Vehicle trajectory prediction method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a vehicle track prediction method and device, electronic equipment and a storage medium. Acquiring the current speed and the current position of the vehicle at the current moment through a sensor; determining a state measurement matrix and a corresponding measurement covariance matrix of the vehicle according to the current speed and the current position; determining a state prediction matrix and a corresponding prediction covariance matrix at the current moment according to the duration coefficient, the correction coefficient, the historical state matrix at the previous moment and a corresponding historical covariance matrix; and according to the state measurement matrix and the corresponding measurement covariance matrix, and the state prediction matrix and the corresponding prediction covariance matrix, determining a state matrix of the vehicle at the next moment so as to predict a driving track of the vehicle. According to the embodiment of the invention, the portability of the vehicle trajectory prediction method is improved.

Description

Technical Field

[0001] The embodiments of the present application relate to intelligent driving technology, and in particular to a vehicle trajectory prediction method, device, electronic device and storage medium. Background Art

[0002] In order to ensure the safety of the vehicle and its passengers while the vehicle is driving, it is necessary to monitor and predict vehicle failures.

[0003] In the prior art, predictions are usually made based on the data from each dial to determine whether the entire vehicle has a fault.

[0004] However, with the development of hybrid vehicles, the vehicle system is becoming more and more complex. Predictions based on data from various dials cannot adapt to vehicle updates and have poor portability. Summary of the invention

[0005] The present application provides a vehicle trajectory prediction method, device, electronic device and storage medium to improve the portability of the vehicle trajectory prediction method.

[0006] In a first aspect, an embodiment of the present application provides a vehicle trajectory prediction method, the vehicle trajectory prediction method comprising:

[0007] The current speed and current position of the vehicle at the current moment are obtained through sensors;

[0008] Determine the vehicle's state measurement matrix and the corresponding measurement covariance matrix based on the current speed and current position;

[0009] Determine the state prediction matrix and the corresponding prediction covariance matrix at the current moment according to the duration coefficient and the correction coefficient, as well as the historical state matrix and the corresponding historical covariance matrix at the previous moment;

[0010] According to the state measurement matrix and the corresponding measurement covariance matrix, as well as the state prediction matrix and the corresponding prediction covariance matrix, the state matrix of the vehicle at the next moment is determined to predict the driving trajectory of the vehicle.

[0011] In a second aspect, an embodiment of the present application further provides a vehicle trajectory prediction device, the vehicle trajectory prediction device comprising:

[0012] The sensor data acquisition module is used to obtain the current speed and current position of the vehicle at the current moment through the sensor;

[0013] A state measurement matrix generation module is used to determine the state measurement matrix and the corresponding measurement covariance matrix of the vehicle according to the current speed and the current position;

[0014] A state prediction matrix determination module is used to determine the state prediction matrix and the corresponding prediction covariance matrix at the current moment according to the duration coefficient and the correction coefficient, as well as the historical state matrix and the corresponding historical covariance matrix at the previous moment;

[0015] The driving trajectory prediction module is used to determine the state matrix of the vehicle at the next moment according to the state measurement matrix and the corresponding measurement covariance matrix, as well as the state prediction matrix and the corresponding prediction covariance matrix, so as to predict the driving trajectory of the vehicle.

[0016] In a third aspect, an embodiment of the present application further provides an electronic device, the electronic device comprising:

[0017] one or more processors;

[0018] A storage device for storing one or more programs;

[0019] When one or more programs are executed by one or more processors, the one or more processors implement any vehicle trajectory prediction method provided in the embodiments of the present application.

[0020] In a fourth aspect, an embodiment of the present application further provides a storage medium comprising computer executable instructions, which, when executed by a computer processor, are used to execute any vehicle trajectory prediction method provided in an embodiment of the present application.

