Vehicle driving parameter prediction method and device

Abstract

The embodiments of the present application provide a method, device, computer-readable medium and electronic device for predicting vehicle driving parameters. The method includes: obtaining driving parameters of at least two vehicles at a first moment, the at least two vehicles include a target vehicle and at least one first reference vehicle; obtaining driving parameters of at least one second reference vehicle at a second moment, the second moment being after the first moment; determining driving parameter change information of the reference vehicle between the first moment and the second moment based on the driving parameters of the at least one first reference vehicle at the first moment and the driving parameters of the second reference vehicle at the second moment; predicting the driving parameters of the target vehicle at the second moment based on the driving parameters of the target vehicle at the first moment and the driving parameter change information. The technical solution of the embodiments of the present application can ensure the real-time determination of vehicle driving parameters.

Description

Technical Field

[0001] The present application relates to the field of computers and safety assisted driving technology, and in particular to a method, device, computer-readable medium and electronic device for predicting vehicle driving parameters. Background Art

[0002] In traffic scenarios, such as on motorway lanes, vehicles have their own rules when uploading their own information. For example, vehicles upload vehicle information containing their own driving parameters (such as GPS information, vehicle speed, vehicle acceleration, etc.) at a fixed period. Therefore, the server can only determine the vehicle driving parameters at a fixed periodic time point, but not at any time within the fixed period. It can be seen that how to ensure the real-time determination of vehicle driving parameters is a technical problem that needs to be solved urgently. Summary of the invention

[0003] The embodiments of the present application provide a method, device, computer-readable medium and electronic device for predicting vehicle driving parameters, thereby ensuring the real-time determination of the vehicle driving parameters at least to a certain extent.

[0004] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present application.

[0005] According to one aspect of an embodiment of the present application, a method for predicting vehicle driving parameters is provided, comprising: obtaining driving parameters of at least two vehicles at a first moment, the at least two vehicles including a target vehicle and at least one first reference vehicle; obtaining driving parameters of at least one second reference vehicle at a second moment, the second moment being after the first moment; determining driving parameter change information of the reference vehicle between the first moment and the second moment based on the driving parameters of the at least one first reference vehicle at the first moment and the driving parameters of the second reference vehicle at the second moment; and predicting the driving parameters of the target vehicle at the second moment based on the driving parameters of the target vehicle at the first moment and the driving parameter change information.

[0006] According to one aspect of an embodiment of the present application, a device for predicting vehicle driving parameters is provided, including: a first acquisition unit, used to acquire driving parameters of at least two vehicles at a first moment, the at least two vehicles including a target vehicle and at least one first reference vehicle; a second acquisition unit, used to acquire driving parameters of at least one second reference vehicle at a second moment, the second moment being after the first moment; a determination unit, used to determine driving parameter change information of the reference vehicle between the first moment and the second moment based on the driving parameters of the at least one first reference vehicle at the first moment and the driving parameters of the second reference vehicle at the second moment; and a prediction unit, used to predict the driving parameters of the target vehicle at the second moment based on the driving parameters of the target vehicle at the first moment and the driving parameter change information.

[0007] In some embodiments of the present application, based on the aforementioned scheme, the determination unit is configured to: determine a first average driving parameter of the first reference vehicle at the first moment according to the driving parameters of the at least one first reference vehicle at the first moment and the number of first reference vehicles; determine a second average driving parameter of the second reference vehicle at the second moment according to the driving parameters of the at least one second reference vehicle at the second moment and the number of second reference vehicles; determine driving parameter change information of the reference vehicle between the first moment and the second moment according to the first average driving parameter and the second average driving parameter.

[0008] In some embodiments of the present application, based on the aforementioned scheme, the determination unit is configured to: determine the ratio between the second average driving parameter and the first average driving parameter as driving parameter change information of the reference vehicle between the first moment and the second moment.

[0009] In some embodiments of the present application, based on the aforementioned scheme, the device also includes a calculation unit, which is used to calculate the second driving risk of the target vehicle at the second moment through a driving risk model based on the driving parameters of the target vehicle at the second moment and the driving parameters of the at least one second reference vehicle at the second moment.

[0010] In some embodiments of the present application, based on the aforementioned scheme, the driving parameters include driving speed and driving acceleration, and the calculation unit is configured as: based on the driving speed of the target vehicle at the second moment and the driving speed at the first moment, calculate the indirect acceleration of the target vehicle at the second moment to obtain a first absolute value of the difference between the driving acceleration of the target vehicle at the second moment and the indirect acceleration; calculate the ratio between the absolute value of the first difference and the driving acceleration of the target vehicle at the second moment to obtain a first ratio; calculate the ratio of the absolute value of the first difference to the indirect acceleration to obtain a second ratio; when both the first ratio and the second ratio do not exceed a first predetermined threshold, calculate the second driving risk of the target vehicle at the second moment through a driving risk model.

[0011] In some embodiments of the present application, based on the aforementioned solution, the device further includes a sending unit, which is used to send the second driving risk to the target vehicle after calculating the second driving risk of the target vehicle at the second moment through a driving risk model.

