Processing method for vehicle accident verification

Abstract

The invention relates to the technical field of vehicle accident verification, in particular to a processing method for vehicle accident verification. The method comprises the following steps: in response to a verification request, acquiring a vehicle running state data set matched with a unique identifier of a target vehicle and an accident occurrence date from a network-connected vehicle data platform; if the data set is matched with the accident occurrence position included in the verification request, judging whether the vehicle running state data of a first preset type included in the data set meets an initial preset judgment condition or not; if yes, judging a first probability that the target vehicle has an accident at each parking time node according to vehicle running state data of a second preset type in a target time period corresponding to each parking time node in the data set; if p0, 1lt; a pilt; and if not, judging whether an accident occurs at the parking time point or not according to the data of the third preset type corresponding to the ith parking time node. According to the invention, the accuracy of checking the accident of the network-connected vehicle can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle accident verification, and in particular to a processing method for vehicle accident verification. Background Art

[0002] When a vehicle has an accident, it is necessary to verify the accident. Currently, there are generally the following ways to verify the accident:

[0003] The first is to provide evidence after the accident, usually by asking the car owner or surveyor to take photos and videos of the car, and verify the time and place of the accident by the time and place of the photos; and use image recognition algorithms to confirm the damaged parts of the car and estimate the amount of loss, so as to verify the authenticity of the accident. This method may cause the car owner to lie about the accident, forge the scene, or even collude with the surveyor to forge the report.

[0004] The second is to use system preset functions or external devices to promptly identify the occurrence of a collision through sensors and collision recognition algorithms, and after the collision occurs, collect evidence of the accident scene by taking photos, videos, and recording various status data of the vehicle. This method requires the use of collision recognition algorithms or external devices, which has the problems of high cost and low coverage.

[0005] At present, there is a common vehicle status data reporting mechanism in domestic connected vehicles. Usually, basic vehicle information (model, vehicle number, etc.) and vehicle operation status data are reported to the connected vehicle data platform. Vehicle operation status data includes vehicle speed, acceleration, angular velocity, geographic location, seat belt status, headlight status, vehicle gear position, door switch status, brake action and throttle action data. These data will be reported at a fixed frequency during the vehicle's driving process, and basically all connected vehicles will have this reporting mechanism, with a high coverage rate. How to improve the accuracy of accident verification of connected vehicles is an urgent problem to be solved. Summary of the invention

[0006] The purpose of the present invention is to provide a method for processing vehicle accident verification to improve the accuracy of accident verification of connected vehicles.

[0007] According to the present invention, a method for processing vehicle accident verification is provided, the method comprising the following steps:

[0008] S100, in response to a verification request, obtaining a vehicle operation status data set matching the unique identification of the target vehicle and the date of the accident from the connected vehicle data platform; the verification request includes the unique identification of the target vehicle, the date of the accident and the location of the accident.

[0009] S200. Determine whether the vehicle operation status data set matches the location where the accident occurred included in the verification request. If it matches, proceed to S300.

[0010] S300. Determine whether the vehicle operation status data of the first preset type included in the vehicle operation status data set meets the initial preset judgment conditions. If it meets, determine that the verification is passed; otherwise, proceed to S400.

[0011] S400. Obtain each parking time node of the target vehicle on the date of the accident according to the preset vehicle parking judgment conditions, and determine the first probability of the target vehicle having an accident at each parking time node according to the vehicle operation status data of the second preset type corresponding to each parking time node in the vehicle operation status data set.

[0012] S500. If p 0,1 < p i < p 0,2 , then proceed to S600; p 0,1 and p 0,2 are the first preset probability and the second preset probability respectively, and p i is the first probability of the target vehicle having an accident at the i-th parking time point.

[0013] S600. Determine whether the target vehicle has an accident at the i-th parking time point according to the vehicle operation status data of the third preset type corresponding to the i-th parking time node in the vehicle operation status data set; if the target vehicle has not had an accident at any parking time point, determine that the verification is not passed; otherwise, determine that the verification is passed.