[0021] The present application obtains the current speed and current position of the vehicle at the current moment through sensors; determines the state measurement matrix and the corresponding measurement covariance matrix of the vehicle according to the current speed and current position; determines the state prediction matrix and the corresponding prediction covariance matrix at the current moment according to the duration coefficient and the correction coefficient, as well as the historical state matrix and the corresponding historical covariance matrix at the previous moment; determines the state matrix of the vehicle at the next moment according to the state measurement matrix and the corresponding measurement covariance matrix, as well as the state prediction matrix and the corresponding prediction covariance matrix, so as to predict the driving trajectory of the vehicle. Trajectory prediction is performed based on the speed parameters and position parameters of the vehicle, so as to improve the universality of the trajectory prediction method, be able to adapt to different vehicle systems, and have strong portability, and increase the correction coefficient to improve the accuracy of trajectory prediction. Therefore, the technical solution of the present application solves the problem that prediction through the data of each dial cannot adapt to the update of the vehicle and has poor portability, and achieves the effect of improving the portability of the vehicle trajectory prediction method. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a flow chart of a vehicle trajectory prediction method in Embodiment 1 of the present application;

[0023] Figure 2 is a flow chart of a vehicle trajectory prediction method in Embodiment 2 of the present application;

[0024] Figure 3 is a flow chart of a vehicle trajectory prediction method in Embodiment 3 of the present application;

[0025] Figure 4 It is a structural schematic diagram of a vehicle trajectory prediction device in Embodiment 4 of the present application;

[0026] Figure 5 It is a structural diagram of an electronic device in Embodiment 5 of the present application. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0028] It should be noted that the terms "first" and "second" etc. in the specification and claims of the present application 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 interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] Embodiment 1

[0030] Figure 1 This is a flowchart of a vehicle trajectory prediction method provided in Example 1 of the present application. This embodiment can be applied to situations where the driving trajectory of a vehicle is predicted. The method can be executed by a vehicle trajectory prediction device, which can be implemented using software and / or hardware and specifically configured in a vehicle, such as a hybrid vehicle.

[0031] See also Figure 1 The vehicle trajectory prediction method shown in the figure specifically includes the following steps:

[0032] S110. Obtain the current speed and current position of the vehicle at the current moment through sensors.

[0033] The sensor may include a speed sensor and a position sensor in the vehicle, and is used to obtain speed information and position information of the vehicle. The current speed may be the speed information obtained by the speed sensor in the vehicle at the current moment. The current position may be the position information obtained by the position sensor in the vehicle at the current moment.

[0034] S120. Determine a state measurement matrix and a corresponding measurement covariance matrix of the vehicle according to the current speed and the current position.

[0035] The state measurement matrix of the vehicle can be a matrix composed of the current speed and current position of the vehicle. Exemplarily, the state measurement matrix is ​​as follows:

[0036]

[0037] Among them, p is the current position, v is the current speed, is the state measurement matrix corresponding to the current time k.

[0038] The measurement covariance matrix may be a covariance matrix of a state measurement matrix. Exemplarily, the state measurement matrix is ​​as follows:

[0039]

[0040] P k is the measurement covariance matrix corresponding to the current moment k.

[0041] S130, determining the state prediction matrix and the corresponding prediction covariance matrix at the current moment according to the duration coefficient and the correction coefficient, as well as the historical state matrix and the corresponding historical covariance matrix at the previous moment.

[0042] The duration coefficient can be a coefficient matrix of the state prediction matrix determined by the historical state matrix according to the duration interval between the current moment and the previous moment, and is used to determine the state prediction matrix at the current moment according to the historical state matrix at the previous moment. Specifically: The relationship between the current position and the historical position at the previous moment is shown in the following formula:

[0043] p k =p k-1 +Δtv k-1 ;

[0044] Among them, p k is the current position, p k-1 is the historical position of the previous moment, Δt is the time interval between the current moment and the previous moment, k is the current moment, and k-1 is the moment before moment k;

[0045] Correspondingly, the relationship between the historical state matrix at the previous moment and the state prediction matrix is ​​shown as follows:

[0046]

[0047] Among them, F k is the duration coefficient.

