Road traffic accident auxiliary responsibility determination method based on class case analysis

Abstract

The invention discloses a road traffic accident auxiliary responsibility determination method based on class case analysis, and belongs to the field of traffic safety, and the method comprises the following steps: constructing a case library based on open source information; extracting features of target cases to be subjected to responsibility determination to obtain an extracted feature set; comparing the similarity between each case in the case library and the target case to be subjected to responsibility determination to obtain a class case library; and constructing a random forest model, and performing classification prediction on the class case library based on the random forest model to obtain a responsibility determination result of the target case to be subjected to responsibility determination. According to the road traffic accident auxiliary responsibility determination method based on class case analysis, the accuracy and efficiency of accident responsibility determination are effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of traffic safety, and in particular relates to a method for auxiliary responsibility determination for road traffic accidents based on similar case analysis. Background Art

[0002] In the handling of traffic accidents, the determination of accident responsibility is a crucial link, and its results are directly related to the criminal and civil liabilities of the parties involved in the accident. However, the current process of determining accident responsibility faces many challenges and problems. The results of accident responsibility determination are easily affected by environmental factors and human subjective cognition, leading to the dilemma of "similar cases with different judgments". In addition, the emergence of assisted driving cars has made the determination of traffic accident responsibility more complicated, because it involves the definition of responsibility between drivers and assisted driving systems, the challenges of legal application, the difficulty of technical evaluation, and the impact on traffic management and law enforcement. These issues are intertwined, increasing the uncertainty and difficulty of determining responsibility.

[0003] In view of the above problems, although the existing technical means can improve the accuracy and efficiency of accident responsibility determination to a certain extent, they still have limitations. For example, although the traffic accident investigation and responsibility determination system can monitor and record relevant data in real time through intelligent monitoring cameras and sensors and other equipment to assist the traffic management department in determining the responsibility of the accident, the system mainly relies on the collection and analysis of on-site data and cannot fully utilize the experience of historical accident responsibility determination cases. Therefore, the present invention proposes a road traffic accident auxiliary responsibility determination method based on similar case analysis. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a method for auxiliary determination of responsibility for road traffic accidents based on similar case analysis to solve the problems existing in the above-mentioned prior art.

[0005] To achieve the above-mentioned purpose, the present invention provides a method for auxiliary determination of liability for road traffic accidents based on similar case analysis, comprising:

[0006] Build a case library based on open source information;

[0007] Extract the features of the target case to be determined to obtain an extracted feature set;

[0008] Comparing the similarity between each case in the case library and the target case to be determined to obtain a similar case library;

[0009] A random forest model is constructed, and classification prediction is performed on the case library based on the random forest model to obtain the accountability results of the target cases to be determined.

[0010] Optionally, the process of building a case library includes:

[0011] Gather cases based on open source information;

[0012] Extracting case features of the cases and establishing corresponding data entries for each case;

[0013] A case library is constructed based on a number of the data items.

[0014] Optionally, the extracted case features are divided into: basic information, right of way and safety obligations of both parties, special circumstances of both parties, and liability determination.

[0015] Optionally, the expression for calculating the similarity between the pending target case and each case in the case library is:

[0016] v i =r i / p*100%

[0017] In the formula, v i represents the similarity, p is the number of features of the target case c, r i For case b i The number of features that have the same value as the target case c.

[0018] Optionally, the steps of building a random forest model include:

[0019] The case determination results in the similar case database are used as labels, and the case characteristics are used as feature variables;

[0020] The characteristic variables are used to train a random forest model to predict the accountability outcome of the case.

[0021] Optionally, the process of performing classification prediction on the similar case library based on the random forest model includes:

[0022] Input the features of the target case to be determined into the trained random forest model;

[0023] The trained random forest model predicts the liability determination results of the target case to be determined based on the input features, where the liability determination results include the probabilities of no liability, primary and secondary liability, and equal liability.

[0024] The present invention also provides a computer terminal device, comprising:

[0025] one or more processors;

[0026] A memory, coupled to the processor, for storing one or more programs;

[0027] When the one or more programs are executed by the one or more processors, the one or more processors implement a method for assisting in determining responsibility for road traffic accidents based on similar case analysis.

[0028] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, a method for auxiliary determination of responsibility for road traffic accidents based on similar case analysis is implemented.

