Traffic risk value determining method and apparatus, electronic device, and storage medium

Abstract

The present application discloses a traffic risk value determining method and apparatus, an electronic device, and a storage medium. In the method, by means of real-time vehicle information of vehicles, a first real-time risk value of another vehicle to a target vehicle can be determined; by means of the real-time vehicle information of each vehicle and real-time environmental information, a second real-time risk value of a traffic accident-prone area can be determined; when the first real-time risk value or the second real-time risk value increases, a target real-time risk value increases accordingly. The driving behaviors of adjacent vehicles are analyzed, and whether a certain section of road has safety risks can also be determined, thereby improving the accuracy of risk analysis, effectively avoiding accidents and ensuring the safety of users.

Description

Method, device, electronic device and storage medium for determining traffic risk value

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This disclosure is based on and claims the priority of Chinese patent application with application number 202311619333.8 and application date November 29, 2023. The entire content of the Chinese patent application is hereby incorporated into this disclosure as a reference. Technical Field

[0003] The present disclosure belongs to the technical field of vehicle safe driving, and in particular relates to a method, device, electronic device and storage medium for determining a traffic risk value. Background Art

[0004] When a vehicle is driving, accidents may occur due to various reasons.

[0005] In existing technologies, accident prevention often relies on analyzing driver behavior, such as fatigue and speeding, or conducting risk analysis on the driving behavior of adjacent vehicles. However, these analyses often rely on limited data, resulting in inaccurate risk analysis results. For example, when a vehicle reaches a certain road section, existing technologies are unable to determine whether the road poses a safety risk, thus failing to effectively protect user safety.

[0006] Therefore, the existing technology has the problem that the accuracy of risk analysis is poor and the safety of users cannot be effectively guaranteed. Summary of the Invention

[0007] The embodiments of the present disclosure provide a method, device, electronic device, and storage medium for determining a traffic risk value, which solve the problem of poor accuracy of risk analysis and inability to effectively ensure user safety.

[0008] In a first aspect, an embodiment of the present disclosure provides a method for determining a traffic risk value, comprising:

[0009] Acquiring real-time information of a preset accident-prone area, wherein the real-time information includes real-time environmental information and real-time vehicle information of vehicles in the accident-prone area;

[0010] Determine a first real-time risk value of other vehicles to the target vehicle based on the real-time vehicle information; determine a second real-time risk value of a traffic accident-prone area based on the real-time information;

[0011] A target real-time risk value is determined according to the first real-time risk value and the second real-time risk value, wherein the target real-time risk value is positively correlated with the first real-time risk value and the second real-time risk value, respectively.

[0012] In some possible implementations, the real-time vehicle information includes position information and speed information; and determining a first real-time risk value of other vehicles to the target vehicle based on the real-time vehicle information includes:

[0013] Calculate the relative information between the target vehicle and other vehicles based on the position information and speed information, the relative information includes relative speed and relative distance;

[0014] Divide the relative speed by the relative distance to obtain a first real-time risk value.

[0015] In some possible implementations, determining a second real-time risk value for a traffic accident-prone area based on real-time information includes:

[0016] Determine the real-time risk contribution value of each vehicle in accident-prone areas based on real-time information;

[0017] The real-time risk contribution value of each vehicle is added together to obtain the second real-time risk value of the accident-prone area.

[0018] In some possible implementations, the method further includes:

[0019] Determining whether the real-time information meets a target trigger condition among multiple preset trigger conditions;

[0020] When the target trigger conditions are met, the preset high risk value is used as the second real-time risk value for the area with frequent traffic accidents.

[0021] In some possible implementations, the real-time vehicle information includes vehicle type information and load information; and determining a target real-time risk value based on the first real-time risk value and the second real-time risk value includes:

[0022] Adding the second real-time risk value to the vehicle type risk value to obtain a third real-time risk value, wherein the vehicle type risk value is obtained based on the vehicle type information and the load information;

[0023] multiplying the first real-time risk value by the third real-time risk value respectively to obtain a plurality of fourth real-time risk values;

[0024] The plurality of fourth real-time risk values ​​are added together to obtain a target real-time risk value.

[0025] In some possible implementations, before determining whether the real-time information satisfies a target trigger condition among a plurality of preset trigger conditions, the method further includes:

[0026] Obtaining historical information on the circumstances and causes of historical accidents that occurred in accident-prone areas, where the historical circumstances information includes historical environmental information within a preset time period before the accident and historical vehicle information for each historical risk vehicle;

[0027] Analyze the potential causes of historical accidents based on historical information;

[0028] Generate trigger conditions for historical accidents based on potential causes and historical causes.

[0029] In some possible implementations, before obtaining historical information about circumstances and causes of historical accidents that occurred in a traffic accident-prone area, the method further includes:

[0030] Obtain historical vehicle information for historical accidents that occurred in accident-prone areas;

[0031] In the preset correspondence between vehicle information and weight, the target weight corresponding to the historical vehicle information of the historical accident is obtained;

[0032] Determine the risk contribution value of each historical vehicle based on the target weight;

[0033] Determine the risk contribution value that is greater than the preset contribution threshold as the target risk contribution value;

[0034] The historical vehicles corresponding to the target risk contribution value are regarded as historical risk vehicles.

[0035] In some possible implementations, the method further includes:

[0036] When the target real-time risk value is greater than the preset risk threshold, a warning reminder message is sent to the target vehicle.

[0037] In some possible implementations, before obtaining real-time information of a preset traffic accident-prone area, the method further includes:

[0038] According to the number of accidents, electronic fences are drawn to obtain areas with high traffic accident incidence.

[0039] In a second aspect, an embodiment of the present disclosure further provides a device for determining a traffic risk value, comprising:

[0040] An acquisition module is used to acquire real-time information of a preset traffic accident-prone area, wherein the real-time information includes real-time environmental information and real-time vehicle information of vehicles in the traffic accident-prone area;

[0041] a determination module for determining a first real-time risk value of other vehicles to the target vehicle based on the real-time vehicle information; the determination module is further configured to determine a second real-time risk value of an area prone to traffic accidents based on the real-time information;

[0042] The determination module is further configured to determine a target real-time risk value based on the first real-time risk value and the second real-time risk value, wherein the target real-time risk value is positively correlated with the first real-time risk value and the second real-time risk value, respectively.

