CN104392328A - Uncertainty evaluation method of vehicle-pedestrian traffic accident - Google Patents

Abstract

The invention discloses an uncertainty evaluation method for a vehicle-pedestrian collision accident. The probability density function of the vehicle-pedestrian state parameter before the collision is identified through the probability density function PDF of the measured parameter after the collision. The invention not only effectively identifies and obtains the range of vehicle-pedestrian state parameters before the collision, but also objectively provides the probability of occurrence of these state parameters, thereby providing important basic data and basis for more reasonable analysis and evaluation of traffic accidents.

Description

An Uncertainty Evaluation Method for Vehicle-Pedestrian Traffic Accidents

technical field

The invention belongs to the field of traffic, and in particular relates to a method for evaluating uncertainty of vehicle-pedestrian traffic accidents, in particular to a method for identifying and evaluating the uncertainty of pre-collision state parameters in vehicle-pedestrian traffic accidents.

Background technique

The frequent occurrence of road traffic accidents has become a very serious social problem. In order to protect the safety of pedestrians, careful investigation and scientific analysis of accidents must be carried out. Therefore, after a traffic accident, how to reconstruct the vehicle-pedestrian state before the collision according to the scene of the accident has important research value and social significance for analyzing the collision law between vehicles and pedestrians, and reasonably carrying out traffic accident identification and responsibility division.

Traditional traffic accident reconstruction is based on deterministic models, and traditional traffic accident reconstruction methods can only give a set of definite pre-collision state parameters. However, traffic accidents are the result of the joint action of many factors, and there are more or less various uncertain factors, such as: errors in field measurement data, friction between vehicles and road surfaces, and braking time. If the coupling effect of these uncertain factors is not considered in traffic accident reconstruction, it may lead to a large deviation between the identified vehicle-pedestrian traffic accident pre-collision state parameters and the actual situation, and it may even be impossible to correctly divide the accident responsibility.

Contents of the invention

Considering the difficulty of traffic accident reconstruction under uncertain factors, the object of the present invention is to propose a PDF of the vehicle-pedestrian state parameters before the collision using the probability density function (Probabilitydensity function, PDF) of the vehicle-pedestrian measurement parameters after the collision In order to provide a basis for the reasonable evaluation of traffic accidents.

According to one aspect of the present invention, a kind of uncertainty assessment method of vehicle-pedestrian traffic accident is proposed, comprising:

Step A: Obtain the first state parameter of the measured vehicle-pedestrian after the collision, and determine the first moments of the first probability density function PDF of the first state parameter, wherein the first state parameter includes: The distance and angle of the pedestrian relative to the vehicle in all directions, the length of the brake marks, the position of the local deformation of the body, the mass and center of mass position of the vehicle and the pedestrian, and the height of the pedestrian;

Step B: Using the reliability calculation method, according to the vehicle-pedestrian collision simulation model constructed by the first state parameter, calculate the second PDF of the first state parameter of the vehicle-pedestrian after the collision, and determine the first state The second moments of the second PDF of the parameter, wherein the reliability calculation method includes: a second-order moment method, a second-order second-order moment method, a JC method and a Monte Carlo method;

Step C: According to the inverse objective function constructed by the first moments and the second moments, determine the second state parameters of the vehicle-pedestrian before the collision, wherein the second state parameters include: vehicle driving Speed, pedestrian walking speed, collision position between pedestrian and vehicle, relative angle between pedestrian and vehicle, vehicle braking time, friction coefficient between vehicle and ground during braking.

Wherein, step A includes:

At the scene of a traffic accident, the first state parameters of vehicles and pedestrians after the collision are measured multiple times;

Obtaining the first PDF of the first state parameter through statistics;

Perform integral processing on the first PDF to determine first moments of each order of the first PDF.

Wherein, step B includes:

Constructing a vehicle-pedestrian collision simulation model according to the first state parameter;

Using a reliability calculation method, according to the vehicle-pedestrian collision simulation model, calculate the second PDF of the first state parameter of the vehicle-pedestrian after the collision;

Integral processing is performed on the second PDF to determine second moments of each order of the second PDF.