[0012] In some embodiments of the present application, based on the aforementioned scheme, the sending unit is configured to: determine a first driving risk of the target vehicle at a first moment; calculate a second absolute value of the difference between the first driving risk and the second driving risk; and send the second driving risk to the target vehicle when the ratio between the absolute value of the second difference and the first driving risk does not exceed a second predetermined threshold.

[0013] In some embodiments of the present application, based on the aforementioned scheme, the sending unit is configured to: when the ratio between the absolute value of the second difference and the first driving risk exceeds a second predetermined threshold, calculate the credibility of the second driving risk by the ratio between the absolute value of the second difference and the first driving risk; and send the second driving risk and the credibility corresponding to the second driving risk to the target vehicle.

[0014] In some embodiments of the present application, based on the aforementioned scheme, the sending unit is configured to: calculate the first total mass of the at least one first reference vehicle based on the vehicle mass of the first reference vehicle; calculate the second total mass of the at least one second reference vehicle based on the vehicle mass of the second reference vehicle; calculate the absolute value of the third difference between the first total mass and the second total mass; and send the second driving risk to the target vehicle when the ratio between the absolute value of the third difference and the first total mass does not exceed a third predetermined threshold.

[0015] According to one aspect of an embodiment of the present application, a computer-readable medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method for predicting vehicle driving parameters as described in the above embodiment is implemented.

[0016] According to one aspect of an embodiment of the present application, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method for predicting vehicle driving parameters as described in the above embodiments.

[0017] In the technical solutions provided in some embodiments of the present application, the driving parameters of the vehicles including the target vehicle and at least one first reference vehicle at the first moment, and the driving parameters of at least one second reference vehicle at the second moment are first obtained, and then the driving parameter change information of the reference vehicle between the first moment and the second moment is determined through the driving parameters of the first reference vehicle at the first moment and the driving parameters of the second reference vehicle at the second moment, and the driving parameters of the target vehicle at the second moment are predicted through the driving parameter change information based on the driving parameters of the target vehicle at the first moment. Since the vehicle will be restricted by the surrounding vehicles when driving on the road, that is, the vehicles are interrelated, the average change of the driving state of all vehicles can reflect the change of the driving state of a certain vehicle to a certain extent, therefore, the driving parameters of the target vehicle at any second moment can be predicted through the driving parameter change information, so that the real-time determination of the vehicle driving parameters can be ensured.

[0018] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0020] Figure 1 A schematic diagram showing an exemplary system architecture to which the technical solution of the embodiments of the present application can be applied;

[0021] Figure 2 A flow chart showing a method for predicting vehicle driving parameters according to an embodiment of the present application is shown;

[0022] Figure 3A detailed flow chart of determining driving parameter change information of a reference vehicle according to an embodiment of the present application is shown;

[0023] Figure 4 A method flow chart before calculating a second driving risk of a target vehicle at a second moment according to an embodiment of the present application is shown;

[0024] Figure 5 A method flow chart before sending the second driving risk to the target vehicle according to an embodiment of the present application is shown;

[0025] Figure 6 A method flow chart before sending the second driving risk to the target vehicle according to an embodiment of the present application is shown;

[0026] Figure 7 A method flow chart before sending the second driving risk to the target vehicle according to an embodiment of the present application is shown;

[0027] Figure 8 A schematic diagram showing prediction of vehicle driving parameters based on the cloud according to an embodiment of the present application is shown;

[0028] Fig. 9 A block diagram of a vehicle driving parameter prediction device according to an embodiment of the present application is shown;

[0029] Fig.10 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0030] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.

[0031] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0032] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0033] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0034] Figure 1 A schematic diagram of an exemplary system architecture to which the technical solution of the embodiments of the present application can be applied is shown.

[0035] like Figure 1 As shown, the system architecture may include terminal devices (such as Figure 1 One or more of the smartphone 101, tablet computer 102 and portable computer 103 shown in the figure, which may also be a desktop computer, smart speaker, smart watch, etc., but are not limited to these), network 104 and server 105. Network 104 is used to provide a medium for a communication link between the terminal device and the server 105. Network 104 may include various connection types, such as wired communication links, wireless communication links, etc., which are not limited in this application. It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. According to the implementation requirements, there may be any number of terminal devices, networks and servers. For example, the server 105 may be a server cluster composed of multiple servers.

[0036] In one embodiment of the present application, Figure 1 The terminal devices shown in can all be placed in the target vehicle described in the present application, wherein the terminal devices shown can periodically upload the vehicle driving parameters of the target vehicle itself to the server, and the server 105 can periodically receive the vehicle driving parameters uploaded by the target vehicle, and can also predict the vehicle driving parameters of the target vehicle at any second moment within the period.