[0048] The historical state matrix of the previous moment is multiplied by the duration coefficient to obtain the initial state prediction matrix of the current moment. According to the initial state prediction matrix, the covariance matrix of the initial state prediction matrix is ​​calculated to determine the corresponding initial prediction covariance matrix.

[0049] The correction coefficient may be a coefficient for correcting the initial state prediction matrix and the corresponding initial prediction covariance matrix obtained by the duration coefficient and the historical state matrix according to the current actual situation. Exemplarily, the correction coefficient may include a driver coefficient and a noise coefficient. By correcting the initial state prediction matrix and the corresponding initial prediction covariance matrix according to the correction coefficient, a state measurement matrix and a corresponding measurement covariance matrix may be obtained. For example, the initial state prediction matrix may be corrected by the driver coefficient, and the initial prediction covariance matrix may be corrected by the noise coefficient.

[0050] S140, determining the state matrix of the vehicle at the next moment according to the state measurement matrix and the corresponding measurement covariance matrix, as well as the state prediction matrix and the corresponding prediction covariance matrix, so as to predict the driving trajectory of the vehicle.

[0051] According to the state measurement matrix and the corresponding measurement covariance matrix, as well as the state prediction matrix and the corresponding prediction covariance matrix, the state matrix of the vehicle at the next moment can be determined through the Kalman filter algorithm. Specifically, the state measurement matrix and the corresponding measurement covariance matrix, as well as the state prediction matrix and the corresponding prediction covariance matrix are multiplied, and the state matrix of the vehicle at the next moment is obtained according to the Kalman gain to predict the driving trajectory of the vehicle. After obtaining the measurement value at the next moment, the above iterative process can be repeated to achieve subsequent predictions.

[0052] The technical solution of this embodiment obtains the current speed and current position of the vehicle at the current moment through sensors; determines the state measurement matrix and the corresponding measurement covariance matrix of the vehicle according to the current speed and current position; determines the state prediction matrix and the corresponding prediction covariance matrix at the current moment according to the duration coefficient and the correction coefficient, as well as the historical state matrix and the corresponding historical covariance matrix at the previous moment; determines the state matrix of the vehicle at the next moment according to the state measurement matrix and the corresponding measurement covariance matrix, as well as the state prediction matrix and the corresponding prediction covariance matrix, so as to predict the driving trajectory of the vehicle. Trajectory prediction is performed based on the speed parameters and position parameters of the vehicle, so as to improve the universality of the trajectory prediction method, be able to adapt to different vehicle systems, and have strong portability, and increase the correction coefficient to improve the accuracy of trajectory prediction. Therefore, the technical solution of this application solves the problem that prediction through the data of each dial cannot adapt to the update of the vehicle and has poor portability, and achieves the effect of improving the portability of the vehicle trajectory prediction method.

[0053] Embodiment 2

[0054] Figure 2 This is a flow chart of a vehicle trajectory prediction method provided in Example 2 of the present application. The technical solution of this embodiment is further refined on the basis of the above technical solution.

[0055] Furthermore, "determine the state prediction matrix and the corresponding prediction covariance matrix at the current moment according to the duration coefficient and the correction coefficient, as well as the historical state matrix and the corresponding historical covariance matrix at the previous moment" is refined as: "correct the historical state matrix and the corresponding historical covariance matrix at the previous moment according to the duration coefficient to obtain the initial state prediction matrix and the corresponding initial prediction covariance matrix; correct the initial state prediction matrix and the corresponding initial prediction covariance matrix according to the correction coefficient to obtain the state prediction matrix and the corresponding prediction covariance matrix at the current moment" to determine the state prediction matrix and the corresponding prediction covariance matrix at the current moment.

[0056] See also Figure 2 A vehicle trajectory prediction method shown includes:

[0057] S210. Obtain the current speed and current position of the vehicle at the current moment through sensors.

