Adverse weather high-impact road section risk prediction and safety management method, device and equipment

Abstract

The application relates to the technical field of traffic safety, and particularly discloses a method, device and equipment for risk prediction and safety management of a high-impact road section in severe weather, which comprises the following steps: predicting the severe weather grade of a target road section through a preset prediction model; determining the traffic control grade of the target road section under severe weather based on the traffic control grade and the severe weather grade of the target road section; and matching the target road section with a corresponding safety improvement strategy based on the starting information of the traffic control under severe weather of the target road section within a preset time range, wherein the starting information of the traffic control under severe weather comprises the starting number of the traffic control under severe weather and the traffic control grade under severe weather that is started. In this way, the high-impact road section in severe weather can be quantitatively predicted under severe weather, and the corresponding safety improvement strategy can be determined based on the prediction grade, so that the traffic safety of the high-impact road section in severe weather is greatly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of traffic safety, and in particular to a method, device and equipment for risk prediction and safety management of a high-impact road section in severe weather. BACKGROUND

[0002] In many traffic accidents, traffic accidents occur directly or indirectly due to severe weather, especially for high-impact road sections in severe weather. Adverse weather conditions affect the road visibility, field of view, road surface adhesion coefficient, etc., which will bring a series of safety problems.

[0003] Currently, when optimizing and improving the high-impact road section in severe weather, that is, improving the safety of the road section through some safety strategy scheme, due to the different actual severe weather impact conditions in different places, a unified improvement work scheme cannot be given, and the actual driving safety risk of each road section cannot be quantitatively evaluated, and thus the corresponding optimization and improvement work cannot be carried out according to the risk level.

[0004] Therefore, how to quantitatively evaluate the safety risk of the road section in severe weather, and develop a matching optimization and improvement scheme according to the driving safety risk, and how to share the dynamic traffic data in severe weather among multiple industry departments, so as to ensure the safety of the high-impact road section in severe weather, has become a problem to be solved. SUMMARY

[0005] Therefore, the purpose of the present application is to provide a method, device and equipment for risk prediction and safety management of a high-impact road section in severe weather, to overcome the problem that the high-impact road section in severe weather cannot guarantee traffic safety in severe weather.

[0006] To achieve the above purpose, the present application adopts the following technical scheme:

[0007] In a first aspect, the present application provides a method for risk prediction and safety management of a high-impact road section in severe weather, comprising:

[0008] predicting the severe weather level of the target road section through a preset prediction model;

[0009] determining the traffic control level in severe weather of the target road section based on the traffic control level of the target road section and the severe weather level;

[0010] matching a corresponding safety improvement strategy for the target road section based on the start information of the traffic control in severe weather of the target road section within a preset time range, the start information of the traffic control in severe weather including the number of starts of the traffic control in severe weather and the level of the started traffic control in severe weather.

[0011] Optionally, the constructing process of the prediction model comprises:

[0012] constructing a simulation model in a preset driving dynamics software; the simulation model comprises a vehicle simulation model, a driver simulation model and a road simulation model;

[0013] determining a target simulation model and inputting preset test parameters to obtain target data; wherein, the preset test parameters include vehicle driving speed, road curve radius, road longitudinal slope and road friction coefficient, and the target data includes lateral force and vertical force of the vehicle tire relative to the road;

[0014] the ratio of the lateral force of the vehicle tire relative to the road to the vertical force of the vehicle tire relative to the road is taken as a critical adhesion coefficient; wherein, the critical adhesion coefficient is used to represent the probability of vehicle rollover, and the greater the critical adhesion coefficient, the greater the probability of vehicle rollover, and the smaller the critical adhesion coefficient, the smaller the probability of vehicle rollover;

[0015] performing regression analysis on the preset test parameters and the critical adhesion coefficient to obtain a basic prediction model for predicting the critical adhesion coefficient based on test parameters;

[0016] obtaining a prediction model for predicting the severe weather grade based on the basic prediction model.

[0017] Optionally,

[0018] the construction of the vehicle simulation model comprises: constructing the vehicle simulation model based on vehicle system parameters and vehicle body parameters in a preset driving dynamics software; wherein, the vehicle system parameters include engine, suspension, steering system, brake system and tire information; and the vehicle body parameters include the mass and size of the vehicle;

[0019] the construction of the driver simulation model comprises: constructing the driver simulation model based on speed control and direction control in a preset driving dynamics software; wherein, the speed is a constant value, and the direction is controlled based on the tracking trajectory of the road;

[0020] the construction of the road simulation model comprises: constructing the road simulation model based on road plane linear parameters, road longitudinal section parameters and road transverse section parameters in a preset driving dynamics software.

[0021] Optionally, the prediction of the severe weather grade of the target road section through the preset prediction model comprises:

[0022] obtaining real-time test parameters of the target road section and inputting them into the prediction model to obtain real-time critical adhesion coefficients corresponding to the real-time test parameters of the target road section;

[0023] Based on the real-time critical adhesion coefficient and the correspondence between the critical adhesion coefficient and the sideslip risk, the real-time sideslip risk corresponding to the real-time critical adhesion coefficient is obtained.

[0024] Based on the real-time skid risk and the correspondence between skid risk and severe weather level, the severe weather level corresponding to the real-time skid risk is obtained and determined as the severe weather level of the target road section.

[0025] Optionally, determining the traffic control level of the target road segment under severe weather conditions based on the traffic control level of the target road segment and the severe weather level includes:

[0026] Based on the traffic control level of the target road segment, the severe weather level, and the preset formula, the traffic control level of the target road segment under severe weather is obtained;

[0027] The preset formula includes:

[0028] T e =5-e, e=1,2,3,4

[0029] S j =5-j, j=1,2,3,4

[0030]

[0031] z = 5 - D z ;

[0032] Where: Te is the severe weather risk value, e is the severe weather level, Sj is the traffic control risk value, j is the traffic control level; Dz is the traffic risk value under severe weather, z is the traffic control level under severe weather.

[0033] Optionally, the step of matching a corresponding safety enhancement strategy for the target road segment based on the traffic control activation information under severe weather conditions within a preset time range includes:

[0034] For traffic control activation information under severe weather conditions, the basic safety enhancement strategy is matched to the target road segment within the first preset level range and the first preset range.