[0037] Specifically, the prediction process can be as follows: the server 105 can first obtain the driving parameters of the vehicles including the target vehicle and at least one first reference vehicle at the first moment, and the driving parameters of at least one second reference vehicle at the second moment, and then determine the driving parameter change information of the reference vehicle between the first moment and the second moment through the driving parameters of the first reference vehicle at the first moment and the driving parameters of the second reference vehicle at the second moment, and finally, based on the driving parameters of the target vehicle at the first moment, predict the driving parameters of the target vehicle at the second moment through the driving parameter change information.

[0038] It should be noted that the vehicle driving parameter prediction method provided in the embodiment of the present application is generally executed by the server 105, and accordingly, the vehicle driving parameter prediction device is generally set in the server 105. However, in other embodiments of the present application, the terminal device may also have similar functions as the server, so as to execute the vehicle driving parameter prediction scheme provided in the embodiment of the present application.

[0039] It should be noted that the server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0040] It should be noted that cloud computing as described above is a computing model that distributes computing tasks on a resource pool composed of a large number of computers, so that various application systems can obtain computing power, storage space and information services as needed. The network that provides resources is called a "cloud". The resources in the "cloud" are infinitely expandable in the eyes of users, and can be obtained at any time, used on demand, and expanded at any time. By establishing a cloud computing resource pool (referred to as a cloud platform, generally referred to as an IaaS (Infrastructure as a Service) platform, various types of virtual resources are deployed in the resource pool for external customers to choose to use. The cloud computing resource pool mainly includes: computing devices (virtualized machines, including operating systems), storage devices, and network devices.

[0041] The implementation details of the technical solution of the embodiment of the present application are described in detail below:

[0042] See also Figure 2 , which shows a flow chart of a method for predicting vehicle driving parameters according to an embodiment of the present application. The method for predicting vehicle driving parameters can be executed by a device having a computing and processing function, such as Figure 1 The server 105 shown in FIG. 1 may also be executed by Figure 1 The process can be performed by the terminal device shown in , or by a cloud server with cloud computing function. Figure 2As shown, the vehicle driving parameter prediction method at least includes steps 210 to 270:

[0043] In step 210 , driving parameters of at least two vehicles at a first moment are acquired, where the at least two vehicles include a target vehicle and at least one first reference vehicle.

[0044] In step 230 , driving parameters of at least one second reference vehicle at a second time are acquired, where the second time is after the first time.

[0045] In the present application, the driving parameters may include the speed, acceleration, driving direction, and mass of the vehicle.

[0046] It should be noted that, in the present application, the first moment may refer to the moment when the target vehicle and the first reference vehicle actively upload driving parameters, and the second moment may refer to any moment between the first moment and the moment when the target vehicle actively uploads its own driving parameters next time, therefore, the target vehicle does not actively upload its own driving parameters at the second moment. For the second reference vehicle, the second moment is the moment when it actively uploads its driving parameters. Based on this, it can be understood by those skilled in the art that, in the present application, the at least one first reference vehicle and the at least one second reference vehicle may not be the same batch of vehicles.

[0047] In step 250 , driving parameter change information of the reference vehicle between the first moment and the second moment is determined based on the driving parameters of the at least one first reference vehicle at the first moment and the driving parameters of the second reference vehicle at the second moment.

[0048] In one embodiment of the present application, based on the driving parameters of the at least one first reference vehicle at the first moment and the driving parameters of the second reference vehicle at the second moment, the driving parameter change information of the reference vehicle between the first moment and the second moment can be determined as follows: Figure 3 Follow the steps shown.

[0049] See also Figure 3 , shows a detailed flow chart of determining driving parameter change information of a reference vehicle according to an embodiment of the present application. It specifically includes steps 251 to 253:

[0050] Step 251: Determine a first average driving parameter of the first reference vehicle at the first moment based on the driving parameter of the at least one first reference vehicle at the first moment and the number of first reference vehicles.

[0051] Specifically, for example, at the first moment t latest The number of at least one first reference vehicle obtained is n latest, where the driving parameters of each first reference vehicle include speed, acceleration, driving direction angle, and mass. By averaging these driving parameters, we can get latest The average speed, average acceleration, average driving direction angle, and average mass of the first reference vehicle obtained are recorded as v latest 、a latest ,θ latest 、m latest , and then obtain the first average driving parameter of the first reference vehicle at the first moment.

[0052] Step 252: Determine a second average driving parameter of the second reference vehicle at the second moment according to the driving parameter of the at least one second reference vehicle at the second moment and the number of second reference vehicles.

[0053] Specifically, for example, at the second moment t current The number of at least one second reference vehicle obtained is n current , where the driving parameters of each second reference vehicle include speed, acceleration, driving direction angle, and mass. By averaging these driving parameters, we can get current The average speed, average acceleration, average driving direction angle, and average mass of the second reference vehicle are obtained and are denoted as v current , a current ,θ current , m current , and then obtain the second average driving parameter of the second reference vehicle at the second moment.

[0054] Step 253: Determine driving parameter change information of the reference vehicle between the first moment and the second moment according to the first average driving parameter and the second average driving parameter.

