Vehicle early warning method and device

Abstract

The embodiment of the present application provides a vehicle early warning method and device. The vehicle early warning method includes: obtaining the driving status data of the target vehicle and the driving status data of other vehicles located around the target vehicle; calculating the first driving risk value between the target vehicle and the other vehicles based on the driving status data of the target vehicle and the driving status data of the other vehicles; obtaining the driving behavior probabilities of historical vehicles in the lane where the target vehicle is located corresponding to different categories of driving behaviors, and calculating the correlation coefficient between any two of the driving behavior probabilities; calculating the second driving risk value between the target vehicle and the other vehicles based on the first driving risk value and the correlation coefficient, so as to warn the target vehicle based on the second driving risk value. The technical solution of the embodiment of the present application improves the accuracy of the vehicle driving risk calculation, thereby ensuring the early warning effect on the vehicle.

Description

Technical Field

[0001] The present application relates to the field of driving safety technology, and in particular to a vehicle early warning method and device. Background Art

[0002] When a vehicle is driving on the road, other vehicles driving around it may bring certain driving risks to the vehicle. In the current technical solution, the driving risk between the current vehicle and other vehicles is calculated based on the driving parameters of the current vehicle and other vehicles, so as to warn the current vehicle based on the driving risk. However, when a vehicle is driving on the road, other vehicles around it may also affect each other and thus affect the driving safety of the current vehicle. Therefore, how to improve the accuracy of driving risk calculation and thus ensure the warning effect of the vehicle has become a technical problem that needs to be solved urgently. Summary of the invention

[0003] The embodiments of the present application provide a vehicle warning method and device, which can improve the accuracy of driving risk calculation to at least a certain extent, thereby ensuring the warning effect on the vehicle.

[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 vehicle early warning method is provided, the method comprising:

[0006] Acquire driving status data of a target vehicle and driving status data of other vehicles located around the target vehicle;

[0007] Calculating a first driving risk value between the target vehicle and the other vehicles according to the driving state data of the target vehicle and the driving state data of the other vehicles;

[0008] Obtaining driving behavior probabilities of historical vehicles in the lane where the target vehicle is located corresponding to different categories of driving behaviors, and calculating a correlation coefficient between any two of the driving behavior probabilities;

[0009] A second driving risk value between the target vehicle and the other vehicles is calculated according to the first driving risk value and the correlation coefficient, so as to issue an early warning to the target vehicle according to the second driving risk value.

[0010] According to one aspect of an embodiment of the present application, a vehicle warning device is provided, the device comprising:

[0011] A first acquisition module is used to acquire the driving state data of the target vehicle and the driving state data of other vehicles located around the target vehicle;

[0012] A calculation module, used for calculating a first driving risk value between the target vehicle and the other vehicles according to the driving state data of the target vehicle and the driving state data of the other vehicles;

[0013] A second acquisition module is used to obtain driving behavior probabilities of historical vehicles in the lane where the target vehicle is located corresponding to different types of driving behaviors, and calculate the correlation coefficient between any two of the driving behavior probabilities;

[0014] A processing module is used to calculate a second driving risk value between the target vehicle and the other vehicles according to the first driving risk value and the correlation coefficient, so as to issue an early warning to the target vehicle according to the second driving risk value.

[0015] Based on the aforementioned scheme, in some embodiments of the present application, the processing module is configured to: determine the vehicle to be calculated among the other vehicles; determine the target correlation coefficient corresponding to the relative position from the correlation coefficient based on the relative position between the vehicles other than the vehicle to be calculated among the other vehicles and the target vehicle; calculate the second driving risk value between the target vehicle and the vehicle to be calculated according to the first driving risk value corresponding to the vehicle to be calculated, the target correlation coefficient and the first driving risk value corresponding to the target correlation coefficient.

[0016] Based on the aforementioned scheme, in some embodiments of the present application, the second acquisition module is configured to: obtain the lane information of the target vehicle; generate a driving behavior data request for the historical vehicle based on the lane information, and send the driving behavior data request to the server; receive the driving behavior data sent by the server, and calculate the driving behavior probability based on the driving behavior data; and calculate the correlation coefficient between the driving behavior probabilities based on the driving behavior probability.

[0017] Based on the aforementioned scheme, in some embodiments of the present application, the second acquisition module is configured to: obtain time information corresponding to the driving behavior according to the driving behavior data; divide the driving behavior according to the time information to obtain the corresponding driving behavior in each historical unit time; and calculate the driving behavior probabilities of different categories of driving behaviors in each historical unit time according to the categories of driving behaviors corresponding to each historical unit time.

[0018] Based on the aforementioned scheme, in some embodiments of the present application, the second acquisition module is configured to: obtain the driving behavior probabilities of the same category of driving behaviors within each historical unit time to obtain a driving behavior probability set corresponding to each category of driving behavior; and calculate the correlation coefficient between the driving behavior probabilities of any two of the driving behaviors based on the driving behavior probabilities contained in the driving behavior probability set.

[0019] Based on the aforementioned scheme, in some embodiments of the present application, the second acquisition module is configured to: determine the lane information of the target vehicle; generate a driving behavior probability request for historical vehicles based on the lane information, and send the driving behavior probability request to the server; receive the driving behavior probability of historical vehicles sent by the server, and calculate the correlation coefficient between any two of the driving behavior probabilities based on the driving behavior probability.

[0020] Based on the aforementioned solution, in some embodiments of the present application, the processing module is configured to: if the second driving risk value is greater than or equal to a predetermined threshold, issue an early warning to the target vehicle.

[0021] Based on the aforementioned scheme, in some embodiments of the present application, the processing module is configured to: perform corresponding warning processing based on the threshold interval in which the second driving risk value is located, and the importance level of the warning processing is positively correlated with the second driving risk value represented by the threshold interval.

[0022] Based on the aforementioned scheme, in some embodiments of the present application, the driving status data includes vehicle data and road condition data, and the calculation module is configured to: calculate the first driving risk value between the target vehicle and the other vehicles based on the vehicle data and road condition data of the target vehicle and the vehicle data and road condition data of the other vehicles.

