Driving state recognition method, device, equipment and computer-readable storage medium

Abstract

A driving state recognition method, apparatus, device, and computer-readable storage medium are disclosed. The method includes: correcting an acquired vehicle speed based on a previously obtained speed correction value to obtain a speed correction value; detecting whether the speed correction value is greater than a threshold; and determining that the vehicle is in a high-speed driving state if the speed correction value is greater than the threshold. By correcting the vehicle speed to obtain a speed correction value, and using the speed correction value as a benchmark for determining the vehicle's driving state, the method can prevent the impact of speed fluctuations on the recognition results under certain special operating conditions, thereby ensuring the accuracy of driving state recognition results.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a driving state recognition method, device, equipment and computer-readable storage medium. Background Art

[0002] In existing technology, a vehicle's driving state is generally determined by its location. For example, if a vehicle is determined to be on a highway or overpass, it is considered to be in a high-speed driving state. However, in some special circumstances, such as traffic jams, even if the vehicle is on a highway or overpass, it is not actually in a high-speed driving state. Therefore, the method of determining the vehicle's driving state based on its location lacks accuracy. Summary of the Invention

[0003] The present application provides a driving status recognition method, device, equipment and computer-readable storage medium, which can solve the technical problem in the prior art that the accuracy of judging the driving status of a vehicle is not high enough.

[0004] In a first aspect, an embodiment of the present application provides a driving state recognition method, the driving state recognition method comprising:

[0005] Correcting the acquired vehicle speed based on the last obtained vehicle speed correction value to obtain a vehicle speed correction value;

[0006] detecting whether the vehicle speed correction value is greater than a threshold;

[0007] If the vehicle speed correction value is greater than the threshold, it is determined that the vehicle is in a high-speed driving state.

[0008] In combination with the first aspect, in one embodiment, the step of correcting the acquired vehicle speed based on the last obtained vehicle speed correction value to obtain the vehicle speed correction value includes:

[0009] Substitute the last obtained vehicle speed correction value and the acquired vehicle speed into the first formula to obtain the vehicle speed correction value. The first formula is:

[0010]

[0011] in, is the vehicle speed correction value, V ego,k is the vehicle speed, is the vehicle speed correction value obtained last time, and α is a positive number less than 1.

[0012] In combination with the first aspect, in one embodiment, after the step of detecting whether the vehicle speed correction value is greater than a threshold, the method further includes:

[0013] If the vehicle speed correction value is not greater than the threshold, detecting whether the number of target point clouds within a preset range centered on the vehicle is greater than a preset value based on the radar point cloud data, wherein the target point cloud is a point cloud having a radial velocity greater than a preset radial velocity;

[0014] If the number of target point clouds within a preset range centered on the vehicle is greater than a preset value, it is determined that the vehicle is in a high-speed driving state.

[0015] In conjunction with the first aspect, in one embodiment, after the step of detecting whether the number of target point clouds within a preset range centered on the vehicle is greater than a preset value based on the radar point cloud data, the method further includes:

[0016] If the number of target point clouds within a preset range centered on the vehicle is not greater than a preset value, calculating a probability that the vehicle is in a high-speed driving state and a probability that the vehicle is not in a high-speed driving state based on the vehicle speed correction value;

[0017] If the probability that the vehicle is in a high-speed driving state is greater than the probability that the vehicle is not in a high-speed driving state, then it is determined that the vehicle is in a high-speed driving state;

[0018] If the probability that the vehicle is in a high-speed driving state is less than the probability that the vehicle is not in a high-speed driving state, it is determined that the vehicle is not in a high-speed driving state.

[0019] In conjunction with the first aspect, in one embodiment, the step of calculating the probability that the vehicle is in a high-speed driving state and the probability that the vehicle is not in a high-speed driving state based on the vehicle speed correction value includes:

[0020] Substituting the vehicle speed correction value into the second formula to obtain the probability that the vehicle is not in a high-speed driving state;

[0021] Substituting the vehicle speed correction value into the third formula to obtain the probability that the vehicle is in a high-speed driving state;

[0022] Among them, the second formula is:

[0023]

[0024] The third formula is:

[0025]

[0026] Among them, P1 is the probability that the vehicle is not in a high-speed driving state, P2 is the probability that the vehicle is in a high-speed driving state, is the vehicle speed correction value, μ1 and μ2 are the preset speed averages, σ1 and σ2 are the preset speed standard deviations, e is a natural constant, v is the preset speed, and p1 and p2 are prior probabilities.

