Risk control processing system and method based on target vehicle ETC data

Abstract

The invention relates to the technical field of vehicle risk control processing, in particular to a risk control processing system and method based on target vehicle ETC data. The method comprises the steps of obtaining an average speed of a target vehicle in a historical road section according to ETC data of the target vehicle; according to the inertial acceleration of the target vehicle, the road condition complexity coefficient of the target road section and the historical driving behavior safety coefficient of a driver of the target vehicle, the average acceleration of the target vehicle decelerating from the initial speed to the safe speed on the target road section is obtained; acquiring a safety prompt distance according to the average acceleration, the initial speed and the safety speed; and in response to the fact that the distance between the target vehicle and the target potential safety hazard position is smaller than or equal to the safety prompt distance, sending speed reduction prompt information to the target vehicle. According to the invention, targeted speed reduction prompts can be provided for different large vehicles, and the driving safety and the passing efficiency are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle risk control processing, in particular to a risk control processing system and method based on target vehicle ETC data. BACKGROUND

[0002] With the rapid development of expressways, ETC systems (Electronic Toll Collection system) have been widely applied, providing convenience for vehicle fast passage. The ETC gantry system is a key component of the expressway ETC system, which is usually arranged above the expressway and communicates with the ETC device on the vehicle through microwave, radio frequency and other technologies to realize the identification and positioning of the vehicle. However, there are deficiencies in the risk prevention and control of large vehicles (such as trucks) on the expressway; if a large vehicle encounters a road congestion situation due to an accident (such as a vehicle collision) in front of the road section during normal driving, the large vehicle often needs to slow down, but due to the large weight of the large vehicle, the sudden deceleration behavior of the large vehicle often has a large safety hazard. The prior art discloses a method of providing a safety prompt to a vehicle when the distance from the road congestion section is a certain preset distance, but different large vehicles have different inertial accelerations on the same section, the driving behavior habits of drivers are different, and the complexity of the road conditions on different sections of the highway is also different. The above-mentioned preset distance is not enough for some large vehicles to slow down safely, while for other large vehicles, it is too large, resulting in unnecessary early deceleration and affecting the passage efficiency; how to determine the corresponding safety prompt distance for different large vehicles to provide targeted speed reduction prompts for different large vehicles and improve driving safety and passage efficiency is a problem to be solved. SUMMARY

[0003] The present application aims to provide a risk control processing system and method based on target vehicle ETC data to provide targeted speed reduction prompts for different large vehicles and improve driving safety and passage efficiency.

[0004] According to a first aspect of the present application, a risk control processing method based on target vehicle ETC data is provided, which comprises the following steps: S100, obtaining the average speed of the target vehicle on the historical road section according to the target vehicle ETC data; the target vehicle ETC data includes the time of the target vehicle passing through each ETC gantry system on the historical road section; the weight of the target vehicle is greater than or equal to a preset weight threshold, and the volume of the target vehicle is greater than or equal to a preset volume threshold.

[0005] S200, obtain an average acceleration of the target vehicle from an initial speed to a safe speed on the target road section according to an inertial acceleration of the target vehicle, a road condition complexity coefficient of the target road section and a historical driving behavior safety coefficient of a driver of the target vehicle; the target vehicle has a weight greater than or equal to a preset weight threshold and a volume greater than or equal to a preset volume threshold; the inertial acceleration and the road condition complexity coefficient of the target road section are positively correlated with the average acceleration, and the driving behavior safety coefficient of the driver of the target vehicle is negatively correlated with the average acceleration; the inertial acceleration is greater than or equal to the average acceleration; the initial speed is obtained according to the average speed; and the target road section is a road section between a current position of the target vehicle and the target safety hidden danger position.

[0006] S300, obtain a safety prompt distance according to the average acceleration, the initial speed and the safe speed; the average acceleration and the initial speed are positively correlated with the safety prompt distance, and the safety prompt distance is negatively correlated with the safe speed.

[0007] S400, in response to a distance between the target vehicle and the target safety hidden danger position being less than or equal to the safety prompt distance, send prompt information of deceleration to the target vehicle.

