Turning trajectory prediction method, device, equipment, medium and product

Abstract

The invention discloses a turning track prediction method, device and equipment, a medium and a product. The method comprises the steps of obtaining a current driving mode and current state information of a vehicle; querying a database according to the current driving mode of the vehicle to obtain a target turning displacement model and a target turning speed model corresponding to the current driving mode of the vehicle; the target turning track is determined according to the current state information of the vehicle, the target turning displacement model and the target turning speed model, through the technical scheme, the turning track meeting the driving habit can be generated, and the driving experience of a driver is improved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of vehicle technology, and in particular to a turning trajectory prediction method, device, equipment, medium and product. Background Art

[0002] After years of development in the current automotive industry, self-driving cars are still not widely accepted by the public, and consumers are unwilling to pay for self-driving functions. The main reason is that the driving behavior of self-driving systems is very different from that of humans, and research in this area is still not sufficient.

[0003] At present, the research and application of autonomous driving mostly tend to be global->local. First, a global route is planned, and after finding an optimal route, the global route is split into multiple small driving plans. For example, to get from point A to point B, a global route from A to B is first planned, and then the route is split into several parts. Each part is called a driving plan, and each plan is executed sequentially to complete the entire driving route.

[0004] However, current autonomous driving lacks consideration of the driving habits of different drivers when predicting the trajectory corresponding to each driving plan, which in turn causes obvious psychological discomfort to the drivers. Summary of the invention

[0005] Embodiments of the present invention provide a turning trajectory prediction method, device, equipment, medium and product that at least partially solve the above-mentioned problems, and can generate a turning trajectory that conforms to driving habits and improve the driver's driving experience.

[0006] According to one aspect of the present invention, a turning trajectory prediction method is provided, comprising:

[0007] Get the vehicle's current driving mode and current status information;

[0008] Querying a database according to the current driving mode of the vehicle to obtain a target turning displacement model and a target turning speed model corresponding to the current driving mode of the vehicle;

[0009] A target turning trajectory is determined according to the current state information of the vehicle, the target turning displacement model, and the target turning speed model.

[0010] According to another aspect of the present invention, a turning trajectory prediction device is provided, the turning trajectory prediction device comprising:

[0011] An acquisition module, used to acquire the current driving mode and current state information of the vehicle;

[0012] A target turning displacement model and a target turning speed model determination module, used to query a database according to the current driving mode of the vehicle to obtain a target turning displacement model and a target turning speed model corresponding to the current driving mode of the vehicle;

[0013] The target turning trajectory determination module is used to determine the target turning trajectory according to the current state information of the vehicle, the target turning displacement model and the target turning speed model.

[0014] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0015] at least one processor; and

[0016] a memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the turning trajectory prediction method described in any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the turning trajectory prediction method described in any embodiment of the present invention when executed.

[0019] According to another aspect of the present invention, a computer program product is provided. When the computer program is executed by a processor, the computer program implements the turning trajectory prediction method as described in any one of the embodiments of the present invention.

[0020] The embodiment of the present invention obtains the target turning displacement model and the target turning speed model corresponding to the current driving mode of the vehicle by querying the database, and determines the target turning trajectory according to the current state information of the vehicle, the target turning displacement model and the target turning speed model. It is possible to generate a turning trajectory that conforms to driving habits and improve the driving experience of the driver.

[0021] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 is a flow chart of a turning trajectory prediction method in an embodiment of the present invention;

[0024] Figure 2 is a schematic diagram of a driving trajectory of a turning condition in an embodiment of the present invention;

[0025] Figure 3 is a schematic diagram of speed change in a turning condition in an embodiment of the present invention;

[0026] Figure 4 is a schematic structural diagram of a turning trajectory prediction device in an embodiment of the present invention;

[0027] Figure 5 It is a structural schematic diagram of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first", "second", etc. in the specification 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 sequence. It should be understood that the data used in this way can be interchanged 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, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0031] Embodiment 1

[0032] Figure 1 Flow chart of a turning trajectory prediction method provided by an embodiment of the present invention. This embodiment is applicable to the case of turning trajectory prediction. The method can be executed by a turning trajectory prediction device in an embodiment of the present invention. The device can be implemented in software and / or hardware. Figure 1 As shown, the method specifically comprises the following steps:

[0033] S110, obtaining the current driving mode and current status information of the vehicle.

