A VRU road traffic accident injury and disability grade evaluation system and method

Abstract

The present application relates to the field of traffic safety assessment, in particular to a VRU road traffic accident injury grade evaluation system and method, the system comprises a collection module, a processing module and a traffic accident injury module; the method comprises, according to the collision form and contact site of VRU road traffic accident, the accident is classified to obtain the collision type;When traffic accident occurs, the collision information of traffic accident is collected;The collected collision information is classified and stored according to the collision type;With the minimum number set of the collision type of different VRU road traffic accidents, the human body injury mechanics parameter and the actual human body injury disability degree injury grade establish corresponding data model, analyze the optimal solution of the relationship of the corresponding injury grade of collision information;The predicted injury grade is determined according to the optimal solution.The present application can obtain the predicted injury grade in the first time of traffic accident, more timely, after the collection of relevant information, the analysis and prediction of injury grade are automatically carried out, and the evaluation time of injury grade is short.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of traffic safety assessment, in particular to a VRU road traffic accident injury and disability grade evaluation system and method. BACKGROUND

[0002] VRU road refers to vulnerable road users, including pedestrians, bicycle riders, electric bicycle riders, etc. With the rapid growth of "people, vehicles and roads", the road mileage and the number of motor vehicles and drivers are rapidly increasing, and the number of injured persons caused by VRU road traffic accidents is also rapidly increasing. When a road traffic accident occurs, the personnel handling the first scene of the accident are traffic police, and the treatment of the injured in the accident is handled by medical institutions. Due to departmental responsibilities, the departments handling the scene and the treatment basically do not pay attention to the injury and disability grade of the injured at the first time.

[0003] The injury and disability grade of the injured is identified by a professional identification agency after the treatment of the injured is completed, or is evaluated by a claim settlement department in the later compensation. Although the existing injury and disability grade identification mode is relatively accurate, it is lagging behind and the evaluation period is particularly long. The long period is not conducive to the resolution of accident disputes and cannot meet the urgent need to know the injury and disability situation. SUMMARY

[0004] The present application aims to provide a VRU road traffic accident injury and disability grade evaluation method to solve the problem of time lag and long period of the existing evaluation mode.

[0005] The VRU road traffic accident injury and disability grade evaluation method in the present application comprises the following steps:

[0006] S1, classifying the accident according to the collision form and contact position of the VRU road traffic accident to obtain the collision type;

[0007] S2, collecting collision information of the traffic accident after the traffic accident occurs, the collision information including the contact position, the injury-causing object, the collision point, the collision speed, the deceleration and acceleration at the time of collision;

[0008] S3, classifying and storing the collected collision information according to the collision type;

[0009] S4, taking the collision types of different VRU road traffic accidents as the minimum set, establishing a corresponding data model of the human body injury mechanics parameters and the actual human body injury disability degree injury and disability grade based on the minimum set, and analyzing the optimal solution of the relationship between the collision information and the injury and disability grade;

[0010] S5, determining the predicted injury and disability grade according to the optimal solution obtained in S4.

[0011] The beneficial effects of the present scheme are:

[0012] By classifying the collision type of the traffic accident, collecting the collision information of the traffic accident, and determining the optimal solution of the correspondence relationship between the injury level and the collision type and the collision information, the predicted injury level is determined. The present scheme is based on the relevant information at the time of the traffic accident to predict the injury level, which can obtain the predicted injury level within the first time of the traffic accident, and the relevant information is collected automatically to analyze and predict the injury level, and the evaluation time of the injury level is short.

[0013] Further, the following steps are further included:

[0014] S6, tracking the injury identification information of the injured person after the accident, and obtaining the injury identification result of the injured person;

[0015] S7, verifying the predicted injury level according to the injury identification result, and optimizing the analysis method of S5 according to the injury identification result.

[0016] The beneficial effects are: by tracking the injury identification information of the injured person after the accident, and comparing with the predicted level, and optimizing the process of predicting the injury level, which can be optimized.

[0017] Further, in S1, the collision type includes pedestrian accident, bicycle accident, two-wheeled motorcycle accident, three-wheeled motorcycle accident, two-wheeled electric vehicle accident, three-wheeled electric vehicle accident, handcart accident, animal-drawn vehicle accident and frontal collision accident.