[0008] According to the second aspect of the present application, a risk control processing system based on ETC data of a target vehicle is also provided, the system comprising a non-transitory computer readable storage medium and a processor; wherein the storage medium stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the above-mentioned risk control processing method based on ETC data of a target vehicle.

[0009] Compared with the prior art, the present application has at least the following beneficial effects: In the present application, the average acceleration of the target vehicle is referred to in determining the safety prompt distance of the target vehicle, and the average acceleration comprehensively considers the inertial acceleration of the target vehicle, the road condition complexity coefficient of the target road section and the historical driving behavior safety coefficient of the driver of the target vehicle. Therefore, the safety prompt distance of the target vehicle determined by the present application is more matched with the target vehicle, the road condition of the target road section and the driving behavior of the driver of the target vehicle, a larger safety prompt distance is provided for the target vehicle when the inertial acceleration of the target vehicle is smaller, the road condition complexity coefficient of the target road section is larger and the historical driving behavior safety coefficient of the driver of the target vehicle is smaller, the driving safety of the target vehicle is increased; and a smaller safety prompt distance is provided for the target vehicle when the inertial acceleration of the target vehicle is larger, the road condition complexity coefficient of the target road section is smaller and the historical driving behavior safety coefficient of the driver of the target vehicle is larger, the passing efficiency of the target vehicle is improved under the premise of ensuring the driving safety. BRIEF DESCRIPTION OF DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments description. Obviously, the drawings in the following description only show some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort based on these drawings.

[0011] Figure 1 The flowchart of the risk control processing method based on the target vehicle ETC data provided for the first embodiment of the present application; Figure 2 The flowchart of the inertia acceleration acquisition process of the target vehicle provided for the first embodiment of the present application; Figure 3 The flowchart of the initial speed acquisition process provided for the first embodiment of the present application. DETAILED DESCRIPTION

[0012] The technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some 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 any creative effort are within the scope of protection of the present application. EMBODIMENT

[0013] According to the present embodiment, as shown in Figure 1 , a risk control processing method based on target vehicle ETC data is provided, which comprises the following steps: S100, acquiring the average speed of the target vehicle on the historical road section according to the target vehicle ETC data; the target vehicle ETC data comprises the time of the target vehicle passing through each ETC gantry system on the historical road section; the weight of the target vehicle is greater than or equal to a preset weight threshold value, and the volume of the target vehicle is greater than or equal to a preset volume threshold value.

[0014] In the present embodiment, the target vehicle is a large vehicle, such as a truck, and the weight of the target vehicle is greater than or equal to a preset weight threshold value, and the volume of the target vehicle is greater than or equal to a preset volume threshold value. The weight threshold value and the volume threshold value are both empirical values. Optionally, the weight threshold value is 4 tons, and the volume threshold value is 26.62m 3 , which corresponds to a length of 5.5m, a width of 2.2m, and a height of 2.2m.

[0015] In the present embodiment, the target vehicle is driving on a highway, and the historical road section is a highway. As a specific embodiment, the historical road section is the road section between the starting position of the target vehicle entering the highway and the current position of the target vehicle.

[0016] As a specific embodiment, the target vehicle ETC data comprises not only the time of the target vehicle passing each ETC gantry system on the historical road section, but also the distance between any two adjacent ETC gantry systems on the historical road section, for example, the target vehicle ETC data is [(t1, 0), (t2, d2), …, (t i i ), …, (t n , d n )], t i is the time of the target vehicle passing the i-th ETC gantry system on the historical road section, d i is the distance between the i-th ETC gantry system and the (i-1)-th ETC gantry system on the historical road section, i ranges from 2 to n, n is the number of ETC gantry systems on the historical road section; t1 is the time of the target vehicle passing the first ETC gantry system on the historical road section. Then, the average speed of the target vehicle on the historical road section is (∑ n i=2 (d i / (t i -t i-1 )) / (n-1).