[0058] S220. Determine a state measurement matrix and a corresponding measurement covariance matrix of the vehicle according to the current speed and the current position.

[0059] S230, modifying the historical state matrix and the corresponding historical covariance matrix at the previous moment according to the duration coefficient to obtain an initial state prediction matrix and a corresponding initial prediction covariance matrix.

[0060] The initial state prediction matrix can be a matrix obtained by modifying the historical state matrix by the duration coefficient. The covariance of the initial state prediction matrix is ​​calculated to obtain the initial state prediction matrix and the corresponding initial prediction covariance matrix. For example, the initial state prediction matrix can be obtained by Get, where F k is the duration coefficient, is the historical state matrix at time k-1, that is, the historical state matrix at the previous moment. Correspondingly, the corresponding initial prediction covariance matrix is Among them, P k-1 is the initial prediction covariance matrix.

[0061] S240. Correct the initial state prediction matrix and the corresponding initial prediction covariance matrix according to the correction coefficient to obtain the state prediction matrix and the corresponding prediction covariance matrix at the current moment.

[0062] In an optional embodiment, the correction coefficient includes: a driver coefficient and a noise coefficient.

[0063] The driver coefficient can be used to modify the initial state prediction matrix according to the driver's behavior habits and current driving parameters. For example, the driver parameters may include the accelerator pedal opening and brake pedal opening of the vehicle. For example, the driver's behavior habits can be calculated by the driver influence matrix E k Indicates that the current driving parameters can be influenced by the driver vector express.

[0064] The noise coefficient may be a noise matrix of an external environment, specifically a noise matrix of a sensor, which is used to correct the initial prediction covariance matrix.

[0065] In an optional embodiment, the initial state prediction matrix and the corresponding initial prediction covariance matrix are corrected according to the correction coefficient to obtain the state prediction matrix and the corresponding prediction covariance matrix at the current moment, including: correcting the initial state prediction matrix according to the driver coefficient to obtain the state prediction matrix at the current moment, and correcting the initial prediction covariance matrix according to the noise coefficient to obtain the prediction covariance matrix.

[0066] The state prediction matrix at the current moment can be obtained by the following formula:

[0067]

[0068] The prediction covariance matrix at the current moment can be obtained by the following formula:

[0069]

[0070] Among them, T kis the noise factor.

[0071] S250, determining the state matrix of the vehicle at the next moment according to the state measurement matrix and the corresponding measurement covariance matrix, as well as the state prediction matrix and the corresponding prediction covariance matrix, so as to predict the driving trajectory of the vehicle.

[0072] In an optional embodiment, determining the state matrix of the vehicle at the next moment according to the state measurement matrix and the corresponding measurement covariance matrix, as well as the state prediction matrix and the corresponding prediction covariance matrix, includes: determining the Kalman gain according to the measurement covariance matrix and the prediction covariance matrix; and determining the state matrix of the vehicle at the next moment according to the Kalman gain, the state measurement matrix and the state prediction matrix.

[0073] The Kalman gain can be determined based on the measurement covariance matrix and the prediction covariance matrix. The Kalman gain is used to determine the state matrix of the vehicle at the next moment through Kalman filtering. Specifically, the Kalman gain can be determined by the following formula:

[0074]

[0075] Where K is the Kalman gain, H k is the state measurement matrix reading at the current moment, is the prediction covariance matrix, R k is the measurement covariance matrix corresponding to the state measurement matrix.

[0076] The state matrix at the next moment can be determined by the following formula:

[0077]

[0078] in, is the state measurement matrix.

[0079] The Kalman gain is determined based on the measurement covariance matrix and the prediction covariance matrix; the state matrix of the vehicle at the next moment is determined based on the Kalman gain, the state measurement matrix and the state prediction matrix. Based on the Kalman filter algorithm, the state matrix of the vehicle at the next moment is determined, thereby improving the accuracy of the prediction of the state matrix of the vehicle at the next moment.