[0018] The beneficial effects are: by classifying the collision target, the weak user side in the road traffic accident can be considered for analysis, which can improve the accuracy of classification and accurately analyze the injury level after the accident.

[0019] Further, in S2, the collision information is collected by monitoring video, vehicle event data recording system and vehicle event data recording system on the vehicle.

[0020] The beneficial effects are: from multiple aspects, the collision information can be collected from multiple aspects, which can collect the collision information from multiple aspects, improve the accuracy and completeness of the collision information collection.

[0021] Further, in S2, when the collision information cannot be collected by monitoring video, vehicle event data recording system and vehicle event data recording system on the vehicle, the scene pictures are taken, and the collision speed, deceleration and acceleration at the time of collision are calculated from the scene traces on the scene pictures, vehicle deformation, and the contact part of the injured person, the injury object and the collision point of the collision target are obtained.

[0022] The beneficial effect is that when the collision information cannot be directly obtained, the collision information is obtained in an alternative way, and the disability assessment after the traffic accident can be timely performed.

[0023] Further, in the S5, the process of determining the predicted disability grade according to the optimal solution is that the disability grades of a plurality of injured parts of each injured person in the traffic accident are obtained, the maximum disability grade of the plurality of injured parts is taken as the main grade, the grades other than the maximum disability grade are taken as additional grades, the sizes of the additional grades are taken as additional grade numbers in a manner of increasing by %1, and the sum of the disability index of the maximum disability grade and each additional grade number is taken as the predicted disability grade.

[0024] The beneficial effect is that the disability grades of each part of the injured person in the traffic accident are combined to calculate the predicted disability grade, and the accuracy and completeness of the disability grade prediction are improved.

[0025] Further, in the S5, the disability grades include grades 1 to 10, the disability indices corresponding to the disability grades decrease by 10% from 100% to 10%, and the additional grade numbers corresponding to the disability grades are 10%-1%.

[0026] The beneficial effect is that the disability indices corresponding to the disability grades and the additional grades are defined, the data model is quantitatively calculated, and the accuracy of the calculation is improved.

[0027] The VRU road traffic accident disability grade evaluation system comprises a collection module, a processing module, and a traffic accident disability module.

[0028] The collection module is used to collect the collision information of the traffic accident.

[0029] The processing module is used to obtain the collision information from the collection module, classify the traffic accident to obtain the collision type, and send the collision type and the collision learning to the traffic accident disability module.

[0030] The traffic accident disability module is used to establish the data model of the disability evaluation according to the above steps, analyze the optimal solution of the relationship between the collision information and the disability grade, and determine the predicted disability grade according to the obtained optimal solution. BRIEF DESCRIPTION OF DRAWINGS

[0031] Figure 1 The flowchart of the VRU road traffic accident disability grade evaluation method embodiment of the present application is shown.

[0032] Figure 2 The schematic block diagram of the VRU road traffic accident disability grade evaluation system embodiment of the present application is shown. DETAILED DESCRIPTION

[0033] The following will be further described in detail through specific embodiments.

[0034] Embodiment one

[0035] The VRU road traffic accident injury grade evaluation system, as shown in the accompanying drawings, comprises a collection module, a processing module and a traffic accident injury module. Figure 2

[0036] The collection module is used to collect the collision information of the traffic accident.

[0037] The processing module is used to obtain the collision information from the collection module, classify the traffic accident to obtain the collision type, and send the collision type and the collision learning to the traffic accident injury module.

[0038] The traffic accident injury module is used to establish the data model of the injury evaluation according to the steps in the following VRU road traffic accident injury grade evaluation method, analyze the optimal solution of the relationship between the collision information and the injury grade, and determine the predicted injury grade according to the obtained optimal solution.

[0039] The VRU road traffic accident injury grade evaluation method, as shown in the accompanying drawings, comprises the following steps: Figure 1

[0040] S1. According to the collision shape and contact position of the VRU road traffic accident, the accident is classified to obtain the collision type, and the collision type includes pedestrian accident, bicycle accident, two-wheeled motorcycle accident, three-wheeled motorcycle accident, two-wheeled electric vehicle accident, three-wheeled electric vehicle accident, handcart accident, animal-drawn vehicle accident and head-on collision accident.