[0043] In some possible implementations, the real-time vehicle information includes location information and speed information; the determination module is configured to determine a first real-time risk value of other vehicles to the target vehicle based on the real-time vehicle information, and the determination module includes:

[0044] a calculation unit, configured to calculate relative information between the target vehicle and other vehicles based on the position information and the speed information, the relative information including relative speed and relative distance;

[0045] The calculation unit is further configured to divide the relative speed by the relative distance to obtain a first real-time risk value.

[0046] In some possible implementations, the determination module is further configured to determine a second real-time risk value for the traffic accident-prone area based on the real-time information. In this case, the determination module includes:

[0047] a determination unit, configured to determine a real-time risk contribution value of each vehicle in a traffic accident-prone area based on real-time information;

[0048] The calculation unit is used to add the real-time risk contribution value of each vehicle to obtain a second real-time risk value of the area with frequent traffic accidents.

[0049] In some possible implementations, the apparatus further includes:

[0050] A judgment module, used to judge whether the real-time information meets a target trigger condition among a plurality of preset trigger conditions;

[0051] The judgment module is also used to use the preset high risk value as the second real-time risk value of the area with high traffic accident incidence when the target trigger condition is met.

[0052] In some possible implementations, the real-time vehicle information includes vehicle model information and load information; the determination module is further configured to determine a target real-time risk value based on the first real-time risk value and the second real-time risk value. In this case, the determination module includes:

[0053] a calculation unit, configured to add the second real-time risk value to the vehicle type risk value to obtain a third real-time risk value, wherein the vehicle type risk value is obtained based on the vehicle type information and the load information;

[0054] The calculation unit is further configured to multiply the first real-time risk value by the third real-time risk value to obtain a plurality of fourth real-time risk values;

[0055] The calculation unit is further configured to add the plurality of fourth real-time risk values ​​to obtain a target real-time risk value.

[0056] In some possible implementations, before determining whether the real-time information satisfies a target trigger condition among a plurality of preset trigger conditions, the apparatus further includes an analysis module and a generation module:

[0057] An acquisition module is used to obtain historical information on the circumstances and causes of historical accidents that occurred in accident-prone areas, wherein the historical circumstances information includes historical environmental information within a preset time period before the accident and historical vehicle information of each historical risk vehicle;

[0058] Analysis module, used to analyze the potential causes of historical accidents based on historical situation information;

[0059] The generation module is used to generate trigger conditions for triggering historical accidents based on potential causes and historical causes.

[0060] In some possible implementations, before the acquisition module is used to acquire historical information about circumstances and causes of historical accidents that occurred in an area prone to traffic accidents, the device further includes:

[0061] An acquisition module is used to obtain historical vehicle information of historical accidents that occurred in areas with a high incidence of traffic accidents;

[0062] The acquisition module is further used to obtain the target weight corresponding to the historical vehicle information of the historical accident in the preset correspondence between the vehicle information and the weight;

[0063] A determination module is used to determine the risk contribution value of each historical vehicle based on the target weight;

[0064] The determination module is further configured to determine a risk contribution value greater than a preset contribution threshold as a target risk contribution value;

[0065] The determination module is also used to take the historical vehicles corresponding to the target risk contribution value as historical risk vehicles.

[0066] In some possible implementations, the apparatus further includes:

[0067] The sending module is used to send early warning reminder information to the target vehicle when the target real-time risk value is greater than the preset risk threshold.

[0068] In some possible implementations, before the acquisition module is used to acquire real-time information of a preset traffic accident-prone area, the device further includes:

[0069] The demarcation module is used to demarcate electronic fences based on the number of accidents and obtain areas with high traffic accident incidence.

[0070] In a third aspect, an embodiment of the present disclosure further provides an electronic device comprising a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the method for determining the traffic risk value in the first aspect, or any possible implementation of the first aspect, is implemented.

[0071] In a fourth aspect, an embodiment of the present disclosure further provides a computer storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method for determining the traffic risk value in the first aspect, or any possible implementation of the first aspect, is implemented.

[0072] In a fifth aspect, an embodiment of the present disclosure provides a computer program product. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device is enabled to execute the method for determining the traffic risk value in the first aspect, or any possible implementation of the first aspect.

[0073] The traffic risk value determination method, device, electronic device, and storage medium of the disclosed embodiments obtain real-time information of a preset accident-prone area. This real-time information may include real-time environmental information and real-time vehicle information of vehicles in the accident-prone area. Based on the real-time vehicle information, a first real-time risk value of other vehicles to a target vehicle is determined. Simultaneously, based on the real-time information, a second real-time risk value of the accident-prone area is determined. A target real-time risk value is then determined based on the first and second real-time risk values. The target real-time risk value is positively correlated with the first and second real-time risk values, respectively. The first real-time risk value of other vehicles to the target vehicle is determined based on the real-time vehicle information of the vehicles. The second real-time risk value of the accident-prone area is determined based on the real-time vehicle information and real-time environmental information of each vehicle. As the first or second real-time risk value increases, the target real-time risk value also increases. This method not only analyzes the driving behavior of adjacent vehicles but also determines whether a road section poses a safety risk, improving the accuracy of risk analysis and effectively ensuring user safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0075] FIG1 is a flow chart of a method for determining a traffic risk value provided by an embodiment of the present disclosure;

[0076] FIG2 is a flow chart of another method for determining a traffic risk value provided by an embodiment of the present disclosure;

[0077] FIG3 is a schematic diagram of a system structure for determining a traffic risk value provided by an embodiment of the present disclosure;

[0078] FIG4 is a schematic diagram of a traffic accident-prone area provided by an embodiment of the present disclosure;

[0079] FIG5 is a schematic diagram of a device for determining a traffic risk value provided by an embodiment of the present disclosure;

[0080] FIG6 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0081] The features and exemplary embodiments of various aspects of the present disclosure will be described in detail below. In order to make the purposes, technical solutions and advantages of the present disclosure clearer, the present disclosure will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present disclosure, rather than to limit the present disclosure. For those skilled in the art, the present disclosure can be implemented without the need for some of these specific details. The following description of the embodiments is merely to provide a better understanding of the present disclosure by illustrating examples of the present disclosure.