[0055] In a specific implementation of this embodiment, the driving parameter change information of the reference vehicle between the first moment and the second moment is determined based on the first average driving parameter and the second average driving parameter, and the ratio between the second average driving parameter and the first average driving parameter is determined as the driving parameter change information of the reference vehicle between the first moment and the second moment.

[0056] Specifically, for example, the average speed v of the second reference vehicle can be current With the average speed v of the first reference vehicle latest The ratio between current / v latest , the average acceleration a of the second reference vehicle current The average acceleration a of the first reference vehicle latest The ratio between current / alatest , the average driving direction angle θ of the second reference vehicle current The average driving direction angle θ with the first reference vehicle latest The ratio θ current / θ latest , the average mass of the second reference vehicle m current The average mass m of the first reference vehicle latest The ratio m current / m latest Determined as the first moment t latest With the second moment t current Reference is made to the vehicle's driving parameter change information.

[0057] In step 270 , based on the driving parameters of the target vehicle at the first moment and the driving parameter change information, the driving parameters of the target vehicle at the second moment are predicted.

[0058] Specifically, for example, the driving parameters of the target vehicle at the first moment may include a speed v target1 , acceleration a target1 、 Driving direction angle θ target1 、Mass m target1 Furthermore, the relative speed change v obtained above can be current / v latest The speed v of the target vehicle at the second moment can be predicted and calculated target1 ·v current / v latest , according to the relative change of acceleration a obtained above current / a latest The acceleration a of the target vehicle at the second moment can be predicted and calculated target1 ·a current / a latest , according to the relative change of the driving direction angle θ obtained above current / θ latest The target vehicle's driving direction angle θ at the second moment can be predicted and calculated target1 ·θ current / θ latest .

[0059] It should be noted that since the mass of the target vehicle does not change between the first moment and the second moment, there is no need to calculate the relative mass change m. current / m latest Predict the vehicle mass of the target vehicle at the second moment.

[0060] In the present application, since a vehicle is restricted by surrounding vehicles when driving on the road, that is, the vehicles are interrelated, the average change in the driving status of all vehicles can reflect the change in the driving status of a certain vehicle to a certain extent. Therefore, the advantage of predicting the driving parameters of the target vehicle at the second moment in the above manner is that the accuracy of the prediction of the driving parameters of the target vehicle at the second moment can be guaranteed.

[0061] In the present application, after predicting the driving parameters of the target vehicle at the second moment based on the driving parameters of the target vehicle at the first moment and the driving parameter change information, the following steps may also be performed:

[0062] Based on the driving parameters of the target vehicle at the second moment and the driving parameters of the at least one second reference vehicle at the second moment, a second driving risk of the target vehicle at the second moment is calculated by a driving risk model.

[0063] In the present application, before calculating the second driving risk of the target vehicle at the second moment through the driving risk model, the road parameters of the road section where the target vehicle is located can also be obtained.

[0064] Specifically, when calculating the second driving risk of the target vehicle at the second moment through the driving risk model, the driving risks between the target vehicle and each reference vehicle at the second moment can be first calculated, and then the driving risks between the target vehicle and each reference vehicle at the second moment can be summed to obtain the second driving risk of the target vehicle at the second moment.

[0065] When calculating the driving risk between the target vehicle and each reference vehicle at the second moment, it is necessary to simultaneously consider the driving parameters of the target vehicle at the second moment, the driving parameters of the reference vehicle at the second moment, and the road parameters of the section where the target vehicle is located.

[0066] In order to enable those skilled in the art to better understand the present application, the driving risk model in the prior art will be briefly described below.

[0067] The following is the calculation formula for the driving risk value between two moving vehicles (moving objects):

[0068]

[0069] Among them, SPE V_ab represents the driving risk between vehicle (object) a and vehicle (object) b; G is a constant (similar to the gravitational constant); R a Represents the road condition parameters of the road where the vehicle (object) a is located. The road condition parameters are used to comprehensively measure the viscosity, humidity, slope and temperature of the road surface. They are generally related to R bEqual; R b Represents the road condition parameters of the road where vehicle (object) b is located. The road condition parameters are used to comprehensively measure the viscosity, humidity, slope and temperature of the road surface. They are generally related to R a Equal; M a represents the mass of vehicle a; M b represents the mass of vehicle b; k3 is a constant (equal to the speed of light); k1 is a constant (usually 3 in air); represents the straight-line distance between vehicle a and vehicle b; represents the relative speed between vehicle a and vehicle b; θ a Represents the angle between the driving direction of vehicle a and the driving direction of vehicle j.

[0070] In one embodiment of the present application, the driving parameters may include driving speed and driving acceleration. Before calculating the second driving risk of the target vehicle at the second moment by using the driving risk model, the following steps may also be performed: Figure 4 Steps shown.

[0071] See also Figure 4 , shows a flow chart of a method before calculating the second driving risk of the target vehicle at the second moment according to an embodiment of the present application. It specifically includes steps 271 to 274:

[0072] Step 271, based on the driving speed of the target vehicle at the second moment and the driving speed of the target vehicle at the first moment, calculate the indirect acceleration of the target vehicle at the second moment to obtain a first absolute value of the difference between the driving acceleration of the target vehicle at the second moment and the indirect acceleration.