[0041] S2. After the traffic accident occurs, the collision information of the traffic accident is collected, and the collision information is collected through monitoring video, vehicle event data recording system on the vehicle and vehicle event data recording system on the vehicle. The vehicle event data recording system can trace the whole process of the traffic accident, such as the driving state of the vehicle when the vehicle collides, and the operation of the driver, including the accelerator pedal, emergency brake, steering wheel angle, etc. When the collision information cannot be collected by monitoring video, vehicle event data recording system and vehicle event data recording system on the vehicle, the scene pictures are taken, and the collision speed, deceleration and acceleration at the time of collision of the collision information are calculated from the scene traces on the scene pictures and the deformation of the vehicle. The contact position of the injured person of the collision target, the injury-causing object and the collision point are obtained, and the collision information includes the contact position, the injury-causing object, the collision point, the collision speed, the deceleration and the acceleration at the time of collision.

[0042] S3. The collected collision information is classified and stored according to the collision type.

[0043] ​​S4, with the minimum number set of collision types of different VRU road traffic accidents, a data model is established between the human body injury mechanics parameters and the actual human body injury disability degree disability grade based on the minimum number set, the data model is established based on the key parts of the head, neck, chest and limbs of the human body when being collided, for example, the human body injury mechanics parameters of head injury are:

[0044]

[0045] Wherein, t represents time (s), a (t) represents the three-axis combined acceleration of the head gravity center (unit m / s 2 );

[0046] Or the human body injury mechanics parameters of head injury are,

[0047] HIP=4.5a x ∫a x dt+4.5a y ∫a y dt+4.5a z ∫a z dt+0.016a x ∫a x dt+0.024a y ∫a y dt+0.022a z ∫a z dt,

[0048] Wherein, a x , a y , a z are the accelerations in three directions;

[0049] The human body injury mechanics parameters of neck injury are:

[0050]

[0051] In the formula, F z is the axial force, M y is the bending moment of flexion / extension, and the subscript int indicates the intercept of the load and the moment intersecting the axis;

[0052] The human body injury mechanics parameters of chest injury are TTI is an evaluation standard with acceleration as a parameter, which is determined by age, measured body weight and thoracic acceleration.

[0053] TTI=1.4·A+0.5(RI+T)·Ms / Mstd

[0054] In the formula: A is age (years);RI is the maximum acceleration absolute value of the 4th and 8th ribs (unit m / s 2) ; T is the maximum acceleration absolute value of the lower thoracic vertebra along the lateral axis (unit: m / s 2 ) ; Ms is the body weight (unit: kg) ; Mstd is the standard body weight (50 percentile male adult of 75 kg).

[0055] The optimal solution of analyzing the relationship between the collision information and the disability grade.

[0056] The method is based on historical data, but only gives a gradually weakening degree of influence, that is, as the data is far away, a gradually converging weight of zero is given, and the formula is:

[0057] S t = ay t +(1-a)S t-1 ,

[0058] In the formula, S t is the smoothing value of time series t;

[0059] y t is the actual value at time t;

[0060] S t-1 is the smoothing value at time t-1;

[0061] a is a smoothing constant, whose value range is [0, 1];

[0062] The initial value y0 of the prediction method is determined by using the full period mean method and the least square method based on the existing historical data.

[0063] S5, the optimal solution obtained according to S4 is used to determine the predicted disability grade, that is, the optimal solution is the disability grade of each part of the wounded under the collision information, and the process of determining the predicted disability grade according to the disability grade of each part of the wounded is: for each wounded in a traffic accident, the disability grades of multiple injured parts are obtained, the maximum disability grade of the multiple injured parts is taken as the main grade, and the grades other than the maximum disability grade are taken as additional grades, the sizes of the additional grades are numbered in the form of increasing by 1%, and the sum of the disability index of the maximum disability grade and each additional grade number is taken as the predicted disability grade, and the data model is represented as:

[0064] C = Ih + ∑Ia (∑Ia≤10%, i = 1, 2, 3...n multiple injuries),

[0065] In the formula: C is the disability coefficient of compound disability, expressed in percentage (%);

[0066] Ih is the disability compensation index of the highest disability grade, that is, for multiple grade disability, the highest disability grade coefficient is expressed in percentage (%), 0≤Ia≤10%.