[0027] In a second aspect, an embodiment of the present application provides a driving state recognition device, the driving state recognition device comprising:

[0028] A correction processing module, configured to correct the acquired vehicle speed based on the vehicle speed correction value obtained last time, to obtain a vehicle speed correction value;

[0029] A first detection module, configured to detect whether the vehicle speed correction value is greater than a threshold;

[0030] The determination module is configured to determine that the vehicle is in a high-speed driving state if the vehicle speed correction value is greater than a threshold value.

[0031] In conjunction with the second aspect, in one embodiment, the correction processing module is configured to:

[0032] Substitute the last obtained vehicle speed correction value and the acquired vehicle speed into the first formula to obtain the vehicle speed correction value. The first formula is:

[0033]

[0034] in, is the vehicle speed correction value, V ego,k is the vehicle speed, is the vehicle speed correction value obtained last time, and α is a positive number less than 1.

[0035] In conjunction with the second aspect, in one embodiment, the driving state recognition device further includes a second detection module, configured to:

[0036] If the vehicle speed correction value is not greater than the threshold, detecting whether the number of target point clouds within a preset range centered on the vehicle is greater than a preset value based on the radar point cloud data, wherein the target point cloud is a point cloud having a radial velocity greater than a preset radial velocity;

[0037] The determination module is further configured to determine that the vehicle is in a high-speed driving state if the number of target point clouds within a preset range centered on the vehicle is greater than a preset value.

[0038] In a third aspect, an embodiment of the present application provides a driving state recognition device, which includes a processor, a memory, and a driving state recognition program stored on the memory and executable by the processor, wherein when the driving state recognition program is executed by the processor, the steps of the driving state recognition method described in the first aspect are implemented.

[0039] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a driving state recognition program is stored, wherein when the driving state recognition program is executed by a processor, the steps of the driving state recognition method described in the first aspect are implemented.

[0040] The beneficial effects of the technical solutions provided in the embodiments of the present application include:

[0041] In this embodiment of the present application, the acquired vehicle speed is corrected based on the previously obtained speed correction value to obtain a speed correction value; the speed correction value is then detected to determine whether it is greater than a threshold; if the speed correction value is greater than the threshold, the vehicle is determined to be in a high-speed driving state. By correcting the vehicle speed to obtain a speed correction value and using the speed correction value as a reference for determining the vehicle's driving state, the impact of speed jumps on the recognition results under certain special operating conditions can be avoided, thereby ensuring the accuracy of the driving state recognition results. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a flow chart of the first embodiment of the driving state recognition method of the present application;

[0043] Figure 2 This is a flow chart of the second embodiment of the driving state recognition method of the present application;

[0044] Figure 3 This is a flow chart of the third embodiment of the driving state recognition method of the present application;

[0045] Figure 4 This is a schematic diagram of the functional modules of an embodiment of the driving state recognition device of the present application;

[0046] Figure 5 This is a schematic diagram of the hardware structure of the driving status recognition device involved in the embodiment of the present application. DETAILED DESCRIPTION

[0047] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0048] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0049] In a first aspect, an embodiment of the present application provides a driving status identification method.

[0050] In one embodiment, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the driving state recognition method of this application. Figure 1As shown, the driving state recognition method includes:

[0051] Step S10, correcting the acquired vehicle speed based on the previously obtained vehicle speed correction value to obtain a vehicle speed correction value;

[0052] In this embodiment, the vehicle speed can be obtained by obtaining the wheel speed monitored by the sensor and then performing relevant calculations on the wheel speed according to the built-in formula, thereby obtaining the vehicle speed, that is, the speed obtained by the execution subject of this embodiment. This part is conventional technology and will not be described in detail here.