Further, the vehicle-pedestrian collision simulation model is a multi-rigid body dynamics model.

Wherein, step C includes:

According to the sum of squares of the differences between the first moments and the second moments, an inverse objective function for vehicle-pedestrian traffic accident reconstruction is established;

Using a genetic algorithm to optimize and solve the inverse objective function;

When the inverse objective function satisfies the optimization condition, determine the third PDF of the second state parameter of the vehicle-pedestrian before the collision;

According to the third PDF, the probability density distribution of the second state parameter and its value range are determined.

According to another aspect of the present invention, there is provided a method for evaluating the uncertainty of vehicle-pedestrian traffic accidents, comprising the steps of:

Step 1: According to the scene conditions of the traffic accident, measure the vehicle-pedestrian state parameters after the collision several times, and obtain the PDF of the measured state parameters through statistics;

Step 2: According to the types of vehicles and pedestrians, establish a vehicle-pedestrian collision simulation model corresponding to traffic accidents;

Step 3: Parametrically describe the PDF of the vehicle-pedestrian state parameters before the collision, and set the value range and initial value of the parameters;

Step 4: Based on the established vehicle-pedestrian collision simulation model, call the second-order moment and other reliability calculation methods multiple times to obtain the PDF of the vehicle-pedestrian state parameters after the collision;

Step 5: Compare the PDF of the vehicle-pedestrian state parameters after the collision calculated in step 4 with the PDF of the vehicle-pedestrian state parameters after the collision measured in step 1, and establish an inverse objective function for vehicle-pedestrian traffic accident reconstruction;

Step 6: Use the optimization method to iteratively solve the inverse objective function. If the iterative convergence condition is not satisfied, generate the next-generation new value of the PDF describing the vehicle-pedestrian state parameters before the collision within the parameter value range, and then turn back to Step 4, if the convergence condition is met, proceed to Step 7;

Step 7: Use the optimized parameter values to construct the PDF of the vehicle-pedestrian state parameters before the collision, so as to realize the uncertainty evaluation of traffic accidents.

Among them, the vehicle-pedestrian state parameters after the collision include: the distance and angle of the pedestrian relative to the vehicle in all directions after the collision, the length of the brake marks, the position of the local deformation of the vehicle body, the mass and center of mass position of the vehicle and the pedestrian, and the height of the pedestrian.

Among them, the vehicle-pedestrian state parameters before the collision include: vehicle speed, pedestrian walking speed, pedestrian-vehicle collision position, pedestrian-vehicle relative angle, vehicle braking time, vehicle-ground friction during braking Coefficient etc.

Among them, the PDF of the vehicle-pedestrian state parameters before the collision is described by a parameterized form, and the recognition result is not a set of usual definite values, but the possible range and probability density distribution of the vehicle-pedestrian state parameters before the collision.

Optionally, in the above step 2, the vehicle-pedestrian collision simulation model is a multi-rigid body dynamics model.

Optionally, in the above step 4, the reliability calculation methods used to obtain the PDF of the vehicle-pedestrian state parameters after the collision include: the first-order second-order moment method, the second-order second-order moment method, the JC method and the Monte Carlo method wait.

Optionally, in the above step 5, when establishing the inverse objective function, the compared data include the PDF of the measured post-collision vehicle-pedestrian state parameter and the calculated PDF of the post-collision vehicle-pedestrian state parameter. Points and moments or cumulants of the PDF, etc.

Optionally, in the above step 6, a genetic algorithm is used as an optimization method, wherein the generation of new values of the next generation is realized through operations such as selection, crossover, and mutation of the genetic algorithm.

As can be seen from the above, a vehicle-pedestrian traffic accident uncertainty evaluation method proposed by the present invention considers the influence of uncertainty factors (especially measurement uncertainty factors) on the reconstruction results of traffic accidents, aiming at evaluating the impact of vehicles before the collision. -Pedestrian state parameter PDF for parametric description. By comparing the PDF of the measured vehicle-pedestrian state parameters after the collision with the calculated PDF of the vehicle-pedestrian state parameters after the collision, the inverse objective function is established, and the PDF of the vehicle-pedestrian state parameters before the collision is realized by using the optimization method Therefore, it provides important basic data and basis for more reasonable analysis and evaluation of traffic accidents.