[0029] Compared with the prior art, the present invention has the following advantages and technical effects:

[0030] The present invention effectively improves the accuracy and efficiency of accident responsibility determination through an auxiliary responsibility determination method for road traffic accidents based on similar case analysis. By constructing a case library, extracting target case features, and comparing similarity with cases in the library, a similar case library with strong relevance is screened out. The random forest model is further used to classify and predict the similar case library to obtain the responsibility determination results of the target case to be determined. This method reduces the impact of human subjectivity and regional differences on responsibility determination, improves the consistency and fairness of responsibility determination, and provides strong technical support for traffic accident handling. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0032] Figure 1 This is a flow chart of a method for assisting in determining liability in road traffic accidents based on similar case analysis according to an embodiment of the present invention. DETAILED DESCRIPTION

[0033] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0034] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0035] Embodiment 1

[0036] like Figure 1As shown, this embodiment provides a method for auxiliary determination of responsibility for road traffic accidents based on similar case analysis, which is based on matching and pushing similar cases of historical accident responsibility identification to assist in accident determination. The main technical solution of the present invention is to establish a case library; input the characteristics of the target case; match similar cases to obtain a similar case library; and determine the results and characteristics of the similar case library. The specific method is as follows: construct a case library based on open source information; extract the characteristics of the target case to be determined to obtain an extracted feature set; compare the similarity of each case in the case library with the target case to be determined to obtain a similar case library; construct a random forest model, and classify and predict the similar case library based on the random forest model to obtain the determination results of the target case to be determined.

[0037] As a specific implementation of this embodiment, it includes:

[0038] 1. Establish a case library: The process of building a case library includes: collecting cases based on open source information; extracting case features of cases and establishing corresponding data entries for each case; and building a case library based on several data entries.

[0039] Furthermore, (1) cases are collected. Case texts of court judgments on motor vehicle traffic accident liability disputes that are open source on the Internet, in-depth accident investigation reports disclosed by the government, and historical accident cases actually handled by users are collected. (2) Case features such as basic case information, fault information, and liability determination are extracted. The features that can be selected are not limited to those in Table 1. The features finally selected based on the features in Table 1 are shown in Table 2. (3) For each accident, a piece of data is created, and the processed data is filled into the corresponding entry to form a case data set, as shown in Table 3. (4) The selected feature set is recorded as The case library formed is B = {b1, b2, ... b n},in, For case b i Features As a specific implementation of this embodiment, the feature set selected in this embodiment is The case library formed is B = {b1, b2, ... b 9088},in, For case b i Features The value of .

[0040] Table 1

[0041]

[0042]

[0043]

[0044] Table 2

[0045]

[0046]

[0047] Table 3

[0048] case <![CDATA[X1]]> <![CDATA[X2]]> <![CDATA[X3]]> <![CDATA[X4]]> <![CDATA[X5]]> ...... <![CDATA[X 42 ]]> <![CDATA[X 43 ]]> <![CDATA[b1]]> 1 1 2 3 12 1 1 <![CDATA[b2]]> 3 2 1 4 12 1 1 ...... <![CDATA[b 9088 ]]> 3 1 1 4 12 4 1

[0049] 2. Input target case features

[0050] Enter the target case features. Get the target case features to be determined. The optional features are F b The extracted features can be determined based on the case and the current information. The extracted feature set is The target case to be determined is recorded as in, Characteristics of the target case The value of .

[0051] As a specific implementation of this embodiment, the target case is a road traffic accident involving an assisted driving car. A small passenger car with adaptive cruise control turned on rear-ended a heavy truck driving normally on the highway. The electronic data of the small passenger car showed that the adaptive cruise control status of the small passenger car was "faulty" at the time of the accident. Based on the case and the current information, the feature G of the target case is extracted. c ={X1,X2,X3,X5,X6,X7,X8,X9,X 10 , X 11 , X 12 , X 13 , X 14 , X 15 , X 16 , X 17 , X 18 , X 22 , X 24 , X 25 , X 26 , X 27 , X 28 , X 29 , X 33}, and the target case is denoted as c = {3, 1, 1, 12, K3, 1, 6, H1, 1, 2, 8, 3, 1, 1, 1, 1, 1, 2, 3, 3, 4, 3, 2}.

[0052] 3. Match similar cases to obtain a case library

[0053] Compare the similarity between the characteristics of each case in case library B and the characteristics of the target case to be determined i , filter out cases similar to the target case c, and obtain a similar case database S = {s1, s2, ..., sq},s j Case b is the case in case library B whose case characteristics are similar to the target case above the threshold i Case b i Similarity v to target case c i =r i / p*100%, where p is the number of features of target case c, r i For case b i The number of features with the same value as the target case c. The threshold can be adjusted based on business needs or expert opinions.