[0067] The disability grade includes grades one to ten, i.e., grade one, grade two, grade three, grade four, grade five, grade six, grade seven, grade eight, grade nine, and grade ten, and the corresponding disability index of the disability grade decreases by 10% from 100% to 10%, i.e., grade one: 100%; grade two: 90%; grade three: 80%; grade four: 70%; grade five: 60%; grade six: 50%; grade seven: 40%; grade eight: 30%; grade nine: 20%; and grade ten: 10%, and the corresponding additional grade number of the disability grade is 10%-1%, i.e., grade one disability: 10%; grade two disability: 9%; grade three disability: 8%; grade four disability: 7%; grade five disability: 6%; grade six disability: 5%; grade seven disability: 4%; grade eight disability: 3%; grade nine disability: 2%; and grade ten disability: 1%.

[0068] In this embodiment, based on the traffic accident disability grade prediction processing mode of the system and method, after the traffic accident occurs, the collision type of the traffic accident is classified, the collision information of the traffic accident is collected, and the optimal solution of the disability grade corresponding relationship is determined according to the collision type and the collision information, the optimal solution is used to determine the predicted disability grade, and the predicted disability grade is calculated in a quantitative manner. When the light injury of the wounded person cannot reach the evaluation of the disability grade, the reaction of this situation can also be performed in a quantitative calculation manner, and the prediction result is more accurate. Based on the related information when the traffic accident occurs, the disability grade is predicted, and the predicted disability grade can be obtained in the first time after the traffic accident occurs, which is more timely. After the related information is collected, the disability grade is automatically analyzed and predicted, and the evaluation time of the disability grade is short.

[0069] Since when the traffic accident causes the wounded person to have a relatively serious physical injury, the evaluation target of the disability grade is to cause permanent physical injury to the wounded person, and to affect the work or activity intensity and type of the wounded person, the evaluation time of the disability grade is to identify after the treatment of the primary injury and the complications associated therewith is completed or the clinical treatment effect is stable. At present, the prediction of the disability grade by the insurance institutions, medical institutions, and appraisal institutions is at least based on the injury basis after the examination and diagnosis results of the wounded person are determined, so when the improvement research on the disability grade evaluation technology is performed, the disability evaluation is generally based on the stable condition of the wounded person or the action limitation degree collected in the activity and work process. In addition, when the traffic accident occurs, the physical condition of the wounded person is not yet determined, and the disability grade is generally not predicted immediately after the traffic accident occurs. The embodiment is based on injury mechanics to predict the disability grade, and the disability grade can be understood in advance and in a timely manner.

[0070] Embodiment two

[0071] The VRU road traffic accident disability grade evaluation method further includes the following steps:

[0072] S6, track the later disability identification information of the accident victim, and obtain the disability identification result of the victim;

[0073] S7, verify the predicted disability grade according to the disability identification result, and optimize the analysis method of S5 according to the disability identification result, the optimization of the analysis method of S5 is performed by adjusting the main grade by a preset step size when the predicted disability grade is larger than the disability identification result, and adjusting the main grade by a preset step size when the predicted disability grade is smaller than the disability identification result, and the preset step size is set according to the actual situation.

[0074] Based on the comparison and optimization of the predicted disability grade and the later disability identification result, the traffic accident disability grade evaluation method can be improved in the actual operation process, and the accuracy of the disability grade evaluation and prediction is improved.

[0075] The above is only an embodiment of the present application, and the specific structure and characteristics of the scheme are not described in detail. It should be pointed out that for those skilled in the art, without departing from the structure of the present application, a number of modifications and improvements can be made, which should be regarded as the protection scope of the present application, and these will not affect the effect and practicality of the present application. The protection scope of the present application should be subject to the content of its claims, and the specific implementation mode and the like recorded in the specification can be used to explain the content of the claims.