[0053] After obtaining the vehicle's speed, directly using it to determine driving status can easily lead to misjudgments. For example, if the vehicle is actually traveling at high speed, but due to an intermittent sensor failure, the sensor's detected wheel speed is lower than the actual wheel speed, the calculated vehicle speed will also be lower than the actual value. If driving status recognition is performed based on this speed, the recognition result will not match the actual situation.

[0054] Therefore, in order to improve the accuracy of subsequent recognition results, it is necessary to correct the vehicle speed in combination with the vehicle speed correction value obtained last time.

[0055] Furthermore, in one embodiment, step S10 includes:

[0056] Substitute the last obtained vehicle speed correction value and the acquired vehicle speed into the first formula to obtain the vehicle speed correction value. The first formula is:

[0057]

[0058] in, is the vehicle speed correction value, V ego,k is the vehicle speed, is the vehicle speed correction value obtained last time, and α is a positive number less than 1.

[0059] In this embodiment, the larger the value of α, the greater the influence of the acquired vehicle speed on the subsequent judgment result. When the performance of the components on the vehicle related to determining the vehicle speed is good, it can be considered that the accuracy of the acquired vehicle speed is high, and α can be set to a larger value; otherwise, α can be set to a smaller value. The value of α is set according to actual conditions and is not limited here.

[0060] Step S20, detecting whether the vehicle speed correction value is greater than a threshold;

[0061] In step S30 , if the vehicle speed correction value is greater than the threshold, it is determined that the vehicle is in a high-speed driving state.

[0062] In this embodiment, the threshold is set according to actual conditions, for example, 60 km / h, 80 km / h, etc. This is only a schematic illustration of the threshold and does not constitute a limitation on the threshold.

[0063] When the vehicle speed correction value is greater than the threshold, it is considered that the vehicle is traveling at a high speed, thereby determining that the vehicle is in a high-speed driving state.

[0064] In this embodiment of the present application, the vehicle speed is obtained and corrected to obtain a corrected speed value; the corrected speed value is detected to determine whether it is greater than a threshold; if the corrected speed value is greater than the threshold, the vehicle is determined to be in a high-speed driving state. By correcting the vehicle speed to obtain a corrected speed value and using the corrected speed value as a reference for determining the vehicle's driving state, the impact of speed jumps on the recognition results under certain special conditions can be avoided, thereby ensuring the accuracy of the driving state recognition results.

[0065] Furthermore, in one embodiment, referring to Figure 2 , Figure 2 This is a flow chart of the second embodiment of the driving state recognition method of this application. Figure 2 As shown, after step S20, the following steps are further included:

[0066] Step S40: If the vehicle speed correction value is not greater than the threshold, detecting whether the number of target point clouds within a preset range centered on the vehicle is greater than a preset value based on the radar point cloud data, wherein the target point cloud is a point cloud having a radial velocity greater than a preset radial velocity;

[0067] In this embodiment, if the vehicle speed correction value is not greater than a threshold, the radar point cloud data is used to determine a point cloud within a preset range centered on the vehicle. From these point clouds, the point cloud with a radial velocity greater than the preset radial velocity is then identified and recorded as the target point cloud. Leveraging high-resolution millimeter-wave radar sensors, the target detection layer within the radar processing algorithm framework is able to detect multiple reflection points of actual physical targets and generate a set of corresponding measurement results. In some cases, there may be thousands of measurement results per frame. These measurement results are referred to as "radar point cloud data." Each measurement result represents a reflection point and includes information such as radial distance, azimuth, and radial velocity.

[0068] The preset range is set based on actual needs. For example, it can be a circle with a radius of 15 meters centered on the vehicle. Alternatively, it can be a rectangular area with a width of 2 meters and a length of 20 meters centered on the vehicle. This is merely an illustrative example of the preset range and does not constitute a limitation.

[0069] The preset radial speed is set according to actual needs, for example, 8 m / s. This is only a schematic illustration of the preset radial speed and does not constitute a limitation on the preset radial speed.

[0070] In step S50 , if the number of target point clouds within a preset range centered on the vehicle is greater than a preset value, it is determined that the vehicle is in a high-speed driving state.