[0080] The technical solution of this embodiment is to correct the historical state matrix and the corresponding historical covariance matrix of the previous moment according to the duration coefficient to obtain the initial state prediction matrix and the corresponding initial prediction covariance matrix, and obtain the corresponding value of the next moment through the duration coefficient; according to the correction coefficient, the initial state prediction matrix and the corresponding initial prediction covariance matrix are corrected to obtain the state prediction matrix and the corresponding prediction covariance matrix at the current moment, and the initial state prediction matrix and the corresponding initial prediction covariance matrix are further corrected by the correction coefficient. The state prediction matrix and the corresponding prediction covariance matrix can be corrected considering various influencing factors to improve the accuracy of the state prediction matrix and the corresponding prediction covariance matrix.

[0081] Embodiment 3

[0082] Figure 3 This is a flow chart of a vehicle trajectory prediction method provided in Example 3 of the present application. The technical solution of this embodiment is further refined on the basis of the above technical solution.

[0083] Furthermore, after determining the state matrix of the vehicle at the next moment based on the state measurement matrix and the corresponding measurement covariance matrix, as well as the state prediction matrix and the corresponding prediction covariance matrix, add: "Compare the state prediction matrix with the measurement value of the vehicle at the next moment. If the number of consecutive large differences is greater than a preset threshold, it is determined that the vehicle's driving trajectory has deviated, and a trajectory deviation warning is issued" to issue a trajectory deviation warning.

[0084] See also Figure 3 A vehicle trajectory prediction method shown includes:

[0085] S310, obtaining the current speed and current position of the vehicle at the current moment through sensors.

[0086] S320: Determine a state measurement matrix and a corresponding measurement covariance matrix of the vehicle according to the current speed and the current position.

[0087] S330, determining the state prediction matrix and the corresponding prediction covariance matrix at the current moment according to the duration coefficient and the correction coefficient, as well as the historical state matrix and the corresponding historical covariance matrix at the previous moment.

[0088] S340, determining the state matrix of the vehicle at the next moment according to the state measurement matrix and the corresponding measurement covariance matrix, as well as the state prediction matrix and the corresponding prediction covariance matrix, so as to predict the driving trajectory of the vehicle.

[0089] S350, comparing the state prediction matrix with the measurement value of the vehicle at the next moment. If the number of consecutive large differences is greater than a preset threshold, it is determined that the vehicle's driving trajectory has deviated, and a trajectory deviation warning is issued.

[0090] A large difference can be a comparison between the state prediction matrix and the measured value of the vehicle at the next moment, which is outside the specified range. The specified range can be determined by professional technicians based on experiments or experience, and this application does not make specific restrictions on this. Compare the vehicle speed and position information with the vehicle model information of the same section and similar configuration in the Internet of Vehicles Big Data Platform. If it is found that the number of consecutive comparison gaps outside the specified range is greater than the preset number threshold, the vehicle state is determined to be abnormal, and a trajectory deviation warning is issued. The preset number threshold can be determined by professional technicians based on experiments or experience, and this application does not make specific restrictions on this. For example, the preset number threshold can be 5 times.

[0091] The Controller Area Network (CAN) network broadcast can send trajectory deviation warning instructions to the corresponding instrument in the form of text warnings and voice warnings; when the instrument receives the trajectory deviation warning instructions in the form of text warnings and voice warnings broadcast by the CAN network, it triggers the buzzer to beep three times at a high pitch, and displays the text prompt "vehicle status abnormal" on the LCD screen, and guides the vehicle controller to control the vehicle according to the big data control strategy read by the Internet of Vehicles platform. The number of buzzer beeps and the text display content are controlled by the vehicle control unit (VCU) through the CAN network. When the key switch is turned off, if the buzzer and LCD screen have not completed the VCU's command action, the VCU broadcasts the trajectory deviation warning instructions in the form of text warnings and voice warnings, and controls the voice broadcast and text display to stop.