[0035] For traffic control activation information under severe weather conditions, the standard version of the safety enhancement strategy is matched to the target road section within the second preset level range and the second preset range.

[0036] For target road sections within the third preset level range and within the third preset range of traffic control activation information under severe weather conditions, an enhanced safety enhancement strategy will be matched.

[0037] Optionally, the security enhancement strategy includes:

[0038] The system receives meteorological information from the acquisition module through a pre-set background information processing module and sends the information back to the acquisition module.

[0039] Control measures are sent to the preset front-end devices through the preset back-end information processing module;

[0040] The system sends security alerts to the information publishing module through a pre-defined backend information processing module.

[0041] The preset background information processing module includes preset systems for public security departments, transportation departments, and meteorological departments.

[0042] Optionally, in the basic security enhancement strategy, the information collection module includes a meteorological department sub-module and an intelligent video sub-module, and the control measures include speed limit release, route guidance, and merging warning;

[0043] In the standard version of the safety enhancement strategy, the information collection module includes a meteorological service sub-module and a fixed meteorological sensing sub-module; the control measures include: vehicle speed measurement, vehicle distance measurement, exit warning, intelligent video, variable speed limit, route guidance, merging warning and notification combination screen; the standard version of the safety enhancement strategy also includes receiving device status information from front-end devices;

[0044] In the enhanced security strategy, the information collection module includes a meteorological service submodule, a fixed meteorological sensing submodule, and a vehicle-mounted mobile meteorological sensing submodule; the control measures include: vehicle speed measurement, vehicle distance measurement, exit warning, intelligent video, variable speed limit, route guidance, merging warning, gradient warning, and notification combination screen; the enhanced security strategy also includes receiving device status information from front-end devices.

[0045] Secondly, embodiments of this application also provide a risk prediction and safety management device for road sections with high impact from severe weather, comprising:

[0046] The prediction module is used to predict the severity of severe weather on the target road section using a preset prediction model.

[0047] The determination module is used to determine the traffic control level of the target road segment under severe weather conditions based on the traffic control level of the target road segment and the severe weather level.

[0048] The matching module is used to match a corresponding safety enhancement strategy for the target road segment based on the traffic control activation information under severe weather conditions within a preset time range. The activation information under severe weather conditions includes the number of times traffic control is activated under severe weather conditions and the activation level of traffic control under severe weather conditions.

[0049] In a third aspect, the embodiments of the present application also provide a device for risk prediction and safety management of a high-impact road section in severe weather, comprising a processor and a memory, the processor being connected to the memory:

[0050] The processor is configured to call and execute a program stored in the memory.

[0051] The memory is configured to store the program, which is used to execute the above-mentioned method for risk prediction and safety management of a high-impact road section in severe weather.

[0052] The present application relates to the technical field of traffic safety, and specifically discloses a method, device and equipment for risk prediction and safety management of a high-impact road section in severe weather, which comprises the following steps: predicting the severity of severe weather on a target road section by using a preset prediction model; determining the traffic control level in severe weather on the target road section based on the traffic control level and the severity of severe weather on the target road section; and matching a corresponding safety improvement strategy for the target road section based on the start information of traffic control in severe weather on the target road section within a preset time range, wherein the start information of traffic control in severe weather includes the number of times of starting traffic control in severe weather and the level of traffic control in severe weather that has been started. In this way, the high-impact road section in severe weather can be quantitatively predicted, and a corresponding safety improvement strategy can be determined based on the prediction level, thereby greatly improving the traffic safety of the high-impact road section in severe weather. BRIEF DESCRIPTION OF DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative effort based on these drawings.

[0054] Figure 1 The flowchart of the method for risk prediction and safety management of a high-impact road section in severe weather provided by the embodiments of the present application is shown in the figure.

[0055] Figure 2 The flowchart of the construction of the prediction model in the method for risk prediction and safety management of a high-impact road section in severe weather provided by the embodiments of the present application is shown in the figure.

[0056] Figure 3 The relationship between the test parameters and the critical adhesion coefficient in the simulation model in the method for risk prediction and safety management of a high-impact road section in severe weather provided by the embodiments of the present application is shown in the figure.

[0057] Figure 4The principle schematic diagram of the basic version safety promotion strategy in the severe weather high-impact road section risk prediction and safety management method provided by the embodiment of the application is shown in the figure;

[0058] Figure 5 The application schematic diagram of the basic version safety promotion strategy in the severe weather high-impact road section risk prediction and safety management method provided by the embodiment of the application is shown in the figure;

[0059] Figure 6 The principle schematic diagram of the standard version safety promotion strategy in the severe weather high-impact road section risk prediction and safety management method provided by the embodiment of the application is shown in the figure;

[0060] Figure 7 The application schematic diagram of the standard version safety promotion strategy in the severe weather high-impact road section risk prediction and safety management method provided by the embodiment of the application is shown in the figure;

[0061] Figure 8 The principle schematic diagram of the promotion version safety promotion strategy in the severe weather high-impact road section risk prediction and safety management method provided by the embodiment of the application is shown in the figure;

[0062] Figure 9 The application schematic diagram of the promotion version safety promotion strategy in the severe weather high-impact road section risk prediction and safety management method provided by the embodiment of the application is shown in the figure;

[0063] Figure 10 The structure schematic diagram of the severe weather high-impact road section risk prediction and safety management device provided by the embodiment of the application is shown in the figure;

[0064] Figure 11 The structure schematic diagram of the severe weather high-impact road section risk prediction and safety management device provided by the embodiment of the application is shown in the figure. DETAILED DESCRIPTION

[0065] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described in detail below. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work belong to the scope of protection of the present application.

[0066] Summary of the application:

[0067] In many traffic accidents, the situation of traffic accidents caused directly or indirectly by bad weather occurs from time to time. The adverse weather conditions affect the road visibility, field of view, road surface adhesion coefficient and the like, which will bring a series of safety problems. Different bad weather has different influence mechanism on traffic safety, and rain, snow, fog and other bad weather all bring adverse effects on traffic safety from the aspect of reducing the friction coefficient of the road surface. After analysis, it is determined that the influence of different bad weather on traffic safety by affecting the friction coefficient of the road surface mainly reflects in the following aspects:

[0068] Under the condition of rainy day, the road surface is wet and slippery, the friction coefficient between the automobile tire and the ground is reduced, and the safe braking distance is increased; the "water film slippery phenomenon" appearing on the waterlogged road surface is easy to cause vehicle out of control and the like. Heavy rain may even induce geological disasters, wash away the road surface, roadbed and other highway facilities, and cause traffic interruption.