[0023] 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 vehicle warning method as described in the above embodiment is implemented.

[0024] 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 vehicle warning method as described in the above embodiments.

[0025] In the technical solutions provided in some embodiments of the present application, by obtaining the driving status data of the target vehicle and the driving status data of other vehicles located around the target vehicle, the first driving risk value between the target vehicle and the other vehicles is calculated based on the driving status data of the target vehicle and the driving status data of the other vehicles, and then the driving behavior probabilities of the historical vehicles in the lane where the target vehicle is located corresponding to different categories of driving behaviors are obtained, and the correlation coefficient between any two driving behavior probabilities is calculated, and the second driving risk value between the target vehicle and the other vehicles is calculated based on the first driving risk value and the correlation coefficient, so as to warn the target vehicle based on the second driving risk value. Therefore, the driving risk brought to the target vehicle by the mutual correlation between other vehicles can be expressed through the second driving risk value, which ensures the accuracy of the calculation of the driving risk, and further ensures the warning effect on the target vehicle.

[0026] 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

[0027] 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:

[0028] 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;

[0029] Figure 2 A schematic diagram of a vehicle early warning method according to an embodiment of the present application is shown;

[0030] Figure 3 An embodiment according to the present application is shown. Figure 2 A schematic flow chart of step S240 in the vehicle early warning method;

[0031] Figure 4 An embodiment according to the present application is shown. Figure 2 A schematic flow chart of step S230 in the vehicle early warning method;

[0032] Figure 5 An embodiment according to the present application is shown. Figure 4 A flow chart of calculating the probability of driving behavior in a vehicle early warning method;

[0033] Figure 6 An embodiment according to the present application is shown. Figure 4 A schematic flow chart of step S440 in the vehicle early warning method;

[0034] Figure 7 Another embodiment according to the present application is shown. Figure 2 A schematic flow chart of step S230 in the vehicle early warning method;

[0035] Figure 8 A schematic diagram of a vehicle early warning method according to an embodiment of the present application is shown;

[0036] Fig. 9 A block diagram of a vehicle warning device according to an embodiment of the present application is shown;

[0037] 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

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] Figure 1A schematic diagram of an exemplary system architecture to which the technical solution of the embodiments of the present application can be applied is shown.

[0043] like Figure 1 As shown, the system architecture may include a vehicle-mounted terminal 110 and a server 120 , wherein the vehicle-mounted terminal 110 and the server 120 interact via a network, and the network may include various connection types, such as a wired communication link, a wireless communication link, and the like.

[0044] It should be understood that Figure 1 The number of vehicle terminals 110 and servers 120 in the embodiment is only for illustration. Any number of vehicle terminals and servers may be provided as required. For example, the vehicle terminals 110 may be vehicle terminals configured for all vehicles traveling on the road, and the servers 120 may be a server cluster composed of multiple servers.

[0045] The vehicle terminal 110 can interact with the server 120 through the network to receive or send information, etc. The server 120 can be a server that provides various services. The vehicle terminal 110 can obtain the driving state data of the target vehicle and the driving state data of other vehicles located around the target vehicle, and calculate the first driving risk value between the target vehicle and the other vehicles based on the driving state data of the target vehicle and the driving state data of the other vehicles, obtain the driving behavior probabilities of the historical vehicles in the lane where the target vehicle is located corresponding to different categories of driving behaviors, and calculate the correlation coefficient between any two driving behavior probabilities, and calculate the second driving risk value between the target vehicle and the other vehicles based on the first driving risk value and the correlation coefficient, so as to warn the target vehicle according to the second driving risk value.

[0046] It should be noted that the vehicle warning method provided in the embodiment of the present application is generally executed by the vehicle terminal 110, and accordingly, the vehicle warning device is generally arranged in the vehicle terminal 110. However, in other embodiments of the present application, the server 120 may also have similar functions as the vehicle terminal, so as to execute the solution of the vehicle warning method provided in the embodiment of the present application.

[0047] The implementation details of the technical solution of the embodiment of the present application are described in detail below:

[0048] Figure 2 FIG. 1 is a flow chart of a vehicle warning method according to an embodiment of the present application. Figure 2 As shown, the vehicle warning processing method at least includes steps S210 to S240, which are described in detail as follows:

[0049] In step S210, the driving status data of the target vehicle and the driving status data of other vehicles located around the target vehicle are acquired.

[0050] The driving state data may be parameter information used to describe the driving behavior of the vehicle on the road and the environment in which it is located. For example, the driving state data may include but is not limited to the vehicle's driving speed, acceleration, driving direction, positioning information, road slope, road viscosity, and visibility, etc. The current situation of the vehicle when driving on the road can be known based on the driving state data.

[0051] In one embodiment of the present application, the vehicle-mounted terminal configured on the target vehicle can obtain the driving status data of the target vehicle through the sensors set on the target vehicle, for example, the driving speed of the target vehicle can be obtained through the speed sensor, the acceleration of the target vehicle can be obtained through the accelerometer, the positioning information and driving direction of the target vehicle can be obtained through the GPS (Global Positioning System), etc. The vehicle-mounted terminal can also communicate with the server of a third-party department through the network to obtain the driving status data, for example, it can communicate with the server of the meteorological department to obtain the visibility of the current road, and communicate with the server of the traffic control department to obtain the slope of the current road, road viscosity, etc.

[0052] In addition, the vehicle-mounted terminal can also obtain the driving status data of other vehicles around the target vehicle by communicating with the server. Specifically, the vehicle-mounted terminal can generate a driving status data request for other vehicles based on the positioning information of the target vehicle, and send the driving status data request to the server. The server can determine other vehicles around the target vehicle based on the positioning information included in the driving status data request, and send the driving status data of the other vehicles to the vehicle-mounted terminal.

[0053] It should be noted that other vehicles located around the target vehicle may be the vehicles closest to the target vehicle in all directions of the target vehicle. For example, the other vehicles may be the vehicle located behind the target vehicle and closest to the target vehicle, the vehicle located in front of the target vehicle and closest to the target vehicle, the vehicle located in front of the left of the target vehicle and closest to the target vehicle, or the vehicle located on the left side of the target vehicle and closest to the target vehicle, and so on.

