A risk control processing system and method based on target vehicle ETC data

Abstract

This invention relates to the field of vehicle risk control processing technology, and in particular to a risk control processing system and method based on target vehicle ETC data. The method includes: obtaining the target vehicle's average speed on historical road segments based on its ETC data; obtaining the target vehicle's average acceleration from its initial speed to a safe speed on the target road segment based on its inertial acceleration, the road condition complexity coefficient of the target road segment, and the driver's historical driving behavior safety coefficient; obtaining a safety warning distance based on the average acceleration, initial speed, and safe speed; and issuing a speed reduction warning to the target vehicle 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. This invention can provide targeted speed reduction warnings for different large vehicles, improving driving safety and traffic efficiency.

Description

Technical Field

[0001] This invention relates to the field of vehicle risk control processing technology, and in particular to a risk control processing system and method based on target vehicle ETC data. Background Technology

[0002] With the rapid development of highways, the ETC (Electronic Toll Collection) system has been widely used, providing convenience for vehicles to pass through quickly. The ETC gantry system is a key component of the highway ETC system. ETC gantry systems are typically installed above highways and communicate with the ETC devices on vehicles using microwave, radio frequency, and other technologies to identify and locate vehicles. However, there are currently shortcomings in the safety risk control for large vehicles (such as trucks) driving on highways. If a large vehicle encounters traffic congestion ahead due to an accident (such as a collision), it often needs to slow down. However, due to their large weight, sudden deceleration by large vehicles often poses significant safety hazards. Existing technologies disclose methods for providing safety warnings to vehicles when they are at a preset distance from congested road sections. However, different large vehicles have different inertial accelerations on the same road segment, drivers have different driving habits, and the road conditions on different sections of highways vary in complexity. The preset distance may be insufficient for safe deceleration for some large vehicles, while it may be too large for others, leading to unnecessary premature deceleration and affecting traffic efficiency. How to determine the corresponding safety warning distance for different large vehicles to provide targeted deceleration warnings and improve driving safety and traffic efficiency is an urgent problem to be solved. Summary of the Invention

[0003] The purpose of this invention is to provide a risk control processing system and method based on target vehicle ETC data, so as to provide targeted speed reduction prompts for different large vehicles, thereby improving driving safety and traffic efficiency.

[0004] According to a first aspect of the present invention, a risk control processing method based on target vehicle ETC data is provided, the method comprising the following steps:

[0005] 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 target vehicle's weight is greater than or equal to a preset weight threshold, and the target vehicle's volume is greater than or equal to a preset volume threshold.

[0006] 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 target vehicle's weight is greater than or equal to a preset weight threshold, and the target vehicle's volume is greater than or equal to a preset volume threshold; the inertial acceleration and the road condition complexity coefficient of the target road segment are both positively correlated with the average acceleration, and 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 segment between the target vehicle's current position and the target safety hazard location.

[0007] 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.

[0008] 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.

[0009] According to a second aspect of the present invention, a risk control processing system based on target vehicle ETC data is also provided. The system includes a non-transitory computer-readable storage medium and a processor. The storage medium stores at least one instruction or at least one program, which is loaded and executed by the processor to implement the above-described risk control processing method based on target vehicle ETC data.

[0010] Compared with the prior art, the present invention has at least the following beneficial effects:

[0011] This invention references the average acceleration of a target vehicle when determining its safe warning distance. This average acceleration comprehensively considers the vehicle's inertial acceleration, the road condition complexity coefficient of the target road segment, and the driver's historical driving behavior safety factor. Therefore, the safe warning distance determined by this invention is more closely matched to the target vehicle, the road condition of the target road segment, and the driver's driving behavior. It can provide a larger safe warning distance when the vehicle's inertial acceleration is low, the road condition complexity coefficient of the target road segment is high, and the driver's historical driving behavior safety factor is low, thus increasing the vehicle's driving safety. Conversely, it can provide a smaller safe warning distance when the vehicle's inertial acceleration is high, the road condition complexity coefficient of the target road segment is low, and the driver's historical driving behavior safety factor is high, thereby improving the vehicle's traffic efficiency while ensuring driving safety. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart of a risk control processing method based on target vehicle ETC data provided in Embodiment 1 of the present invention;

[0014] Figure 2 A flowchart illustrating the process of obtaining the inertial acceleration of a target vehicle according to Embodiment 1 of the present invention;

[0015] Figure 3 This is a flowchart illustrating the process of obtaining the initial velocity as provided in Embodiment 1 of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example

[0017] According to this embodiment, as Figure 1 As shown, a risk control processing method based on target vehicle ETC data is provided, the method including the following steps:

[0018] 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 target vehicle's weight is greater than or equal to a preset weight threshold, and the target vehicle's volume is greater than or equal to a preset volume threshold.