[0073] Specifically, in the present application, the indirect acceleration of the target vehicle at the second moment can be calculated by the following formula:

[0074]

[0075] Then, the first absolute value of the difference between the driving acceleration of the target vehicle at the second moment and the indirect acceleration is obtained:

[0076]

[0077] Step 272, calculating the ratio between the absolute value of the first difference and the driving acceleration of the target vehicle at the second moment to obtain a first ratio.

[0078] Specifically, in the present application, the first ratio Q1 is:

[0079]

[0080] Step 273, calculating the ratio of the absolute value of the first difference to the indirect acceleration to obtain a second ratio.

[0081] Specifically, in the present application, the second ratio Q2 is:

[0082]

[0083] Step 274: When both the first ratio and the second ratio do not exceed a first predetermined threshold, a second driving risk of the target vehicle at a second moment is calculated using a driving risk model.

[0084] In the present application, the first predetermined threshold may be set to a historical traffic accident rate P of the location of the target vehicle, wherein the historical traffic accident rate P of the location of the target vehicle may be obtained from a traffic management department.

[0085] Specifically, when Q1≤P and Q2≤P, the second driving risk of the target vehicle at the second moment is calculated by the driving risk model. Because when Q1≤P and Q2≤P, it can be determined to a certain extent that the difference between the calculated predicted indirect acceleration and the predicted driving acceleration of the target vehicle at the second moment is within a tolerable range, thereby ensuring the rationality of predicting the driving acceleration of the target vehicle at the second moment, and then the second driving risk of the target vehicle at the second moment can be calculated based on the driving parameters of the target vehicle at the second moment.

[0086] In the present application, when Q1>P and Q2>P, it is not necessary to calculate the second driving risk of the target vehicle at the second moment, because the purpose of predicting the driving parameters of the target vehicle at the second moment is to reduce the historical traffic accident rate with reference to the calculated target vehicle. If Q1>P and Q2>P, it can be considered that the unreasonable degree of the predicted driving parameters of the target vehicle at the second moment exceeds the historical traffic accident rate, making the error rate of the calculated driving risk of the target vehicle at the second moment higher than the historical traffic accident rate, which in turn causes the calculated second driving risk of the target vehicle at the second moment to no longer be of reference value.

[0087] It should be noted that, in the present application, the first predetermined threshold may also be set to other values.

[0088] In one embodiment of the present application, after calculating the second driving risk of the target vehicle at the second moment by using the driving risk model, the following steps may also be performed:

[0089] The second driving risk is sent to the target vehicle.

[0090] In one embodiment of the present application, before sending the second driving risk to the target vehicle, the following steps may also be performed: Figure 5 Steps shown.

[0091] See also Figure 5 , shows a method flow chart before sending the second driving risk to the target vehicle according to an embodiment of the present application. It specifically includes steps 281 to 283:

[0092] Step 281, determining a first driving risk of the target vehicle at a first moment.

[0093] Specifically, for example, the target vehicle at the first moment t latest The first driving risk is E latest .

[0094] Step 282: Calculate a second absolute value of the difference between the first driving risk and the second driving risk.

[0095] Specifically, for example, the target vehicle at the second time t current The second driving risk is E current , the absolute value of the second difference is |E current -E latest |.

[0096] Step 283: When the ratio between the absolute value of the second difference and the first driving risk does not exceed a second predetermined threshold, the second driving risk is sent to the target vehicle.

[0097] Specifically, for example, the ratio between the absolute value of the second difference and the first driving risk is |E current -E latest | / E latest .

[0098] In the present application, the second predetermined threshold may be set to the historical traffic accident rate P of the location of the target vehicle, wherein the historical traffic accident rate P of the location of the target vehicle may be obtained from the traffic management department.

[0099] It should be noted that, in the present application, the second predetermined threshold may also be set to other values.

[0100] In the above embodiment, it is also possible to perform the following Figure 6 Steps shown.

[0101] See also Figure 6 , shows a flow chart of a method before sending the second driving risk to the target vehicle according to an embodiment of the present application. It specifically includes steps 284 to 285:

[0102] Step 284: When the ratio between the absolute value of the second difference and the first driving risk exceeds a second predetermined threshold, the credibility of the second driving risk is calculated by the ratio between the absolute value of the second difference and the first driving risk.

[0103] In the present application, the credibility of the second driving risk can be: 1-|E current -E latest | / E latest For those skilled in the art, it is understandable that when 1-|E current -E latest | / E latest The smaller the value of , the lower the credibility of the second driving risk.

[0104] Step 285: Send the second driving risk and the credibility corresponding to the second driving risk to the target vehicle.

[0105] In one embodiment of the present application, the driving parameter includes vehicle mass. Before sending the second driving risk to the target vehicle, the following may also be performed: Figure 7 Steps shown.