[0054] In one embodiment of the present application, eight directions can be divided based on the location of the target vehicle, namely, front, left front, right front, left side, left rear, right side, right rear, and rear. Thus, the server can determine other vehicles located at various directions of the target vehicle based on the location of the target vehicle. The directions of each other vehicle are then associated with the driving status data of the other vehicle and sent to the vehicle-mounted terminal of the target vehicle, so that the vehicle-mounted terminal can determine the direction of a certain other vehicle and the driving status data of the other vehicle.

[0055] In step S220, a first driving risk value between the target vehicle and the other vehicles is calculated according to the driving state data of the target vehicle and the driving state data of the other vehicles.

[0056] The first driving risk value may be a value used to describe the current driving risk of the target vehicle. The larger the first driving risk value is, the higher the current driving risk of the target vehicle is.

[0057] In this embodiment, the vehicle terminal calculates the first driving risk value between the target vehicle and each other vehicle based on the driving state data of the target vehicle and the driving state data of other vehicles, so as to clarify the driving risk brought by each other vehicle to the target vehicle. It should be understood that because the driving state data of each other vehicle is different, the first driving risk value calculated based on the driving state data of each other vehicle may also be different.

[0058] In one embodiment of the present application, the vehicle terminal can calculate the collision intensity between the target vehicle and other vehicles based on the driving status data of the target vehicle and the driving status data of other vehicles, and use the collision intensity as the first driving risk value. The higher the collision intensity, the greater the damage to the target vehicle, and therefore the higher the driving risk; conversely, the lower the collision intensity, the lower the damage to the target vehicle, and the lower the driving risk.

[0059] In step S230, the driving behavior probabilities corresponding to different categories of driving behaviors of historical vehicles in the lane where the target vehicle is located are obtained, and the correlation coefficient between any two of the driving behavior probabilities is calculated.

[0060] The historical vehicle may be a vehicle that has traveled through the lane where the target vehicle is located.

[0061] Driving behavior may be the driver's control behavior of the vehicle, which may include multiple categories. For example, the driving behavior may include driving straight ahead (i.e. accelerating), driving straight behind (i.e. decelerating), driving straight left (i.e. changing lanes quickly to the left), driving straight right (i.e. changing lanes quickly to the right), driving to the left front, driving to the right front, driving to the left rear, and driving to the right rear.

[0062] The driving behavior probability may be used to represent the probability of possible driving behaviors of the vehicle, for example, the probability of the vehicle driving straight ahead is 20%, the probability of driving straight left is 15%, etc. Through the driving behavior probability, the probability of each driving behavior of the vehicle can be known.

[0063] In this embodiment, the server can record the driving behavior of historical vehicles passing through a certain lane through a camera device installed on the road, and count the number of driving behaviors of vehicles passing through the lane per unit time, so as to calculate the driving behavior probabilities of different categories of driving behaviors of historical vehicles.

[0064] The vehicle-mounted terminal can obtain the driving behavior probabilities of different categories of driving behaviors of historical vehicles in the lane where the target vehicle is located in each unit time from the server, and calculate the correlation coefficient between any two driving behavior probabilities based on the driving behavior probabilities, so as to express the degree of linear correlation between the two driving behavior probabilities based on the correlation coefficient.

[0065] It should be understood that the correlation coefficient between the driving behavior probabilities should correspond to the two driving behaviors. For example, the correlation coefficient calculated based on the driving behavior probability of driving due left and the driving behavior probability of driving due forward should correspond to the driving behavior of driving due left and the driving behavior of driving due forward.

[0066] In step S240, a second driving risk value between the target vehicle and the other vehicles is calculated according to the first driving risk value and the correlation coefficient, so as to issue an early warning to the target vehicle according to the second driving risk value.

[0067] In this embodiment, the vehicle terminal can calculate the second driving risk value according to the first driving risk value of other vehicles and the correlation coefficients corresponding to other vehicles. The second driving risk value can be used to indicate the driving risk brought to the target vehicle by a certain vehicle among other vehicles affected by other vehicles other than the vehicle. It should be understood that each other vehicle corresponds to a second driving risk value, and the second driving risk values ​​of different other vehicles can be the same or different.

[0068] exist Figure 2 In the embodiment shown, the first driving risk value between the target vehicle and other vehicles is calculated based on the driving state data of the target vehicle and the driving state data of other vehicles located around the target vehicle, and then the driving behavior probabilities of the historical vehicles in the lane where the target vehicle is located corresponding to different categories of driving behaviors are obtained, and the correlation coefficient between any two driving behavior probabilities is calculated, and the second driving risk value between the target vehicle and other vehicles is calculated based on the first driving risk value and the correlation coefficient, so as to warn the target vehicle according to the second driving risk value. Thus, the second driving risk value can be used to indicate the driving risk brought to the target vehicle by a certain vehicle among other vehicles affected by other vehicles other than the target vehicle, so as to improve the accuracy of calculating the driving risk of the target vehicle, thereby ensuring the warning effect on the target vehicle.

[0069] based on Figure 2The embodiment shown, Figure 3 An embodiment according to the present application is shown. Figure 2 Schematic diagram of the process of step S240 in the vehicle warning method. Figure 3 As shown, step S240 at least includes steps S310 to S330, which are described in detail as follows:

[0070] In step S310, a vehicle to be calculated among the other vehicles is determined.

[0071] In one embodiment of the present application, the vehicle-mounted terminal can determine the vehicle to be calculated among other vehicles according to a predetermined selection order. For example, based on the position of the target vehicle, taking other vehicles directly in front of the target vehicle as the starting point, the vehicles to be calculated are selected in turn in a clockwise direction to calculate the second driving risk value between the target vehicle and each vehicle to be calculated.

[0072] In another embodiment of the present application, the vehicle-mounted terminal can determine the distance between the target vehicle and each other vehicle based on the location information of the target vehicle and the location information of other vehicles, and determine the vehicles to be calculated among the other vehicles in order of distance from small to large, thereby quickly determining the second driving risk value of other vehicles that are closer to the target vehicle, so as to warn the target vehicle as soon as possible, provide the driver of the target vehicle with more reaction time, and ensure the warning effect.