[0019] In this embodiment, the target vehicle is a large vehicle, such as a truck. 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. Both the weight threshold and the volume threshold are empirical values; optionally, the weight threshold is 4 tons and the volume threshold is 26.62m³. 3 The volume threshold corresponds to a length of 5.5m, a width of 2.2m, and a height of 2.2m.

[0020] In this embodiment, the target vehicle is traveling on a highway, and the historical road segment is the highway; as a specific implementation, the historical road segment is the section between the starting position of the target vehicle entering the highway and the current position of the target vehicle.

[0021] As a specific implementation, the target vehicle's ETC data includes not only the time the target vehicle passes through each ETC gantry system on the historical road segment, but also the distance between any two adjacent ETC gantry systems on the historical road segment. For example, the target vehicle's ETC data is [(t1,0),(t2,d2),…,(t i ,d i ),…,(t n ,d n )],t i Let d be the time it takes for the target vehicle to pass through the i-th ETC gantry system on the historical road segment. i Let t1 be the distance between the i-th ETC gantry system and the (i-1)-th ETC gantry system on the historical road segment, where i ranges from 2 to n, and n is the number of i-th ETC gantry systems on the historical road segment; t1 is the time it takes for the target vehicle to pass through the first ETC gantry system on the historical road segment. Then, the average speed of the target vehicle on the historical road segment is (∑ n i=2 (d i / (t i -t i-1 )) / (n-1).

[0022] In a preferred embodiment, before S100, it is determined whether the target vehicle has passed through a designated ETC gantry system. If it has, proceed to S100; otherwise, continue determining whether the target vehicle has passed through a designated ETC gantry system. The designated ETC gantry system is the ETC gantry system located on both sides of the accident location and closer to the target vehicle's starting position. For example, if the accident location is between the first and second ETC gantry systems, the target vehicle will first pass through the first ETC gantry system and then the second ETC gantry system according to its travel direction. That is, the first gantry system is the ETC system closer to the target vehicle's starting position between the first and second ETC gantry systems, and thus the first ETC gantry system is the designated ETC gantry system. Based on this preferred embodiment, starting S100-S400 at the ETC gantry system ahead of the accident location reduces unnecessary calculations and saves computing resources.

[0023] S200. Obtain the average acceleration of the target vehicle from the initial speed to the safe speed on the target road section based on 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. 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. Both the inertial acceleration and the road condition complexity coefficient of the target road section are positively correlated with the average acceleration, and the historical 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 based on the average vehicle speed. The target road section is the road section between the current position of the target vehicle and the target safety hazard position.

[0024] In this embodiment, the target safety hazard position is the upstream congestion point of the accident impact area on the target road section. Upstream refers to the side closer to the starting position of the target vehicle. The target road section is a highway.

[0025] 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. It is safer for the target vehicle to travel on the target road section with a larger acceleration. The acceleration in this embodiment is negative, and a larger acceleration corresponds to less deceleration in the same time. As an optional specific implementation manner, the road condition complexity coefficient of the target road section is obtained based on 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 obtain the average radius of all curves based on each curve radius, obtain the average slope of all slopes based on each slope of the slope, and obtain the average slope length of all slopes based on each slope length of the slope. Among them, the average 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 slope, and the average slope length are all positively correlated with the road condition complexity coefficient of the target road section. Optionally, a weighted summation formula is used to obtain the road condition complexity coefficient of the target road section, k1 = a1×b1 - a2×b2 + a3×b3 + a4×b4 + a5×b5, where k1 is the road condition complexity coefficient of the target road section, a1, a2, a3, a4, and a5 are the weights corresponding to the number of curves, the average radius, the number of slopes, the average slope, and the average slope length respectively, b1, b2, b3, b4, and b5 are the normalized number of curves, the average radius, the number of slopes, the average slope, and the average slope length 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. Those skilled in the art know that any normalization method in the prior art falls within the protection scope of the present invention and will not be elaborated here.