[0034] The current driving mode of the vehicle may be any one of an aggressive mode, a normal mode and a conservative mode. The current state information may include the current position and driving direction of the vehicle, and the current state information may also include the lateral acceleration of the vehicle. The embodiment of the present invention does not limit this.

[0035] Specifically, the current driving mode and current state information of the vehicle can be obtained by: obtaining the current driving mode set by the user, and if the user has not modified the driving mode, determining that the current driving mode is a pre-set normal mode. The current state information of the vehicle is collected by sensors deployed inside the vehicle (for example, the vehicle position can be obtained through GPS).

[0036] S120, querying a database according to the current driving mode of the vehicle to obtain a target turning displacement model and a target turning speed model corresponding to the current driving mode of the vehicle.

[0037] The database pre-stores a plurality of turning displacement models and turning speed models corresponding to the driving modes.

[0038] The target turning displacement model corresponding to the current driving mode may be:

[0039]

[0040] Among them, k1, k2, k3, k4, k5, m1, m2, m3, m4, m5, m6 and m7 are all parameters corresponding to the current driving mode. k1, k2, k3, k4, k5, m1, m2, m3, m4, m5, m6 and m7 are all known quantities.

[0041] The target turning speed model can be:

[0042]

[0043] Among them, v1 is the speed at the starting point of the first arc, v3 is the speed at the end point of the third arc, a1 is the acceleration of the first arc segment corresponding to the current driving mode, and a2 is the acceleration of the third arc segment corresponding to the current driving mode.

[0044] Optionally, before querying the database according to the current driving mode of the vehicle to obtain the target turning displacement model and the target turning speed model corresponding to the current driving mode of the vehicle, the method further includes:

[0045] Get historical driving data;

[0046] Determining turning displacement models and turning speed models corresponding to a plurality of driving modes according to the historical driving data, the initial turning displacement model, and the initial turning speed model;

[0047] A database is generated based on the turning displacement models and turning speed models corresponding to various driving modes.

[0048] The historical driving data may be pre-collected driving data of different drivers under turning conditions. The historical driving data includes: historical driving data corresponding to a plurality of turning conditions, and the historical driving data corresponding to each turning condition includes: three sections of historical driving data, namely: historical driving data corresponding to the first arc, historical driving data corresponding to the second arc, and historical driving data corresponding to the third arc, wherein the historical driving data corresponding to the first arc includes: the angle between the tangent of the first arc starting point and the x-axis, the angle between the tangent of the first arc end point and the x-axis, the radius of the first arc, the speed at the starting point of the first arc, the duration of the first arc, and the length of the first arc, the historical driving data corresponding to the second arc includes: the radius of the second arc, the speed of the second arc, and the duration of the second arc, and the historical driving data corresponding to the third arc includes: the angle between the tangent of the third arc starting point and the x-axis, the angle between the tangent of the third arc end point and the x-axis, the radius of the third arc, the length of the third arc, the speed at the end point of the third arc, and the duration of the third arc.

[0049] Specifically, the method for obtaining historical driving data may be: collecting historical data of multiple vehicles, splitting the historical data to obtain multiple segments of data, screening the multiple segments of data to obtain multiple segments of turning driving data, and determining the multiple segments of turning driving data as historical driving data.

[0050] Specifically, the method for determining the turning displacement models and turning speed models corresponding to the multiple driving modes according to the historical driving data, the initial turning displacement model, and the initial turning speed model can be: screening the historical driving data to obtain multiple sections of turning driving data, and determining the turning displacement models and turning speed models corresponding to the multiple driving modes according to the multiple sections of turning driving data, the initial turning displacement model, and the initial turning speed model.