[0106] See also Figure 7 , shows a method flow chart before sending the second driving risk to the target vehicle according to an embodiment of the present application. It specifically includes steps 291 to 294:

[0107] Step 291: Calculate a first total mass of the at least one first reference vehicle based on the vehicle mass of the first reference vehicle.

[0108] Step 292: Calculate a second total mass of the at least one second reference vehicle based on the vehicle mass of the second reference vehicle.

[0109] Step 293: Calculate a third absolute value of a difference between the first total mass and the second total mass.

[0110] Step 294: When the ratio between the third absolute value of the difference and the first total mass does not exceed a third predetermined threshold, the second driving risk is sent to the target vehicle.

[0111] In the present application, the third predetermined threshold may be set to a historical traffic accident rate P of the location of the target vehicle, wherein the historical traffic accident rate P of the location of the target vehicle may be obtained from a traffic management department.

[0112] It should be noted that, in the present application, the third predetermined threshold value may also be set to other values.

[0113] In this application, in the traffic scenario, in the embodiment of predicting the driving parameters of the target vehicle, it is also possible to build a fusion car cloud, regional cloud and edge cloud to realize the prediction of vehicle driving parameters for each vehicle in the Internet of Vehicles through the cloud car system, such as Figure 8 , shows a schematic diagram of predicting vehicle driving parameters based on the cloud according to an embodiment of the present application. The system consists of a cloud and a car network. Among them, all computing functions of this solution can be implemented on the car cloud, and the vehicle can periodically obtain its own driving parameters and upload them to the car cloud.

[0114] Specifically, the automobile cloud first obtains driving parameters of vehicles including a target vehicle and at least one first reference vehicle at a first moment, and driving parameters of at least one second reference vehicle at a second moment, and then determines driving parameter change information of the reference vehicles between the first moment and the second moment through the driving parameters of the first reference vehicle at the first moment and the driving parameters of the second reference vehicle at the second moment, and predicts the driving parameters of the target vehicle at the second moment based on the driving parameters of the target vehicle at the first moment through the driving parameter change information.

[0115] Moreover, the inventor of the present application compared the method of the present application with the prior art by repeating 10 simulation experiments. Specifically, based on the prediction method of vehicle driving parameters in the present application, an early warning of the driving risk of the target vehicle was issued. The statistical experimental results are shown in Table 1.

[0116] Experimental level sequence Ratio of sample variance of early warning risk value between the present invention and the prior art First experiment 0.75 Second experiment 0.71 The third experiment 0.76 The fourth experiment 0.72 The fifth experiment 0.79 The sixth experiment 0.72 The seventh experiment 0.78 The eighth experiment 0.71 The ninth experiment 0.76 The tenth experiment 0.75

[0117] Table 1

[0118] It can be seen from Table 1 that the sample variance of the prediction value of the prior art is greater than the sample variance of the prediction value of the present invention. The sample variance is an unbiased estimate of the variance and can reflect the warning error. It can be seen that the prediction method of the vehicle driving parameters in the present application can ensure the accuracy and real-time performance of determining the vehicle driving parameters, thereby providing a warning of the driving risk of the target vehicle, which has higher accuracy than the prior art.

[0119] In the technical solutions provided in some embodiments of the present application, firstly, the driving parameters of the vehicles including the target vehicle and at least one first reference vehicle at the first moment, and the driving parameters of at least one second reference vehicle at the second moment are obtained, and then the driving parameter change information of the reference vehicle between the first moment and the second moment is determined through the driving parameters of the first reference vehicle at the first moment and the driving parameters of the second reference vehicle at the second moment, and the driving parameters of the target vehicle at the second moment are predicted through the driving parameter change information based on the driving parameters of the target vehicle at the first moment. Since the vehicle will be restricted by the surrounding vehicles when driving on the road, that is, the vehicles are interrelated, the average change of the driving state of all vehicles can reflect the change of the driving state of a certain vehicle to a certain extent, therefore, the driving parameters of the target vehicle at any second moment can be predicted through the driving parameter change information, so that the real-time determination of the vehicle driving parameters can be ensured.

[0120] The following describes an embodiment of the device of the present application, which can be used to execute the method for predicting vehicle driving parameters in the above embodiment of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the method for predicting vehicle driving parameters in the above embodiment of the present application.

[0121] Fig. 9 A block diagram of a vehicle driving parameter prediction device according to an embodiment of the present application is shown.

[0122] Reference Fig. 9 As shown, a vehicle driving parameter prediction device 900 according to an embodiment of the present application includes: a first acquisition unit 901 , a second acquisition unit 902 , a determination unit 903 , and a prediction unit 904 .

[0123] Among them, the first acquisition unit 901 is used to acquire the driving parameters of at least two vehicles at a first moment, and the at least two vehicles include a target vehicle and at least one first reference vehicle; the second acquisition unit 902 is used to acquire the driving parameters of at least one second reference vehicle at a second moment, and the second moment is after the first moment; the determination unit 903 is used to determine the driving parameter change information of the reference vehicle between the first moment and the second moment according to the driving parameters of the at least one first reference vehicle at the first moment and the driving parameters of the second reference vehicle at the second moment; the prediction unit 904 is used to predict the driving parameters of the target vehicle at the second moment based on the driving parameters of the target vehicle at the first moment and the driving parameter change information.