[0069] Under the condition of snowy day, the snowfall makes the road surface become wet and slippery, and the friction force rapidly decreases, the vehicle is easy to idle or slip, and once the braking is too strong, danger may occur, and when a certain thickness of accumulated snow is crushed by the running vehicle, it is very likely to form ice under the condition of low temperature, further reducing the friction coefficient of the road surface, leading to the occurrence of accidents.

[0070] Under the condition of foggy day, the water vapor content in the fog is large, the water droplets in the air mix with the near-ground dust and the dust on the road surface, adhere to the tire and the road surface, reduce the friction coefficient between the tire and the road surface, and thus adversely affect the traffic safety.

[0071] In the prior art, when carrying out optimization and promotion work of bad weather high-impact road section, due to the different actual bad weather influence conditions in different places, it is impossible to give a unified promotion work scheme, the actual driving safety risk of each road section cannot be quantitatively evaluated, and then the corresponding optimization and promotion work cannot be carried out according to the risk level. Therefore, how to carry out the matching optimization and promotion scheme according to the driving safety risk level becomes a problem to be solved. In the actual optimization and promotion work, designing a scientific and effective work scheme is the basic guarantee of the bad weather high-impact road section. Setting warning prompts, speed limit control distance and other facilities on both sides of the road can effectively improve the traffic safety effect, however, single and isolated traffic safety facilities cannot fully play a role, and the facility information needs to be circulated and shared. However, the information sources of the optimization and promotion work of the bad weather high-impact road section involve meteorology, public security traffic management, transportation and other industry departments. In the prior art, effective circulation and sharing cannot be displayed, therefore, how to share the dynamic data among multiple industry departments is also a problem to be solved in the prior art.

[0072] In view of the above technical problems, the application provides a severe weather high-impact road section risk prediction and safety management method, device and equipment, which is used to at least partially solve the above technical problems, and will be described in detail in the form of an embodiment.

[0073] Figure 1 A flowchart of the severe weather high-impact road section risk prediction and safety management method provided by the embodiment of the application is shown in Figure 1 The embodiment can include the following steps:

[0074] S101, predicting a severe weather grade of a target road section by a preset prediction model.

[0075] Specifically, the prediction model can be a prediction model generated after a simulation model based on a preset driving dynamics software, which is used to directly obtain the severe weather grade of the target road section according to the information of the target road section including the severe weather high-impact road section acquired in real time.

[0076] S102, determining a traffic control grade under severe weather of the target road section based on the traffic control grade of the target road section and the severe weather grade.

[0077] Specifically, the traffic control grade of a road section can be the control grade of the target road section under daily conditions, which can be obtained through a preset system or website of a traffic or other department. Based on the traffic control grade of the target road section and the severe weather grade predicted by the above prediction model, the traffic control grade under severe weather of the target road section is obtained, thereby providing a data basis for subsequent matching of safety strategies for the target road section.

[0078] S103, matching a corresponding safety improvement strategy for the target road section based on the start information of the traffic control under severe weather of the target road section within a preset time range.

[0079] The start information of the traffic control under severe weather includes the number of starts of the traffic control under severe weather and the traffic control grade under severe weather that is started.

[0080] Specifically, some safety improvement strategies for different traffic control grades under severe weather can be determined in advance, and the number of starts of the traffic control under severe weather within a preset time range, such as one year, of the target road section and the grade of each start are obtained, thereby matching different safety improvement strategies for the target road section.

[0081] For example, for some road sections that are frequently controlled under adverse weather conditions and have high specific levels within a year, the highest safety promotion strategy is matched; for some road sections that are frequently controlled under adverse weather conditions but have low specific levels within a year, or for some road sections that are rarely controlled under adverse weather conditions but have high specific levels within a year, a general safety promotion strategy is matched; and for some road sections that are rarely controlled under adverse weather conditions and have low specific levels within a year, a lower safety promotion strategy is matched.

[0082] The method for predicting and managing risks of high-impact road sections under adverse weather conditions provided in the present application first predicts the adverse weather level of a target road section through a preset prediction model; then determines the traffic control level of the target road section under adverse weather conditions based on the traffic control level and the adverse weather level of the target road section; and finally matches a corresponding safety promotion strategy for the target road section based on the start information of traffic control under adverse weather conditions of the target road section within a preset time range, wherein the start information of traffic control under adverse weather conditions includes the number of times of starting traffic control under adverse weather conditions and the level of traffic control under adverse weather conditions that is started. In this way, the method can realize quantitative prediction of high-impact road sections under adverse weather conditions, and determine the corresponding safety promotion strategy based on the predicted level, thereby greatly improving the traffic safety of high-impact road sections under adverse weather conditions.

[0083] Figure 2 For the flowchart of constructing the prediction model in the method for predicting and managing risks of high-impact road sections under adverse weather conditions provided in the embodiments of the present application, please refer to Figure 2 The construction of the prediction model can include at least the following steps:

[0084] S201, constructing a simulation model in a preset driving dynamics software.

[0085] The simulation model includes a vehicle simulation model, a driver simulation model, and a road simulation model.

[0086] Specifically, driving dynamics software such as CARSIM can be applied to simulate and analyze driving safety under different adverse weather conditions, including constructing a vehicle simulation model, a road simulation model, and a driver simulation model.

[0087] In some embodiments, the construction of the vehicle simulation model includes constructing the vehicle simulation model based on vehicle system parameters and vehicle body parameters in a preset driving dynamics software; wherein the vehicle system parameters include engine, suspension, steering system, braking system, and tire information; and the vehicle body parameters include the mass and size of the vehicle and other appearance information. In this way, one or more vehicle simulation models are constructed to provide model support for subsequent driving safety of target road sections.