[0017] As a preferred embodiment, before S100, it is judged whether the target vehicle passes the specified ETC gantry system, if yes, S100 is entered; otherwise, it is continued to judge whether the target vehicle passes the specified ETC gantry system; the specified ETC gantry system is the ETC gantry system located on both sides of the accident occurrence position and close to the starting position of the target vehicle. For example, the accident occurrence position is located between the first ETC gantry system and the second ETC gantry system, and the target vehicle will pass the first ETC gantry system first and then the second ETC gantry system according to the driving direction of the target vehicle, that is, the first gantry system is the ETC system close to the starting position of the target vehicle among the first ETC gantry system and the second ETC gantry system, and then the first ETC gantry system is the specified ETC gantry system. Based on the preferred embodiment, the ETC gantry system in front of the accident occurrence position is started to execute S100-S400, which can reduce unnecessary operation and save computing resources.

[0018] ​S200, obtaining an average acceleration of the target vehicle from an initial speed to a safe speed on the target road section according to an inertial acceleration of the target vehicle, a road condition complexity coefficient of the target road section, and a historical driving behavior safety coefficient of a driver of the target vehicle; the target vehicle has a weight greater than or equal to a preset weight threshold, and has a volume greater than or equal to a preset volume threshold; the inertial acceleration and the road condition complexity coefficient of the target road section are positively correlated with the average acceleration, and the driving behavior safety coefficient of the driver of the target vehicle on the historical road section is negatively correlated with the average acceleration; the inertial acceleration is greater than or equal to the average acceleration; the initial speed is obtained according to the average speed; and the target road section is a road section between a current position of the target vehicle and the target safety hidden danger position.

[0019] In this embodiment, the target safety hidden danger position is an upstream congestion point of an accident influence area on the target road section, and the upstream refers to a side closer to the starting position of the target vehicle. The target road section is a highway.

[0020] In this embodiment, the greater the road condition complexity coefficient of the target road section, the more complex the road condition of the target road section, and it is safer for the target vehicle to travel on the target road section at a greater acceleration. The acceleration in this embodiment is a negative value, and a greater acceleration corresponds to less deceleration in the same time. As an optional specific implementation, the road condition complexity coefficient of the target road section is obtained according to the number of curves, the curve radius, the number of slopes, the slope and the slope length included in the target road section, for example, first obtaining an average value of the radius of all curves according to the radius of each curve, obtaining an average value of the slope of all slopes according to the slope of each slope, and obtaining an average value of the slope length of all slopes according to the slope length of each slope, wherein the average value of the radius is negatively correlated with the road condition complexity coefficient of the target road section, and the number of curves, the number of slopes, the average value of the slope and the average value of the slope length are positively correlated with the road condition complexity coefficient of the target road section; optionally, the road condition complexity coefficient of the target road section is obtained by using a weighted summation formula, k1=a1×b1-a2×b2+a3×b3+a4×b4+a5×b5, wherein k1 is the road condition complexity coefficient of the target road section, a1, a2, a3, a4 and a5 are weights corresponding to the number of curves, the average value of the radius, the number of slopes, the average value of the slope and the average value of the slope length respectively, b1, b2, b3, b4 and b5 are the number of curves, the average value of the radius, the number of slopes, the average value of the slope and the average value of the slope length after normalization respectively, a1, a2, a3, a4 and a5 all satisfy the condition of being greater than 0 and less than 1, and the sum of a1, a2, a3, a4 and a5 is 1, 0<k1<1, and any normalization method in the prior art falls within the protection scope of the present application, which will not be described here.

[0021] The smaller the historical driving behavior safety factor of the driver of the target vehicle is, the less safe the historical driving behavior of the driver of the target vehicle is, and the safer the target vehicle is when driving on the target road section at a larger acceleration. As an optional specific embodiment, the historical driving behavior safety factor of the driver of the target vehicle is obtained according to the number of sudden accelerations, the number of sudden braking, the number of lane changes and the speed fluctuation degree of the driver of the target vehicle on the historical road section. For example, the coefficient of variation of the speed of the target vehicle on the historical road section is obtained according to the real-time speed of the target vehicle on the historical road section, and the coefficient of variation is determined as the speed fluctuation degree, wherein the number of sudden accelerations, the number of sudden braking, the number of lane changes and the speed fluctuation degree are all negatively correlated with the historical driving behavior safety factor of the driver of the target vehicle; optionally, the historical driving behavior safety factor of the driver of the target vehicle is obtained by using a weighted summation formula, k2=1-c1×d1-c2×d2-c3×d3-c4×d4, k2 is the historical driving behavior safety factor of the driver of the target vehicle, c1, c2, c3 and c4 are weights corresponding to the number of sudden accelerations, the number of sudden braking, the number of lane changes and the speed fluctuation degree, respectively, d1, d2, d3 and d4 are the normalized number of sudden accelerations, the number of sudden braking, the number of lane changes and the speed fluctuation degree, respectively, c1, c2, c3 and c4 all satisfy the condition of being greater than 0 and less than 1, and the sum of c1, c2, c3 and c4 is 1, 0<k2<1, and any normalization method in the prior art falls within the protection scope of the present application, which will not be described here.