[0051] Optionally, determining turning displacement models and turning speed models corresponding to a plurality of driving modes according to the historical driving data, the initial turning displacement model, and the initial turning speed model includes:

[0052] Screening the historical driving data to obtain at least one section of turning driving data, wherein the turning driving data includes: turning speed and turning radius;

[0053] Determining a driving mode corresponding to each section of turning driving data according to the turning speed and the turning radius;

[0054] Determine a turning displacement model and a turning speed model corresponding to each section of turning driving data according to each section of turning driving data, an initial turning displacement model and an initial turning speed model;

[0055] According to the driving mode corresponding to each section of turning driving data, and the turning displacement model and turning speed model corresponding to each section of turning driving data, the turning displacement models and turning speed models corresponding to the plurality of driving modes are determined.

[0056] Specifically, the historical driving data may be screened to obtain at least one section of turning driving data by splitting the historical driving data to obtain multiple sections of data, and screening the multiple sections of data to obtain multiple sections of turning driving data.

[0057] Specifically, the method of determining the driving mode corresponding to each section of turning driving data according to the turning speed and the turning radius can be: inputting the turning speed and the turning radius into a driving mode determination model to obtain the driving mode corresponding to each section of turning driving data, wherein the driving mode determination model is a pre-trained neural network model.

[0058] Specifically, according to each section of turning driving data, the initial turning displacement model and the initial turning speed model, a method for determining the turning displacement model and the turning speed model corresponding to each section of turning driving data may be: fitting the initial turning displacement model and the initial turning speed model based on the least squares method and each section of turning driving data to obtain a target parameter corresponding to each section of turning driving data; and determining the turning displacement model and the turning speed model corresponding to each section of turning driving data according to the target parameter, the initial turning displacement model and the initial turning speed model corresponding to each section of turning driving data.

[0059] Optionally, determining the driving mode corresponding to each section of turning driving data according to the turning speed and the turning radius includes:

[0060] The turning speed and turning radius in each section of turning driving data are input into a driving mode determination model to obtain a driving mode corresponding to each section of turning driving data, wherein the driving mode determination model is a pre-trained neural network model.

[0061] Among them, the training process of the driving mode determination model is:

[0062] Build a neural network model;

[0063] Acquire a target sample set, wherein the target sample set includes: a driving data sample and a driving mode corresponding to the driving data sample, and the driving data sample includes: a turning speed sample and a turning radius sample;

[0064] Input the driving data samples into the neural network model to obtain the predicted driving pattern;

[0065] Training parameters of the neural network model according to the difference between the predicted driving mode and the driving mode corresponding to the driving data sample;

[0066] The trained neural network model is determined as the driving mode determination model.

[0067] Optionally, determining a turning displacement model and a turning speed model corresponding to each section of turning driving data according to each section of turning driving data, an initial turning displacement model, and an initial turning speed model includes:

[0068] The initial turning displacement model and the initial turning speed model are fitted based on the least square method and each section of turning driving data to obtain the target parameters corresponding to each section of turning driving data;

[0069] According to the target parameters, the initial turning displacement model and the initial turning speed model corresponding to each section of turning driving data, a turning displacement model and a turning speed model corresponding to each section of turning driving data are determined.

[0070] Specifically, the initial turning displacement model and the initial turning speed model are fitted based on the least squares method and each section of turning driving data to obtain the target parameters corresponding to each section of turning driving data. The method may be: based on the fitting formula, the initial turning displacement model and the initial turning speed model are fitted based on the turning driving data of each section to obtain the target parameters corresponding to each section of turning driving data, wherein the fitting formula is: k=f(k0; θ1, θ2, θ3, θ4, v1, v2, v3, R1, R2, R3), k0 is the initial value, and the optimal target parameters are found through continuous iteration to achieve the optimal fitting degree.