[0082] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, the elements defined by the phrase "comprising..." do not exclude the presence of other identical elements in the process, method, article, or device comprising the elements.

[0083] As described in the background art, accidents may occur due to various reasons while a vehicle is driving.

[0084] In existing technologies, accident prevention often relies on analyzing driver behavior, such as fatigue and speeding, or conducting risk analysis on the driving behavior of adjacent vehicles. However, these analyses often use limited data and yield inaccurate risk analysis results. For example, existing technologies cannot determine whether a vehicle is driving on a certain road section, preventing it from presenting safety risks. This makes it impossible to predict safety risks ahead of time, effectively preventing users from being harmed.

[0085] In accident-prone areas, accidents can occur due to the "unavoidable" blind spots of large, heavily loaded freight vehicles, where proximity increases the risk. This can occur due to overloaded vehicles driving on overpasses, causing them to flip over. Environmental factors can also increase the probability of accidents, compromising the safety of surrounding pedestrians and vehicles. Therefore, existing technologies suffer from poor risk analysis accuracy and fail to effectively ensure user safety.

[0086] Based on this, the embodiments of the present disclosure provide a method, device, electronic device and storage medium for determining a traffic risk value, which can solve the problem of poor accuracy of risk analysis and inability to effectively ensure user safety.

[0087] The method for determining the traffic risk value provided by the embodiment of the present disclosure is described in detail below with reference to the accompanying drawings.

[0088] FIG1 is a flow chart of a method for determining a traffic risk value provided by an embodiment of the present disclosure. As shown in FIG1 , the method may include S110 - S130 .

[0089] S110 , obtaining real-time information of a preset traffic accident-prone area, wherein the real-time information includes real-time environmental information and real-time vehicle information of vehicles in the traffic accident-prone area.

[0090] The preset accident-prone areas may be geographical locations where accidents occur more frequently. For example, electronic fences may be manually drawn around accident-prone areas, or they may be automatically drawn around accident-prone areas based on the frequency of accidents.

[0091] The real-time information of the traffic accident-prone area refers to the information of the traffic accident-prone area obtained in real time. The real-time information may include, for example, real-time environmental information and real-time vehicle information of vehicles in the traffic accident-prone area.

[0092] Real-time environmental information refers to real-time information about the environment in accident-prone areas, such as weather conditions such as rainy days, foggy days, and nights.

[0093] Real-time vehicle information refers to vehicle information obtained in real time. This vehicle information is vehicle information that may easily cause an accident, such as vehicle location information, speed information, vehicle model information, and load information.

[0094] Specifically, step S110 may be understood as obtaining real-time information of a preset traffic accident-prone area, where the real-time information may include real-time environmental information and real-time vehicle information of vehicles in the traffic accident-prone area.

[0095] In some embodiments, before obtaining real-time information of a preset traffic accident-prone area, the method further includes:

[0096] According to the number of accidents, electronic fences are drawn to obtain areas with high traffic accident incidence.

[0097] Specifically, when accidents occur frequently, electronic fences of the area can be generated manually or automatically on the map to obtain electronic fences in accident-prone areas. That is, areas with frequent traffic accidents are demarcated in the form of electronic fences. Both manual and automatic electronic fence generation methods are supported, which is more flexible and saves manpower.

[0098] S120, determining a first real-time risk value of other vehicles to the target vehicle based on the real-time vehicle information; and determining a second real-time risk value of the area prone to traffic accidents based on the real-time information.

[0099] Specifically, step S120 can be understood as determining the real-time risk value of other vehicles to the target vehicle based on the real-time vehicle information of the vehicle. This real-time risk value can be called a first real-time risk value. Based on the real-time information, the real-time risk value of the area with frequent traffic accidents can also be determined. This real-time risk value can be called a second real-time risk value.

[0100] In some embodiments, the real-time vehicle information includes location information and speed information; determining a first real-time risk value of other vehicles to the target vehicle based on the real-time vehicle information includes:

[0101] Calculate the relative information between the target vehicle and other vehicles based on the position information and speed information, the relative information includes relative speed and relative distance;

[0102] Divide the relative speed by the relative distance to obtain a first real-time risk value.

[0103] Relative information, relative information between two vehicles. For example, there are N vehicles in an accident-prone area. Each vehicle can be called a target vehicle, and the other N-1 vehicles can be called other vehicles. The target vehicle can calculate relative information with the N-1 other vehicles, such as relative speed and relative distance.

[0104] Specifically, real-time vehicle information may include location information and speed information. Determining a first real-time risk value of other vehicles to the target vehicle based on the vehicle's real-time vehicle information in step S120 may include: calculating relative information between the target vehicle and the other vehicles based on the location information and speed information, where the relative information may include relative speed and relative distance; and then dividing the relative speed by the relative distance to obtain the first real-time risk value. For example, a greater relative speed between the target vehicle and the other vehicle indicates a greater speed difference between the two vehicles, a greater risk, and a greater likelihood of an accident; and a greater first real-time risk value. Conversely, a smaller relative distance between the target vehicle and the other vehicle indicates a closer distance between the two vehicles, a greater risk, and a greater likelihood of an accident; and a greater first real-time risk value. The magnitude of the first real-time risk value can be used to reflect the real-time risk level of other vehicles to the target vehicle, effectively ensuring user safety.

[0105] In some embodiments, determining a second real-time risk value for a traffic accident-prone area based on real-time information includes:

[0106] Determine the real-time risk contribution value of each vehicle in accident-prone areas based on real-time information;

[0107] The real-time risk contribution value of each vehicle is added together to obtain the second real-time risk value of the accident-prone area.

[0108] The real-time risk contribution value refers to the risk value of each vehicle in an area with a high incidence of traffic accidents obtained in real time.