[0088] In practical applications, the vehicle simulation model can be constructed by five sub-modules of engine, suspension, steering system, brake system and tire respectively, so as to ensure the accuracy of the vehicle simulation model.

[0089] In some embodiments, the construction of the driver simulation model comprises: constructing the driver simulation model based on speed control and direction control in the preset vehicle dynamics software; wherein the speed is a constant value, and the direction is controlled based on the tracking trajectory of the road.

[0090] Specifically, in the embodiments of the present application, the driver simulation model can be composed of speed control and direction control. In modeling, in terms of speed control, for example, based on the uniform speed driving of a passenger car, the vehicle speed can be set to a constant value; in terms of direction control, the vehicle can be set to follow a pre-tracked trajectory of the road, simulating the driving behavior of steering, etc.

[0091] In some embodiments, the construction of the road simulation model comprises: constructing the road simulation model based on road plane linear parameters, road longitudinal section parameters and road transverse section parameters in the preset vehicle dynamics software.

[0092] Specifically, in the embodiments of the present application, the construction of the road simulation model can be completed in three directions of plane linearity, road longitudinal section and road transverse section. For example, the road plane linearity and related parameters can be determined by inputting the coordinate values under different stake numbers (or other road markers); the road longitudinal section linearity and related parameters can be determined by inputting the starting and ending point elevations; the road transverse section linearity and related parameters can be determined by inputting the elevations of the left, middle and right lines of the road under a representative transverse section.

[0093] S202, determine the target simulation model, and input the preset test parameters to obtain the target data.

[0094] The preset test parameters include vehicle driving speed, road curve radius, road longitudinal slope and road friction coefficient, and the target data includes lateral force and vertical force of the vehicle tire relative to the road.

[0095] Specifically, determining the target simulation model means determining the specific parameter values or parameter value ranges in the above-mentioned vehicle simulation model, road simulation model and driver simulation model, so as to obtain the target data by applying the established simulation model subsequently.

[0096] S203, taking the ratio of the lateral force of the vehicle tire relative to the road to the vertical force of the vehicle tire relative to the road as the critical adhesion coefficient.

[0097] The critical adhesion coefficient is used to represent the probability of vehicle rollover, and the greater the critical adhesion coefficient, the greater the probability of vehicle rollover, and the smaller the critical adhesion coefficient, the smaller the probability of vehicle rollover.

[0098] In this application, considering the driving safety of vehicles such as passenger cars, by considering the lateral motion and yaw motion of passenger cars, the driving stability of the passenger cars on complex road sections and under abnormal road surface friction coefficients is analyzed, the influencing factors and their strengths are obtained, and the driving risk probability is obtained under different influencing factor values, and the critical adhesion coefficient is selected as the evaluation index. Specifically, the maximum value of the absolute value of the ratio of the lateral force to the vertical force of each wheel of the passenger car under different post numbers is the critical adhesion coefficient under the post number, and the specific calculation method is as follows:

[0099]

[0100] In the formula, μL is the critical adhesion coefficient under different mileage; i = 1, 2, 3, 4, respectively representing the left front wheel, the right front wheel, the left rear wheel, and the right rear wheel; FYi(z) represents the tire lateral force under different post numbers; FZi(z) represents the tire vertical force under different post numbers.

[0101] The critical adhesion coefficient is selected as the evaluation index, considering that it can be directly related to the road adhesion coefficient, and under the influence of various factors, it may cause the decrease of the road adhesion coefficient and the increase of the critical adhesion coefficient. When the two are roughly equal, it can be considered that the passenger car will lose stability and produce side slip and other dangerous working conditions.

[0102] It should be noted that the actual factors affecting the safety of passenger cars mainly include driving speed, circular curve radius, longitudinal slope, and road surface friction coefficient, etc. However, road wetness mainly leads to the decrease of the road adhesion coefficient, and the adhesion coefficient refers to the ratio of the adhesion force (the limit value of the tire tangential reaction force) to the ground normal reaction force acting on the tire.

[0103] That is, when the same type of vehicle drives on the same type of road, the ground normal reaction force acting on the tire remains unchanged. At this time, the change of the adhesion coefficient caused by bad weather only affects the change of the adhesion force. When no side slip behavior occurs, the various resistances suffered by the passenger car must be less than the adhesion force, so the change of the adhesion coefficient has no effect on the research at this time. In the case of side slip behavior, the driving characteristics of the passenger car are difficult to simulate, and the data results are difficult to analyze. In this application, the driving characteristics of the vehicle when no side slip behavior occurs are studied through the simulation model, so as to determine the driving risk of the passenger car on complex road sections and in bad weather.

[0104] Specifically, after the specific parameters of the vehicle simulation model, the driver simulation model and the road simulation model are determined through the above process, the simulation test selects the vehicle driving speed, the road curve radius, the road longitudinal slope and the road friction coefficient as the test parameters, i.e., the preset test parameters, to analyze the vehicle driving risk, so as to obtain the data for constructing the prediction. Table 1 lists the simulation strategies of the test in the present application:

[0105] Table 1 vehicle simulation strategies under different parameter conditions

[0106]

[0107] The 4 groups of data under each number in Table 1 are input into the software mentioned in the above embodiment as the preset test parameters, and the lateral force and the vertical force are set as the output items, i.e., the target data. Finally, 4 groups of 16 groups of output data are obtained, and the data is visualized and displayed. Figure 3 The relationship between the test parameters in the simulation model and the critical adhesion coefficient in the adverse weather high-impact road section risk prediction and safety management method provided in the embodiments of the present application is shown in the schematic diagram, specifically as shown in Figure 3

[0108] Through simulation analysis, it can be concluded that when the vehicle is driving on a straight road section, the critical adhesion coefficient is almost 0, at this time, the road surface friction coefficient caused by adverse weather basically has no effect on driving safety; when the vehicle enters a curve road section from a straight road section, the critical adhesion coefficient gradually increases; when the vehicle is driving at the connection between a straight section and a circular curve section, the critical adhesion coefficient increases rapidly; after the vehicle enters the curve road section for a certain time, the critical adhesion coefficient tends to be stable, at this time, the vehicle is driving stably on the curve road section; when the vehicle exits the curve, the critical adhesion coefficient decreases rapidly.

