiple sets of historical vehicle driving records, each set of historical vehicle driving records including a historical vehicle input and a historical vehicle output. The historical vehicle input is the rear axle axis center distance difference and the yaw angle difference between two adjacent moments, and the historical vehicle output is the rear axle axis center velocity and the yaw angle velocity between two adjacent moments.

[0082] Data processing module 2 is configured to classify the rear axle axis distance difference, yaw angle difference, rear axle axis speed, and yaw angle angular velocity in all historical vehicle driving records into multiple levels according to the numerical value, thereby obtaining multiple distance difference levels, angle difference levels, speed levels, and angular velocity levels;

[0083] The tracking control module 3 is used to perform model training with the distance difference level and the angle difference level as input and the speed level and the angular velocity level as output to obtain a vehicle trajectory tracking model for vehicle trajectory tracking.

[0084] In this embodiment, when controlling the rear axle axis speed and yaw angle velocity based on the rear axle axis distance difference and yaw angle difference, each data is divided into levels and the control model is trained to realize fuzzy control, avoid frequent changes in control quantity caused by frequent changes in state quantity, and improve driving safety and comfort.

[0085] Among them, the functional implementation of each module in the above-mentioned vehicle trajectory tracking system corresponds to each step in the above-mentioned vehicle trajectory tracking method embodiment, and its functions and implementation processes are not repeated here one by one.

[0086] In a third aspect, an embodiment of the present application provides a vehicle trajectory tracking device, which may be a device with data processing capabilities, such as a personal computer (PC), a laptop computer, or a server.

[0087] Reference Figure 4 , Figure 4 FIG1 is a schematic diagram of the hardware structure of the vehicle trajectory tracking device involved in the embodiment of the present application. In the embodiment of the present application, the vehicle trajectory tracking device may include a processor, a memory, a communication interface, and a communication bus.

[0088] The communication bus may be of any type and is used to interconnect the processor, memory, and communication interface.

[0089] Communication interfaces include input / output (I / O), physical, and logical interfaces, which interconnect components within the vehicle tracking device and connect it to other devices (such as other computing devices or user devices). Physical interfaces can include Ethernet, fiber, and ATM interfaces; user devices can include displays and keyboards.

[0090] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0091] The processor may be a general-purpose processor that can invoke a vehicle trajectory tracking program stored in a memory and execute the vehicle trajectory tracking method provided in the embodiments of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The methods executed when the vehicle trajectory tracking program is invoked can be referenced to the various embodiments of the vehicle trajectory tracking method of the present application and will not be further described here.

[0092] Those skilled in the art will understand that Figure 4 The hardware structure shown in the figure does not constitute a limitation to the present application and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0093] In a fourth aspect, an embodiment of the present application also provides a computer-readable storage medium.

[0094] The computer-readable storage medium of the present application stores a vehicle trajectory tracking program, wherein when the vehicle trajectory tracking program is executed by a processor, the steps of the vehicle trajectory tracking method as described above are implemented.

[0095] Among them, the method implemented when the vehicle trajectory tracking program is executed can refer to the various embodiments of the vehicle trajectory tracking method of this application, and will not be repeated here.

[0096] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0097] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "second", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit "second", "second" and "third" to different types.

[0098] In the description of the embodiments of this application, the words "exemplary," "for example," or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary," "for example," or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "for example," or "for example" is intended to present the relevant concepts in a concrete manner.

[0099] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0100] In some processes described in the embodiments of the present application, multiple operations or steps are included that appear in a specific order. However, it should be understood that these operations or steps may not be performed in the order in which they appear in the embodiments of the present application or may be performed in parallel. The sequence numbers of the operations are only used to distinguish between different operations, and the sequence numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations or steps may be performed in sequence or in parallel, and these operations or steps may be combined.

[0101] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, or the part that contributes to the existing technology, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above and includes a number of instructions for enabling a terminal device to execute the methods described in each embodiment of this application.

[0102] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the description and drawings of this application, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A vehicle trajectory tracking method, characterized in that: The method comprises: Collecting multiple sets of vehicle historical driving records, each set of vehicle historical driving records includes vehicle historical input and vehicle historical output, the vehicle historical input is the rear axle axis center distance difference and yaw angle difference between two adjacent moments, and the vehicle historical output is the rear axle axis center speed and yaw angle speed between two adjacent moments; According to the numerical level classification strategy, the rear axle axis distance difference, yaw angle difference, rear axle axis speed, and yaw angle angular velocity in all vehicle historical driving records are divided into multiple levels according to the numerical value, thereby obtaining multiple distance difference levels, angle difference levels, speed levels, and angular velocity levels; The vehicle trajectory tracking model is obtained by using the distance difference level and the angle difference level as input quantities and the speed level and the angular velocity level as output quantities to perform model training, so as to perform vehicle trajectory tracking. The vehicle trajectory tracking according to the vehicle trajectory tracking model specifically includes the following steps: Collect the current vehicle real-time location information and the next vehicle target location information; Calculate the rear axle center distance difference and yaw angle difference at the next moment relative to the current moment based on the vehicle's real-time position information and the vehicle's target position information; The distance difference level corresponding to the rear axle center distance difference and the angle difference level corresponding to the yaw angle difference are input into the vehicle trajectory tracking model to obtain the speed level and angular velocity level; Determine whether the current speed level and angular velocity level are the same as the speed level and angular velocity level obtained according to the model. If so, continue to control the vehicle according to the current rear axle axis speed and yaw angular velocity. If not, switch the current rear axle axis speed and / or yaw angular velocity to the speed level and / or angular velocity level obtained according to the model and continue to control the vehicle.