[0109] Specifically, determining the second real-time risk value for the accident-prone area based on real-time information in step S120 may include determining the real-time risk contribution value of each vehicle in the accident-prone area based on the real-time information, and then summing the real-time risk contribution values ​​of each vehicle to obtain the second real-time risk value for the entire accident-prone area. The magnitude of the second real-time risk value can reflect the real-time risk level of the accident-prone area as a whole, effectively ensuring user safety.

[0110] In some embodiments, the method further comprises:

[0111] Determining whether the real-time information meets a target trigger condition among multiple preset trigger conditions;

[0112] When the target trigger conditions are met, the preset high risk value is used as the second real-time risk value for the area with frequent traffic accidents.

[0113] The preset multiple trigger conditions refer to conditions that are likely to trigger an accident. If one of the preset multiple trigger conditions is met, it means that there is a possibility of an accident occurring.

[0114] The preset high-risk value serves as a second real-time risk value for accident-prone areas when trigger conditions are met. Different trigger conditions trigger different types of accidents, so different trigger conditions can correspond to different high-risk values. A higher high-risk value indicates a more serious accident. For example, the high-risk value corresponding to trigger condition 1 could be 100, and the high-risk value corresponding to trigger condition 2 could be 150.

[0115] Specifically, the method for determining a second real-time risk value for a high-accident-prone area based on real-time information in step S120 may further include determining whether the real-time information satisfies a trigger condition from among multiple preset trigger conditions, which may be referred to as a target trigger condition. If the target trigger condition is met, a preset high-risk value may be used as the second real-time risk value for the high-accident-prone area. In other words, the second real-time risk value may be derived by summing the real-time risk contribution values ​​of each vehicle within the entire area. As the speed, vehicle type, and load of each vehicle increase, the second real-time risk value for the entire high-accident-prone area increases. Furthermore, the magnitude of the second real-time risk value may also be related to the trigger condition. When the real-time information within the high-accident-prone area satisfies the trigger condition, the second real-time risk value is assigned a preset high-risk value, which also increases the second real-time risk value for the entire high-accident-prone area. Therefore, the magnitude of the second real-time risk value can reflect the real-time risk level of the entire high-accident-prone area, improving the accuracy of risk analysis and effectively ensuring user safety.

[0116] S130: Determine a target real-time risk value according to the first real-time risk value and the second real-time risk value, wherein the target real-time risk value is positively correlated with the first real-time risk value and the second real-time risk value, respectively.

[0117] Specifically, step S130 can be understood as determining a target real-time risk value based on the first real-time risk value and the second real-time risk value, wherein the target real-time risk value is positively correlated with the first real-time risk value and the second real-time risk value, respectively. For example, the target real-time risk value can be obtained by directly adding the first real-time risk value and the second real-time risk value, or by multiplying the first real-time risk value and the second real-time risk value by their respective weights and then adding them together. The target real-time risk value can also be obtained by multiplying the first real-time risk value and the second real-time risk value. In other words, the larger the first real-time risk value, the larger the target real-time risk value. Similarly, the larger the second real-time risk value, the larger the target real-time risk value. The target real-time risk value can reflect the degree of danger posed to the target vehicle by the speed and position of other surrounding vehicles, and can also reflect the degree of danger posed to the entire accident-prone area.

[0118] In some embodiments, the real-time vehicle information includes vehicle type information and load information; determining a target real-time risk value based on the first real-time risk value and the second real-time risk value includes:

[0119] Adding the second real-time risk value to the vehicle type risk value to obtain a third real-time risk value, wherein the vehicle type risk value is obtained based on the vehicle type information and the load information;

[0120] multiplying the first real-time risk value by the third real-time risk value respectively to obtain a plurality of fourth real-time risk values;

[0121] The plurality of fourth real-time risk values ​​are added together to obtain a target real-time risk value.

[0122] Among them, the vehicle model risk value can be obtained based on the vehicle model information and load information. For example, if the vehicle model is a large truck, the higher the vehicle model risk value is, and if the load is overweight, the higher the vehicle model risk value is.

[0123] Specifically, determining a target real-time risk value based on the first and second real-time risk values ​​may include: adding the second real-time risk value to the vehicle type risk value, resulting in a third real-time risk value, where the vehicle type risk value is derived based on vehicle type and load information; then multiplying the first real-time risk value by the third real-time risk value to obtain multiple fourth real-time risk values; and then summing the multiple fourth real-time risk values ​​to obtain the target real-time risk value. The target real-time risk value can reflect the degree of danger to the target vehicle posed by the speed, location, vehicle type, and load of surrounding vehicles, and can also reflect the degree of danger in the entire accident-prone area.

[0124] In the method for determining a traffic risk value provided in an embodiment of the present disclosure, real-time information of a preset accident-prone area is obtained. This real-time information may include real-time environmental information and real-time vehicle information of vehicles in the accident-prone area. Based on the real-time vehicle information, a first real-time risk value of other vehicles to a target vehicle is determined. Simultaneously, based on the real-time information, a second real-time risk value for the accident-prone area is determined. A target real-time risk value is then determined based on the first and second real-time risk values. The target real-time risk value is positively correlated with the first and second real-time risk values, respectively. The first real-time risk value of other vehicles to the target vehicle is determined based on the real-time vehicle information of the vehicle. The second real-time risk value for the accident-prone area is determined based on the real-time vehicle information and real-time environmental information of each vehicle. As the first or second real-time risk value increases, the target real-time risk value also increases. This method not only analyzes the driving behavior of adjacent vehicles but also determines whether a safety risk exists on a particular road section. This improves the accuracy of risk analysis, effectively avoids accidents, and ensures user safety.

[0125] In some embodiments, as shown in FIG2 , before determining whether the real-time information satisfies a target trigger condition among a plurality of preset trigger conditions, the method may further include:

[0126] S140, obtaining historical information on the circumstances and causes of historical accidents that occurred in the accident-prone area, wherein the historical information includes historical environmental information within a preset time period before the accident and historical vehicle information of each historical risk vehicle;

[0127] S150, analyzing potential causes of historical accidents based on historical situation information;

[0128] S160: Based on the potential causes and historical causes, a triggering condition for triggering the historical accident is generated.