[0071] Specifically, according to the target parameters corresponding to each section of turning driving data, the initial turning displacement model and the initial turning speed model, the turning displacement model and the turning speed model corresponding to each section of turning driving data may be determined in the following manner: the target parameters corresponding to each section of turning driving data include: the target parameters corresponding to the initial turning displacement model and the target parameters corresponding to the initial turning displacement model; the parameters in the initial turning displacement model are replaced with the target parameters corresponding to the initial turning displacement model; the parameters in the initial turning speed model are replaced with the target parameters corresponding to the initial turning speed model, and the turning displacement model and the turning speed model corresponding to each section of turning driving data are obtained.

[0072] Optionally, the turning driving data also includes: driving data corresponding to the first arc, driving data corresponding to the second arc and driving data corresponding to the third arc, wherein the driving data corresponding to the first arc includes: the angle between the tangent at the starting point of the first arc and the x-axis, the angle between the tangent at the end point of the first arc and the x-axis, the radius of the first arc, the speed at the starting point of the first arc, the duration of the first arc and the length of the first arc, the driving data corresponding to the second arc includes: the radius of the second arc, the speed of the second arc and the duration of the second arc, and the driving data corresponding to the third arc includes: the angle between the tangent at the starting point of the third arc and the x-axis, the angle between the tangent at the end point of the third arc and the x-axis, the radius of the third arc, the length of the third arc, the speed at the end point of the third arc and the duration of the third arc.

[0073] Optionally, the initial turning displacement model is:

[0074]

[0075] Among them, θ1 is the angle between the tangent of the first arc starting point and the x-axis, θ2 is the angle between the tangent of the first arc ending point and the x-axis, θ3 is the angle between the tangent of the third arc starting point and the x-axis, v2 is the velocity of the second arc, R1 is the radius of the first arc, R2 is the radius of the second arc, R3 is the radius of the third arc, t is the time point, t1 is the duration of the first arc, t2 is the duration of the second arc, t3 is the duration of the first arc, l1 is the length of the first arc, and a1 is the acceleration of the first arc.

[0076] Optionally, the initial turning speed model is:

[0077]

[0078]

[0079] Among them, v1 is the velocity at the starting point of the first arc, v3 is the velocity at the end point of the third arc, a2 is the acceleration of the third arc, and l3 is the length of the third arc.

[0080] S130: determining a target turning trajectory according to the current state information of the vehicle, the target turning displacement model, and the target turning speed model.

[0081] Specifically, the method for determining the target turning trajectory according to the current state information of the vehicle, the target turning displacement model, and the target turning speed model may be: if it is determined that the vehicle is in a turning state according to the current state information of the vehicle, then the target turning trajectory is determined according to the current state information of the vehicle, the target turning displacement model, and the target turning speed model. If the current state information of the vehicle includes: the current speed, current position, and current driving direction of the vehicle, then the method for determining the target turning trajectory according to the current state information of the vehicle, the target turning displacement model, and the target turning speed model may also be: determining the corresponding relationship between the turning position and time, and the corresponding relationship between the turning speed and time according to the target turning displacement model and the target turning speed model; determining the target turning trajectory according to the corresponding relationship between the turning position and time, the corresponding relationship between the turning speed and time, the current speed, current position, and current driving direction of the vehicle.

[0082] Optionally, determining a target turning trajectory according to the current state information of the vehicle, the target turning displacement model, and the target turning speed model includes:

[0083] If it is determined that the vehicle is in a turning state according to the current state information of the vehicle, a target turning trajectory is determined according to the current state information of the vehicle, the target turning displacement model, and the target turning speed model.

[0084] Among them, the turning state can be the process of the vehicle turning from straight driving, including but not limited to the vehicle avoiding obstacles and the vehicle turning into a curve. The current state information of the vehicle may include: the current position of the vehicle. By querying the map based on the current position of the vehicle, it can be determined whether the vehicle is in a turning state. The state information of the vehicle may also include: the lateral acceleration of the vehicle. According to the lateral acceleration of the vehicle, it can be determined whether the vehicle is in a turning state. For example, if the absolute value of the lateral acceleration of the vehicle gradually increases from 0, it is determined that the vehicle is in a turning state, and the embodiment of the present invention does not limit this.