[0014] The present invention has at least the following beneficial effects compared with the prior art:

[0015] The present invention first obtains a vehicle operation status data set corresponding to a target vehicle on a corresponding date according to a verification request, and performs multi-level judgments based on the vehicle operation status data set, including verifying whether the position data of the vehicle matches the position where the accident occurred. If it matches, different processing is performed on vehicle operation status data of different preset types in sequence. Among them, the process of judging according to the vehicle operation status data of the first preset type is simple, and a verification pass result can be quickly obtained when the vehicle operation status data of the first preset type meets the initial preset judgment conditions; if the initial preset judgment conditions are not met, each parking time node is judged and analyzed, and it is only determined that the verification fails when no accident occurs at any parking time point of the target vehicle; and when judging and analyzing each parking time node, different processing and comprehensive judgment are performed on vehicle operation status data of different preset types according to vehicle operation status data of different preset types; thus, the present invention achieves the purpose of quickly and accurately verifying accidents of connected vehicles. Description of the Drawings

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for description in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0017] Figure 1 It is a flowchart of a processing method for vehicle accident verification provided by an embodiment of the present invention. Detailed Embodiments

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0019] According to this embodiment, as Figure 1 shown, a processing method for vehicle accident verification is provided, and the method includes the following steps:

[0020] S100, in response to a verification request, obtain a vehicle operation status data set that matches the unique identifier of the target vehicle and the date of the accident from the connected vehicle data platform; the verification request includes the unique identifier of the target vehicle, the date of the accident, and the location of the accident.

[0021] In this embodiment, the connected vehicle data platform stores the unique identifier of each connected vehicle, the data date, and the corresponding vehicle operation status data set. By comparing the unique identifier of the target vehicle and the date of the accident with the unique identifiers of the connected vehicles stored in the connected vehicle data platform and the data dates, a vehicle operation status data set that matches the unique identifier of the target vehicle and the date of the accident can be obtained. Optionally, the unique identifier of the vehicle is the license plate number of the vehicle or the vehicle identification number of the vehicle.

[0022] S200, determine whether the vehicle operation status data set matches the location of the accident included in the verification request. If it matches, enter S300.

[0023] In this embodiment, S200 further includes: if it does not match, it is determined that the verification fails.

[0024] As a specific implementation, the vehicle operation status data set includes the location data of the vehicle. S200 includes:

[0025] S210, obtain the location data of the vehicle from the vehicle operation status data set according to a preset time interval; wherein, the time interval corresponding to any two adjacent location data of the vehicle is the preset time interval.

[0026] In this embodiment, the vehicle operation status data set includes the location data of the vehicle at each acquisition moment. The location data of the vehicle is extracted according to a preset time interval. The preset time interval is an empirical value. Optionally, the preset time interval is 30 minutes or 1 hour.

[0027] As a specific implementation, the location data of the vehicle is Global Positioning System (GPS) data.

[0028] S220, if any location data of the vehicle obtained does not belong to the same district or county as the location of the accident, it is determined that the vehicle operation status data set does not match the location of the accident included in the verification request; otherwise, it is determined that the vehicle operation status data set matches the location of the accident included in the verification request.

[0029] Based on S210 - S220, a preliminary and rapid verification of the vehicle accident location can be achieved.

[0030] S300, determine whether the vehicle operation status data of the first preset type included in the vehicle operation status data set meets the initial preset judgment condition. If it meets, it is determined that the verification passes; otherwise, enter S400.

[0031] In this embodiment, the vehicle operating state of the first preset type is known. The vehicle operating state of the first preset type is the vehicle operating state with the highest correlation with the occurrence of a vehicle accident, which can be obtained based on experience or the Pearson correlation coefficient. For example, the vehicle operating state of the first preset type is the state of the airbag, and the initial preset judgment condition is that the airbag is deployed.

[0032] S400. Obtain each parking time node of the target vehicle on the date of the accident according to the preset vehicle parking judgment condition, and judge the first probability of the target vehicle having an accident at each parking time node according to the vehicle operating state data of the second preset type corresponding to the target time period of each parking time node in the vehicle operating state dataset.

[0033] In this embodiment, the preset vehicle parking judgment condition is determined based on experience. Optionally, the preset vehicle parking judgment condition includes: the vehicle speed is 0 and the duration is greater than or equal to the preset time threshold, or there is no more vehicle operating state data reported after the vehicle speed is 0. Optionally, the preset time threshold is 10 minutes.