[0022] In the embodiment, the inertial acceleration of the target vehicle is related to the speed of the target vehicle, environmental factors (such as wind speed) and the road. As an optional specific embodiment, as shown in FIG. 8, the process of obtaining the inertial acceleration of the target vehicle includes: Figure 2 S201, matching the initial speed, the safe speed and the average wind speed of the target road section in the inertial acceleration list corresponding to the target vehicle; the inertial acceleration list corresponding to the target vehicle includes a plurality of records, and any record includes a starting speed, an ending speed, a wind speed and a corresponding inertial acceleration.

[0023] In the embodiment, the inertial acceleration list corresponding to the target vehicle is a pre-established list, and optionally, the records in the list are realized by vehicle dynamics simulation software, and the simulation parameters are consistent with the parameters when the target vehicle drives on the target road; when the initial speed is the starting speed in a record, the safe speed is the ending speed in the record, and the average wind speed of the target road section is the wind speed in the record, it is determined that the inertial acceleration of the target vehicle is the inertial acceleration in the record.

[0024] S202, determining the inertial acceleration in the matched record as the inertial acceleration of the target vehicle.​

[0025] Based on S201-S202, the embodiment can predict the inertial acceleration of the target vehicle.

[0026] In the embodiment, the average acceleration of the target vehicle from the initial speed to the safe speed on the target section is a relatively safe acceleration determined for the target vehicle according to the inertial acceleration of the target vehicle, the road condition complexity coefficient of the target section and the historical driving behavior safety coefficient of the driver of the target vehicle; as a preferred specific embodiment, x1=x0x(1+r x(1+u1xk1-u2xk2)), x0 is the inertial acceleration of the target vehicle, x1 is the average acceleration of the target vehicle from the initial speed to the safe speed on the target section, u1 and u2 are weights corresponding to the road condition complexity coefficient of the target section and the historical driving behavior safety coefficient of the driver of the target vehicle respectively, u1 and u2 are greater than 0 and less than 1, the sum of u1 and u2 is 1, k1 and k2 are the road condition complexity coefficient of the target section and the historical driving behavior safety coefficient of the driver of the target vehicle respectively, r is a preset adjustment coefficient, r>0, 0<k1<1, 0<k2<1. Optionally, r, u1 and u2 are empirical values. Based on the preferred specific embodiment, the embodiment can increase the average acceleration when the road condition is relatively complex and the historical driving behavior of the driver of the target vehicle is poor, which is beneficial to increase the safety prompt distance and improve the safety of the target vehicle when driving on a relatively complex road section and the historical driving behavior of the driver is poor.

[0027] In the embodiment, the initial speed is a predicted value of the target vehicle when the distance between the target vehicle and the target safety hazard position is the safety prompt distance. As an optional specific embodiment, the average speed of the target vehicle on the historical section is determined as the initial speed. As a preferred specific embodiment, as shown in Figure 3 The process of obtaining the initial speed includes: S210, obtaining a safety risk coefficient of the target vehicle driving on the target section according to the accident complexity degree occurring on the target section, the weather safety hazard coefficient, the historical driving behavior safety coefficient of the driver of the target vehicle and the first safety risk coefficient model corresponding to the target vehicle; the dependent variable of the first safety risk coefficient model corresponding to the target vehicle is the safety risk coefficient of the target vehicle driving on the target section, and the dependent variable of the first safety risk coefficient model corresponding to the target vehicle includes the accident complexity degree occurring on the target section, the weather safety hazard coefficient and the historical driving behavior safety coefficient of the driver of the target vehicle.