[0085] In a specific example, since people's driving habits are mainly reflected in turning conditions, and the focus is on the speed change when entering a turn, the speed change during the turn, and the speed change in the speed adjustment stage after the turn. Since it is difficult to drive strictly in an absolute straight line in actual situations, the embodiment of the present invention mainly models the "arc + arc + arc" driving scheme for turning conditions, such as Figure 2 As shown, the first arc is the preparatory section for entering the turn, which is close to a straight line and can be regarded as an arc with a very large radius. The same is true for the third arc. The second arc is an ordinary turning section with a smaller radius.

[0086] like Figure 3 As shown in the figure, in order to meet driving comfort, the R1 segment is a uniform deceleration segment, the R2 segment is a uniform speed segment, and the R3 segment is a uniform acceleration segment. The speed of the car is the most important factor affecting the passengers' riding experience, and the mileage is related to the speed and time. Therefore, the initial turning displacement model uses a formula that combines the speed and time. The formula is as follows:

[0087]

[0088] Among them, θ1 is the angle between the tangent of the first arc starting point and the x-axis, θ2 is the angle between the tangent of the first arc ending point and the x-axis, θ3 is the angle between the tangent of the second arc starting point and the x-axis, θ4 is the angle between the tangent of the second arc ending point and the x-axis, v1 is the entrance velocity, v2 is the arc segment velocity, and v3 is the exit velocity. R1 is the radius of the first arc, R2 is the radius of the second arc, and R3 is the radius of the third arc.

[0089] Thus, the acceleration of the first arc segment can be obtained:

[0090]

[0091] The acceleration of the third arc segment:

[0092]

[0093] The radii of the three arcs are:

[0094] Construct the initial turning displacement model:

[0095]

[0096] Among them, θ1 is the angle between the tangent of the first arc starting point and the x-axis, θ2 is the angle between the tangent of the first arc ending point and the x-axis, θ3 is the angle between the tangent of the third arc starting point and the x-axis, v2 is the velocity of the second arc, R1 is the radius of the first arc, R2 is the radius of the second arc, R3 is the radius of the third arc, t is the time point, t1 is the duration of the first arc, t2 is the duration of the second arc, t3 is the duration of the first arc, l1 is the length of the first arc, and a1 is the acceleration of the first arc.

[0097] The initial turning speed model is:

[0098]

[0099] Among them, v1 is the velocity at the starting point of the first arc, v3 is the velocity at the end point of the third arc, a2 is the acceleration of the third arc, and l3 is the length of the third arc.

[0100] The above model can be used to determine the current driving strategy based on time, such as speed, acceleration, turning radius, etc.

[0101] It should be noted that the above model parameters are obtained by fitting the initial turning displacement model, the initial turning speed model and the historical driving data. Therefore, the driving habits of the driver in this driving process.

[0102] Since most of the parameters to be identified belong to a low-frequency change trend with a slow change speed, the nonlinear least squares method combined with MATLAB is used to express the initial turning displacement model and the initial turning speed model as a continuous abstract function to achieve the fitting of the driving data of any driver under the turning condition. And the characteristic parameters are fitted, including: arc radius and arc speed. According to the arc radius and arc speed, the unique solution of the driving trajectory and speed change law of drivers with different habits under this condition can be obtained, and the driving habits of the driver in this journey can be analyzed to a certain extent.

[0103] Drawing on the idea of ​​nonlinear least squares fitting, in the embodiment of the present invention, the fitting formula can be:

[0104] k=f(k0; θ1, θ2, θ3, θ4, v1, v2, v3, R1, R2, R3);

[0105] Among them, k0 is the initial value. Through continuous iteration, the optimal parameters are found to achieve the best fitting degree.

[0106] The initial value uses the first driving data of different people, or the expectation of the normal distribution of driving data of different groups of people as the initial value.

[0107] The quantities that can be directly fitted are: θ1, θ2, θ3, θ4, the lengths of the three arcs l1, l2, l3, the position coordinates of the entrance and exit (x1, y1), (x2, y2), the time t1, t2, t3 used to travel through the three arcs, the speed v2 of the second arc, the speed v1 at the starting point of the first arc, and the speed v3 at the end point of the third arc.