[0124] In some embodiments of the present application, based on the aforementioned scheme, the determination unit 903 is configured to: determine the first average driving parameter of the first reference vehicle at the first moment according to the driving parameters of the at least one first reference vehicle at the first moment and the number of first reference vehicles; determine the second average driving parameter of the second reference vehicle at the second moment according to the driving parameters of the at least one second reference vehicle at the second moment and the number of second reference vehicles; determine the driving parameter change information of the reference vehicle between the first moment and the second moment according to the first average driving parameter and the second average driving parameter.

[0125] In some embodiments of the present application, based on the aforementioned scheme, the determination unit 903 is configured to: determine the ratio between the second average driving parameter and the first average driving parameter as driving parameter change information of the reference vehicle between the first moment and the second moment.

[0126] In some embodiments of the present application, based on the aforementioned scheme, the device also includes a calculation unit, which is used to calculate the second driving risk of the target vehicle at the second moment through a driving risk model based on the driving parameters of the target vehicle at the second moment and the driving parameters of the at least one second reference vehicle at the second moment.

[0127] In some embodiments of the present application, based on the aforementioned scheme, the driving parameters include driving speed and driving acceleration, and the calculation unit is configured as: based on the driving speed of the target vehicle at the second moment and the driving speed at the first moment, calculate the indirect acceleration of the target vehicle at the second moment to obtain a first absolute value of the difference between the driving acceleration of the target vehicle at the second moment and the indirect acceleration; calculate the ratio between the absolute value of the first difference and the driving acceleration of the target vehicle at the second moment to obtain a first ratio; calculate the ratio of the absolute value of the first difference to the indirect acceleration to obtain a second ratio; when both the first ratio and the second ratio do not exceed a first predetermined threshold, calculate the second driving risk of the target vehicle at the second moment through a driving risk model.

[0128] In some embodiments of the present application, based on the aforementioned solution, the device further includes a sending unit, which is used to send the second driving risk to the target vehicle after calculating the second driving risk of the target vehicle at the second moment through a driving risk model.

[0129] In some embodiments of the present application, based on the aforementioned scheme, the sending unit is configured to: determine a first driving risk of the target vehicle at a first moment; calculate a second absolute value of the difference between the first driving risk and the second driving risk; and send the second driving risk to the target vehicle when the ratio between the absolute value of the second difference and the first driving risk does not exceed a second predetermined threshold.

[0130] In some embodiments of the present application, based on the aforementioned scheme, the sending unit is configured to: when the ratio between the absolute value of the second difference and the first driving risk exceeds a second predetermined threshold, calculate the credibility of the second driving risk by the ratio between the absolute value of the second difference and the first driving risk; and send the second driving risk and the credibility corresponding to the second driving risk to the target vehicle.

[0131] In some embodiments of the present application, based on the aforementioned scheme, the sending unit is configured to: calculate the first total mass of the at least one first reference vehicle based on the vehicle mass of the first reference vehicle; calculate the second total mass of the at least one second reference vehicle based on the vehicle mass of the second reference vehicle; calculate the absolute value of the third difference between the first total mass and the second total mass; and send the second driving risk to the target vehicle when the ratio between the absolute value of the third difference and the first total mass does not exceed a third predetermined threshold.

[0132] Fig.10 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown.

[0133] It should be noted that Fig.10 The computer system 1000 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0134] like Fig.10As shown, the computer system 1000 includes a central processing unit (CPU) 1001, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 1002 or the program loaded from the storage part 1008 to the random access memory (RAM) 1003, such as executing the method described in the above embodiment. In the RAM 1003, various programs and data required for system operation are also stored. The CPU 1001, the ROM 1002 and the RAM 1003 are connected to each other through the bus 1004. The input / output (I / O) interface 1005 is also connected to the bus 1004.

[0135] The following components are connected to the I / O interface 1005: an input section 1006 including a keyboard, a mouse, etc.; an output section 1007 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the I / O interface 1005 as needed. A removable medium 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1010 as needed so that a computer program read therefrom is installed into the storage section 1008 as needed.

[0136] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part 1009, and / or installed from a removable medium 1011. When the computer program is executed by a central processing unit (CPU) 1001, various functions defined in the system of the present application are executed.

[0137] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, - but not limited to - an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a 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 above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by an instruction execution system, device or device or used in combination with it. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0138] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0139] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0140] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the method described in the above embodiment.

[0141] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0142] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the implementation methods of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation methods of the present application.

[0143] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not disclosed in the present application.