[0028] In the embodiment, the greater the accident complexity degree, the more complex the accident, the higher the risk of the target vehicle driving at a larger speed on the target road section and being unable to timely slow down, and the less safe; as an optional specific implementation, the accident complexity degree is obtained according to the number of vehicles having accidents on the target road and the number of lanes having accidents on the target road, and the number of vehicles having accidents on the target road and the number of lanes having accidents on the target road are both positively correlated with the accident complexity degree. Optionally, the accident complexity degree is obtained by using a weighted summation formula, k3 = e1 x f1 + e2 x f2, k3 is the accident complexity degree, e1 and e2 are weights corresponding to the number of vehicles having accidents on the target road and the number of lanes having accidents on the target road, respectively, f1 and f2 are the number of vehicles having accidents on the target road and the number of lanes having accidents on the target road after normalization, respectively, e1 and e2 both satisfy the condition of being greater than 0 and less than 1, and the sum of e1 and e2 is 1. It is known to those skilled in the art that any normalization method in the prior art falls within the protection scope of the present application, and thus will not be described here.

[0029] In the embodiment, the greater the weather safety hidden danger coefficient, the worse the weather factor, and the higher the risk of the target vehicle driving at a larger speed on the target road section. As an optional specific implementation, a mapping relationship between various weathers and the weather safety hidden danger coefficient is established in advance, and the weather safety hidden danger coefficient of the target road section can be obtained according to the weather of the target road section and the mapping relationship. Optionally, the weather safety hidden danger coefficient has a value range of 0-1.

[0030] In the embodiment, the accident complexity degree and the weather safety hidden danger coefficient on the target road section are both positively correlated with the safety risk coefficient of the target vehicle driving on the target road section, and the historical driving behavior safety coefficient of the driver of the target vehicle is negatively correlated with the safety risk coefficient of the target vehicle driving on the target road section.

[0031] In the embodiment, the first safety risk coefficient model corresponding to the target vehicle is a pre-established model; as an optional specific implementation, a curve fitting method is used to obtain the relationship between the accident complexity degree, the weather safety hidden danger coefficient, the historical driving behavior safety coefficient of the driver, and the safety risk coefficient corresponding to the target vehicle, which is the first safety risk coefficient model corresponding to the target vehicle. As another optional specific implementation, a weighted summation method is used to obtain the relationship between the accident complexity degree, the weather safety hidden danger coefficient, the historical driving behavior safety coefficient of the driver, and the safety risk coefficient corresponding to the target vehicle.

[0032] S220, acquire the first speed corresponding to the target vehicle according to the safety risk coefficient of the target vehicle driving on the target road section and the second safety risk coefficient model corresponding to the target vehicle; the dependent variable of the second safety risk coefficient model corresponding to the target vehicle is the safety risk coefficient of the target vehicle driving on the target road section, and the independent variable of the second safety risk coefficient model corresponding to the target vehicle is the speed of the target vehicle.

[0033] In the embodiment, the second safety risk coefficient model corresponding to the target vehicle is a pre-established model; as an optional specific implementation, a curve fitting method is used to acquire the relationship between the speed corresponding to the target vehicle and the safety risk coefficient, which is the second safety risk coefficient model corresponding to the target vehicle. Under the premise that the second safety risk coefficient model corresponding to the target vehicle is known, the safety risk coefficient of the target vehicle driving on the target road section is substituted into the second safety risk coefficient model, so that the first speed corresponding to the target vehicle can be acquired.

[0034] Optionally, the safety speed is an empirical value or is obtained according to a preset safety risk coefficient threshold and the second safety risk coefficient model corresponding to the target vehicle, and the preset safety risk coefficient threshold is an empirical value.

[0035] S230, if the average speed is greater than or equal to the first speed, the average speed is determined as the initial speed; otherwise, the first speed is determined as the initial speed.