[0073] In step S320, based on the relative position between the target vehicle and the other vehicles except the vehicle to be calculated, a target correlation coefficient corresponding to the relative position is determined from the correlation coefficients.

[0074] In this embodiment, the vehicle-mounted terminal can determine the relative position between the target vehicle and other vehicles based on the position information of the target vehicle and the position information of other vehicles, for example, the other vehicle is located directly in front of the target vehicle, directly to the left of the target vehicle, or in front of the left of the target vehicle, etc.

[0075] According to the relative position of the target vehicle and other vehicles except the vehicle to be calculated, the target correlation coefficient corresponding to the relative position can be determined from the correlation coefficient. Specifically, based on the relative position of other vehicles and the target vehicle, the relative position of the vehicle to be calculated and the relative position of the vehicles except the vehicle to be calculated are combined in pairs, so as to select the correlation coefficient corresponding to the combined relative position as the target correlation coefficient. For example, there are other vehicles directly in front of, directly to the left and in front of the left of the target vehicle. If it is determined that the other vehicle directly in front of the target vehicle is the vehicle to be calculated, the combination methods include directly in front and directly to the left and directly in front and left in front. Then, the target correlation coefficient to be selected is the correlation coefficient between the probability of driving behavior of driving directly in front and driving directly to the left, and the correlation coefficient between the probability of driving behavior of driving directly in front and driving in front of the left.

[0076] It should be understood that when there are other vehicles in different directions of the target vehicle, if other vehicles in a certain direction pose a threat to the target vehicle, the driving behavior of the target vehicle is also affected by other vehicles in other directions. For example, when the vehicle directly in front of the target vehicle poses a threat to the target vehicle, if there is a vehicle directly to the left of the target vehicle, the target vehicle cannot drive to the left to avoid the risk, that is, the driving risk brought to the target vehicle by the vehicle directly to the left of the target vehicle through the vehicle directly in front of the target vehicle. Therefore, according to the relative position between the target vehicle and other vehicles, the target correlation coefficient is selected from the correlation coefficients, which can express the connection between the driving risks posed to the target vehicle by other vehicles located in two directions, thereby ensuring the accuracy of the second driving risk value.

[0077] In step S330, a second driving risk value between the target vehicle and the vehicle to be calculated is calculated according to the first driving risk value corresponding to the vehicle to be calculated, the target correlation coefficient, and the first driving risk value corresponding to the target correlation coefficient.

[0078] In this embodiment, when calculating the second driving risk value between the target vehicle and the vehicle to be calculated, the vehicle-mounted terminal can perform calculation according to the first driving risk value corresponding to the vehicle to be calculated, the target correlation coefficient, and the first driving risk value corresponding to the target correlation coefficient.

[0079] It should be noted that the first driving risk value corresponding to the target correlation coefficient is the first driving risk value corresponding to the other vehicles at the two relative positions corresponding to the target correlation coefficient, except for the vehicle to be calculated. For example, the target correlation coefficient is the correlation coefficient between the driving behavior probabilities of driving straight ahead and driving straight left. If the other vehicle located directly in front of the target vehicle is the vehicle to be calculated, then the first driving risk value corresponding to the target correlation coefficient is the first driving risk value corresponding to the other vehicle located directly left of the target vehicle.

[0080] Specifically, the first driving risk value corresponding to the vehicle to be calculated can be added to the product of the target correlation coefficient multiplied by the first driving risk value corresponding to the target correlation coefficient to obtain the second driving risk value between the target vehicle and the vehicle to be calculated. For example, if there are other vehicles directly in front of, directly to the left of, and in front of the left of the target vehicle, if it is determined that the other vehicle directly in front of the target vehicle is the vehicle to be calculated, then the second driving risk value between the target vehicle and the vehicle to be calculated is:

[0081] E front =e front +C front,left e left +C front,left-front e left-front

[0082] Among them, e front is the first driving risk value corresponding to the vehicle to be calculated, C front，left is the correlation coefficient between the probability of driving forward and driving to the left, e left is the first driving risk value corresponding to other vehicles located to the left of the target vehicle, C front，left-front is the correlation coefficient between the driving behaviors of driving forward and driving forward to the left, e left-front is the first driving risk value corresponding to other vehicles located in front of the left side of the target vehicle.

[0083] Thus, the second driving risk value between each other vehicle and the target vehicle can be calculated one by one.

[0084] exist Figure 3 In the embodiment shown, by determining the vehicle to be calculated among other vehicles, and based on the relative position between the target vehicle and the vehicles other than the vehicle to be calculated among other vehicles, the target correlation coefficient corresponding to the relative position is determined from the correlation coefficient, and then the second driving risk value between the target vehicle and the vehicle to be calculated is calculated according to the first driving risk value corresponding to the vehicle to be calculated, the target correlation coefficient, and the first driving risk value corresponding to the target correlation coefficient. In this way, the second driving risk value between the target vehicle and the other vehicles can be accurately calculated, and the second driving risk value can express the driving risk brought to the target vehicle by other vehicles other than the vehicle to be calculated through the vehicle to be calculated, so that the calculated second driving risk value can be closer to the actual situation, ensuring the warning effect on the target vehicle.

[0085] based on Figure 2 The embodiment shown, Figure 4 An embodiment according to the present application is shown. Figure 2 Schematic diagram of the process of step S230 in the vehicle warning method. Figure 4As shown, step S230 at least includes steps S410 to S440, which are described in detail as follows:

[0086] In step S410, the lane information of the target vehicle is obtained.

[0087] In this embodiment, the vehicle terminal can compare the positioning information of the target vehicle with the position information corresponding to each lane to determine the lane information where the target vehicle is located, for example, the target vehicle is located in the left lane of XXX Road, etc.

[0088] In step S420, a driving behavior data request for historical vehicles is generated according to the lane information, and the driving behavior data request is sent to a server.

[0089] The driving behavior data may be information used to describe the driving behavior of the vehicle, and the driving behavior data may include but is not limited to time information of the driving behavior and the category of the driving behavior, etc.