[0109] Figure 3 (a) is the influence of the curve radius R (i.e., the vehicle curve radius) on the critical adhesion coefficient, as shown in Figure 3 (a), the curve radius has a significant effect on the critical adhesion coefficient. Specifically, when the curve radius is the general minimum radius of 200 m, the maximum value of the critical adhesion coefficient is about 0.28. With the increase of the curve radius, the maximum value of the critical adhesion coefficient gradually decreases. The peak values under different curve radii all appear near the same driving distance, which can reflect that the driver has good control over the vehicle under these conditions, and the vehicle can drive according to the expected trajectory of the driver.

[0110] Figure (b) is the influence of the longitudinal slope i (i.e., the road longitudinal slope) on the critical adhesion coefficient, as shown in Figure 3 ​(b) shows that longitudinal slope gradient has a significant impact on the critical adhesion coefficient. When the longitudinal slope gradient is the maximum slope of 6%, the maximum value of the critical adhesion coefficient is about 0.32. With the increase of the longitudinal slope gradient, the critical adhesion coefficient gradually increases. The peak value under different longitudinal slope gradients is slightly different, and the greater the longitudinal slope gradient, the earlier the peak value appears, and the worse the handling performance of the vehicle.

[0111] Figure (c) is the influence of speed v (i.e. vehicle driving speed) on the critical adhesion coefficient, as shown in Figure 3 (c) shows that speed has a significant impact on the critical adhesion coefficient. When the driving speed is 80km, the maximum value of the critical adhesion coefficient is about 0.37. With the increase of the driving speed, the maximum value of the critical adhesion coefficient increases. The peak value under different driving speeds is slightly different, and the greater the speed, the earlier the peak value appears, and the worse the handling performance of the vehicle.

[0112] Figure (d) is the influence of the friction coefficient (i.e. road friction coefficient) on the critical adhesion coefficient, as shown in Figure 3 (d) shows that the friction coefficient has a significant impact on the critical adhesion coefficient. When the friction coefficient is 0.4, the maximum value of the critical adhesion coefficient is about 0.31. With the decrease of the friction coefficient, the maximum value of the critical adhesion coefficient increases, and the peak value under different road friction coefficients is basically consistent, which can reflect that under these conditions, the driver has good handling performance on the vehicle, and the vehicle can travel according to the expected trajectory of the driver.

[0113] S204, regression analysis is performed on the preset test parameters and the critical adhesion coefficient to obtain a prediction model for predicting the critical adhesion coefficient based on the test parameters.

[0114] Specifically, regression analysis is performed on each group of data obtained by the above simulation to obtain a prediction model of the critical adhesion coefficient, i.e. a basic prediction model, which is as follows:

[0115]

[0116] wherein: μ L is the critical adhesion coefficient under different distances; v is the driving speed (km / h); R is the radius of the circular curve (m); i is the road transverse slope (%); and f is the road friction coefficient.

[0117] S204, a prediction model for predicting the adverse weather grade is obtained based on the basic prediction model.

[0118] Specifically, after obtaining the above basic prediction model, r can be defined as the risk of occurrence of side slip, according to the statistical theory, the probability of occurrence of side slip is taken as the risk of occurrence of side slip, and different risks of occurrence of side slip are classified, and then based on the above basic prediction model, a final prediction model for predicting the adverse weather grade is obtained, and the specific model is:

[0119]

[0120] wherein, r is the risk of occurrence of side slip, corresponding to the adverse weather grade mentioned in the above embodiments of the present application, f is the road friction coefficient, and the specific corresponding relationship is shown in Table 2 below:

[0121] Table 2 Corresponding relationship between occurrence planning risk and adverse weather grade

[0122] Risk r [0.8-1] [0.5-0.8) [0.2-0.5) [0-0.2) Risk rating Primary Secondary Tertiary Quaternary Adverse weather rating 1 2 3 4

[0123] On the basis of the above embodiments, in actual application, as long as the real-time test parameters of the corresponding preset test parameters of the target road section are obtained and input into the above prediction model, the occurrence of side slip risk of the target road section under adverse weather and the adverse weather grade can be obtained.

[0124] Further, in the present application, when the safety promotion strategy matching is performed, the traffic control level of the target road section is also considered.

[0125] Specifically, the traffic control level can be customized according to the classification of the traffic management department or based on the information of the road.

[0126] For example, the road traffic management level can be divided into four levels, including level one, level two, level three and level four, and the specific classification standards are shown in Table 3 below:

[0127] Table 4 Classification table of road traffic safety control conditions

[0128]

[0129] On this basis, based on the traffic control level of the target road section, the adverse weather grade and the preset formula, the traffic control level of the target road section under adverse weather is obtained. The specific formula and determination method are shown in the following formula:

[0130] T e = 5 - e, e = 1, 2, 3, 4

[0131] S j = 5 - j, j = 1, 2, 3, 4

[0132]

[0133] z = 5 - D z

[0134] Wherein, Te is the adverse weather risk value, e is the adverse weather grade; Sj is the traffic safety control risk value, j is the traffic control grade; Dz is the traffic risk value under adverse weather, z is the traffic control grade under adverse weather. It should be noted that the above adverse weather traffic control grade classification can not consider the mutual influence between events, and the events are independent by default.

[0135] In practical applications, the adverse weather traffic control can be executed according to different grades, for example, the adverse weather traffic control grade is divided into four levels, and the adverse weather traffic control grade is identified by different colors, from low to high, in turn: blue (low risk) (fourth level), yellow (medium risk) (third level), orange (high risk) (second level) and red (extremely high risk) (first level).

[0136] On the basis of the above embodiment, the application matches the corresponding safety promotion strategy for the target road section by presetting the starting information of the adverse weather traffic control of the target road section within a certain period of time, such as one year. Wherein, the starting information includes the number of starts and the specific level of the start.

[0137] Specifically, the basic version safety promotion strategy can be matched for the target road section within the first preset level range and the first preset range of the adverse weather traffic control starting information; the standard version safety promotion strategy can be matched for the target road section within the second preset level range and the second preset range of the adverse weather traffic control starting information; and the enhanced version safety promotion strategy can be matched for the target road section within the third preset level range and the third preset range of the adverse weather traffic control starting information.