[0034] In this embodiment, the vehicle operating state of the second preset type is known, which can be determined based on experience or using the Pearson correlation coefficient, such as vehicle speed, acceleration, and steering angle of the steering wheel. The correlation between the vehicle operating state of the second preset type and the occurrence of a vehicle accident is less than the correlation between the vehicle operating state of the first preset type and the occurrence of a vehicle accident.

[0035] Optionally, use a machine learning or deep learning model (such as a neural network model, support vector machine, or random forest, etc.) to judge the first probability of the target vehicle having an accident at each parking time node. As a preferred specific implementation manner, S400 includes:

[0036] S410. Obtain the vehicle operating state data A of the second preset type corresponding to the target time period of the i-th parking time node; the upper limit of the target time period corresponding to the i-th parking time node is the moment that is the first preset duration before and earlier than the i-th parking time node, and the lower limit of the target time period corresponding to the i-th parking time node is the moment that is the first preset duration after and later than the i-th parking time node.

[0037] In this embodiment, the first preset duration is an empirical value. Optionally, the first preset duration is 10 seconds.

[0038] S420. Construct the feature vector F of the i-th parking time node according to A; F = (F 1 , F 2 , …, F j , …, F m ), F jIt is a data sequence of the vehicle operation state of the j-th second preset type, where the value range of j is from 1 to m, and m is the number of vehicle operation states of the second preset type; F j =(F j,1 , F j,2 , …, F j,r , …, F j,R ), where F j,r is the data at the r-th acquisition moment within the target time period corresponding to the i-th parking time node for the vehicle operation state of the j-th second preset type. The value range of r is from 1 to R, and R is the number of acquisition moments included in the target time period corresponding to the i-th parking time node.

[0039] S430. Input the feature vector of the i-th parking time node into the trained target neural network model, and determine the output of the target neural network model as the first probability that the target vehicle has an accident at the i-th parking time node; the trained target neural network model has the function of inferring the probability of an accident based on the input feature vector.

[0040] Those skilled in the art know that any training method of a neural network model in the prior art falls within the protection scope of the present invention. Optionally, the target neural network model is a multi-layer perceptron (MLP).

[0041] In this embodiment, the vehicle operation state of the second preset type is a vehicle operation state that has a relatively large correlation with the vehicle having an accident. Based on S410 - S430, the probability of the vehicle having an accident can be obtained according to the vehicle operation state data of the second preset type, and based on this probability, it is possible to quickly determine whether the vehicle has an accident.

[0042] S500. If p 0,1 < p i < p 0,2 , then enter S600; p 0,1 and p 0,2 are the first preset probability and the second preset probability respectively, and p i is the first probability that the target vehicle has an accident at the i-th parking time point.

[0043] In this embodiment, S500 further includes: if p i ≥ p 0,2 , then it is determined that the target vehicle has an accident at the i-th parking time point, and the verification passes. If p i ≤ p 0,1 , then it is determined that the target vehicle has no accident at the i-th parking time point; if the target vehicle has no accident at any parking time point, then it is determined that the verification fails; otherwise, it is determined that the verification passes.

[0044] In this embodiment, the value range of i is from 1 to n, where n is the number of parking time nodes of the target vehicle on the date of the accident.

[0045] In this embodiment, p 0,1 and p 0,2 are both empirical values. Optionally, p 0,1 = 0.2, p 0,2 = 0.8.

[0046] S600. Determine whether an accident occurred to the target vehicle at the i-th parking time point according to the vehicle operation state data of the third preset type corresponding to the i-th parking time node in the vehicle operation state dataset; if no accident occurred to the target vehicle at any parking time point, it is determined that the verification fails; otherwise, it is determined that the verification passes.

[0047] As a preferred specific implementation manner, S600 includes:

[0048] S610. Determine the second probability f i of an accident occurring to the target vehicle at the i-th parking time point according to the vehicle operation state data of the third preset type corresponding to the i-th parking time node in the vehicle operation state dataset; the correlation coefficient between the vehicle operation state data of the third preset type and vehicle accidents is less than the correlation coefficient between the vehicle operation state data of the second preset type and vehicle accidents.