Description of drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required in the embodiments of the present invention. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For Those of ordinary skill in the art can also obtain other drawings based on these drawings without making creative efforts.

Fig. 1 is the flowchart of the uncertainty evaluation method of vehicle-pedestrian traffic accident of the present invention;

Fig. 2 is a flow chart of the steps of vehicle-pedestrian traffic accident uncertainty evaluation in a specific embodiment of the present invention;

Fig. 3 (a) is the state of vehicle-pedestrian before collision among the present invention;

Fig. 3 (b) is the state of vehicle-pedestrian before collision among the present invention;

Fig. 4 (a) is the probability density curve of the pre-collision vehicle speed υ identified in the present invention;

Fig. 4 (b) is the probability density curve of the friction coefficient μ between the vehicle and the ground in the braking process identified in the present invention;

Fig. 4 (c) is the probability density curve of the angle α between the vehicle and the pedestrian before the collision identified in the present invention;

Fig. 5 (a) is the probability density curve of the X-direction throwing distance _lx of the pedestrian relative to the vehicle front wheel center after the collision measured and calculated in the present invention;

Fig. 5 (b) is the probability density curve of the Y-direction thrown distance l _y of the pedestrian relative to the vehicle front wheel center after the collision measured and calculated in the present invention;

Fig. 5(c) is the probability density curve of the angle θ between the X-direction of the pedestrian and the center of the front wheel of the vehicle after the collision measured and calculated in the present invention.

Detailed ways

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 part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

In view of the fact that the traditional reconstruction of traffic accidents cannot consider the uncertain factors, the present invention fully considers the influence of the uncertain factors on the inverse results, and not only provides the reasonable value range of the vehicle-pedestrian state parameters before the collision, but also more importantly The most important thing is to give its probability density curve, which provides an important basis for a more reasonable evaluation of traffic accidents.

Moreover, the present invention will parametrically describe the PDF of the vehicle-pedestrian state parameters before the collision, and use the reliability calculation method to solve the PDF of the vehicle-pedestrian state parameters after the collision, so as to improve the efficiency and accuracy of uncertain reconstruction of traffic accidents.

In addition, the present invention can obtain PDFs of multiple pre-collision state parameters at one time, including vehicle speed, pedestrian speed, collision position between pedestrian and vehicle, relative angle between pedestrian and vehicle, vehicle braking time, braking process The PDF of parameters such as the coefficient of friction between the vehicle and the ground.

In the following, the method for evaluating vehicle-pedestrian traffic accidents of the present invention will be described in detail by taking the evaluation of vehicle-pedestrian traffic accidents under the consideration of measurement response uncertainty as an example in conjunction with accompanying drawing 1 .

As shown in FIG. 1 , the vehicle-pedestrian traffic accident uncertainty evaluation method of the present invention includes the following steps A-C.

Step A: Obtain the measured first state parameter of the vehicle-pedestrian after the collision, and determine the first moments of the first PDF of the first state parameter.

Here, the first state parameters include the distance and angle of the pedestrian relative to the vehicle in all directions after the collision, the length of the brake marks, the position of the local deformation of the vehicle body, the mass and center of mass position of the vehicle and the pedestrian, and the height of the pedestrian.

Specifically, at the scene of a traffic accident, the first state parameters of vehicles and pedestrians after the collision are measured multiple times; then, the first PDF of the first state parameter is obtained statistically; and the first PDF is integrated to determine the first PDF. First moments.

Step B: Using the reliability calculation method, according to the vehicle-pedestrian collision simulation model constructed by the first state parameter, calculate the second PDF of the first state parameter of the vehicle-pedestrian after the collision, and determine the second PDF of the first state parameter The second moments of the PDF.

Specifically, according to the first state parameter, a vehicle-pedestrian collision simulation model is constructed. The vehicle-pedestrian collision simulation model here may be a multi-rigid body dynamics model. Then, by using the reliability calculation method and according to the vehicle-pedestrian collision simulation model, the second PDF of the first state parameter of the vehicle-pedestrian after the collision is calculated.