[0090] The request for the driving behavior data of the historical vehicle may be information for requesting the driving behavior data of the historical vehicle in the lane where the target vehicle is located.

[0091] In this embodiment, the vehicle terminal can generate a driving behavior data request for the historical vehicle according to the lane information of the target vehicle, and send the driving behavior data request to the server. The server parses the received driving behavior data request to obtain the road information of the target vehicle, and obtains the driving behavior data of the historical vehicle corresponding to the road information based on the road information.

[0092] In step S430, the driving behavior data sent by the server is received, and the driving behavior probability is calculated according to the driving behavior data.

[0093] In one embodiment of the present application, the vehicle terminal may receive driving behavior data sent by the server, and sort each driving behavior according to the sequence of time information in the driving behavior data, and calculate the driving behavior probability of different types of driving behaviors within each predetermined number interval according to each predetermined number interval. The predetermined number interval may be a pre-configured value for dividing driving behaviors, for example, the predetermined number interval may be 100, 150 or 200, etc. The above is only an illustrative example, and the present application does not make any special limitation on this.

[0094] Thus, the vehicle terminal can select driving behaviors of a predetermined number of intervals from the driving behavior data in chronological order for calculation. For example, if the predetermined number of intervals is 100, the driving behavior probabilities of different types of driving behaviors are calculated in every 100 driving behaviors in chronological order. Thus, the driving behavior probabilities of different types of driving behaviors in each predetermined number of intervals of driving behaviors can be obtained.

[0095] In step S440, a correlation coefficient between any two driving behavior probabilities is calculated based on the driving behavior probabilities.

[0096] In this embodiment, the vehicle terminal can calculate the correlation coefficient between any two driving behavior probabilities based on the driving behavior probabilities of different categories of driving behaviors, wherein the method for calculating the correlation coefficient is a prior art and will not be repeated in this application.

[0097] exist Figure 4 In the embodiment shown, by obtaining the lane information where the target vehicle is located, and generating a driving behavior data request for historical vehicles based on the lane information to send to the server, the server obtains the driving behavior data of historical vehicles in the lane where the target vehicle is located based on the driving behavior data request, and sends it to the vehicle terminal, so that the vehicle terminal can calculate the driving behavior probability based on the driving behavior data, and then calculate the correlation coefficient between any two driving behavior probabilities. In this way, the server can collect a sufficient number of driving behavior data of historical vehicles in advance to ensure the reliability of the collected driving behavior data, and then ensure the accuracy of the calculation results of the driving behavior probability and the correlation coefficient.

[0098] based on Figure 2 and Figure 4 The embodiment shown, Figure 5 An embodiment according to the present application is shown. Figure 4 Schematic diagram of the process of calculating the probability of driving behavior in the vehicle warning method. Figure 5 As shown, calculating the driving behavior probability according to the driving behavior data at least includes steps S510 to S530, which are described in detail as follows:

[0099] In step S510, time information corresponding to the driving behavior is acquired according to the driving behavior data.

[0100] In this embodiment, the vehicle terminal can obtain the time information corresponding to each driving behavior from the received driving behavior data, and can determine the time when the driving behavior occurred based on the time information. For example, if the time information corresponding to a driving behavior is 2020.05.23.15.30.46, it means that a historical vehicle has this driving behavior in the lane where the target vehicle is located at 15:30:46 on May 23, 2020, and so on.

[0101] In step S520, the driving behavior is divided according to the time information to obtain the corresponding driving behavior in each historical unit time.

[0102] In this embodiment, the historical unit time can be pre-set time information for dividing the driving behavior of historical vehicles. It should be understood that in order to calculate the correlation coefficient between different driving behavior probabilities, the historical unit time should be multiple, wherein the "multiple" mentioned in this application is at least two or any number of more than two. For example, the server collects the driving behavior of historical vehicles on a certain day, and the total duration is 24 hours. If the historical unit time is 1 hour, there are 24 corresponding historical unit times. According to the time information of the driving behavior, each driving behavior can be divided into 24 historical unit times to obtain the driving behaviors corresponding to the 24 historical unit times, and so on. The above is only an illustrative example, and this application does not make any special limitation on this.

[0103] In other embodiments of the present application, the historical unit time can also be evenly divided according to the total collection time of the server to obtain a target number of historical unit times. For example, the total collection time of the server is 600 minutes, and the target number of historical unit times is 10. Then the length of each historical unit time is 60 minutes. Therefore, according to the time information of the driving behavior, each driving behavior can be divided into 10 historical unit times to obtain the corresponding driving behavior in 10 historical unit times.

[0104] In step S530, the driving behavior probabilities of different types of driving behaviors in each historical unit time are calculated according to the types of driving behaviors corresponding to each historical unit time.

[0105] In this embodiment, according to the corresponding driving behaviors in each historical unit time obtained, according to the category of driving behavior, the number of driving behaviors in each category and the total number of driving behaviors in a single historical unit time can be obtained respectively, thereby calculating the driving behavior probability of each category of driving behavior in each historical unit time respectively, that is, dividing the number of driving behaviors in each category by the total number of driving behaviors to obtain the driving behavior probability of each category of driving behavior.

[0106] based on Figure 5In the embodiment shown, the time information corresponding to the driving behavior is obtained according to the driving behavior data, and the driving behavior is divided according to the time information to obtain the driving behavior corresponding to each historical unit time, and then the driving behavior probability of the driving behavior of different categories in each historical unit time is calculated according to the category of the driving behavior corresponding to each historical unit time. In this way, the driving behavior probability of the driving behavior of different categories in each historical unit time can be accurately calculated, which ensures the accuracy of the subsequent calculation of the correlation coefficient between any two driving behavior probabilities, and further ensures the accuracy of the calculation of the second driving risk value.

[0107] based on Figure 2 , Figure 4 and Figure 5 The embodiment shown, Figure 6 An embodiment according to the present application is shown. Figure 4 Schematic diagram of the process of step S440 in the vehicle warning method. Figure 6 As shown, step S440 at least includes steps S610 to S620, which are described in detail as follows:

[0108] In step S610, the driving behavior probabilities of the same category of driving behaviors within each historical unit time are obtained to obtain a driving behavior probability set corresponding to each category of driving behavior.