[0049] In this embodiment, the vehicle operation state of the third preset type is known and can be determined based on experience or using the Pearson correlation coefficient. For example, the working state of the hazard lights and the working state of the turn signals, etc.; compared with the vehicle operation state of the second preset type, the correlation coefficient between the vehicle operation state of the third preset type and vehicle accidents is relatively small, but there is also an association between the vehicle operation state of the third preset type and vehicle accidents. In this embodiment, the vehicle operation state of the third preset type is used for auxiliary judgment in the case of p 0,1 <p i <p 0,2 situation.

[0050] As a preferred specific implementation manner, S610 includes:

[0051] S611. Obtain the vehicle operation state data B of the third preset type corresponding to the i-th parking time node; B = (B 1 , B 2 , …, B x , …, B y ), B xData corresponding to the running state of the x-th vehicle of the third preset type within the x-th time period corresponding to the i-th parking time node, where the value range of x is from 1 to y, and y is the number of running states of the vehicle of the third preset type.

[0052] In this embodiment, the duration and start time of the x-th time period are empirical values. The durations and start times of different x-th time periods may be the same or different. For example, the running state of the first vehicle of the third preset type is the working state of the hazard lights, and the first time period is within 5 minutes after the parking time node; the running state of the second vehicle of the third preset type is the working state of the turn signal, and the second time period is within 5 minutes after the parking time node.

[0053] S612, traverse B and determine whether B x meets the x-th preset condition.

[0054] In this embodiment, the x-th preset condition is used to represent the condition that the running state of the x-th vehicle of the third preset type usually meets at the parking time node when the vehicle has an accident. The x-th preset condition can be set in advance according to experience. For example, the running state of the first vehicle of the third preset type is the working state of the hazard lights, and the first preset condition is that the hazard lights are turned on within 5 minutes after the parking time node; the running state of the second vehicle of the third preset type is the working state of the turn signal, and the second preset condition is that the turn signal is turned on within 5 minutes after the parking time node.

[0055] S613, determine the ratio of the number of B in B that meet the corresponding preset condition to y as f x i .

[0056] In this embodiment, the larger the value of f i , the greater the probability that the target vehicle has an accident at the i-th parking time node.

[0057] S620, adjust p according to f i , and determine whether the target vehicle has an accident at the i-th parking time point according to the adjusted result p'; p' i = k × f i + p i , where k is a preset adjustment coefficient, 0 < k ≤ 1 - p i . i . 0,2

[0058] In this embodiment, f i is positively correlated with p'. i

[0059] In this embodiment, S620 further includes: if p' i ≥ p 0,2, it is determined that the target vehicle has an accident at the i-th parking time point; otherwise, it is determined that the target vehicle has no accident at the i-th parking time point.

[0060] As a preferred specific embodiment, the process of obtaining k includes:

[0061] S621, obtain a sample vehicle data set; the sample vehicle data set includes the vehicle operation state data of the second preset type and the corresponding vehicle operation state data of the third preset type corresponding to a target time period of several sample vehicles at a parking time point.

[0062] S622, obtain the accuracy rate c when 1 - p 0,2 is determined as k 0 .

[0063] As a specific embodiment, according to the vehicle operation state data of the second preset type corresponding to the target time period of each sample vehicle included in the sample vehicle data set at a parking time point, obtain the first probability that each sample vehicle has an accident at the corresponding parking time point; if the first probability that a certain sample vehicle has an accident at the corresponding parking time point is greater than p 0,1 and less than p 0,2 , then determine that the sample vehicle is a first-class vehicle, and judge the second probability that the sample vehicle has an accident at the corresponding parking time point according to the vehicle operation state data of the third preset type corresponding to the sample vehicle at the corresponding parking time node, and adjust the corresponding first probability according to the second probability that the sample vehicle has an accident at the corresponding parking time point. When adjusting, the value of k is 1 - p 0,2 , and judge whether an accident occurs according to the adjustment result; determine the ratio of the number of times of correct judgment according to the adjustment result to the number of first-class vehicles as the accuracy rate c 0 .

[0064] S623, if c 0 is less than the preset accuracy rate threshold, initialize the first variable e to 1.

[0065] In this embodiment, the preset accuracy rate threshold is an empirical value. Optionally, the preset accuracy rate threshold is 0.8.

[0066] S624, obtain the accuracy rate c when 1 - p 0,2 - e × d is determined as k e ; d is the preset adjustment step size.