[0026] In this embodiment, the smaller the safety factor of the historical driving behavior of the driver of the target vehicle, the less safe the historical driving behavior of the driver of the target vehicle. When the target vehicle is driving on the target road section, it is safer to drive with a larger acceleration. As an optional specific implementation, the safety factor of the historical driving behavior of the driver of the target vehicle is obtained based on the number of hard accelerations, the number of hard brakes, the number of lane changes, and the speed fluctuation degree of the driver of the target vehicle on the historical road section. For example, first obtain the coefficient of variation of the speed of the target vehicle on the historical road section according to the real-time speed of the target vehicle on the historical road section, and determine this coefficient of variation as the speed fluctuation degree. Among them, the number of hard accelerations, the number of hard brakes, the number of lane changes, and the speed fluctuation degree are all negatively correlated with the safety factor of the historical driving behavior of the driver of the target vehicle; optionally, a weighted summation formula is used to obtain the safety factor of the historical driving behavior of the driver of the target vehicle, k2 = 1 - c1×d1 - c2×d2 - c3×d3 - c4×d4, where k2 is the safety factor of the historical driving behavior of the driver of the target vehicle, c1, c2, c3, and c4 are the weights corresponding to the number of hard accelerations, the number of hard brakes, the number of lane changes, and the speed fluctuation degree respectively, d1, d2, d3, and d4 are the normalized number of hard accelerations, the number of hard brakes, 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. Those skilled in the art know that any normalization method in the prior art falls within the protection scope of the present invention and will not be elaborated here.

[0027] In this 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 implementation, as Figure 2 shown, the process of obtaining the inertial acceleration of the target vehicle includes:

[0028] S201, match the initial speed, the safety 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 several records, and any record includes a starting speed, an ending speed, a wind speed, and the corresponding inertial acceleration.

[0029] In this embodiment, the inertial acceleration list corresponding to the target vehicle is a pre-established list. Optionally, the records in the list are implemented through vehicle dynamics simulation software, and the simulation parameters are the same as the parameters when the target vehicle is driving on the target road; when the initial speed is the starting speed in a certain record, the safety speed is the ending speed in this record, and the average wind speed of the target road section is the wind speed in this record, it is determined that the inertial acceleration of the target vehicle is the inertial acceleration in this record.

[0030] S202. Determine the inertial acceleration in the matched record as the inertial acceleration of the target vehicle.

[0031] Based on S201 - S202, this embodiment can predict the inertial acceleration of the target vehicle.

[0032] In this embodiment, the average acceleration at which the target vehicle decelerates from the initial speed to the safe speed on the target road 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 road section, and the historical driving behavior safety coefficient of the driver of the target vehicle. As a preferred specific implementation, x1 = x0×(1 + r×(1 + u1×k1 - u2×k2)), where x0 is the inertial acceleration of the target vehicle, x1 is the average acceleration at which the target vehicle decelerates from the initial speed to the safe speed on the target road section, u1 and u2 are the weights corresponding to the road condition complexity coefficient of the target road section and the historical driving behavior safety coefficient of the driver of the target vehicle respectively, both 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 road 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 this preferred specific implementation, this 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 increasing the safety prompt distance and improving the safety of the target vehicle when driving on a road section with relatively complex road conditions and the historical driving behavior of the driver is poor.

[0033] In this embodiment, the initial speed is the predicted value of the speed of the target vehicle when the distance between the target vehicle and the target safety hazard location is the safety prompt distance. As an optional specific implementation, determine the average vehicle speed of the target vehicle on the historical road section as the initial speed. As a preferred specific implementation, as Figure 3 shown, the process of obtaining the initial speed includes:

[0034] S210. Obtain the safety risk coefficient of the target vehicle when driving on the target road section according to the accident complexity degree that occurred on the target road 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 when driving on the target road section, and the dependent variable of the first safety risk coefficient model corresponding to the target vehicle includes the accident complexity degree that occurred on the target road section, the weather safety hazard coefficient, and the historical driving behavior safety coefficient of the driver of the target vehicle.

[0035] In this embodiment, a higher accident complexity indicates a more complex accident, a higher risk of the target vehicle traveling at a high speed on the target road segment without timely deceleration, and a less safe environment. As an optional implementation, the accident complexity is obtained based on the number of vehicles involved in the accident and the number of lanes involved in the accident on the target road. Both the number of vehicles involved in the accident and the number of lanes involved in the accident on the target road are positively correlated with the accident complexity. Optionally, a weighted summation formula can be used to obtain the accident complexity: k3 = e1 × f1 + e2 × f2, where k3 is the accident complexity, e1 and e2 are the weights corresponding to the number of vehicles involved in the accident and the number of lanes involved in the accident on the target road, respectively, and f1 and f2 are the normalized number of vehicles involved in the accident and the number of lanes involved in the accident on the target road, respectively. Both e1 and e2 satisfy the condition of being greater than 0 and less than 1, and the sum of e1 and e2 is 1. Those skilled in the art will understand that any normalization method in the prior art falls within the protection scope of this invention, and will not be elaborated here.