[0108] After obtaining more historical driving data for each person or each group, the driving habits of this person or this driver group are divided into three driving modes: aggressive, normal and conservative through the kmeans clustering method. Different driving modes of different people correspond to different parameters, thus realizing personalized parameter configuration.

[0109] The technical solution of this embodiment obtains the current driving mode and current state information of the vehicle; queries a database according to the current driving mode of the vehicle to obtain a target turning displacement model and a target turning speed model corresponding to the current driving mode of the vehicle; determines a target turning trajectory according to the current state information of the vehicle, the target turning displacement model and the target turning speed model, and can generate a turning trajectory that conforms to driving habits, thereby improving the driver's driving experience.

[0110] Embodiment 2

[0111] Figure 4 This is a schematic diagram of the structure of a turning trajectory prediction device provided by an embodiment of the present invention. This embodiment is applicable to the case of turning trajectory prediction. The device can be implemented in software and / or hardware. The device can be integrated in any device that provides a turning trajectory prediction function, such as Figure 4 As shown, the turning trajectory prediction device specifically includes: an acquisition module 410, a target turning displacement model and a target turning speed model determination module 420 and a target turning trajectory determination module 430.

[0112] The acquisition module is used to obtain the current driving mode and current state information of the vehicle;

[0113] A target turning displacement model and a target turning speed model determination module, used to query a database according to the current driving mode of the vehicle to obtain a target turning displacement model and a target turning speed model corresponding to the current driving mode of the vehicle;

[0114] The target turning trajectory determination module is used to determine the target turning trajectory according to the current state information of the vehicle, the target turning displacement model and the target turning speed model.

[0115] The above-mentioned product can execute the method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0116] Embodiment 3

[0117] Figure 5 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device 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 processing, 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 required herein.

[0118] like Figure 5 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and 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 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0119] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0120] The processor 11 may be a variety of general and / or special processing components 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 special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the turning trajectory prediction method.

[0121] In some embodiments, the turning trajectory prediction method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the turning trajectory prediction method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the turning trajectory prediction method in any other appropriate manner (e.g., by means of firmware).

[0122] Various implementations of the systems and techniques described above 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), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including 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.

[0123] 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, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0124] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0125] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: 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 a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0126] The systems and techniques described herein may be implemented in a computing system that includes backend 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 frontend components (e.g., a user computer with a graphical user interface or a 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 backend components, middleware components, or frontend components. The components of the system may 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.

[0127] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0128] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0129] An embodiment of the present invention further provides a computer program product, including a computer program, wherein when the computer program is executed by a processor, the turning trajectory prediction method according to any embodiment of the present invention is implemented.

[0130] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0131] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A turning trajectory prediction method, characterized in that: include: Get the vehicle's current driving mode and current status information; Querying a database according to the current driving mode of the vehicle to obtain a target turning displacement model and a target turning speed model corresponding to the current driving mode of the vehicle; A target turning trajectory is determined according to the current state information of the vehicle, the target turning displacement model, and the target turning speed model.

2. The method according to claim 1, characterized in that Before querying the database according to the current driving mode of the vehicle to obtain the target turning displacement model and the target turning speed model corresponding to the current driving mode of the vehicle, the method further includes: Get historical driving data; Determining turning displacement models and turning speed models corresponding to a plurality of driving modes according to the historical driving data, the initial turning displacement model, and the initial turning speed model; A database is generated based on the turning displacement models and turning speed models corresponding to various driving modes.

3. The method according to claim 2, characterized in that Determining turning displacement models and turning speed models corresponding to a plurality of driving modes according to the historical driving data, the initial turning displacement model, and the initial turning speed model, including: Screening the historical driving data to obtain at least one section of turning driving data, wherein the turning driving data includes: turning speed and turning radius; Determining a driving mode corresponding to each section of turning driving data according to the turning speed and the turning radius; Determine a turning displacement model and a turning speed model corresponding to each section of turning driving data according to each section of turning driving data, an initial turning displacement model and an initial turning speed model; According to the driving mode corresponding to each section of turning driving data, and the turning displacement model and the turning speed model corresponding to each section of turning driving data, the turning displacement models and the turning speed models corresponding to the plurality of driving modes are determined.