[0071] In this embodiment, the preset value is set according to actual needs. If the number of target point clouds within the preset range centered on the vehicle is greater than the preset value, it is determined that the vehicle is in a high-speed driving state.

[0072] Furthermore, in one embodiment, referring to Figure 3 , Figure 3 This is a flow chart of the third embodiment of the driving state recognition method of this application. Figure 3 As shown, after step S40, the following steps are further included:

[0073] Step S60: If the number of target point clouds within a preset range centered on the vehicle is not greater than a preset value, then calculating the probability that the vehicle is in a high-speed driving state and the probability that the vehicle is not in a high-speed driving state based on the vehicle speed correction value;

[0074] Step S70: If the probability that the vehicle is in a high-speed driving state is greater than the probability that the vehicle is not in a high-speed driving state, then it is determined that the vehicle is in a high-speed driving state;

[0075] Step S80: If the probability that the vehicle is in a high-speed driving state is less than the probability that the vehicle is not in a high-speed driving state, it is determined that the vehicle is not in a high-speed driving state.

[0076] In this embodiment, when the vehicle speed correction value is not greater than the threshold value and the number of target point clouds within a preset range centered on the vehicle is not greater than a preset value, the probability that the vehicle is in a high-speed driving state and the probability that the vehicle is not in a high-speed driving state are calculated based on the vehicle speed correction value, and then the probabilities corresponding to the two driving states are compared to determine the driving state of the vehicle.

[0077] Furthermore, in one embodiment, the step of calculating the probability that the vehicle is in a high-speed driving state and the probability that the vehicle is not in a high-speed driving state based on the vehicle speed correction value includes:

[0078] Substituting the vehicle speed correction value into the second formula to obtain the probability that the vehicle is not in a high-speed driving state;

[0079] Substituting the vehicle speed correction value into the third formula to obtain the probability that the vehicle is in a high-speed driving state;

[0080] Among them, the second formula is:

[0081]

[0082] The third formula is:

[0083]

[0084] Among them, P1 is the probability that the vehicle is not in a high-speed driving state, P2 is the probability that the vehicle is in a high-speed driving state, is the vehicle speed correction value, μ1 and μ2 are the preset speed averages, σ1 and σ2 are the preset speed standard deviations, e is a natural constant, v is the preset speed, and p1 and p2 are prior probabilities.

[0085] In this embodiment, μ1, μ2, σ1, σ2, v, p1, and p2 are set according to actual needs, for example: μ1 = 30, μ2 = 140, σ1 = 160, σ2 = 400, v = 140, p1 = 0.8, and p2 = 0.2. This is merely an illustrative description of μ1, μ2, σ1, σ2, v, p1, and p2 and does not constitute a limitation on μ1, μ2, σ1, σ2, v, p1, and p2.

[0086] In a second aspect, an embodiment of the present application also provides a driving status recognition device.

[0087] In one embodiment, referring to Figure 4 , Figure 4 This is a functional module diagram of an embodiment of the driving state recognition device of the present application. Figure 4 As shown, the driving state recognition device includes:

[0088] A correction processing module 10 is used to correct the acquired vehicle speed based on the vehicle speed correction value obtained last time to obtain a vehicle speed correction value;

[0089] A first detection module 20 is used to detect whether the vehicle speed correction value is greater than a threshold;

[0090] The determination module 30 is configured to determine that the vehicle is in a high-speed driving state if the vehicle speed correction value is greater than a threshold value.

[0091] Furthermore, in one embodiment, the correction processing module 10 is configured to:

[0092] Substitute the last obtained vehicle speed correction value and the acquired vehicle speed into the first formula to obtain the vehicle speed correction value. The first formula is:

[0093]

[0094] in, is the vehicle speed correction value, V ego,k is the vehicle speed, is the vehicle speed correction value obtained last time, and α is a positive number less than 1.

[0095] Furthermore, in one embodiment, the driving state recognition device further includes a second detection module, configured to:

[0096] If the vehicle speed correction value is not greater than the threshold, detecting whether the number of target point clouds within a preset range centered on the vehicle is greater than a preset value based on the radar point cloud data, wherein the target point cloud is a point cloud having a radial velocity greater than a preset radial velocity;

[0097] The determination module 30 is further configured to determine that the vehicle is in a high-speed driving state if the number of target point clouds within a preset range centered on the vehicle is greater than a preset value.