[0109] In this embodiment, according to the category of driving behavior, the driving behavior probabilities of the same category in each historical unit time can be obtained and integrated to obtain a driving behavior probability set corresponding to each category of driving behavior. Each driving behavior probability set corresponds to a category of driving behavior. The order of the driving behavior probabilities included in the driving behavior probability set can be sorted according to the order of the corresponding historical unit time.

[0110] In step S620, a correlation coefficient between the driving behavior probabilities of any two driving behaviors is calculated based on the driving behavior probabilities included in the driving behavior probability set.

[0111] In this embodiment, according to the driving behavior probability set corresponding to each category of driving behavior and the driving behavior probabilities contained in the driving behavior probability set, the correlation coefficient between the driving behavior probabilities of any two driving behaviors can be calculated to obtain the degree of linear correlation between different categories of driving behaviors according to the correlation coefficient.

[0112] In one embodiment of the present application, the correlation coefficient r(X, Y) between the driving behavior probabilities of any two driving behaviors can be calculated by the following formula:

[0113]

[0114] Where X is the driving behavior probability set of driving behavior A, Y is the driving behavior probability set of driving behavior B, Cov(X,Y) is the covariance of X and Y, Var[X] is the variance of X, and Var[Y] is the variance of Y.

[0115] based on Figure 2 The embodiment shown, Figure 7 Another embodiment according to the present application is shown. Figure 2 Schematic diagram of the process of step S230 in the vehicle warning method. Figure 7 As shown, step S230 at least includes steps S710 to S730, which are described in detail as follows:

[0116] In step S710, the lane information of the target vehicle is determined.

[0117] In this embodiment, the vehicle terminal can compare the positioning information of the target vehicle with the position information corresponding to each lane to determine the lane information where the target vehicle is located, for example, the target vehicle is located in the right lane of XXX Road, etc.

[0118] In step S720, a driving behavior probability request for historical vehicles is generated according to the lane information, and the driving behavior probability request is sent to the server.

[0119] The request for the driving behavior probability of historical vehicles may be information for requesting the driving behavior probabilities of historical vehicles in the lane where the target vehicle is located corresponding to different categories of driving behaviors.

[0120] In this embodiment, the server can obtain the lane information of the target vehicle according to the received request for the driving behavior probability of the historical vehicle, and obtain the driving behavior probability of the historical vehicle in the lane corresponding to different categories of driving behaviors according to the lane information. Specifically, after collecting the driving behavior of the historical vehicle, the server can pre-calculate the driving behavior probability of the historical vehicle without the need for the vehicle terminal to perform the calculation, so as to improve the early warning efficiency of the vehicle terminal.

[0121] In step S730, the historical driving behavior probabilities of the vehicles sent by the server are received, and the correlation coefficients between the driving behavior probabilities are calculated based on the driving behavior probabilities.

[0122] In this embodiment, the vehicle terminal can calculate the correlation coefficient between any two driving behavior probabilities based on the received historical vehicle driving behavior probabilities corresponding to different categories of driving behaviors. The calculation method is as described above and will not be repeated in this application.

[0123] exist Figure 7In the illustrated embodiment, by precalculating the driving behavior probability of historical vehicles through the server, the efficiency of the vehicle-mounted terminal in calculating the correlation coefficient can be improved, thereby improving the efficiency of early warning for the target vehicle.

[0124] based on Figure 2 In the embodiment shown, in one embodiment of the present application, the target vehicle is warned according to the second driving risk value, including:

[0125] If the second driving risk value is greater than or equal to a predetermined threshold, an early warning is issued to the target vehicle.

[0126] The predetermined threshold may be pre-configured threshold information used to determine whether the target vehicle is at a high driving risk.

[0127] In this embodiment, the second driving risk value is compared with a predetermined threshold value. If the second driving risk value is greater than or equal to the predetermined threshold value, it indicates that the driving risk of the target vehicle is a high driving risk, and the driver of the target vehicle should be reminded to pay attention to driving safety. If the second driving risk value is less than the predetermined threshold value, it indicates that the driving risk of the target vehicle is a low driving risk, and therefore there is no need to warn the driver of the target vehicle to reduce the situation of false warnings.

[0128] Based on the above embodiment, in an embodiment of the present invention, if the second driving risk value is greater than or equal to a predetermined threshold, the target vehicle is warned, including:

[0129] Based on the threshold interval in which the second driving risk value is located, corresponding warning processing is performed, and the importance level of the warning processing is positively correlated with the second driving risk value represented by the threshold interval.

[0130] In this embodiment, it should be understood that the higher the second driving risk value is, the more attention the driver of the target vehicle should pay. Therefore, those skilled in the art can pre-set the corresponding driving risk threshold, and compare the second driving risk value with each driving risk threshold to obtain the threshold interval in which the second driving risk value is located, and determine the warning processing level according to the threshold interval.

[0131] It should be noted that the warning processing level is positively correlated with the size of the second driving risk value included in the threshold interval, that is, the larger the second driving risk value included in the threshold interval, the higher the corresponding warning processing level. For example, the warning processing level corresponding to the threshold interval [50,60) is medium, the warning processing level corresponding to [60,70) is high, and so on. The above is only an illustrative example, and this application does not make any special limitation on this.

[0132] In one embodiment of the present application, a person skilled in the art can pre-set different warning strategies corresponding to different warning processing levels, such as voice reminder, display screen display or tightening seat belt, etc. According to the warning processing level determined by the second driving risk value, a warning strategy corresponding to the warning processing level is selected. In this way, the driver of the target vehicle can be more concerned about the warning to ensure the warning effect.

[0133] Based on the above embodiment, in one embodiment of the present application, the driving state data includes vehicle data and road condition data, and the first driving risk value between the target vehicle and the other vehicles is calculated according to the driving state data of the target vehicle and the driving state data of the other vehicles, including:

[0134] A first driving risk value between the target vehicle and the other vehicles is calculated based on the vehicle data and the road condition data of the target vehicle and the vehicle data and the road condition data of the other vehicles.