[0129] Historical situation information refers to the historical environmental information within a preset time period before the accident and the historical vehicle information of each historical risk vehicle.

[0130] The preset time period refers to a period of time before the accident occurs, which can be 10 seconds, 30 seconds, etc. In the embodiment of the present disclosure, the preset time period can be set based on demand and is not limited in the embodiment of the present disclosure.

[0131] Historical risk vehicles refer to risky vehicles among the vehicles in the accident-prone area within a preset time period before the accident occurs.

[0132] Specifically, in step S120, it is determined whether the real-time information satisfies a target trigger condition among the preset multiple trigger conditions. If the target trigger condition is satisfied, before the preset high risk value is used as the second real-time risk value for the traffic accident-prone area, the trigger condition may be generated by the following steps S140 to S160:

[0133] Step S140 can be understood as obtaining historical situation information and historical cause information of historical accidents that occurred in areas with high traffic accident incidence, wherein the historical situation information includes historical environmental information within a preset time period before the accident and historical vehicle information of each historical risk vehicle.

[0134] In some embodiments, before obtaining historical information on circumstances and causes of historical accidents that occurred in the accident-prone area, the method further includes:

[0135] Obtain historical vehicle information for historical accidents that occurred in accident-prone areas;

[0136] In the preset correspondence between vehicle information and weight, the target weight corresponding to the historical vehicle information of the historical accident is obtained;

[0137] Determine the risk contribution value of each historical vehicle based on the target weight;

[0138] Determine the risk contribution value that is greater than the preset contribution threshold as the target risk contribution value;

[0139] The historical vehicles corresponding to the target risk contribution value are regarded as historical risk vehicles.

[0140] Specifically, before obtaining historical information on the circumstances and causes of historical accidents occurring in a traffic accident-prone area, the method may further include determining historical risk vehicles: obtaining historical vehicle information for historical accidents occurring in the traffic accident-prone area, such as the location, speed, vehicle model, and load of each historical vehicle; then obtaining a target weight corresponding to the historical vehicle information for the historical accident based on a preset correspondence between vehicle information and weights; then, based on the target weight, determining a risk contribution value for each historical vehicle; and determining a risk contribution value greater than a preset contribution threshold as a target risk contribution value. The historical vehicle corresponding to the target risk contribution value may then be designated as a historical risk vehicle. Thus, through the historical vehicle information, a risk vehicle that caused the accident is screened out from multiple historical vehicles. Although this risk vehicle is not a direct cause of the accident, it may be an indirect cause of the accident. For example, the historical risk vehicle may be a large truck with a blind spot. Because the large truck is close to the accident vehicle, the accident vehicle collides with a pedestrian in the blind spot. By searching for historical risk vehicles, we can find the indirect causes of accidents, that is, potential causes, at a deeper level. We can use the potential causes and direct causes together as the historical root causes of the accident, and use this as the trigger condition for the real-time accident. When it is detected that the real-time information meets the trigger condition, we can issue early warnings in time to avoid the occurrence of real-time accidents.

[0141] Step S150 can be understood as analyzing the potential causes of the historical accident based on the historical environmental information within a preset time period before the accident and the historical vehicle information of each historically risky vehicle. For example, a potential cause could be foggy weather with visibility less than 50 meters within the preset time period before the accident. Another potential cause could be rainy weather with 2-5 cm of accumulated water, and the historically risky vehicles around the accident vehicle being speeding.

[0142] Step S160 can be understood as generating the root cause of a historical accident based on potential and historical causes. This root cause serves as the triggering condition for the historical accident. The triggering condition for the historical accident can be analyzed using the historical information and causes of the historical accident. Therefore, by determining whether real-time information meets the triggering condition, the risk level of accident-prone areas can be determined, i.e., a second real-time risk value for accident-prone areas can be determined. This makes the risk analysis more accurate, effectively preventing accidents and ensuring driving safety.

[0143] In some embodiments, the method further comprises:

[0144] When the target real-time risk value is greater than the preset risk threshold, a warning reminder message is sent to the target vehicle.

[0145] The preset risk threshold refers to the risk value of an impending accident. In the embodiment of the present disclosure, the risk threshold can be set based on demand and is not limited in the embodiment of the present disclosure.

[0146] Specifically, when the target real-time risk value is greater than the preset risk threshold, it means that the vehicles around the target vehicle and the accident-prone areas pose a high risk to the target vehicle. An early warning reminder message can be sent to the target vehicle, thereby ensuring the driving safety of the target vehicle and effectively preventing accidents.

[0147] In one example, as shown in Figure 3, the traffic risk determination system can be a V2X-based driving environment safety dynamic assessment method and warning system. This system can include: a V2X vehicle / on-board terminal and a V2X vehicle-infrastructure collaborative operation management platform. The on-board terminal has a built-in V2X-SDK functional interface, a high-precision positioning module, a data communication module, and an alarm module. The V2X vehicle-infrastructure collaborative operation management platform provides information analysis and management services such as an electronic fence management system for accident-prone areas, vehicle management, road information management, equipment management, an alarm system, and a driving environment safety assessment system. The V2X vehicle / on-board terminal can communicate with the V2X vehicle-infrastructure collaborative operation management platform via a cellular network.

[0148] The steps for determining traffic risk value in the system are as follows:

[0149] 1) Demarcation of electronic fences and scene restoration in accident-prone areas: After demarcating accident-prone areas, historical accident scenes in the area are restored to obtain the historical causes that directly caused the accidents.

[0150] Specifically, as shown in Figure 4, the V2X platform administrator can set up electronic fences in accident-prone areas through the V2X operation management platform. For example, the circular dotted area in Figure 4 can be manually demarcated to define electronic fences in accident-prone areas of any range, and the time of historical accidents and the accident impact factor R can be recorded. i 、Influence Factor R i Corresponding occurrence condition C i .

[0151] Among them, the impact factor R i It can be vehicle attributes or driving behavior, such as vehicle type, speeding, overweight, and the influencing factor R i It can also be environmental factors such as rainy days, foggy days, nights, etc.