[0092] The parallel-parallel hybrid power system includes VCU, instrument, CAN communication network and key switch; VCU includes digital input switch (electronic handbrake pull-up signal and electronic handbrake release signal, etc.), analog input switch (such as air tank air pressure and brake pedal voltage, etc.), power output switch (brake pressure reducing valve, brake pressure increasing valve and brake backup pressure valve) and CAN communication network; the instrument includes LCD display device and buzzer alarm device; CAN communication network includes physical wiring harness; key switch is used to start or shut down VCU and instrument power; when the key switch is turned on, VCU is controlled to work, vehicle input and output signals are collected, vehicle speed and position are estimated through Kalman filter algorithm, and compared with big data through Internet of Vehicles technology. If the vehicle speed or position signal is abnormal at the next moment, a fault warning signal is generated.

[0093] The technical solution of this embodiment is to compare the state prediction matrix with the measurement value of the vehicle at the next moment. If the number of consecutive large differences is greater than the preset number threshold, it is determined that the vehicle's driving trajectory has deviated, and a trajectory deviation warning is issued. This can timely issue an early warning of the vehicle's trajectory deviation phenomenon, reminding the driver to conduct corresponding inspections to ensure the safety of the vehicle and passengers.

[0094] Embodiment 4

[0095] Figure 4 The figure is a schematic diagram of the structure of a vehicle trajectory prediction device provided in the fourth embodiment of the present application. This embodiment can be applied to the case of predicting the driving trajectory of a vehicle and is configured in a vehicle. The specific structure of the vehicle trajectory prediction device is as follows:

[0096] The sensor data acquisition module 410 is used to acquire the current speed and current position of the vehicle at the current moment through the sensor;

[0097] The state measurement matrix generation module 420 is used to determine the state measurement matrix and the corresponding measurement covariance matrix of the vehicle according to the current speed and the current position;

[0098] The state prediction matrix determination module 430 is used to determine the state prediction matrix and the corresponding prediction covariance matrix at the current moment according to the duration coefficient and the correction coefficient, as well as the historical state matrix and the corresponding historical covariance matrix at the previous moment;

[0099] The driving trajectory prediction module 440 is used to determine the state matrix of the vehicle at the next moment according to the state measurement matrix and the corresponding measurement covariance matrix, as well as the state prediction matrix and the corresponding prediction covariance matrix, so as to predict the driving trajectory of the vehicle.

[0100] The technical solution of this embodiment obtains the current speed and current position of the vehicle at the current moment through sensors; determines the state measurement matrix and the corresponding measurement covariance matrix of the vehicle according to the current speed and current position; determines the state prediction matrix and the corresponding prediction covariance matrix at the current moment according to the duration coefficient and the correction coefficient, as well as the historical state matrix and the corresponding historical covariance matrix at the previous moment; determines the state matrix of the vehicle at the next moment according to the state measurement matrix and the corresponding measurement covariance matrix, as well as the state prediction matrix and the corresponding prediction covariance matrix, so as to predict the driving trajectory of the vehicle. Trajectory prediction is performed based on the speed parameters and position parameters of the vehicle, so as to improve the universality of the trajectory prediction method, be able to adapt to different vehicle systems, and have strong portability, and increase the correction coefficient to improve the accuracy of trajectory prediction. Therefore, the technical solution of this application solves the problem that prediction through the data of each dial cannot adapt to the update of the vehicle and has poor portability, and achieves the effect of improving the portability of the vehicle trajectory prediction method.

[0101] Optionally, the state prediction matrix determination module 430 includes:

[0102] A duration coefficient correction unit, used to correct the historical state matrix and the corresponding historical covariance matrix at the previous moment according to the duration coefficient, to obtain an initial state prediction matrix and a corresponding initial prediction covariance matrix;

[0103] The correction coefficient correction unit is used to correct the initial state prediction matrix and the corresponding initial prediction covariance matrix according to the correction coefficient to obtain the state prediction matrix and the corresponding prediction covariance matrix at the current moment.