[0138] For example, the target road section can be matched with the safety promotion strategy by Table 4 as follows:

[0139] Table 4 Safety promotion strategy matching table

[0140]

[0141] In the above table 4, the basic version, the standard version and the enhanced version correspond to the basic version safety promotion strategy, the standard version safety promotion strategy and the enhanced version safety promotion strategy respectively, three kinds of safety promotion strategies, safety promotion effect components are strengthened, so as to meet the needs of different adverse weather traffic control.

[0142] Further, in the above embodiments, among the three safety promotion strategies, the meteorological information of the collection module is received by the preset background information processing module or the application software and feedback information is fed back to the collection module, the control measures are sent to the preset front-end device by the background information processing module or the application software, and the safety promotion is sent to the information publishing module by the background information processing module or the application software. Among them, the preset background information processing module can include a public security department preset system such as a road traffic safety information collection and publishing system, a transportation department preset system such as a highway network operation management and service system, and a meteorological department preset system such as a road traffic intelligent meteorological service system, or corresponding application software, etc., which is communicated with the information publishing module to send safety prompts to the information publishing module, and the information publishing module can realize safety prompt publishing based on map navigation, WeChat, Weibo, traffic broadcast, SMS and various APPs and other applications or ways. The three different levels of safety promotion strategies also have differences, thereby realizing different levels of safety promotion.

[0143] The three safety promotion strategies mentioned above will be described in detail below.

[0144] Figure 4 The principle diagram of the basic version safety promotion strategy in the adverse weather high-impact road section risk prediction and safety management method provided by the embodiments of the present application is shown in Figure 5 The application diagram of the basic version safety promotion strategy in the adverse weather high-impact road section risk prediction and safety management method provided by the embodiments of the present application is shown in Figure 4 and Figure 5 As shown in the basic version safety promotion strategy, the information collection module includes a meteorological department sub-module and an intelligent video sub-module, and the control measures include speed limit publishing, route induction and merging warning.

[0145] Specifically, in the basic version scheme, i.e. the basic version safety promotion strategy, the adverse weather warning information provided by the meteorological department is relied on, and fixed meteorological information collection equipment can be added in the collection module according to the situation. Speed limit publishing, route profile warning (route induction), merging warning, intelligent video, monitoring and other front-end devices are set at the roadside of the adverse weather high-impact road section, which are used for control measures under the control of the background information processing module. Synchronous variable information boards, broadcast warning prompt facilities are added at the positions of upstream and downstream adjacent service areas, parking areas, toll stations, etc., and the functions of traffic police event automatic detection, multi-department joint early warning and disposal, multi-channel social publishing, etc. are realized through the management system.

[0146] In some embodiments, the above functions can also be realized by various module units, for example, Figure 5As shown, it can be equipped with variable speed limit signs, merging warning signs, and intelligent video cameras; the route guidance unit can provide distance warnings, collision avoidance trails, and road condition warnings; the merging warning unit can sense and warn merging vehicles on the main road / ramp, enabling linkage between main and branch roads, road flow, congestion detection, and sound and light warnings and fatigue warnings.

[0147] Figure 6 This is a schematic diagram illustrating the principle of the standard safety enhancement strategy in the method for risk prediction and safety management of high-impact road sections in severe weather provided in the embodiments of this application. Figure 7 This is a schematic diagram illustrating the application of the standard safety enhancement strategy in the risk prediction and safety management method for high-impact road sections in severe weather provided in the embodiments of this application. Figure 6 and Figure 7 As shown, the standard version of the safety enhancement strategy includes a meteorological service sub-module and a fixed meteorological sensing sub-module in the information collection module; control measures include: vehicle speed measurement, vehicle distance measurement, exit warning, intelligent video, variable speed limit, route guidance, merging warning and notification combination screen; the standard version of the safety enhancement strategy also includes receiving equipment status information from front-end devices.

[0148] Specifically, the standard version of the solution, namely the standard version of the safety enhancement strategy, involves constructing fixed meteorological information collection, vehicle speed measurement, vehicle distance measurement, exit warning, intelligent video, variable speed limit, route guidance, merging warning, and notification combination screen front-end equipment on road sections with high impact from severe weather. Through the "One Road, Three Parties" system platform, information such as severe weather warnings and traffic control is shared, enabling functions such as automatic detection of traffic incidents, multi-departmental joint early warning and handling, and multi-channel release to the public. It is applicable to road sections with medium and high impact from severe weather.

[0149] In some embodiments, the above functions can also be implemented through various modular units, for example, specifically as follows: Figure 7 As shown, it can set exit warning signs, variable speed limit signs, and intelligent video cameras; realize vehicle distance capture function through the vehicle distance measurement unit; realize speed capture by measuring the speed of vehicles on the road through the vehicle speed measurement unit; realize road profile and road condition warning, vehicle distance warning, and severe weather warning through the route guidance unit, including various weather types such as rain, fog, ice, snow, and wind; collect data such as road condition, visibility, and six meteorological elements through the meteorological acquisition unit; realize vehicle distance warning, collision avoidance tail tracks, and road condition warning through the route guidance unit; sense and warn merging vehicles on the main road / ramp through the merging warning unit, realize the linkage between main and branch roads, road flow, congestion detection, and realize audible and visual warnings and fatigue prevention warnings.

[0150] Figure 8 This is a schematic diagram illustrating the principle of the enhanced safety improvement strategy in the method for risk prediction and safety management of high-impact road sections under severe weather provided in the embodiments of this application. Figure 9An application diagram of an improved version of a safety promotion strategy in a severe weather high-impact road section risk prediction and safety management method provided by the embodiments of the present application is shown in Figure 8 and Figure 9 In the improved version of the safety promotion strategy, the information collection module includes a meteorological service sub-module, a fixed meteorological sensing sub-module, and a vehicle-mounted mobile meteorological sensing sub-module; the control measures include vehicle speed measurement, vehicle distance measurement, exit warning, intelligent video, variable speed limit, route induction, inflow warning, gradient early warning, and a combination screen; the improved version of the safety promotion strategy also includes receiving device state information of a front-end device.