[0152] Condition C occurs i Represents the impact factor R i Meeting Ci conditions will cause an accident. For example, a historical accident occurred in an accident-prone area, and the impact factor of the vehicle R i The corresponding occurrence condition is 120km / h.i For speeds exceeding 100 km / h, the impact factor R i If this condition is reached, an accident may occur.

[0153] 2) Analyze the potential causes of the accident based on the historical situation information within the preset time period before the historical accident occurred.

[0154] Specifically, the V2X operation management platform collects information such as the vehicle model, speed, heading angle, and lane of all vehicles in the electronic fence area of ​​the current accident-prone area 10 seconds before the accident based on the recorded historical accident occurrence time, and analyzes the vehicle driving behavior and its influencing factors in the accident-prone area before the accident. i 、The impact factor R of the accident i Corresponding condition C i .

[0155] 3) The potential causes that indirectly caused the accident and the historical causes that directly caused the accident are regarded as the root causes of the accident, that is, the triggering conditions that triggered the accident.

[0156] For example, the direct cause of a historical accident was a collision between a car and a pedestrian at an intersection. Then, the potential cause was analyzed based on the situation 10 seconds before the accident. 10 seconds before the accident, a large truck was driving in front of the car. Due to the obstruction of the large truck, the car could not recognize the status of the traffic light at the intersection. As a result, the large truck left the current lane when the traffic light was about to turn red (that is, the current light was green). The car followed the large truck and wanted to leave the current lane. At this time, the traffic light had changed from green to red. Due to the obstruction of the large truck, the car could not recognize the status of the traffic light at the intersection in time, which caused the car to collide with the pedestrian. Therefore, the potential cause was that at the intersection, the vehicle in front of the target vehicle was a large truck, the traffic light was about to change from green to red, and there were pedestrians and bicycles waiting to cross the road.

[0157] For example, the direct cause of a historical accident was a collision between two cars, with the rear car rear-ending the front car. Based on the historical information at the time of the accident, the environmental information at the time of the accident was foggy, visibility was 50 meters, and the road surface humidity was wet. The following distance between the two cars before the accident was 5 meters. Therefore, the potential cause was foggy, visibility ≤ 50 meters, wet or over-wet road surface humidity, and following distance ≤ 5 meters.

[0158] 4) Obtain real-time information in accident-prone areas, determine whether the trigger conditions are met, and determine the real-time risk value of each vehicle.

[0159] The V2X vehicle-mounted device sends the high-precision location information and vehicle information of the vehicle to the V2XServer platform at a fixed frequency, and then when the vehicle v kDriving into the fenced area triggers the accident factor R set for the current accident-prone area. i Corresponding condition C i , then vehicle v k Danger factors affecting the driving environment in this area Increase.

[0160] 5) Determine the second real-time risk value R for areas prone to traffic accidents d For example, real-time estimation of the safety factor of the driving environment in accident-prone areas

[0161] 6) Determine a first real-time risk value of the other vehicles to the target vehicle. For example, if the relative speed of the target vehicle and the other vehicles is Δv, and the relative distance between the target vehicle and the other vehicles is Δs, the first real-time risk value may be Δv / Δs.

[0162] 7) Determine a target real-time risk value based on the first real-time risk value of other vehicles to the target vehicle and the second real-time risk value of the entire accident-prone area. If the target real-time risk value is greater than a risk threshold, send an early warning reminder to the target vehicle.

[0163] Specifically, the current vehicle driving environment safety factor, namely the target real-time risk value RT, is estimated in real time based on the vehicle model data, vehicle speed, vehicle spacing, road conditions, vehicle lanes, etc. of the current road section. i .

[0164] Where i represents the i-th other vehicle, Δv i / Δs i It represents the first real-time risk value of other vehicles to the target vehicle. The greater the relative speed and the closer the relative distance, the greater the risk to the target vehicle. i Indicates the risk impact coefficient of other vehicle models and loads on the driving environment, such as the risk impact coefficient p of large vehicles such as heavy-load freight and tank trucks i Bigger, R d Indicates the risk value of accident-prone areas. The higher the risk level of accident-prone areas, the larger the Rd value. If the current area is not an accident-prone area, R d Can be 0;

[0165] = is the relative change value of the target vehicle’s real-time risk within ΔT time. If the real-time information triggers the trigger condition, and the target vehicle’s Rapidly increasing, or the real-time risk value RT of the target vehicle i If the risk threshold D is exceeded, the vehicles in the area will be warned to avoid accidents.i Effective reminders are given based on the different needs of each vehicle, thus reducing the safety risks of the current driving environment. i It can be vehicle attributes or driving behavior, such as vehicle type, speeding, overweight, and the influencing factor R i It can also be environmental factors such as rainy days, foggy days, nights, etc.

[0166] FIG5 is a diagram of a traffic risk value determination device provided in an embodiment of the present disclosure. The device 500 may include an acquisition module 510 and a determination module 520.

[0167] An acquisition module 510 is configured to acquire real-time information of a preset accident-prone area, wherein the real-time information includes real-time environmental information and real-time vehicle information of vehicles in the accident-prone area;

[0168] The determination module 520 is configured to determine a first real-time risk value of other vehicles to the target vehicle based on the real-time vehicle information. The determination module is further configured to determine a second real-time risk value of a traffic accident-prone area based on the real-time information.

[0169] The determination module 520 is further configured to determine a target real-time risk value based on the first real-time risk value and the second real-time risk value, wherein the target real-time risk value is positively correlated with the first real-time risk value and the second real-time risk value, respectively.