[0104] Optional, correction factors, including: driver factor and noise factor.

[0105] Optional, correction factor correction unit, including:

[0106] The driver coefficient and the noise coefficient correspond to the correction subunit, which is used to correct the initial state prediction matrix according to the driver coefficient to obtain the state prediction matrix at the current moment, and to correct the initial prediction covariance matrix according to the noise coefficient to obtain the prediction covariance matrix.

[0107] Optionally, the driving trajectory prediction module 440 includes:

[0108] A Kalman gain determination unit, used for determining a Kalman gain according to a measurement covariance matrix and a prediction covariance matrix;

[0109] The state matrix prediction unit is used to determine the state matrix of the vehicle at the next moment according to the Kalman gain, the state measurement matrix and the state prediction matrix.

[0110] Optionally, the vehicle trajectory prediction device further includes:

[0111] The trajectory deviation warning module is used to compare the state prediction matrix with the measurement value of the vehicle at the next moment. If the number of consecutive large differences is greater than the preset threshold, it is determined that the vehicle's driving trajectory has deviated and a trajectory deviation warning is issued.

[0112] The vehicle trajectory prediction device provided in the embodiments of the present application can execute the vehicle trajectory prediction method provided in any embodiment of the present application, and has the corresponding functional modules and beneficial effects for executing the vehicle trajectory prediction method.

[0113] Embodiment 5

[0114] Figure 5 A schematic diagram of the structure of an electronic device provided in Embodiment 5 of the present application is shown in FIG. Figure 5As shown, the electronic device includes a processor 510, a memory 520, an input device 530 and an output device 540; the number of the processor 510 in the electronic device can be one or more. Figure 5 A processor 510 is taken as an example; the processor 510, the memory 520, the input device 530 and the output device 540 in the electronic device can be connected via a bus or other means. Figure 5 The example of connecting through bus is taken in the following.

[0115] The memory 520, as a computer-readable storage medium, can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the vehicle trajectory prediction method in the embodiment of the present application (for example, the sensor data acquisition module 410, the state measurement matrix generation module 420, the state prediction matrix determination module 430 and the driving trajectory prediction module 440). The processor 510 executes various functional applications and data processing of the electronic device by running the software programs, instructions and modules stored in the memory 520, that is, realizing the above-mentioned vehicle trajectory prediction method.

[0116] The memory 520 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 520 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 520 may further include a memory remotely arranged relative to the processor 510, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0117] The input device 530 may be used to receive input character information and generate key signal input related to user settings and function control of the electronic device. The output device 540 may include a display device such as a display screen.

[0118] According to an embodiment of the present invention, the present invention also provides an electronic device, a readable storage medium and a computer program product.

[0119] Embodiment 6

[0120] Embodiment 6 of the present application also provides a storage medium containing computer executable instructions, which are used to execute a vehicle trajectory prediction method when executed by a computer processor. The method includes: obtaining the current speed and current position of the vehicle at the current moment through a sensor; determining the state measurement matrix and the corresponding measurement covariance matrix of the vehicle according to the current speed and the current position; determining the state prediction matrix and the corresponding prediction covariance matrix at the current moment according to the duration coefficient and the correction coefficient, and the historical state matrix and the corresponding historical covariance matrix at the previous moment; determining the state matrix of the vehicle at the next moment according to the state measurement matrix and the corresponding measurement covariance matrix, and the state prediction matrix and the corresponding prediction covariance matrix, so as to predict the driving trajectory of the vehicle.

[0121] Of course, the storage medium containing computer executable instructions provided in an embodiment of the present application, whose computer executable instructions are not limited to the method operations described above, can also execute related operations in the vehicle trajectory prediction method provided in any embodiment of the present application.

[0122] Through the above description of the implementation method, the technicians in the relevant field can clearly understand that the present application can be implemented with the help of software and necessary general hardware, and of course it 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 the present application can be essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for an electronic device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0123] It is worth noting that in the embodiment of the above-mentioned vehicle trajectory prediction device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application.