Among them, the reliability calculation methods include the first-order second-order moment method, the second-order second-order moment method, the JC method and the Monte Carlo method. Integral processing is performed on the second PDF to determine second moments of each order of the second PDF.

Step C: Determine the second state parameter of the vehicle-pedestrian before the collision according to the inverse objective function constructed by the first moments and the second moments.

Wherein, the second state parameter includes vehicle speed, pedestrian walking speed, collision position between pedestrian and vehicle, relative angle between pedestrian and vehicle, vehicle braking time, and friction coefficient between vehicle and ground during braking.

Specifically, according to the sum of the squares of the differences between the first moments and the second moments, the inverse objective function for vehicle-pedestrian traffic accident reconstruction is established; the genetic algorithm is used to optimize and solve the inverse objective function; when the inverse The objective function satisfies the optimization condition, and the third PDF of the second state parameter of the vehicle-pedestrian before the collision is determined; according to the third PDF, the probability density distribution and value range of the second state parameter are determined.

As can be seen from the above, a vehicle-pedestrian traffic accident uncertainty evaluation method proposed by the present invention considers the influence of uncertainty factors (especially measurement uncertainty factors) on the reconstruction results of traffic accidents, aiming at evaluating the impact of vehicles before the collision. -Pedestrian state parameter PDF for parametric description. By comparing the PDF of the measured vehicle-pedestrian state parameters after the collision with the calculated PDF of the vehicle-pedestrian state parameters after the collision, the inverse objective function is established, and the PDF of the vehicle-pedestrian state parameters before the collision is realized by using the optimization method , thus providing an important basis for a more reasonable analysis of traffic accidents.

After a vehicle-pedestrian traffic accident, due to the uncertainty in the measurement of accident scene data, in order to consider the impact of such uncertainty on the reconstruction of vehicle-pedestrian collision accidents, according to the accident scene conditions (that is, the measured post-collision Vehicle-pedestrian state parameters) first establish a vehicle-pedestrian collision simulation model, and parametrically describe the PDF of the vehicle-pedestrian state parameters before the collision; on this basis, the reliability calculation method can be used to obtain the vehicle-pedestrian state parameters after the collision PDF; compare the measured and calculated moments of each order of the vehicle-pedestrian state parameter PDF after the collision to form an inverse objective function, where the moments of each order of PDF can be obtained by integrating the PDF; use the genetic algorithm to optimize the solution to obtain the collision PDF of the preceding vehicle-pedestrian state parameters.

Fig. 2 shows the step process of vehicle-pedestrian traffic accident uncertainty evaluation method in the specific embodiment of the present invention, and specific implementation steps are as follows:

Step 1: After the vehicle-pedestrian traffic accident occurs, measure the vehicle-pedestrian state parameter Z ^u several times after the collision, including the X-direction and Y-direction throwing distances l _x , ly _y of the pedestrian relative to the center of the front wheel of the vehicle after the collision, and The angle θ between the pedestrian and the center of the front wheel of the vehicle in the X direction, and the PDF of l _x , l _y and θ are obtained statistically. The graphical expression of the PDF is the probability density curve shown in Figure 5(a)-5(c). At the same time, according to the PDF of l _x , _ly and θ, the corresponding moments of each order of the PDF of l _x , _ly and θ can be calculated Among them, i represents the i-th state parameter, j represents the j-order moment, and m represents that each order moment is obtained through measurement.

Step 2: According to the measured vehicle-pedestrian state parameter Z ^u after the collision, establish a multi-rigid body simulation model of the vehicle-pedestrian collision accident as shown in Figure 3(a)-3(b). The purpose of uncertain reconstruction of traffic accidents is to identify the state parameters before the collision, where the state parameters before the collision include the vehicle speed υ before the collision, the friction coefficient μ between the vehicle and the ground during braking, and the angle α between the pedestrian and the vehicle ;

Step 3: Use formula (1) to describe the PDF of the vehicle-pedestrian state parameters before the collision:

${\mathrm{<span\; class="notranslate">\; P</span>}}_{{\mathrm{<span\; class="notranslate">\; x</span>}}^{\mathrm{<span\; class="notranslate">\; u</span>}}}^{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}^{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span><span\; class="notranslate">\; \{</span>\frac{{\mathrm{<span\; class="notranslate">\; \&kappa;</span>}}_{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}}{\sqrt{{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; 4</span>}\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; x</span>}}^{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; )</span>}}<span\; class="notranslate">\; \&Center\; Dot;</span>{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{<span\; class="notranslate">\; (</span>\frac{{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>\sqrt{{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; 4</span>}\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; x</span>}}^{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; )</span>}}{{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

where ^Xu is the state parameter of the vehicle-pedestrian before the collision, which means υ, μ and α; here, b ₀ , b ₁ , b ₂ , λ, κ _χ are coefficients describing the parameterized PDF of ^Xu . If the parameters b ₀ , b ₁ , b ₂ , and λ can be identified, the PDF of the vehicle-pedestrian state parameters before the collision can be obtained by formula (1).

Step 4: When any set of b ₀ , b ₁ , b ₂ , λ is given, based on the multi-rigid body simulation model of the established vehicle-pedestrian collision accident, the reliability calculation method such as the first-order second-order moment method is called multiple times to calculate The PDF of the vehicle-pedestrian state parameters after the collision is obtained, and the specific process is as follows.

First, divide the value range of the measured vehicle-pedestrian state parameter Z ^u into n equal parts, and take the step size as Δz=(z _U -z _L )/n, where z _L and z _U respectively define Z ^u The lower limit and upper limit of the value, n is a positive integer greater than or equal to 2; secondly, for the kth step, ε=z _L +kΔz is used to construct the functional function G ^u =g(X ^u )-ε, where g(X ^u ) represents the numerical model corresponding to the multi-rigid body simulation model of the vehicle-pedestrian collision accident; again, the cumulative probability F(z ^{u )} of the vehicle-pedestrian state parameter Z ^u after the collision is calculated by using the reliability calculation method of the first-order second-order moment method , that is, to use the first-order second-order moment method to solve equation (2):

$\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; z</span>}}^{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; \&Integral;</span>}_{{\mathrm{<span\; class="notranslate">\; G</span>}}^{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\mathrm{<span\; class="notranslate">\; P</span>}}_{{\mathrm{<span\; class="notranslate">\; x</span>}}^{\mathrm{<span\; class="notranslate">\; u</span>}}}^{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}^{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; dx</span>}}^{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

Here, k is from 0 to n, calling the second-order moment method multiple times can calculate the PDF of the vehicle-pedestrian state parameters after the collision, and on this basis, calculate the corresponding order of the PDF of the vehicle-pedestrian state parameters after the collision moment Among them, i represents the i-th state parameter, j represents the j-order moment, and c represents that each order moment is obtained through calculation.

Step 5: Calculate the corresponding moments of each order of the vehicle-pedestrian state parameter PDF after the collision and the measured moment of each order corresponding to the vehicle-pedestrian state parameter PDF after the collision For comparison, the sum of the squares of the difference between the two is used as the reverse objective function, as shown in the following formula (3):

$\begin{array}{cc}\mathrm{<span\; class="notranslate">\; min</span>}& \underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; ij</span>}}^{\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}^{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; ij</span>}}^{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>\end{array}$

In the formula and are the moments of each order corresponding to _the PDF of the measured and calculated vehicle-pedestrian state parameters after the collision; n ₁ is the order of the moment, which is 4 in this example; The number is 3 in this example.

Step 6: Use the genetic algorithm to optimize and solve the inverse objective function. If the iterative convergence condition is not satisfied, generate the next-generation new values of the parameters b ₀ , b ₁ , b ₂ , and λ through operations such as selection, crossover, and mutation, and then Go back to step 4; if the convergence condition is met, go to step 7.