[0151] Specifically, the improved version, i.e., the improved version of the safety promotion strategy, is based on the standard version, and vehicle-mounted mobile meteorological sensing and full-road gradient warning and prompting devices are added to the severe weather high-impact road section to achieve full coverage of meteorological information collection, early warning and prompting, traffic control, and information release, which is suitable for severe weather high-impact road sections.

[0152] In some embodiments, the above functions can also be implemented through various module units, for example, as shown in Figure 9 On the basis of the safety promotion strategy of the real-time example described above, the on-site detection weather live information can be obtained through a vehicle-mounted mobile meteorological collection unit, real-time meteorological warning information can be received, and traffic control information can be released, as shown in Figure 9 which will not be described one by one here.

[0153] The severe weather high-impact road section risk prediction and safety management method provided by the present application builds a human-vehicle-road model under severe weather conditions through the application of driving dynamics simulation analysis for simulation, forming a prediction model for severe weather driving risk prediction, quantitatively predicting and analyzing the severity of severe weather, and being suitable for road traffic driving safety risk prediction under different scenarios such as rain, snow, and fog, and being able to quantitatively evaluate the driving safety under severe weather conditions.

[0154] At the same time, the traffic control level is adapted to the severity of severe weather, so as to determine the traffic control level under severe weather, and the traffic control level under severe weather is divided into blue (low risk), yellow (medium risk), orange (high risk), and red (extremely high risk), so as to directly and quickly determine the traffic control level under severe weather conditions.

[0155] and on this basis, according to the grade and the number of times of starting traffic control under adverse weather of the target road section in a preset time range, the target road section is matched with three safety promotion strategies of a basic version, a standard version and an improved version, so that corresponding optimization and promotion work is carried out for different grades of adverse weather high-impact road sections. Through targeted information collection, front-end equipment setting, back-end system processing logic, information release mode and multi-department, all-round and multi-channel traffic safety control, a closed-loop work flow is formed, which can provide technical support for adverse weather high-impact road section optimization and promotion work, provide theoretical method support for the implementation of the plan decision of the grass-roots public security traffic management department, provide a basic guarantee for adverse weather high-impact road section driving safety, and has good popularization and application effect.

[0156] Based on the same inventive concept, the application also provides an adverse weather high-impact road section risk prediction and safety management device, Figure 10 The adverse weather high-impact road section risk prediction and safety management device provided by the embodiment of the application comprises:

[0157] The prediction module 101 is configured to predict the adverse weather grade of the target road section by using a preset prediction model.

[0158] The determination module 102 is configured to determine the traffic control grade under adverse weather of the target road section based on the traffic control grade and the adverse weather grade of the target road section.

[0159] The matching module 103 is configured to match the corresponding safety promotion strategy for the target road section based on the starting information of traffic control under adverse weather of the target road section in a preset time range, wherein the starting information of traffic control under adverse weather includes the number of times of starting traffic control under adverse weather and the grade of traffic control under adverse weather that is started.

[0160] The specific implementation of the adverse weather high-impact road section risk prediction and safety management device provided by the embodiment of the application can refer to the implementation of the adverse weather high-impact road section risk prediction and safety management method of any of the above embodiments, which will not be described here.

[0161] Based on the same inventive concept, the application also provides an adverse weather high-impact road section risk prediction and safety management device, Figure 11 The adverse weather high-impact road section risk prediction and safety management device provided by the embodiment of the application comprises: a processor 111 and a memory 112, wherein the processor 111 is connected with the memory 112. The processor 111 is configured to call and execute a program stored in the memory 112; the memory 112 is configured to store the program, and the program is at least used for the adverse weather high-impact road section risk prediction and safety management method mentioned in the above method embodiment.

[0162] The specific implementation of the severe weather high-impact road section risk prediction and safety management device provided by the embodiments of the present application can refer to the implementation of the severe weather high-impact road section risk prediction and safety management method of any of the above embodiments, which will not be repeated here.

[0163] It can be understood that the same or similar parts in the above embodiments can be mutually referred to, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0164] It should be noted that in the description of the present application, the terms "first", "second", etc. are only for the purpose of description, and cannot be understood as indicating or implying relative importance. In addition, in the description of the present application, unless otherwise specified, the meaning of "a plurality of" is at least two.

[0165] Any process or method descriptions in flow charts or described elsewhere herein can be understood as representing code modules, segments, or portions of code that include one or more executable instructions for implementing specific logic functions or other processes, and the various embodiments of the present application include additional implementations in which the functions described with reference to the figures are implemented by hardware, software, firmware, or combinations thereof. The order in which the functions are described is not necessarily the order in which the functions are performed, as some functions can be performed in different order or substantially concurrently with each other.

[0166] It should be understood that parts of the present application can be realized by hardware, software, firmware or their combinations. In the above embodiments, a plurality of steps or methods can be realized by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if realized by hardware, and as in another embodiment, it can be realized by any one or their combinations of the following technologies known in the art: discrete logic circuit with logic gate circuit for implementing logic function on data signal, application specific integrated circuit with suitable combination logic gate circuit, programmable gate array (PGA), field programmable gate array (FPGA) and the like.

[0167] Those skilled in the art of the present technology can understand that all or part of the steps carried out by the above-mentioned embodiments can be completed by programs instructing relevant hardware, and the programs can be stored in a computer readable storage medium, and when executed, include one or a combination of steps of the method embodiments.