2. The vehicle trajectory tracking method according to claim 1, wherein: The plurality of distance difference levels include no difference, relatively close, very close, far, and very far; The upper limit of the numerical range of the no-difference level is smaller than the lower limit of the numerical range of the relatively close level, the upper limit of the numerical range of the relatively close level is smaller than the lower limit of the numerical range of the very close level, the upper limit of the numerical range of the very close level is smaller than the lower limit of the numerical range of the far level, and the upper limit of the numerical range of the far level is smaller than the lower limit of the numerical range of the very far level.

3. The vehicle trajectory tracking method according to claim 1, wherein: The plurality of angle difference levels include a first negative major level, a first negative minor level, a first zero level, a first positive minor level, and a first positive major level; The upper limit of the numerical range of the first negative major level is smaller than the lower limit of the numerical range of the first negative minor level, the upper limit of the numerical range of the first negative minor level is smaller than the lower limit of the numerical range of the first zero level, the upper limit of the numerical range of the first zero level is smaller than the lower limit of the numerical range of the first positive minor level, and the upper limit of the numerical range of the first positive minor level is smaller than the lower limit of the numerical range of the first positive major level.

4. The vehicle trajectory tracking method according to claim 1, wherein: The plurality of speed levels include slow, very slow, fast, very fast, and very fast; The upper limit of the numerical range of the slow level is less than the lower limit of the numerical range of the very slow level, the upper limit of the numerical range of the very slow level is less than the lower limit of the numerical range of the fast level, the upper limit of the numerical range of the fast level is less than the lower limit of the numerical range of the very fast level, and the upper limit of the numerical range of the very fast level is less than the lower limit of the numerical range of the very fast level.

5. The vehicle trajectory tracking method according to claim 1, wherein: The plurality of angular velocity levels include a second negative major level, a second negative minor level, a second zero level, a second positive minor level, and a second positive major level; The upper limit of the numerical range of the second negative major level is smaller than the lower limit of the numerical range of the second negative minor level, the upper limit of the numerical range of the second negative minor level is smaller than the lower limit of the numerical range of the second zero level, the upper limit of the numerical range of the second zero level is smaller than the lower limit of the numerical range of the second positive minor level, and the upper limit of the numerical range of the second positive minor level is smaller than the lower limit of the numerical range of the second positive major level.

6. The vehicle trajectory tracking method according to claim 1, wherein: The rear axle axis center distance difference, yaw angle difference, rear axle axis center speed, and yaw angle speed are all based on the geodetic coordinate system; When the vehicle's driving route is along the positive direction of the X-axis, if the vehicle's real-time position at the current moment is to the left of the vehicle's target position at the next moment, the yaw angle difference and the yaw angle velocity are both positive.

7. A vehicle trajectory tracking system, characterized in that: The vehicle trajectory tracking system comprises: a data acquisition module for collecting multiple sets of historical vehicle driving records, each set of historical vehicle driving records including vehicle historical inputs and vehicle historical outputs, wherein the vehicle historical inputs are the rear axle axis center distance difference and the yaw angle difference between two adjacent moments, and the vehicle historical outputs are the rear axle axis center velocity and the yaw angle velocity between two adjacent moments; a data processing module configured to classify rear axle axis distance difference, yaw angle difference, rear axle axis speed, and yaw angle angular velocity in all historical vehicle driving records into multiple levels according to numerical values, thereby obtaining multiple distance difference levels, angle difference levels, speed levels, and angular velocity levels; The tracking control module is configured to perform model training using the distance difference level and the angle difference level as input quantities and the speed level and the angular velocity level as output quantities to obtain a vehicle trajectory tracking model for vehicle trajectory tracking. The vehicle trajectory tracking according to the vehicle trajectory tracking model specifically includes collecting the vehicle's real-time position information at a current moment and the vehicle's target position information at a next moment; calculating based on the vehicle's real-time position information and the vehicle's target position information to obtain the rear axle axis distance difference and the yaw angle difference at the next moment relative to the current moment; inputting the distance difference level corresponding to the rear axle axis distance difference and the angle difference level corresponding to the yaw angle difference as input quantities into the vehicle trajectory tracking model to obtain the speed level and the angular velocity level; and determining whether the speed level and angular velocity level at the current moment are the same as the speed level and angular velocity level obtained according to the model. If so, the vehicle is continued to be controlled according to the rear axle axis speed and yaw angle velocity at the current moment; if not, the rear axle axis speed and / or yaw angle velocity at the current moment are switched to the speed level and / or angular velocity level obtained according to the model and the vehicle is continued to be controlled.

8. A vehicle trajectory tracking device, characterized in that: The vehicle trajectory tracking device includes a processor, a memory, and a vehicle trajectory tracking program stored in the memory and executable by the processor, wherein when the vehicle trajectory tracking program is executed by the processor, the steps of the vehicle trajectory tracking method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a vehicle trajectory tracking program, wherein when the vehicle trajectory tracking program is executed by a processor, the steps of the vehicle trajectory tracking method according to any one of claims 1 to 6 are implemented.

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