Step 7: Substituting the optimized parameter values b ₀ , b ₁ , b ₂ , and λ shown in Table 1 into formula (1), and obtaining the probability density curves of the recognized vehicle-pedestrian state parameters υ, μ, and α before the collision , as shown in Figure 4(a) to 4(c). The results of Figures 4(a) to 4(c) not only provide the reasonable value range of the vehicle-pedestrian state parameters before the collision, but more importantly, their probability density curves, which provide a more reasonable evaluation of traffic accidents. an important basis. Substituting the results in Table 1 into step 4, the optimal calculated PDF of the vehicle-pedestrian state parameters after the collision is obtained, as shown in Figures 5(a) to 5(c).

Table 1 Recognition results of vehicle-pedestrian state parameter PDF before collision

As shown in Figures 5a) to 5(c), the PDF of the calculated post-collision vehicle-pedestrian state parameters is substantially the same as the measured PDF of the post-collision vehicle-pedestrian state parameters, thereby verifying the vehicle-pedestrian traffic of the present invention The correctness and effectiveness of the accident evaluation method.

As can be seen from the above, a vehicle-pedestrian traffic accident uncertainty evaluation method proposed by the present invention considers the influence of uncertainty factors (especially measurement uncertainty factors) on the reconstruction results of traffic accidents, aiming at evaluating the impact of vehicles before the collision. -Pedestrian state parameter PDF for parametric description. By comparing the PDF of the measured vehicle-pedestrian state parameters after the collision with the calculated PDF of the vehicle-pedestrian state parameters after the collision, the inverse objective function is established, and the PDF of the vehicle-pedestrian state parameters before the collision is realized by using the optimization method Therefore, it provides important basic data and basis for more reasonable analysis and evaluation of traffic accidents.

The above is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Anyone skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present invention. Should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims (5)

1. a uncertainty assessment method for vehicle-pedestrian's traffic hazard, comprising:

Steps A: the first state parameter obtaining the collision rift vehicle-pedestrian measured, and determine first each rank square of the first probability density function PDF of described first state parameter, wherein said first state parameter comprises: the height of the quality of the position of the Distance geometry angle of all directions of collision rift pedestrian relative vehicle, braking mark length, vehicle body local deformation, vehicle and pedestrian and centroid position, pedestrian;

Step B: utilize reliability degree calculation method, according to the vehicle-pedestrian collision's realistic model built by described first state parameter, calculate the 2nd PDF of first state parameter of collision rift vehicle-pedestrian, and determine second each rank square of the 2nd PDF of described first state parameter, wherein said reliability degree calculation method comprises: FOSM, second-order second-moment method, JC method and Monte Carlo method;

Step C: according to the reverse objective function constructed by described first each rank square and described second each rank square, determine to collide second state parameter of vehicle in front-pedestrian, wherein said second state parameter comprises: the friction factor on vehicle and ground in the relative angle of the position of the speed of Vehicle Speed, pedestrian's walking, pedestrian and vehicle collision, pedestrian and vehicle, the time of car brakeing, braking procedure.

2. evaluation method according to claim 1, is characterized in that, described steps A comprises:

In the scene of a traffic accident, first state parameter of repetitive measurement collision rift vehicle and pedestrian;

Statistics obtains a PDF of described first state parameter;

Integral Processing is carried out to a described PDF, to determine first each rank square of a described PDF.

3. evaluation method according to claim 1, is characterized in that, described step B comprises:

According to described first state parameter, build vehicle-pedestrian collision's realistic model;

Utilize reliability degree calculation method, according to described vehicle-pedestrian collision's realistic model, calculate the 2nd PDF of first state parameter of collision rift vehicle-pedestrian;

Integral Processing is carried out to described 2nd PDF, to determine second each rank square of described 2nd PDF.

4. evaluation method according to claim 3, is characterized in that, described vehicle-pedestrian collision's realistic model is Multi-body dynamic model.

5. method according to any one of claim 1 to 4, is characterized in that, step C comprises:

According to the quadratic sum of the difference of described first each rank square and described second each rank square, set up the reverse objective function of vehicle-pedestrian's accident reconstruction;

Utilize genetic algorithm, described reverse objective function is optimized and solves;

When described reverse objective function meets optimal conditions, determine the 3rd PDF of the second state parameter colliding vehicle in front-pedestrian;

According to described 3rd PDF, determine probability density distribution and the span thereof of described second state parameter.