[0054] As a specific implementation of this embodiment, the calculation case library B = {b1, b2, ... b 9088} is the similarity between each case and the target case c, p is the number of features of the target case c, p = 24, r i For case b i The number of features with the same value as the target case c, in the embodiment r i The value is {r1=7,r2=10,......,r 9088 =17}. Case b i Similarity v to target case c i =r i / p*100%, in the embodiment v i The value is {v1=29.2%, v2=41.7%, ..., v 9088 =70.8%}. Based on business needs and expert opinions, 60% is used as the threshold, and the similarity level v i More than 60% of the cases are included in the similar case library. In the embodiment, a total of 116 cases are included in the similar case library. The similar case library S = {s1, s2, ..., s 116}.

[0055] 4. Analysis of the results and characteristics of the similar case database

[0056] Furthermore, the steps of constructing the random forest model include: using the case accountability results in the case library as labels and the case characteristics as feature variables; using the feature variables to train the random forest model to predict the case accountability results.

[0057] Furthermore, statistics are collected on the proportion of three types of liability in the similar case database: none, primary and secondary, and equal. Statistics are collected on the priority right of way and safety obligations of the parties in each type of liability determination results. Statistics are collected on other information that needs attention in similar cases. A random forest model is established with the case determination results as the dependent variable and the case characteristics as the independent variable, and modeling and analysis is performed on the similar case database data. Based on the analysis results, a classification prediction model for the target case determination results is established to predict the probability of the target case determination results being none, primary and secondary, and equal. The case characteristics that have a significant impact on the similar case determination results are determined to achieve auxiliary determination of the case.

[0058] The target case in the embodiment involves a failure of an assisted driving function. When calculating the results of determining liability, other information that needs to be paid attention to includes whether similar cases involve assisted driving system functions, the types of failures in the assisted driving system functions, and the liability results of related similar cases.

[0059] Among the 116 similar cases in the embodiment, there were 71 cases of no liability, accounting for 61.2%, 34 cases of primary and secondary liability, accounting for 29.3%, and 11 cases of equal liability, accounting for 9.48%. In the 71 cases of no liability, all the parties in the front car had the right of way and did not violate the safety obligation; the parties in the rear car had no right of way, and 65 parties in the rear car violated the safety obligation, accounting for 91.5%, of which 96.9% were speeding, 60.0% were distracted driving, and 36.9% were fatigue driving. In the 34 cases of primary and secondary liability, the parties involved in the front vehicle all had the right of way and violated safety obligations, of which 61.8% were due to slow driving, 47.0% were due to distracted driving, 38.2% were due to fatigue driving, and 23.5% were due to vehicle safety conditions not being met; the parties involved in the rear vehicle all did not have the right of way and violated safety obligations, of which 97.1% were due to speeding, 85.3% were due to distracted driving, and 8.8% involved functional failures of the auxiliary driving system. In the 11 cases of equal liability, the parties involved in the front vehicle all had the right of way and violated safety obligations, of which 90.9% were due to slow driving, 54.5% were due to drunk driving, and 36.4% were due to driving without a license; the parties involved in the rear vehicle all did not have the right of way and violated safety obligations, of which 100% were due to speeding, 36.4% were due to distracted driving, and 18.2% were due to fatigue driving. Among the 116 similar cases in the embodiment, there were 15 cases involving assisted driving cars, accounting for 12.9%, and 3 cases involving functional failures of the assisted driving system, of which 2 involved adaptive cruise system (ACC) failures, accounting for 66.7%, and 1 involved automatic emergency braking system (AEB) failure, accounting for 33.3%. All of them were caused by failures in the assisted driving system of the rear vehicle, and the rear vehicle was primarily responsible.

[0060] A random forest model is established with the case accountability result as the dependent variable and the case characteristics as the independent variable, and the data of the similar case library is modeled and analyzed. According to the analysis results, a classification prediction model for the target case accountability result is established to predict the probability of the target case accountability result being none, primary and secondary, and equal, and to determine the case characteristics that have a significant impact on the accountability results of similar cases, so as to achieve auxiliary accountability for the case. The similar case library in the application embodiment is used for random forest modeling, and the model predicts the accountability results of the target case as follows: 87.37% are none, 11.91% are primary and secondary, and 0.72% are equal. According to the importance score of the influencing factors output by the model, it can be seen that the case characteristics that have a significant impact on the accountability results of similar cases of the target case include X 11 , X 15 , X 16 , X 17 .