[0036] In this embodiment, the higher the weather safety hazard coefficient, the more severe the weather conditions, and the higher the risk for the target vehicle traveling at a high speed on the target road segment. As an optional implementation, a mapping relationship between various weather conditions and the weather safety hazard coefficient is pre-established. The weather safety hazard coefficient for the target road segment can then be obtained based on the weather conditions of the target road segment and this mapping relationship. Optionally, the weather safety hazard coefficient can range from 0 to 1.

[0037] In this embodiment, 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, and 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.

[0038] In this embodiment, the first safety risk coefficient model corresponding to the target vehicle is a pre-established model. As an optional implementation, a curve fitting method is used to obtain the relationship between the accident complexity, weather safety hazard coefficient, driver's historical driving behavior safety coefficient, and safety risk coefficient corresponding to the target vehicle. This relationship is the first safety risk coefficient model corresponding to the target vehicle. As another optional implementation, a weighted summation method is used to obtain the relationship between the accident complexity, weather safety hazard coefficient, driver's historical driving behavior safety coefficient, and safety risk coefficient corresponding to the target vehicle.

[0039] 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.

[0040] In this 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 obtain the relationship between the speed and the safety risk coefficient of the target vehicle, which is the second safety risk coefficient model corresponding to the target vehicle. Given the second safety risk coefficient model corresponding to the target vehicle, the first speed corresponding to the target vehicle can be obtained by substituting the safety risk coefficient of the target vehicle traveling on the target road segment into the second safety risk coefficient model.

[0041] Optionally, the safe speed can be an empirical value or obtained based on a preset safety risk coefficient threshold and a second safety risk coefficient model corresponding to the target vehicle. The preset safety risk coefficient threshold is an empirical value.

[0042] 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.

[0043] In this embodiment, the first speed is calculated based on the safety risk coefficient of the target road segment, reflecting the impact of factors such as current road conditions, weather, and driver behavior on safety. If the first speed is higher than the average speed, it indicates that the current road conditions or environment are more complex or the driver's driving habits are poor. By determining the larger value between the average speed and the first speed as the initial speed, it is beneficial to increase the safety warning distance, provide the driver with a longer deceleration time, and improve the safety of the target vehicle.

[0044] 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.

[0045] As a specific implementation, the safety warning distance is z, z=(v2×v2-v1×v1) / (2×y), where z is the safety warning distance, v1 and v2 are the initial velocity and the safe velocity, respectively, and y is the average acceleration.

[0046] 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.

[0047] In one specific implementation, the prompt information also includes the distance between the target vehicle and the target safety hazard; thus, the driver of the target vehicle can determine the amount of speed reduction based on the distance between the target vehicle and the target safety hazard.

[0048] This embodiment considers the target vehicle's average acceleration when determining the safe warning distance. This average acceleration comprehensively takes into account the target vehicle's inertial acceleration, the road condition complexity coefficient of the target road segment, and the driver's historical driving behavior safety factor. Therefore, the safe warning distance determined by this embodiment is more closely matched with the target vehicle, the road condition of the target road segment, and the driver's driving behavior. It can provide a larger safe warning distance for the target vehicle when the inertial acceleration of the target vehicle is small, the road condition complexity coefficient of the target road segment is large, and the driver's historical driving behavior safety factor is small, thereby increasing the target vehicle's driving safety. Conversely, it can provide a smaller safe warning distance for the target vehicle when the inertial acceleration of the target vehicle is large, the road condition complexity coefficient of the target road segment is small, and the driver's historical driving behavior safety factor is large, thereby improving the target vehicle's traffic efficiency while ensuring driving safety. Example

[0049] This embodiment provides a risk control processing system based on target vehicle ETC data. The system includes a non-transitory computer-readable storage medium and a processor. The storage medium stores at least one instruction or at least one program segment, which is loaded and executed by the processor to implement the above-described risk control processing method based on target vehicle ETC data.

[0050] While specific embodiments of the invention have been described in detail by way of example, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of the invention. It should also be understood that various modifications can be made to the embodiments without departing from the scope and spirit of the invention. The scope of the invention 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; 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.

2. 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.

3. 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.

4. 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.

5. 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.

6. The risk control processing method based on target vehicle ETC data according to claim 1, 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.

7. The risk control processing method based on target vehicle ETC data according to claim 1, 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.

8. The risk control processing method based on target vehicle ETC data according to claim 1, 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.

9. 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 1-8.

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