[0098] Furthermore, in one embodiment, the driving state recognition device further includes a calculation module for:

[0099] If the number of target point clouds within a preset range centered on the vehicle is not greater than a preset value, calculating a probability that the vehicle is in a high-speed driving state and a probability that the vehicle is not in a high-speed driving state based on the vehicle speed correction value;

[0100] The determination module 30 is further configured to:

[0101] If the probability that the vehicle is in a high-speed driving state is greater than the probability that the vehicle is not in a high-speed driving state, then it is determined that the vehicle is in a high-speed driving state;

[0102] If the probability that the vehicle is in a high-speed driving state is less than the probability that the vehicle is not in a high-speed driving state, it is determined that the vehicle is not in a high-speed driving state.

[0103] Furthermore, in one embodiment, the calculation module is configured to:

[0104] Substituting the vehicle speed correction value into the second formula to obtain the probability that the vehicle is not in a high-speed driving state;

[0105] Substituting the vehicle speed correction value into the third formula to obtain the probability that the vehicle is in a high-speed driving state;

[0106] Among them, the second formula is:

[0107]

[0108] The third formula is:

[0109]

[0110] Among them, P1 is the probability that the vehicle is not in a high-speed driving state, P2 is the probability that the vehicle is in a high-speed driving state, is the vehicle speed correction value, μ1 and μ2 are the preset speed averages, σ1 and σ2 are the preset speed standard deviations, e is a natural constant, v is the preset speed, and p1 and p2 are prior probabilities.

[0111] Among them, the functional implementation of each module in the above-mentioned driving state recognition device corresponds to the various steps in the above-mentioned driving state recognition method embodiment, and their functions and implementation processes are no longer repeated here.

[0112] In a third aspect, an embodiment of the present application provides a driving status recognition device, which may be a personal computer (PC), a laptop computer, a server, or other device with data processing capabilities.

[0113] Reference Figure 5 , Figure 5 Schematic diagram of the hardware structure of the driving state recognition device involved in the embodiment of the present application. In the embodiment of the present application, the driving state recognition device may include a processor, a memory, a communication interface and a communication bus.

[0114] The communication bus may be of any type and is used to interconnect the processor, memory, and communication interface.

[0115] Communication interfaces include input / output (I / O) interfaces, physical interfaces, and logical interfaces, used to interconnect components within the driving status recognition device, as well as interfaces used to interconnect the driving status recognition device with other devices (such as other computing devices or user devices). Physical interfaces can include Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc. User devices can include displays, keyboards, etc.

[0116] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0117] The processor may be a general-purpose processor that can call a driving state recognition program stored in a memory and execute the driving state recognition method provided in the embodiments of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the driving state recognition program is called can refer to the various embodiments of the driving state recognition method of the present application and will not be repeated here.

[0118] Those skilled in the art will understand that Figure 5 The hardware structure shown in the figure does not constitute a limitation to the present application and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0119] In a fourth aspect, an embodiment of the present application also provides a computer-readable storage medium.

[0120] The computer-readable storage medium of the present application stores a driving state recognition program, wherein when the driving state recognition program is executed by a processor, the steps of the driving state recognition method as described above are implemented.

[0121] Among them, the method implemented when the driving status recognition program is executed can refer to the various embodiments of the driving status recognition method of this application, and will not be repeated here.

[0122] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0123] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit the "first", "second" and "third" to different types.

[0124] In the description of the embodiments of this application, the words "exemplary," "for example," or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary," "for example," or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "for example," or "for example" is intended to present the relevant concepts in a concrete manner.

[0125] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0126] In some processes described in the embodiments of the present application, multiple operations or steps are included that appear in a specific order. However, it should be understood that these operations or steps may not be performed in the order in which they appear in the embodiments of the present application or may be performed in parallel. The sequence numbers of the operations are only used to distinguish between different operations, and the sequence numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations or steps may be performed in sequence or in parallel, and these operations or steps may be combined.