[0135] The vehicle data may be data related to the driving parameters of the vehicle, such as the speed, acceleration and driving direction of the vehicle.

[0136] The road condition data may be information related to the road on which the vehicle is located, such as the slope, viscosity, and visibility of the road.

[0137] In this embodiment, by acquiring the vehicle data and road condition data of the target vehicle and other vehicles, the driving status of the target vehicle and other vehicles can be fully understood, and then the first driving risk value between the target vehicle and other vehicles is calculated based on the vehicle data and road condition data of the target vehicle and other vehicles, which can ensure the accuracy of the calculation of the first driving risk value, and then ensure the accuracy of the subsequent calculation of the second driving risk value, thereby ensuring the early warning effect on the target vehicle.

[0138] Based on the technical solution of the above embodiment, a specific application scenario of the embodiment of the present application is introduced below:

[0139] Figure 8 FIG. 1 is a flow chart of a vehicle warning method according to an embodiment of the present application. Figure 8 As shown, the vehicle warning method at least includes steps S810 to S890, which are described in detail as follows:

[0140] In step S810, the driving status data of the target vehicle and the driving status data of other vehicles located around the target vehicle are acquired.

[0141] In step S820, a first driving risk value between the target vehicle and the other vehicles is calculated according to the driving state data of the target vehicle and the driving state data of the other vehicles.

[0142] In step S830, the driving behavior data of historical vehicles in the lane where the target vehicle is located is obtained.

[0143] In step S840, the driving behavior probability is calculated according to the driving behavior data.

[0144] In step S850, a correlation coefficient between any two driving behavior probabilities is calculated based on the driving behavior probabilities.

[0145] In step S860, the vehicle to be calculated among the other vehicles is determined.

[0146] In step S870, based on the relative position between the target vehicle and the other vehicles except the vehicle to be calculated, a target correlation coefficient corresponding to the relative position is determined from the correlation coefficients.

[0147] In step S880, a second driving risk value between the target vehicle and the vehicle to be calculated is calculated according to the first driving risk value corresponding to the vehicle to be calculated, the target correlation coefficient, and the first driving risk value corresponding to the target correlation coefficient.

[0148] In step S890, a warning is issued to the target vehicle according to the second driving risk value.

[0149] exist Figure 8 In the illustrated embodiment, by pre-calculating a first driving risk value between the target vehicle and other vehicles, and then calculating the correlation coefficient between any two driving behavior probabilities based on the driving behavior probabilities of historical vehicles on the road where the target vehicle is located corresponding to different categories of driving behaviors, and calculating the second driving risk value between the target vehicle and other vehicles based on the first driving risk value and the correlation coefficient, the driving risk brought to the target vehicle by other vehicles due to their mutual correlation can be expressed, thereby improving the accuracy of the driving risk calculation of the target vehicle, and thereby ensuring the early warning effect on the target vehicle.

[0150] The following describes an embodiment of the device of the present application, which can be used to execute the vehicle warning method 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 vehicle warning method in the above embodiment of the present application.

[0151] Fig. 9 A block diagram of a vehicle warning device according to an embodiment of the present application is shown.

[0152] Reference Fig. 9As shown, a vehicle warning device according to an embodiment of the present application includes:

[0153] A first acquisition module 910 is used to acquire the driving state data of the target vehicle and the driving state data of other vehicles located around the target vehicle;

[0154] A calculation module 920, configured to calculate a first driving risk value between the target vehicle and the other vehicles according to the driving state data of the target vehicle and the driving state data of the other vehicles;

[0155] A second acquisition module 930 is used to obtain driving behavior probabilities of historical vehicles in the lane where the target vehicle is located corresponding to different types of driving behaviors, and calculate the correlation coefficient between any two of the driving behavior probabilities;

[0156] The processing module 940 is used to calculate a second driving risk value between the target vehicle and the other vehicles according to the first driving risk value and the correlation coefficient, so as to issue an early warning to the target vehicle according to the second driving risk value.

[0157] Based on the aforementioned scheme, in some embodiments of the present application, the processing module 940 is configured to: determine the vehicle to be calculated among the other vehicles; determine the target correlation coefficient corresponding to the relative position from the correlation coefficient based on the relative position between the vehicles other than the vehicle to be calculated among the other vehicles and the target vehicle; calculate the second driving risk value between the target vehicle and the vehicle to be calculated according to the first driving risk value corresponding to the vehicle to be calculated, the target correlation coefficient and the first driving risk value corresponding to the target correlation coefficient.

[0158] Based on the aforementioned scheme, in some embodiments of the present application, the second acquisition module 930 is configured to: obtain the lane information of the target vehicle; generate a driving behavior data request for a historical vehicle based on the lane information, and send the driving behavior data request to a server; receive the driving behavior data sent by the server, and calculate the driving behavior probability based on the driving behavior data; and calculate the correlation coefficient between the driving behavior probabilities based on the driving behavior probabilities.

[0159] Based on the aforementioned scheme, in some embodiments of the present application, the second acquisition module 930 is configured to: obtain time information corresponding to the driving behavior according to the driving behavior data; divide the driving behavior according to the time information to obtain the corresponding driving behavior in each historical unit time; and calculate the driving behavior probabilities of different categories of driving behaviors in each historical unit time according to the categories of driving behaviors corresponding to each historical unit time.

[0160] Based on the aforementioned scheme, in some embodiments of the present application, the second acquisition module 930 is configured to: obtain the driving behavior probabilities of the same category of driving behaviors within each historical unit time to obtain a driving behavior probability set corresponding to each category of driving behavior; and calculate the correlation coefficient between the driving behavior probabilities of any two of the driving behaviors based on the driving behavior probabilities contained in the driving behavior probability set.

[0161] Based on the aforementioned scheme, in some embodiments of the present application, the second acquisition module 930 is configured to: determine the lane information of the target vehicle; generate a driving behavior probability request for historical vehicles based on the lane information, and send the driving behavior probability request to the server; receive the driving behavior probability of historical vehicles sent by the server, and calculate the correlation coefficient between any two of the driving behavior probabilities based on the driving behavior probability.