4. The method according to claim 3, characterized in that Determining a driving mode corresponding to each section of turning driving data according to the turning speed and the turning radius includes: The turning speed and turning radius in each section of turning driving data are input into a driving mode determination model to obtain a driving mode corresponding to each section of turning driving data, wherein the driving mode determination model is a pre-trained neural network model.

5. The method according to claim 3, characterized in that: According to each section of turning driving data, the initial turning displacement model and the initial turning speed model, a turning displacement model and a turning speed model corresponding to each section of turning driving data are determined, including: The initial turning displacement model and the initial turning speed model are fitted based on the least square method and each section of turning driving data to obtain the target parameters corresponding to each section of turning driving data; According to the target parameters, the initial turning displacement model and the initial turning speed model corresponding to each section of turning driving data, a turning displacement model and a turning speed model corresponding to each section of turning driving data are determined.

6. The method according to claim 3, characterized in that: The turning driving data also includes: driving data corresponding to the first arc, driving data corresponding to the second arc, and driving data corresponding to the third arc, wherein the driving data corresponding to the first arc includes: the angle between the tangent of the starting point of the first arc and the x-axis, the angle between the tangent of the end point of the first arc and the x-axis, the radius of the first arc, the speed at the starting point of the first arc, the duration of the first arc, and the length of the first arc; the driving data corresponding to the second arc includes: the radius of the second arc, the speed of the second arc, and the duration of the second arc; the driving data corresponding to the third arc includes: the angle between the tangent of the starting point of the third arc and the x-axis, the angle between the tangent of the end point of the third arc and the x-axis, the radius of the third arc, the length of the third arc, the speed at the end point of the third arc, and the duration of the third arc.

7. The method according to claim 6, characterized in that The initial turning displacement model is: Among them, θ1 is the angle between the tangent of the first arc starting point and the x-axis, θ2 is the angle between the tangent of the first arc ending point and the x-axis, θ3 is the angle between the tangent of the third arc starting point and the x-axis, v2 is the velocity of the second arc, R1 is the radius of the first arc, R2 is the radius of the second arc, R3 is the radius of the third arc, t is the time point, t1 is the duration of the first arc, t2 is the duration of the second arc, t3 is the duration of the first arc, l1 is the length of the first arc, and a1 is the acceleration of the first arc.

8. The method according to claim 7, characterized in that The initial turning speed model is: Among them, v1 is the velocity at the starting point of the first arc, v3 is the velocity at the end point of the third arc, a2 is the acceleration of the third arc, and l3 is the length of the third arc.

9. The method according to claim 1, characterized in that: Determining a target turning trajectory according to the current state information of the vehicle, the target turning displacement model, and the target turning speed model includes: If it is determined that the vehicle is in a turning state according to the current state information of the vehicle, a target turning trajectory is determined according to the current state information of the vehicle, the target turning displacement model, and the target turning speed model.

10. A turning trajectory prediction device, characterized in that: include: An acquisition module, used to acquire the current driving mode and current state information of the vehicle; A target turning displacement model and a target turning speed model determination module, used to query a database according to the current driving mode of the vehicle to obtain a target turning displacement model and a target turning speed model corresponding to the current driving mode of the vehicle; The target turning trajectory determination module is used to determine the target turning trajectory according to the current state information of the vehicle, the target turning displacement model and the target turning speed model.

11. 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, and the computer program is executed by the at least one processor so that the at least one processor can execute the turning trajectory prediction method according to any one of claims 1 to 9.

12. 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 turning trajectory prediction method according to any one of claims 1 to 9 when executed.

13. A computer program product, characterized in that The computer program product comprises a computer program, which, when executed by a processor, implements the turning trajectory prediction method according to any one of claims 1 to 9.

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