[0036] In the embodiment, the first speed is calculated according to the safety risk coefficient of the target road section, which reflects the influence of the current road condition, weather and driver behavior and other factors on safety; if the first speed is higher than the average speed, it indicates that the current road condition or environment is relatively complex or the driver's driving behavior habit is not good, and by determining the larger value between the average speed and the first speed as the initial speed, it is beneficial to increase the safety prompt distance, to provide a longer speed reduction time for the driver, and to improve the safety of the target vehicle driving.

[0037] S300, acquire the safety prompt distance according to the average acceleration, the initial speed and the safety speed; the average acceleration and the initial speed are positively correlated with the safety prompt distance, and the safety prompt distance is negatively correlated with the safety speed.

[0038] As an specific implementation, the safety prompt distance is z, z=(v2×v2-v1×v1) / (2×y), wherein z is the safety prompt distance, v1 and v2 are the initial speed and the safety speed respectively, and y is the average acceleration.

[0039] S400, in response to the distance between the target vehicle and the target safety hidden danger position being less than or equal to the safety prompt distance, send a speed reduction prompt information to the target vehicle.

[0040] As a specific implementation, the prompt information further comprises a distance between the target vehicle and the target safety hazard position; thus, the driver of the target vehicle can determine the magnitude of the speed reduction according to the distance between the target vehicle and the target safety hazard position.

[0041] The embodiment refers to the average acceleration of the target vehicle when determining the safety prompt distance of the target vehicle, which comprehensively considers the inertial acceleration of the target vehicle, the road condition complexity coefficient of the target road section and the historical driving behavior safety coefficient of the driver of the target vehicle, thus, the safety prompt distance of the target vehicle determined by the embodiment is more matched with the target vehicle, the road condition of the target road section and the driving behavior of the driver of the target vehicle, can provide a larger safety prompt distance for the target vehicle when the inertial acceleration of the target vehicle is smaller, the road condition complexity coefficient of the target road section is larger and the historical driving behavior safety coefficient of the driver of the target vehicle is smaller, increase the driving safety of the target vehicle; and can provide a smaller safety prompt distance for the target vehicle when the inertial acceleration of the target vehicle is larger, the road condition complexity coefficient of the target road section is smaller and the historical driving behavior safety coefficient of the driver of the target vehicle is larger, improve the passing efficiency of the target vehicle under the premise of ensuring the driving safety. Embodiment

[0042] The embodiment provides a risk control processing system based on target vehicle ETC data, the system comprises a non-transitory computer readable storage medium and a processor; wherein the storage medium stores at least one instruction or at least one program, the at least one instruction or the at least one program is loaded and executed by the processor to realize the above-mentioned risk control processing method based on target vehicle ETC data.

[0043] Although some specific embodiments of the present application have been described in detail by examples, those skilled in the art should understand that the above examples are only for illustration, not for limiting the scope of the present application. Those skilled in the art should also understand that various modifications can be made to the embodiments without departing from the scope and spirit of the present application. The scope of the present application is defined by the appended claims.

Claims

1. A risk control processing method based on target vehicle ETC data, characterized in that, The method includes the following steps: S100, obtain the average speed of the target vehicle on the historical road segment based on the target vehicle's ETC data; the target vehicle's ETC data includes the time the target vehicle spends passing through each ETC gantry system on the historical road segment; the weight of the target vehicle is greater than or equal to a preset weight threshold, and the volume of the target vehicle is greater than or equal to a preset volume threshold. S200: Based on the target vehicle's inertial acceleration, the road condition complexity coefficient of the target road segment, and the driver's historical driving behavior safety coefficient, the average acceleration of the target vehicle as it decelerates from its initial speed to a safe speed on the target road segment is obtained. The inertial acceleration and the road condition complexity coefficient of the target road segment are both positively correlated with the average acceleration, while the driver's historical driving behavior safety coefficient on the target road segment is negatively correlated with the average acceleration. The inertial acceleration is greater than or equal to the average acceleration. The initial speed is obtained based on the average vehicle speed. The target road segment is the section between the target vehicle's current position and the location of the target safety hazard. S300, obtain the safety warning distance based on the average acceleration, initial velocity, and safe velocity; the average acceleration and the initial velocity are both positively correlated with the safety warning distance, and the safety warning distance is negatively correlated with the safe velocity; S400, in response to the distance between the target vehicle and the target safety hazard location being less than or equal to the safety warning distance, issues a speed reduction warning message to the target vehicle.