[0162] Based on the aforementioned solution, in some embodiments of the present application, the processing module 940 is configured to: if the second driving risk value is greater than or equal to a predetermined threshold, issue an early warning to the target vehicle.

[0163] Based on the aforementioned scheme, in some embodiments of the present application, the processing module 940 is configured to: perform corresponding warning processing based on the threshold interval in which the second driving risk value is located, and the importance level of the warning processing is positively correlated with the second driving risk value represented by the threshold interval.

[0164] Based on the aforementioned scheme, in some embodiments of the present application, the driving status data includes vehicle data and road condition data, and the calculation module 920 is configured to: calculate the first driving risk value between the target vehicle and the other vehicles based on the vehicle data and road condition data of the target vehicle and the vehicle data and road condition data of the other vehicles.

[0165] 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.

[0166] It should be noted that Fig.10 The computer system 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.

[0167] like Fig.10As shown, the computer system 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 RAM 1003, various programs and data required for system operation are also stored. CPU 1001, ROM 1002 and RAM 1003 are connected to each other through bus 1004. Input / output (I / O) interface 1005 is also connected to bus 1004.

[0168] 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.

[0169] 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 computer program 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 section 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.

[0170] 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 computer program 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 computer program 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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 vehicle early warning method, characterized in that: include: Acquire driving status data of a target vehicle and driving status data of other vehicles located around the target vehicle, wherein the driving status data includes vehicle data and road condition data; Calculating a first driving risk value between the target vehicle and the other vehicles based on the vehicle data and road condition data of the target vehicle and the vehicle data and road condition data of the other vehicles; Obtaining driving behavior probabilities of historical vehicles in the lane where the target vehicle is located corresponding to different categories of driving behaviors, and calculating a correlation coefficient between any two of the driving behavior probabilities; determining a vehicle to be calculated among the other vehicles; Based on the relative position between the target vehicle and the vehicle other than the vehicle to be calculated among the other vehicles, determining a target correlation coefficient corresponding to the relative position from the correlation coefficients; A second driving risk value between the target vehicle and the vehicle to be calculated is calculated according to the first driving risk value corresponding to the vehicle to be calculated, the target correlation coefficient, and the first driving risk value corresponding to the target correlation coefficient.

2. The method according to claim 1, characterized in that Obtaining the driving behavior probabilities of historical vehicles in the lane where the target vehicle is located corresponding to different categories of driving behaviors, and calculating the correlation coefficient between any two of the driving behavior probabilities, including: Obtaining lane information where the target vehicle is located; generating a driving behavior data request for a historical vehicle according to the lane information, and sending the driving behavior data request to a server; receiving driving behavior data sent by the server, and calculating the driving behavior probability according to the driving behavior data; According to the driving behavior probabilities, a correlation coefficient between the driving behavior probabilities is calculated.

3. The method according to claim 2, characterized in that Calculating the driving behavior probability according to the driving behavior data includes: According to the driving behavior data, obtaining time information corresponding to the driving behavior; Dividing the driving behavior according to the time information to obtain the corresponding driving behavior in each historical unit time; According to the categories of driving behaviors corresponding to the respective historical unit times, driving behavior probabilities of driving behaviors of different categories in the respective historical unit times are calculated.

4. The method according to claim 3, characterized in that Calculating the correlation coefficient between the driving behavior probabilities according to the driving behavior probabilities includes: Obtaining the driving behavior probabilities of the same category of driving behaviors within each historical unit time to obtain a driving behavior probability set corresponding to each category of driving behaviors; According to the driving behavior probabilities included in the driving behavior probability set, a correlation coefficient between the driving behavior probabilities of any two driving behaviors is calculated.

5. The method according to claim 1, characterized in that Obtaining the driving behavior probabilities of historical vehicles in the lane where the target vehicle is located corresponding to different categories of driving behaviors, and calculating the correlation coefficient between any two of the driving behavior probabilities, including: Determine lane information where the target vehicle is located; generating a driving behavior probability request for historical vehicles according to the lane information, and sending the driving behavior probability request to a server; The historical driving behavior probabilities of the vehicles sent by the server are received, and a correlation coefficient between any two driving behavior probabilities is calculated based on the driving behavior probabilities.

6. The method according to claim 1, characterized in that Providing an early warning to the target vehicle according to the second driving risk value includes: If the second driving risk value is greater than or equal to a predetermined threshold, an early warning is issued to the target vehicle.

7. The method according to claim 6, characterized in that If the second driving risk value is greater than or equal to a predetermined threshold, an early warning is issued to the target vehicle, including: Based on the threshold interval in which the second driving risk value is located, corresponding warning processing is performed, and the importance level of the warning processing is positively correlated with the second driving risk value represented by the threshold interval.

8. A vehicle warning device, characterized in that: include: A first acquisition module is used to acquire driving state data of a target vehicle and driving state data of other vehicles located around the target vehicle, wherein the driving state data includes vehicle data and road condition data; A calculation module, configured to calculate a first driving risk value between the target vehicle and the other vehicles based on the vehicle data and road condition data of the target vehicle and the vehicle data and road condition data of the other vehicles; A second acquisition module is used to obtain driving behavior probabilities of historical vehicles in the lane where the target vehicle is located corresponding to different types of driving behaviors, and calculate the correlation coefficient between any two of the driving behavior probabilities; A processing module, configured to determine a vehicle to be calculated among the other vehicles; and based on a relative position between the target vehicle and the vehicle other than the vehicle to be calculated among the other vehicles, determine a target correlation coefficient corresponding to the relative position from the correlation coefficients; A second driving risk value between the target vehicle and the vehicle to be calculated is calculated according to the first driving risk value corresponding to the vehicle to be calculated, the target correlation coefficient, and the first driving risk value corresponding to the target correlation coefficient.

9. A computer-readable medium, characterized in that A computer program is stored thereon, and when the computer program is executed by a processor, the vehicle warning method as described in any one of claims 1 to 7 is implemented.

10. An electronic device comprising: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, enables the one or more processors to implement the vehicle warning method as described in any one of claims 1 to 7.

11. A computer program product, characterized in that It includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the vehicle warning method as claimed in any one of claims 1 to 7.

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