[0170] The traffic risk value determination device provided in the embodiments of the present disclosure obtains real-time information of a preset accident-prone area. This real-time information may include real-time environmental information and real-time vehicle information of vehicles in the accident-prone area. Based on the real-time vehicle information, a first real-time risk value of other vehicles to a target vehicle is determined. Simultaneously, based on the real-time information, a second real-time risk value of the accident-prone area is determined. A target real-time risk value is then determined based on the first real-time risk value and the second real-time risk value. The target real-time risk value is positively correlated with the first real-time risk value and the second real-time risk value, respectively. The first real-time risk value of other vehicles to the target vehicle is determined based on the real-time vehicle information of the vehicles. The second real-time risk value of the accident-prone area is determined based on the real-time vehicle information and real-time environmental information of each vehicle. As the first real-time risk value or the second real-time risk value increases, the target real-time risk value also increases. This device not only analyzes the driving behavior of adjacent vehicles but also determines whether a road section poses a safety risk. This improves the accuracy of risk analysis, effectively avoids accidents, and ensures user safety.

[0171] In some embodiments, the real-time vehicle information includes location information and speed information; the determination module is configured to determine a first real-time risk value of other vehicles to the target vehicle based on the real-time vehicle information. In this case, the determination module includes:

[0172] a calculation unit, configured to calculate relative information between the target vehicle and other vehicles based on the position information and the speed information, the relative information including relative speed and relative distance;

[0173] The calculation unit is further configured to divide the relative speed by the relative distance to obtain a first real-time risk value.

[0174] In some embodiments, the determination module is further configured to determine a second real-time risk value for a traffic accident-prone area based on the real-time information. In this case, the determination module includes:

[0175] a determination unit, configured to determine a real-time risk contribution value of each vehicle in a traffic accident-prone area based on real-time information;

[0176] The calculation unit is used to add the real-time risk contribution value of each vehicle to obtain a second real-time risk value of the area with frequent traffic accidents.

[0177] In some embodiments, the device further includes a determination module:

[0178] A judgment module, used to judge whether the real-time information meets a target trigger condition among a plurality of preset trigger conditions;

[0179] The judgment module is also used to use the preset high risk value as the second real-time risk value of the area with high traffic accident incidence when the target trigger condition is met.

[0180] In some embodiments, the real-time vehicle information includes vehicle type information and load information; the determination module is further configured to determine a target real-time risk value based on the first real-time risk value and the second real-time risk value. In this case, the determination module includes:

[0181] a calculation unit, configured to add the second real-time risk value to the vehicle type risk value to obtain a third real-time risk value, wherein the vehicle type risk value is obtained based on the vehicle type information and the load information;

[0182] The calculation unit is further configured to multiply the first real-time risk value by the third real-time risk value to obtain a plurality of fourth real-time risk values;

[0183] The calculation unit is further configured to add the plurality of fourth real-time risk values ​​to obtain a target real-time risk value.

[0184] In some embodiments, before determining whether the real-time information satisfies a target trigger condition among a plurality of preset trigger conditions, the apparatus further includes an analysis module and a generation module:

[0185] An acquisition module is used to obtain historical information on the circumstances and causes of historical accidents that occurred in accident-prone areas, wherein the historical circumstances information includes historical environmental information within a preset time period before the accident and historical vehicle information of each historical risk vehicle;

[0186] Analysis module, used to analyze the potential causes of historical accidents based on historical situation information;

[0187] The generation module is used to generate trigger conditions for triggering historical accidents based on potential causes and historical causes.

[0188] In some embodiments, before the acquisition module is used to acquire historical information about historical circumstances and historical causes of historical accidents that occurred in the area prone to traffic accidents, the device further includes:

[0189] An acquisition module is used to obtain historical vehicle information of historical accidents that occurred in areas with a high incidence of traffic accidents;

[0190] The acquisition module is further used to obtain the target weight corresponding to the historical vehicle information of the historical accident in the preset correspondence between the vehicle information and the weight;

[0191] A determination module is used to determine the risk contribution value of each historical vehicle based on the target weight;

[0192] The determination module is further configured to determine a risk contribution value greater than a preset contribution threshold as a target risk contribution value;

[0193] The determination module is also used to take the historical vehicles corresponding to the target risk contribution value as historical risk vehicles.

[0194] In some embodiments, the apparatus further comprises:

[0195] The sending module is used to send early warning reminder information to the target vehicle when the target real-time risk value is greater than the preset risk threshold.

[0196] In some embodiments, before the acquisition module is used to acquire real-time information of a preset traffic accident-prone area, the device further includes a demarcation module:

[0197] The demarcation module is used to demarcate electronic fences based on the number of accidents and obtain areas with high traffic accident incidence.

[0198] The various modules in the traffic risk value determination device provided in the embodiment of the present disclosure can implement the functions of the various steps of the traffic risk value determination method provided in Figures 1 to 4 and achieve their corresponding technical effects. For the sake of brevity, they will not be repeated here.

[0199] FIG6 shows a schematic diagram of the hardware structure of a device for determining a traffic risk value provided by an embodiment of the present disclosure.

[0200] The traffic risk value determination device may include a processor 601 and a memory 602 storing computer program instructions.

[0201] Specifically, the processor 601 may include a central processing unit (CPU) or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present disclosure.

[0202] Memory 602 may include a large-capacity memory for data or instructions. By way of example, and not limitation, memory 602 may include a hard disk drive (HDD), a floppy disk drive, flash memory, an optical disk, a magneto-optical disk, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 602 may include removable or non-removable (or fixed) media. Where appropriate, memory 602 may be internal or external to the device for determining the traffic risk value. In certain embodiments, memory 602 is a non-volatile solid-state memory.

[0203] The memory may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Thus, generally, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0204] The processor 601 reads and executes computer program instructions stored in the memory 602 to implement any one of the methods for determining the traffic risk value in the above embodiments.

[0205] In one example, the traffic risk value determination device may further include a communication interface 603 and a bus 604. As shown in FIG6, the processor 601, the memory 602, and the communication interface 603 are connected via the bus 604 and communicate with each other.

[0206] The communication interface 603 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present disclosure.

[0207] Bus 604 includes hardware, software, or both, and couples the components of the traffic risk value determination device to one another. By way of example, and not limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Linear Predictive Coding (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (Peripheral Component Interconnect-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 604 may include one or more buses. Although embodiments of the disclosure describe and illustrate a particular bus, this disclosure contemplates any suitable bus or interconnect.

[0208] The device can execute the method for determining the traffic risk value in the embodiment of the present disclosure based on the various units / components in the traffic risk value determination apparatus, thereby realizing the method for determining the traffic risk value described in conjunction with FIG. 1 to FIG. 4 .