Claims

1. A method for evaluating the injury and disability grade of VRU road traffic accident, characterized in that, Comprise the following steps: S1, according to the collision form and contact position of VRU road traffic accident, the accident is classified to obtain the collision type, the collision type includes the head-on accident of pedestrian accident, bicycle accident, two-wheeled motorcycle accident, three-wheeled motorcycle accident, two-wheeled electric vehicle accident, three-wheeled electric vehicle accident, handcart accident, animal-drawn vehicle accident; S2, after the traffic accident occurs, the collision information of the traffic accident is collected, and the collision information includes the contact position, the injury object, the collision point, the collision speed, the deceleration and the acceleration at the time of collision; S3, the collected collision information is classified and stored according to the collision type; S4, taking the collision type of different VRU road traffic accident as the minimum set, the data model is established based on the minimum set, the human body injury mechanics parameter and the actual human body injury disability degree disability grade, the data model is established based on the key parts of the head, neck and chest of the human body when the human body is collided, the human body injury mechanics parameter of head injury is, ， Wherein, t represents time (s), a (t) represents the three-axis combined acceleration of the head center of gravity; The human body injury mechanics parameter of neck injury is: ， wherein is the axial force, is the bending moment of flexion / extension, with the subscript int indicating the intercept of the load and moment, respectively, with the axis; The human body injury mechanics parameter of chest injury is TTI, which is determined by age, measured body weight and thoracic acceleration, and is expressed as: TTI=1.4•A+0.5(RI+T)•Ms / Mstd; In the formula: A is age; RI is the maximum acceleration absolute value of the 4th and 8th ribs; T is the maximum acceleration absolute value of the lower thoracic vertebra along the lateral axis; Ms is the body weight; Mstd is the standard body weight; The optimal solution of the relationship between the collision information and the disability grade is analyzed, based on the historical data, only the gradually weakened influence degree is given, that is, as the data is far away, the weight number is gradually convergent to zero; S5, the optimal solution obtained according to S4 is used to determine the predicted disability grade, and the process of determining the predicted disability grade according to the optimal solution is that, for each injured person in the traffic accident, the disability grades of multiple injured parts are obtained, the maximum disability grade of the multiple injured parts is taken as the main grade, the grades other than the maximum disability grade are taken as additional grades, the size of the additional grades is taken as the additional grade number in the form of increasing by 1, and the sum of the disability index of the maximum disability grade and each additional grade number is taken as the predicted disability grade.

2. The method for assessing the injury severity of VRU road traffic accidents according to claim 1, characterized in that: Further comprising the following steps: S6, tracking the post-injury disability identification information of the accident victim, and obtaining the disability identification result of the victim; S7, verifying the predicted disability grade according to the disability identification result, and optimizing the analysis method of S5 according to the disability identification result. 3.The VRU road traffic accident injury grade evaluation method according to claim 1, characterized in that: In the S2, the collision information is collected through monitoring video, vehicle event data recording system and vehicle event data recording system. 4.The VRU road traffic accident injury grade evaluation method according to claim 3, characterized in that: In the S2, when the collision information cannot be collected by monitoring video, vehicle event data recording system and vehicle event data recording system, the collision speed, the deceleration and the acceleration at the time of collision are calculated from the scene traces on the scene pictures, the vehicle deformation, the contact position, the injury object and the collision point of the injured person of the collision target are obtained. 5.The VRU road traffic accident injury grade evaluation method according to claim 1, characterized in that: In the S5, the disability grade includes grades 1-10, the disability index corresponding to the disability grade decreases by 10% from 100% to 10%, and the additional grade number corresponding to the disability grade is 10%-1%.

6. The VRU road traffic accident injury disability grade evaluation system, characterized in that: The application comprises a collection module, a processing module and a traffic accident disability module. The collection module is used for collecting collision information of the traffic accident. The processing module is used for obtaining the collision information from the collection module, classifying the traffic accident to obtain a collision type, and sending the collision type and the collision learning to the traffic accident disability module. The traffic accident disability module is used for establishing a data model of disability evaluation according to the repeated steps in any one of claims 1-5, analyzing an optimal solution of the relationship between the collision information and the disability grade, and determining the predicted disability grade according to the obtained optimal solution.

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