[0168] In addition, each function unit in each embodiment of the present application can be integrated in one processing module, or each unit can be physically present separately, or two or more units can be integrated in one module. The integrated module can be realized in the form of hardware, or in the form of a software function module. When the integrated module is realized in the form of a software function module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0169] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0170] In the description of the present specification, the description referring to the terms "one embodiment", "some embodiments", "an example", "a specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily mean the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0171] Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A method for risk prediction and safety management of road sections highly affected by severe weather, characterized in that, include: Predict the severity of severe weather on the target road section by using a pre-set prediction model; Based on the traffic control level of the target road segment and the severe weather level, determine the traffic control level of the target road segment under severe weather conditions; Based on the traffic control activation information under severe weather conditions within a preset time range for the target road segment, a corresponding safety enhancement strategy is matched for the target road segment. The activation information under severe weather conditions includes the number of times traffic control is activated under severe weather conditions and the level of traffic control under severe weather conditions that is activated. The construction process of the prediction model includes: A simulation model is constructed in a pre-defined vehicle dynamics software; the simulation model includes a vehicle simulation model, a driver simulation model, and a road simulation model. The target simulation model is determined and preset test parameters are input to obtain target data; wherein, the types of preset test parameters include vehicle speed, road curve radius, road longitudinal slope and road friction coefficient, and the target data includes the lateral force and vertical force of vehicle tires relative to the road; The ratio of the lateral force of the vehicle tire relative to the road to the vertical force of the vehicle tire relative to the road is used as the critical adhesion coefficient; wherein, the critical adhesion coefficient is used to characterize the probability of the vehicle rolling over, and the larger the critical adhesion coefficient, the greater the probability of the vehicle rolling over, and the smaller the critical adhesion coefficient, the smaller the probability of the vehicle rolling over. Regression analysis is performed on the preset test parameters and the critical adhesion coefficient to obtain a basic prediction model for predicting the critical adhesion coefficient based on the test parameters. Based on the aforementioned basic prediction model, a prediction model for predicting severe weather levels is obtained; The construction of the vehicle simulation model includes: constructing the vehicle simulation model based on vehicle system parameters and vehicle body parameters in a preset vehicle dynamics software; wherein, the vehicle system parameters include engine, suspension, steering system, braking system and tire information; the vehicle body parameters include the vehicle's mass and dimensions; The construction of the driver simulation model includes: constructing the driver simulation model based on speed control and direction control in a preset driving dynamics software; wherein, the speed is a constant value, and the direction is controlled based on the road tracking trajectory. The construction of the road simulation model includes: constructing the road simulation model based on road horizontal linear parameters, road longitudinal profile parameters, and road cross profile parameters in a preset driving dynamics software; The step of predicting the severity of severe weather on the target road section using a preset prediction model includes: The real-time test parameters of the target road segment are obtained and input into the prediction model to obtain the real-time critical adhesion coefficient corresponding to the real-time test parameters of the target road segment. Based on the real-time critical adhesion coefficient and the correspondence between the critical adhesion coefficient and the sideslip risk, the real-time sideslip risk corresponding to the real-time critical adhesion coefficient is obtained. Based on the real-time skid risk and the correspondence between skid risk and severe weather level, the severe weather level corresponding to the real-time skid risk is obtained and determined as the severe weather level of the target road section.

2. The method for risk prediction and safety management of road sections with high impact from severe weather as described in claim 1, characterized in that, Determining the traffic control level of the target road segment under severe weather conditions based on the traffic control level of the target road segment and the severe weather level includes: Based on the traffic control level of the target road segment, the severe weather level, and the preset formula, the traffic control level of the target road segment under severe weather is obtained; The preset formula includes: ； Wherein: T e S represents the severe weather risk value, e represents the severe weather level, and S represents the severe weather risk value. j denoted as , where j represents the traffic control risk value and z represents the traffic control level; Dz represents the traffic risk value under severe weather conditions and z represents the traffic control level under severe weather conditions.

3. The method for risk prediction and safety management of road sections with high impact from severe weather as described in claim 1, characterized in that, The method of matching corresponding safety enhancement strategies for the target road segment based on the traffic control activation information under severe weather conditions within a preset time range includes: For traffic control activation information under severe weather conditions, the basic safety enhancement strategy is matched to the target road segment within the first preset level range and the first preset range. For traffic control activation information under severe weather conditions, the standard version of the safety enhancement strategy is matched to the target road section within the second preset level range and the second preset range. For target road sections within the third preset level range and within the third preset range of traffic control activation information under severe weather conditions, an enhanced safety enhancement strategy will be matched.

4. The method for risk prediction and safety management of road sections with high impact from severe weather as described in claim 3, characterized in that, The security enhancement strategy includes: The system receives meteorological information from the acquisition module through a pre-set background information processing module and sends the information back to the acquisition module. Control measures are sent to the preset front-end devices through the preset back-end information processing module; The system sends security alerts to the information publishing module through a pre-defined backend information processing module. The preset background information processing module includes preset systems for public security departments, transportation departments, and meteorological departments.

5. The method for risk prediction and safety management of road sections with high impact from severe weather as described in claim 4, characterized in that, In the aforementioned basic security enhancement strategy, the data acquisition module includes a meteorological department sub-module and an intelligent video sub-module, and the control measures include speed limit release, route guidance, and inbound warning; In the standard version of the security enhancement strategy, the data acquisition module includes a meteorological service sub-module and a fixed meteorological sensing sub-module; the control measures include: vehicle speed measurement, vehicle distance measurement, exit warning, intelligent video, variable speed limit, route guidance, merging warning and notification combination screen; the standard version of the security enhancement strategy also includes receiving device status information from front-end devices; In the enhanced security strategy, the data acquisition module includes a meteorological service submodule, a fixed meteorological sensing submodule, and a vehicle-mounted mobile meteorological sensing submodule; the control measures include: vehicle speed measurement, vehicle distance measurement, exit warning, intelligent video, variable speed limit, route guidance, merging warning, gradient warning, and notification combination screen; the enhanced security strategy also includes receiving device status information from front-end devices.

6. A risk prediction and safety management device for high-impact road sections in severe weather, used to perform the method as described in any one of claims 1-5, characterized in that, include: The prediction module is used to predict the severity of severe weather on the target road section using a preset prediction model. The determination module is used to determine the traffic control level of the target road segment under severe weather conditions based on the traffic control level of the target road segment and the severe weather level. The matching module is used to match a corresponding safety enhancement strategy for the target road segment based on the traffic control activation information under severe weather conditions within a preset time range. The activation information under severe weather conditions includes the number of times traffic control is activated under severe weather conditions and the activation level of traffic control under severe weather conditions.

7. A risk prediction and safety management device for road sections with high impact from severe weather, characterized in that, It includes a processor and a memory, wherein the processor is connected to the memory: The processor is used to call and execute the program stored in the memory; The memory is used to store the program, which is at least used to execute the method for risk prediction and safety management of high-impact road sections in severe weather as described in any one of claims 1-5.

Images