[0209] In addition, in conjunction with the methods for determining traffic risk values ​​in the above embodiments, embodiments of the present disclosure may provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when executed by a processor, the computer program instructions implement any of the methods for determining traffic risk values ​​in the above embodiments.

[0210] The present disclosure also provides a computer program product. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes each process of any one of the above-mentioned methods for determining a traffic risk value.

[0211] It should be understood that the present disclosure is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and illustrated as examples. However, the method process of the present disclosure is not limited to the specific steps described and illustrated. Those skilled in the art can make various changes, modifications, and additions, or change the order of the steps after understanding the spirit of the present disclosure.

[0212] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present disclosure are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link via a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, read-only memories (ROMs), flash memories, erasable read-only memories (EROMs), floppy disks, compact disc read-only memories (CD-ROMs), optical discs, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0213] It should also be noted that the exemplary embodiments described in this disclosure describe methods or systems based on a series of steps or devices. However, this disclosure is not limited to the order of the steps described above. In other words, the steps may be performed in the order described in the embodiments, or in a different order, or several steps may be performed simultaneously.

[0214] Aspects of the present disclosure have been described above with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor or a field programmable logic circuit. It is also understood that each box in the block diagram and / or flowchart and the combination of the boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0215] The above is only a specific embodiment of the present disclosure. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the scope of protection of the present disclosure is not limited to this. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present disclosure, and these modifications or replacements should be included in the scope of protection of the present disclosure.

Claims

1. A method for determining a traffic risk value, comprising: Acquire real-time information of a preset traffic accident-prone area, wherein the real-time information includes real-time environmental information and real-time vehicle information of vehicles in the traffic accident-prone area; Determine a first real-time risk value of other vehicles to the target vehicle based on the real-time vehicle information of the vehicle; determine a second real-time risk value of the traffic accident-prone area based on the real-time information; A target real-time risk value is determined according to the first real-time risk value and the second real-time risk value, wherein the target real-time risk value is positively correlated with the first real-time risk value and the second real-time risk value, respectively.

2. The method according to claim 1, wherein: The real-time vehicle information includes position information and speed information; determining a first real-time risk value of other vehicles to the target vehicle based on the real-time vehicle information of the vehicle includes: Calculate relative information between the target vehicle and other vehicles based on the position information and the speed information, wherein the relative information includes a relative speed and a relative distance; The relative speed is divided by the relative distance to obtain a first real-time risk value.

3. The method according to claim 1, wherein: Determining the second real-time risk value of the traffic accident-prone area according to the real-time information includes: Determining a real-time risk contribution value of each vehicle in the traffic accident-prone area according to the real-time information; The real-time risk contribution value of each vehicle is added together to obtain a second real-time risk value of the traffic accident-prone area.

4. The method according to claim 3, wherein: Also includes: Determining whether the real-time information satisfies a target trigger condition among a plurality of preset trigger conditions; When the target trigger condition is met, the preset high risk value is used as the second real-time risk value of the traffic accident-prone area.

5. The method according to any one of claims 1 to 4, wherein: The real-time vehicle information includes vehicle type information and load information; The determining a target real-time risk value according to the first real-time risk value and the second real-time risk value includes: Adding the second real-time risk value to the vehicle type risk value to obtain a third real-time risk value, wherein the vehicle type risk value is obtained based on the vehicle type information and the load information; multiplying the first real-time risk value by the third real-time risk value respectively to obtain a plurality of fourth real-time risk values; The plurality of fourth real-time risk values ​​are added together to obtain a target real-time risk value.

6. The method according to claim 4, wherein: Before determining whether the real-time information satisfies a target trigger condition among a plurality of preset trigger conditions, the method further includes: Obtaining historical situation information and historical cause information of historical accidents that occurred in the accident-prone area, wherein the historical situation information includes historical environmental information within a preset time period before the accident and historical vehicle information of each historical risk vehicle; Analyze the potential causes of the historical accidents based on the historical situation information; Based on the potential causes and the historical causes, a triggering condition for triggering the historical accident is generated.

7. The method according to claim 6, wherein: Before obtaining the historical situation information and historical cause information of historical accidents occurring in the accident-prone area, the method further includes: Acquire historical vehicle information of historical accidents that occurred in the accident-prone area; In the preset correspondence between vehicle information and weight, obtaining the target weight corresponding to the historical vehicle information of the historical accident; Determining the risk contribution value of each of the historical vehicles according to the target weight; Determine the risk contribution value that is greater than a preset contribution threshold as the target risk contribution value; The historical vehicle corresponding to the target risk contribution value is regarded as a historical risk vehicle.

8. The method according to claim 1, wherein: Also includes: When the target real-time risk value is greater than a preset risk threshold, a warning reminder message is sent to the target vehicle.

9. The method according to claim 1, wherein: Before obtaining the real-time information of the preset traffic accident-prone area, the method further includes: According to the number of accidents, electronic fences are drawn to obtain areas with high traffic accident incidence.

10. A device for determining a traffic risk value, comprising: An acquisition module, used to acquire real-time information of a preset traffic accident-prone area, wherein the real-time information includes real-time environmental information and real-time vehicle information of vehicles in the traffic accident-prone area; A determination module, configured to determine a first real-time risk value of other vehicles to a target vehicle based on the real-time vehicle information of the vehicle; and determine a second real-time risk value of the traffic accident-prone area based on the real-time information; The determination module is further used to determine a target real-time risk value according to the first real-time risk value and the second real-time risk value, wherein the target real-time risk value is positively correlated with the first real-time risk value and the second real-time risk value, respectively.

11. An electronic device, comprising: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the method for determining the traffic risk value according to any one of claims 1 to 9 is implemented.

12. A computer-readable storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement the method for determining a traffic risk value according to any one of claims 1 to 9.

13. A computer program product, wherein when instructions in the computer program product are executed by a processor of an electronic device, the electronic device is enabled to execute the method for determining a traffic risk value as claimed in any one of claims 1 to 9.

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