[0124] Note that the above are only preferred embodiments of the present application and the technical principles used. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the appended claims.

Claims

1. A vehicle trajectory prediction method, characterized in that: include: The current speed and current position of the vehicle at the current moment are obtained through sensors; Determine a state measurement matrix and a corresponding measurement covariance matrix of the vehicle according to the current speed and the current position; Determine the state prediction matrix and the corresponding prediction covariance matrix at the current moment according to the duration coefficient and the correction coefficient, as well as the historical state matrix and the corresponding historical covariance matrix at the previous moment; According to the state measurement matrix and the corresponding measurement covariance matrix, as well as the state prediction matrix and the corresponding prediction covariance matrix, the state matrix of the vehicle at the next moment is determined to predict the driving trajectory of the vehicle.

2. The method according to claim 1, characterized in that Determining the state prediction matrix and the corresponding prediction covariance matrix at the current moment according to the duration coefficient and the correction coefficient, as well as the historical state matrix and the corresponding historical covariance matrix at the previous moment, includes: Correcting the historical state matrix and the corresponding historical covariance matrix at the previous moment according to the duration coefficient to obtain an initial state prediction matrix and a corresponding initial prediction covariance matrix; According to the correction coefficient, the initial state prediction matrix and the corresponding initial prediction covariance matrix are corrected to obtain the state prediction matrix and the corresponding prediction covariance matrix at the current moment.

3. The method according to claim 2, characterized in that The correction coefficient includes: a driver coefficient and a noise coefficient.

4. The method according to claim 3, characterized in that The initial state prediction matrix and the corresponding initial prediction covariance matrix are corrected according to the correction coefficient to obtain the state prediction matrix and the corresponding prediction covariance matrix at the current moment, including: The initial state prediction matrix is ​​modified according to the driver coefficient to obtain the state prediction matrix at the current moment, and the initial prediction covariance matrix is ​​modified according to the noise coefficient to obtain the prediction covariance matrix.

5. The method according to claim 1, characterized in that Determining the state matrix of the vehicle at the next moment according to the state measurement matrix and the corresponding measurement covariance matrix, and the state prediction matrix and the corresponding prediction covariance matrix, includes: Determining a Kalman gain based on the measurement covariance matrix and the prediction covariance matrix; The state matrix of the vehicle at the next moment is determined according to the Kalman gain, the state measurement matrix and the state prediction matrix.

6. The method according to claim 1, characterized in that After determining the state matrix of the vehicle at the next moment according to the state measurement matrix and the corresponding measurement covariance matrix, and the state prediction matrix and the corresponding prediction covariance matrix, the method further includes: The state prediction matrix is ​​compared with the measured value of the vehicle at the next moment. If the number of consecutive large differences is greater than a preset threshold, it is determined that the vehicle's driving trajectory has deviated, and a trajectory deviation warning is issued.

7. A vehicle trajectory prediction device, characterized in that: include: The sensor data acquisition module is used to obtain the current speed and current position of the vehicle at the current moment through the sensor; A state measurement matrix generation module, used to determine the state measurement matrix and the corresponding measurement covariance matrix of the vehicle according to the current speed and the current position; A state prediction matrix determination module is used to determine the state prediction matrix and the corresponding prediction covariance matrix at the current moment according to the duration coefficient and the correction coefficient, as well as the historical state matrix and the corresponding historical covariance matrix at the previous moment; The driving trajectory prediction module is used to determine the state matrix of the vehicle at the next moment according to the state measurement matrix and the corresponding measurement covariance matrix, as well as the state prediction matrix and the corresponding prediction covariance matrix, so as to predict the driving trajectory of the vehicle.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the vehicle trajectory prediction method as described in any one of claims 1-6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the vehicle trajectory prediction method as described in any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, which, when executed by a processor, implements the vehicle trajectory prediction method according to any one of claims 1 to 6.

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