[0061] Based on the above technical solution, the present invention achieves the following technical effects:

[0062] From a technical perspective, by constructing an accident responsibility determination case library, the present invention can quickly find historical cases similar to the target case, provide an objective reference for the determination of responsibility for assisted driving car accidents, reduce the phenomenon of "similar cases with different judgments" due to personal experience, subjective judgment or regional differences, and improve the accuracy and consistency of responsibility determination.

[0063] From an economic perspective, the present invention can provide case support for the traffic police department to determine the liability of the parties involved, resolve conflicts in advance, and reduce civil compensation disputes for economic losses caused by accidents.

[0064] From a social perspective, the present invention can reveal the distribution patterns of responsibility for traffic accidents, common accident types and their causes through in-depth analysis of large amounts of data in the case library, provide decision-making support for traffic management departments, and help formulate more accurate and effective traffic safety policies and preventive measures to reduce the occurrence of traffic accidents.

[0065] Embodiment 2

[0066] This embodiment also provides a computer terminal device, including:

[0067] one or more processors;

[0068] A memory, coupled to the processor, for storing one or more programs;

[0069] When one or more programs are executed by one or more processors, the one or more processors implement a method for auxiliary responsibility determination for road traffic accidents based on similar case analysis.

[0070] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, a method for auxiliary determination of responsibility for road traffic accidents based on similar case analysis is implemented.

[0071] Based on the matching and push of similar cases of historical accident responsibility determination, the present invention can realize the rapid matching reference of similar cases of traffic accident responsibility determination, and assist front-line case handlers in responsibility determination. The technical solution of the invention mines common rules from historical accident responsibility determination cases through the steps of establishing a case library, inputting target case characteristics, matching similar cases to obtain a similar case library, and analyzing the results and characteristics of the similar case library, so as to provide reference and assistance for the current accident responsibility determination. This invention is expected to solve the inconsistency, subjectivity and efficiency problems in the current accident responsibility determination process to a certain extent, and improve the fairness and efficiency of accident handling.

[0072] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for auxiliary liability determination of road traffic accidents based on similar case analysis, characterized in that: The following steps are involved: Build a case library based on open source information; Extract the features of the target case to be determined to obtain an extracted feature set; Comparing the similarity between each case in the case library and the target case to be determined to obtain a similar case library; A random forest model is constructed, and classification prediction is performed on the case library based on the random forest model to obtain the accountability results of the target cases to be determined.

2. The method for auxiliary determination of liability for road traffic accidents based on similar case analysis according to claim 1 is characterized in that: The process of building a case library includes: Gather cases based on open source information; Extracting case features of the cases and establishing corresponding data entries for each case; A case library is constructed based on a number of the data items.

3. The method for auxiliary determination of liability for road traffic accidents based on similar case analysis according to claim 2 is characterized in that: The extracted case features are divided into: basic information, right of way and safety obligations of both parties, special circumstances of both parties and determination of liability.

4. The method for auxiliary determination of liability for road traffic accidents based on similar case analysis according to claim 1 is characterized in that: The expression for calculating the similarity between the pending target case and each case in the case library is: v i =r i / p*100% In the formula, v i represents the similarity, p is the number of features of the target case c, r i For case b i The number of features that have the same value as the target case c.

5. The method for auxiliary determination of liability for road traffic accidents based on similar case analysis according to claim 1 is characterized in that: The steps to build a random forest model include: The case determination results in the similar case database are used as labels, and the case characteristics are used as feature variables; The characteristic variables are used to train a random forest model to predict the accountability outcome of the case.

6. The method for auxiliary liability determination of road traffic accidents based on similar case analysis according to claim 5 is characterized in that: The process of classifying and predicting the case database based on the random forest model includes: Input the features of the target case to be determined into the trained random forest model; The trained random forest model predicts the liability determination results of the target case to be determined based on the input features, where the liability determination results include the probabilities of no liability, primary and secondary liability, and equal liability.

7. A computer terminal device, characterized in that: include: one or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the road traffic accident auxiliary responsibility determination method based on similar case analysis as described in any one of claims 1-6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for auxiliary determination of responsibility for road traffic accidents based on similar case analysis as described in any one of claims 1 to 6 is implemented.

Images