[0127] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, of course, 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, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for enabling a terminal device to execute the methods described in each embodiment of the present application.

[0128] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A driving state recognition method, characterized in that: The driving state recognition method includes: Correcting the acquired vehicle speed based on the last obtained vehicle speed correction value to obtain a vehicle speed correction value; detecting whether the vehicle speed correction value is greater than a threshold; If the vehicle speed correction value is greater than the threshold, it is determined that the vehicle is in a high-speed driving state; If the vehicle speed correction value is not greater than the threshold, detecting whether the number of target point clouds within a preset range centered on the vehicle is greater than a preset value based on the radar point cloud data, wherein the target point cloud is a point cloud having a radial velocity greater than a preset radial velocity; If the number of target point clouds within a preset range centered on the vehicle is greater than a preset value, it is determined that the vehicle is in a high-speed driving state; The step of correcting the acquired vehicle speed based on the previously obtained vehicle speed correction value to obtain the vehicle speed correction value includes: Substitute the last obtained vehicle speed correction value and the acquired vehicle speed into the first formula to obtain the vehicle speed correction value. The first formula is: in, is the vehicle speed correction value, is the vehicle speed, is the speed correction value obtained last time, A positive number less than 1.

2. The driving state recognition method according to claim 1, characterized in that: After the step of detecting whether the number of target point clouds within a preset range centered on the vehicle is greater than a preset value based on the radar point cloud data, the method further includes: If the number of target point clouds within a preset range centered on the vehicle is not greater than a preset value, calculating a probability that the vehicle is in a high-speed driving state and a probability that the vehicle is not in a high-speed driving state based on the vehicle speed correction value; If the probability that the vehicle is in a high-speed driving state is greater than the probability that the vehicle is not in a high-speed driving state, then it is determined that the vehicle is in a high-speed driving state; If the probability that the vehicle is in a high-speed driving state is less than the probability that the vehicle is not in a high-speed driving state, it is determined that the vehicle is not in a high-speed driving state.

3. The driving state recognition method according to claim 2, characterized in that: The step of calculating the probability that the vehicle is in a high-speed driving state and the probability that the vehicle is not in a high-speed driving state based on the vehicle speed correction value includes: Substituting the vehicle speed correction value into the second formula to obtain the probability that the vehicle is not in a high-speed driving state; Substituting the vehicle speed correction value into the third formula to obtain the probability that the vehicle is in a high-speed driving state; Among them, the second formula is: The third formula is: in, is the probability that the vehicle is not in a high-speed driving state, is the probability that the vehicle is in high-speed driving state, is the vehicle speed correction value, 、 is the preset speed average value, 、 is the preset speed standard deviation, is a natural constant, is the preset speed, 、 is the prior probability.

4. A driving state recognition device, characterized in that: The driving state recognition device includes: The correction processing module is used to substitute the vehicle speed correction value obtained last time and the obtained vehicle speed into a first formula to obtain a vehicle speed correction value, wherein the first formula is: in, is the vehicle speed correction value, is the vehicle speed, is the speed correction value obtained last time, is a positive number less than 1; A first detection module, configured to detect whether the vehicle speed correction value is greater than a threshold; a determination module, configured to determine that the vehicle is in a high-speed driving state if the vehicle speed correction value is greater than a threshold value; a second detection module configured to detect, based on the radar point cloud data, whether the number of target point clouds within a preset range centered on the vehicle is greater than a preset value if the vehicle speed correction value is not greater than a threshold, wherein the target point clouds are point clouds having radial velocities greater than a preset radial velocity; The determination module is further configured to determine that the vehicle is in a high-speed driving state if the number of target point clouds within a preset range centered on the vehicle is greater than a preset value.

5. A driving status recognition device, characterized in that: The driving state recognition device includes a processor, a memory, and a driving state recognition program stored in the memory and executable by the processor, wherein when the driving state recognition program is executed by the processor, the steps of the driving state recognition method as described in any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a driving state recognition program, wherein when the driving state recognition program is executed by the processor, the steps of the driving state recognition method according to any one of claims 1 to 3 are implemented.

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