[0144] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for predicting vehicle driving parameters, characterized in that: The method comprises: Acquire driving parameters of at least two vehicles at a first moment, wherein the at least two vehicles include a target vehicle and at least one first reference vehicle; Acquire driving parameters of at least one second reference vehicle at a second moment, where the second moment is after the first moment; Determining a first average driving parameter of the at least one first reference vehicle at the first moment according to the driving parameter of the at least one first reference vehicle at the first moment and the number of first reference vehicles; Determining a second average driving parameter of the second reference vehicle at the second moment according to the driving parameter of the at least one second reference vehicle at the second moment and the number of second reference vehicles; Determining driving parameter change information of a reference vehicle between a first moment and a second moment according to the first average driving parameter and the second average driving parameter; Based on the driving parameter of the target vehicle at the first moment and the driving parameter change information, the driving parameter of the target vehicle at the second moment is predicted.

2. The method according to claim 1, characterized in that The step of determining driving parameter change information of the reference vehicle between a first moment and a second moment according to the first average driving parameter and the second average driving parameter comprises: A ratio between the second average driving parameter and the first average driving parameter is determined as driving parameter change information of the reference vehicle between a first time and a second time.

3. The method according to claim 1, characterized in that After predicting the driving parameters of the target vehicle at the second moment based on the driving parameters of the target vehicle at the first moment and the driving parameter change information, the method further includes: Based on the driving parameters of the target vehicle at the second moment and the driving parameters of the at least one second reference vehicle at the second moment, a second driving risk of the target vehicle at the second moment is calculated by a driving risk model.

4. The method according to claim 3, characterized in that The driving parameters include driving speed and driving acceleration. Before calculating the second driving risk of the target vehicle at the second moment by using the driving risk model, the method further includes: Based on the driving speed of the target vehicle at the second moment and the driving speed of the target vehicle at the first moment, calculating the indirect acceleration of the target vehicle at the second moment to obtain a first absolute value of the difference between the driving acceleration of the target vehicle at the second moment and the indirect acceleration; Calculating a ratio between the absolute value of the first difference and the driving acceleration of the target vehicle at the second moment to obtain a first ratio; Calculating a ratio of the absolute value of the first difference to the indirect acceleration to obtain a second ratio; When both the first ratio and the second ratio do not exceed a first predetermined threshold, a second driving risk of the target vehicle at a second moment is calculated by using a driving risk model.

5. The method according to claim 3, characterized in that: After calculating the second driving risk of the target vehicle at the second moment by using the driving risk model, the method further includes: The second driving risk is sent to the target vehicle.

6. The method according to claim 5, characterized in that Before sending the second driving risk to the target vehicle, the method further includes: Determining a first driving risk of the target vehicle at a first moment; calculating a second absolute value of a difference between the first driving risk and the second driving risk; When the ratio between the absolute value of the second difference and the first driving risk does not exceed a second predetermined threshold, the second driving risk is sent to the target vehicle.

7. The method according to claim 6, characterized in that The method further comprises: When the ratio between the absolute value of the second difference and the first driving risk exceeds a second predetermined threshold, calculating the credibility of the second driving risk by the ratio between the absolute value of the second difference and the first driving risk; The second driving risk and the credibility corresponding to the second driving risk are sent to the target vehicle.

8. The method according to claim 5, characterized in that The driving parameter includes vehicle mass. Before sending the second driving risk to the target vehicle, the method further includes: Calculating a first total mass of the at least one first reference vehicle based on the vehicle mass of the first reference vehicle; calculating a second total mass of the at least one second reference vehicle based on the vehicle mass of the second reference vehicle; calculating a third absolute value of a difference between the first total mass and the second total mass; When a ratio between the third absolute value of the difference and the first total mass does not exceed a third predetermined threshold, the second driving risk is sent to the target vehicle.

9. A vehicle driving parameter prediction device, characterized in that: The device comprises: A first acquisition unit is used to acquire driving parameters of at least two vehicles at a first moment, wherein the at least two vehicles include a target vehicle and at least one first reference vehicle; A second acquisition unit is used to acquire the driving parameters of at least one second reference vehicle at a second moment, where the second moment is after the first moment; a determination unit, configured to determine a first average driving parameter of the first reference vehicle at the first moment according to the driving parameter of the at least one first reference vehicle at the first moment and the number of first reference vehicles; determine a second average driving parameter of the second reference vehicle at the second moment according to the driving parameter of the at least one second reference vehicle at the second moment and the number of second reference vehicles; and determine driving parameter change information of the reference vehicle between the first moment and the second moment according to the first average driving parameter and the second average driving parameter; The prediction unit is used to predict the driving parameters of the target vehicle at a second moment based on the driving parameters of the target vehicle at a first moment and the driving parameter change information.

10. A computer readable medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for predicting vehicle driving parameters according to any one of claims 1 to 8 is implemented.

11. A computer device, characterized in that: include: one or more processors; A memory for storing one or more computer programs, which, when executed by the one or more processors, enables the computer device to implement the method for predicting vehicle driving parameters according to any one of claims 1 to 8.

12. A computer program product, characterized in that The computer program product includes a computer program, which is stored in a computer-readable storage medium. A processor of a computer device reads and executes the computer program from the computer-readable storage medium, so that the computer device executes the vehicle driving parameter prediction method according to any one of claims 1 to 8.

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