[0067] In this embodiment, d is an empirical value; optionally, d is 0.01 × (1 - p 0,2 ).

[0068] In this embodiment, obtain 1 - p0,2 - The accuracy rate c when e×d is determined as k e The process of obtaining 1 - p as above 0,2 The accuracy rate c when determined as k 0 is similar to the above process and will not be elaborated here.

[0069] S625, if c e is less than the preset accuracy rate threshold, then update e to e + 1, repeat S624 until c e is greater than or equal to the preset accuracy rate threshold, and determine 1 - p 0,2 - e×d as k.

[0070] In this embodiment, k obtained based on S621 - S625 can improve the accuracy of vehicle accident verification.

[0071] In this embodiment, when p 0,1 < p i < p 0,2 is in this situation, it can accurately assist in judging whether an accident has occurred to the target vehicle at the parking time node based on the vehicle operation state data of the third preset type, improving the accuracy of the subsequent determination result.

[0072] In this embodiment, first obtain the vehicle operation state data set of the target vehicle for the corresponding date according to the verification request, and perform multi-level judgments based on this vehicle operation state data set, including verifying whether the position data of the vehicle matches the accident location. If it matches, then perform different processes on the vehicle operation state data of different preset types in sequence. Among them, the process of judging according to the vehicle operation state data of the first preset type is simple, and it can quickly obtain a verification - passed result when the vehicle operation state data of the first preset type meets the initial preset judgment conditions; if it does not meet the initial preset judgment conditions, then judge and analyze each parking time node, and only determine that the verification fails when no accident occurs at any parking time point of the target vehicle; and when judging and analyzing each parking time node, different processes and comprehensive judgments are performed on the vehicle operation state data of different preset types according to the different correlation coefficients between the vehicle operation state data of different preset types and the vehicle accident. Thus, this embodiment achieves the purpose of quickly and accurately verifying accidents of connected vehicles.

[0073] Although some specific embodiments of the present invention have been described in detail by way of examples, those skilled in the art should understand that the above examples are only for illustration purposes and not for limiting the scope of the present invention. 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 invention. The scope of the present invention is defined by the appended claims.

Claims

1. A method for processing vehicle accident verification, characterized in that: The method comprises the following steps: S100, in response to a verification request, obtaining a vehicle operation status data set matching the unique identifier of the target vehicle and the date of the accident from a connected vehicle data platform; the verification request includes the unique identifier of the target vehicle, the date of the accident, and the location of the accident; S200, determining whether the vehicle operation status data set matches the location of the accident included in the verification request, and if so, proceeding to S300; S300, determining whether the vehicle running status data of the first preset type included in the vehicle running status data set meets the initial preset determination condition, if so, determining that the verification is passed; otherwise, entering S400; S400, obtaining each parking time node of the target vehicle within the date of the accident occurrence according to the preset vehicle parking judgment condition, and judging a first probability of the target vehicle having an accident at each parking time node according to the second preset type of vehicle operation status data of the target time period corresponding to each parking time node in the vehicle operation status data set; S500, if p 0,1 <p i <p 0,2 , then enter S600; p 0,1 and p 0,2 are the first preset probability and the second preset probability respectively, p i is the first probability of the target vehicle having an accident at the i-th parking time point; S600, judging whether the target vehicle has an accident at the i-th parking time point based on the vehicle operation status data of the third preset type corresponding to the i-th parking time node in the vehicle operation status data set; if the target vehicle has no accident at any parking time point, it is determined that the verification has failed; otherwise, it is determined that the verification has passed.

2. The vehicle accident verification processing method according to claim 1, characterized in that: The vehicle operation status data set includes the location data of the vehicle, and S200 includes: S210, acquiring the position data of the vehicle from the vehicle operation status data set according to a preset time interval; wherein the time interval corresponding to any two adjacent position data of the vehicle is the preset time interval; S220: If any of the acquired vehicle location data does not belong to the same district or county as the location where the accident occurred, it is determined that the vehicle operation status data set does not match the location where the accident occurred included in the verification request; otherwise, it is determined that the vehicle operation status data set matches the location where the accident occurred included in the verification request.