2. The risk control processing method based on target vehicle ETC data according to claim 1, characterized in that, The process of obtaining the initial velocity includes: S210, obtain the safety risk coefficient of the target vehicle driving on the target road segment based on the complexity of accidents occurring on the target road segment, the weather safety hazard coefficient, the historical driving behavior safety coefficient of the driver of the target vehicle, and the first safety risk coefficient model corresponding to the target vehicle; the dependent variable of the first safety risk coefficient model corresponding to the target vehicle is the safety risk coefficient of the target vehicle driving on the target road segment, and the dependent variable of the first safety risk coefficient model corresponding to the target vehicle includes the complexity of accidents occurring on the target road segment, the weather safety hazard coefficient, and the historical driving behavior safety coefficient of the driver of the target vehicle; S220, obtain the first speed of the target vehicle based on the safety risk coefficient of the target vehicle traveling on the target road segment and the second safety risk coefficient model corresponding to the target vehicle; the dependent variable of the second safety risk coefficient model corresponding to the target vehicle is the safety risk coefficient of the target vehicle traveling on the target road segment, and the independent variable of the second safety risk coefficient model corresponding to the target vehicle is the speed of the target vehicle; S230, if the average vehicle speed is greater than or equal to the first speed, then the average vehicle speed is determined as the initial speed; otherwise, the first speed is determined as the initial speed.

3. The risk control processing method based on target vehicle ETC data according to claim 1, characterized in that, The process of obtaining the inertial acceleration of the target vehicle includes: S201, Match the initial speed, safe speed, and average wind speed of the target road segment with the inertial acceleration list corresponding to the target vehicle; the inertial acceleration list corresponding to the target vehicle includes several records, and each record includes a starting speed, an ending speed, a wind speed, and a corresponding inertial acceleration; S202, determine the inertial acceleration in the matched record as the inertial acceleration of the target vehicle.

4. The risk control processing method based on target vehicle ETC data according to claim 1, characterized in that, The method further includes: before S100, determining whether the target vehicle has passed through a designated ETC gantry system; if it has, proceeding to S100; otherwise, continuing to determine whether the target vehicle has passed through a designated ETC gantry system; the designated ETC gantry system is an ETC gantry system located on both sides of the accident location and close to the starting position of the target vehicle.

5. The risk control processing method based on target vehicle ETC data according to claim 1, characterized in that, The road condition complexity coefficient of the target road segment is obtained based on the number of curves, curve radii, number of slopes, gradient, and slope length of the target road segment.

6. The risk control processing method based on target vehicle ETC data according to claim 1, characterized in that, The safety factor of the target vehicle's driver's historical driving behavior is obtained based on the number of times the driver of the target vehicle accelerated rapidly, braked suddenly, changed lanes, and experienced speed fluctuations on historical road sections.

7. The risk control processing method based on target vehicle ETC data according to claim 2, characterized in that, The complexity of the accident is determined by the number of vehicles involved in the accident on the target road and the number of lanes on the target road where the accident occurred.

8. The risk control processing method based on target vehicle ETC data according to claim 2, characterized in that, The complexity of accidents and the weather safety hazard coefficient on the target road segment are positively correlated with the safety risk coefficient of the target vehicle driving on the target road segment, while the safety coefficient of the driver's historical driving behavior of the target vehicle is negatively correlated with the safety risk coefficient of the target vehicle driving on the target road segment.

9. The risk control processing method based on target vehicle ETC data according to claim 2, characterized in that, The safe speed is obtained based on a preset safety risk coefficient threshold and a second safety risk coefficient model corresponding to the target vehicle.

10. A risk control processing system based on target vehicle ETC data, characterized in that, The system includes a non-transitory computer-readable storage medium and a processor; wherein the storage medium stores at least one instruction or at least one program, the at least one instruction or the at least one program being loaded and executed by the processor to implement the risk control processing method based on target vehicle ETC data as described in any one of claims 1-9.

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