3. The vehicle accident verification processing method according to claim 1, characterized in that: S600 includes: S610, determining a second probability f of the target vehicle having an accident at the i-th parking time point according to the vehicle operation status data of the third preset type corresponding to the i-th parking time node in the vehicle operation status data set. i ; The correlation coefficient between the vehicle running status data of the third preset type and the vehicle accident is less than the correlation coefficient between the vehicle running status data of the second preset type and the vehicle accident; S620, according to f i P i Make adjustments and use the adjustment result p' i Determine whether the target vehicle has an accident at the i-th parking time point; p' i = k × f i +p i , k is the preset adjustment coefficient, 0 <k≤1-p 0,2 .

4. The vehicle accident verification processing method according to claim 3 is characterized in that: The process of obtaining k includes: S621, obtaining a sample vehicle data set; the sample vehicle data set includes vehicle operation status data of a second preset type and corresponding vehicle operation status data of a third preset type for a target time period corresponding to a parking time point of a number of sample vehicles; S622, obtain 1-p according to the sample vehicle data set 0,2 Determine the accuracy c0 when k is equal to; S623, if c0 is less than a preset accuracy threshold, initializing the first variable e to 1; S624, obtain 1-p 0,2 -e×d determines the accuracy c when k e ; d is the preset adjustment step; S625, if c e If the accuracy is less than the preset threshold, then e is updated to e+1, and S624 is repeated until c e Greater than or equal to the preset accuracy threshold, and 1-p 0,2 -e×d is determined as k.

5. The vehicle accident verification processing method according to claim 1, characterized in that: S610 includes: S611, obtaining vehicle running status data B of a third preset type corresponding to the i-th parking time node; B=(B1, B2, …, B x ,…,B y ), B x is the data of the xth vehicle running state of the third preset type in the xth time period corresponding to the ith parking time node, where the value of x ranges from 1 to y, and y is the number of vehicle running states of the third preset type; S612, traverse B, judge B x Whether the xth preset condition is met; S613, select B in B that meets the corresponding preset condition x The ratio of the number of y is determined as f i .

6. The vehicle accident verification processing method according to claim 1, characterized in that: S400 includes: S410, obtaining vehicle operation status data A of a second preset type for a target time period corresponding to the i-th parking time node; the upper limit of the target time period corresponding to the i-th parking time node is a moment that is a first preset time length away from the i-th parking time node and earlier than the i-th parking time node, and the lower limit of the target time period corresponding to the i-th parking time node is a moment that is a first preset time length away from the i-th parking time node and later than the i-th parking time node; S420, construct a feature vector F of the i-th parking time node according to A; F=(F1, F2, …, F j ,…,F m ), F j is a data sequence of the jth second preset type of vehicle running state, where j ranges from 1 to m, and m is the number of the second preset type of vehicle running states; F j =(F j,1 ,F j,2 ,…,F j,r ,…,F j,R ), F j,r The data of the rth collection time within the target time period corresponding to the i-th parking time node for the j-th second preset type of vehicle operation state, where r ranges from 1 to R, and R is the number of collection times included in the target time period corresponding to the i-th parking time node; S430, input the feature vector of the i-th parking time node into the trained target neural network model, and determine the output of the target neural network model as the first probability of the target vehicle having an accident at the i-th parking time node; the trained target neural network model has the function of inferring the probability of an accident based on the input feature vector.

7. The vehicle accident verification processing method according to claim 1, characterized in that: The preset vehicle parking judgment conditions include: the vehicle speed is 0 and the duration is greater than or equal to a preset time threshold, or there is no more vehicle running status data reported after the vehicle speed is 0.

8. The vehicle accident verification processing method according to claim 1, characterized in that: S620 also includes: if p' i ≥p 0,2 , it is determined that the target vehicle has an accident at the i-th parking time point; otherwise, it is determined that the target vehicle has no accident at the i-th parking time point.

9. The vehicle accident verification processing method according to claim 1, characterized in that: S200 also includes: if there is no match, determining that the verification fails.

10. The vehicle accident verification processing method according to claim 1, characterized in that: S500 also includes: If p i ≥p 0,2 , then it is determined that the target vehicle has an accident at the i-th parking time point and the verification is passed; if p i ≤p 0,1 , it is determined that the target vehicle has no accident at the i-th parking time point.

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