CN113420511A - Vehicle-mounted communication delay modeling method - Google Patents

Vehicle-mounted communication delay modeling method Download PDF

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CN113420511A
CN113420511A CN202110824526.1A CN202110824526A CN113420511A CN 113420511 A CN113420511 A CN 113420511A CN 202110824526 A CN202110824526 A CN 202110824526A CN 113420511 A CN113420511 A CN 113420511A
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常琳
蒋华涛
仲雪君
陈大鹏
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Wuxi Internet Of Things Innovation Center Co ltd
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Abstract

The invention discloses a vehicle-mounted communication delay modeling method, which relates to the technical field of vehicle-mounted communication and comprises the following steps: establishing a vehicle-mounted communication delay mixed model based on a joint probability distribution function; respectively acquiring vehicle-mounted communication delay data in different driving scenes; setting initial parameters for each distribution of the hybrid model; substituting the vehicle-mounted communication delay data into the hybrid model to estimate the hybrid weight of each distribution; re-estimating the distributed parameters according to the vehicle-mounted communication delay data and the mixed weight; substituting the vehicle-mounted communication delay data into the mixed model with updated parameters to re-estimate the distributed mixed weight; re-estimating the parameters of each distribution until the mixing weight and the parameter change obtained by two iterations are smaller than a preset value to obtain a mixing model under each driving scene; the vehicle-mounted communication delay is modeled under different traffic environments, so that the hybrid model can adapt to the current traffic environment.

Description

Vehicle-mounted communication delay modeling method
Technical Field
The invention relates to the technical field of vehicle-mounted communication, in particular to a vehicle-mounted communication delay modeling method.
Background
The intelligent internet (V2X) technology can enhance the environmental perception capability of the single intelligent automatic driving automobile, make correct decision judgment for the automobile and provide favorable data support. However, implementing V2X requires a stable, reliable, low-latency guarantee of vehicle-mounted communication. Most of the current vehicle-mounted communication delay modeling is wireless communication analysis, only the influence of a communication channel environment on delay in the current environment is considered, but barrier shielding, weather, the position, time, condition and the like of traffic cannot be involved, so that the existing modeling method cannot adapt to all traffic environments. And different traffic environments, such as shelters formed by buildings, trees, obstacles and the like, can have great influence on the vehicle-mounted communication delay.
Disclosure of Invention
The invention provides a vehicle-mounted communication delay modeling method aiming at the problems and the technical requirements, and the vehicle-mounted communication delay modeling method can be used for modeling vehicle-mounted communication delay in different traffic environments, so that a hybrid model can be suitable for the current traffic environment.
The technical scheme of the invention is as follows:
a vehicle-mounted communication delay modeling method comprises the following steps:
establishing a vehicle-mounted communication delay mixed model based on a joint probability distribution function;
respectively acquiring vehicle-mounted communication delay data in different driving scenes;
setting initial parameters for each distribution of the hybrid model;
substituting the vehicle-mounted communication delay data in the ith driving scene into the hybrid model to estimate the hybrid weight of each distribution;
re-estimating the parameters of each distribution according to the vehicle-mounted communication delay data and the mixing weight of the mixing model in the ith driving scene;
substituting the vehicle-mounted communication delay data in the ith driving scene into the mixed model with updated parameters to re-estimate the mixed weight of each distribution;
re-estimating the distributed parameters according to the vehicle-mounted communication delay data and the mixed weight of the mixed model in the ith driving scene until the mixed weight and the parameter change obtained by the two iterations are smaller than a preset value, and obtaining the mixed model in the ith driving scene;
and (5) substituting the vehicle-mounted communication delay data in the ith driving scene into the hybrid model to estimate the distributed hybrid weights until the hybrid models in all driving scenes are obtained, wherein i is i + 1.
The further technical scheme is that the expression of the mixed model is as follows:
Figure BDA0003173124700000021
where f (x) is the joint probability distribution function, πkAre mixed weights, and
Figure BDA0003173124700000022
fk(. is) a probability distribution function of model k, θkThe data are parameters of probability distribution functions, K is the number of the probability distribution functions, and x is vehicle-mounted communication delay data.
The further technical scheme is that the method for estimating the mixed weight of each distribution comprises the following steps:
and respectively calculating the probability of the vehicle-mounted communication delay data under the ith driving scene under each distribution function according to the initial parameters or the updated parameters of each distribution, and taking the probability as the mixed weight of each distribution function.
The further technical scheme is that different driving scenes comprise a plurality of influence factors, namely sampling starting time T, position P changing along with time, traffic condition TF and weather W;
the position P is a longitude and latitude coordinate of the intersection;
the traffic condition TF refers to the congestion level of the current traffic, and the congestion level is set to be 0-10 vehicles as level 1, the congestion level is set to be 10-20 vehicles as level 2, the congestion level is set to be 20-40 vehicles as level 3, the congestion level is set to be 40-70 vehicles as level 4, the congestion level is set to be 70-100 vehicles as level 5, and the congestion level is set to be more than 100 vehicles as level 6;
setting the weather as W0, the light rain as W1, the medium rain as W2, the heavy rain as W3, the light snow as W4, the heavy snow as W5 and the haze as W6;
respectively collecting vehicle-mounted communication delay data under different driving scenes, comprising:
expressing the influence factors under each driving scene as [ P, T, TF, W ] in an array form;
and under each driving scene, starting to acquire vehicle-mounted communication delay data according to the T corresponding to the array, and recording the changed influence factor array and the corresponding vehicle-mounted communication delay data in a set sampling time period.
The further technical scheme is that the probability distribution function comprises an exponential distribution function, a normal distribution function and a lognormal distribution function;
the expression of the exponential distribution function is:
fexp(x|λ)＝λe-λxwherein λ is a scaling parameter;
the expression of the normal distribution function is:
Figure BDA0003173124700000031
wherein mu1、σ1Respectively representing the mean and variance of normal distribution;
the expression of the lognormal distribution function is:
Figure BDA0003173124700000032
wherein mu2、σ2Mean and variance of the lognormal distribution are respectively represented.
The further technical scheme is that the method for estimating the parameters of each distribution comprises the following steps:
assuming that the parameters of the three distribution functions are unknown, estimating the parameters of the three distribution functions by adopting a maximum likelihood function through vehicle-mounted communication delay data in the ith driving scene, wherein the maximum likelihood function of the hybrid model is as follows:
Figure BDA0003173124700000033
wherein m is the number of vehicle-mounted communication delay data in the ith driving scene;
and respectively solving partial derivatives of the parameters to be solved, so that the partial derivatives are zero, and estimating the value of each parameter to be solved.
The further technical scheme is that initial parameters are set for each distribution of the mixed model, and the method comprises the following steps:
and initializing the scale parameters, the mean and the variance of the normal distribution and the mean and the variance of the log-normal distribution.
The further technical scheme is that after the mixed models under all driving scenes are obtained, the method further comprises the following steps:
and under each driving scene, inputting corresponding vehicle-mounted communication delay data into the hybrid model and predicting to obtain the vehicle-mounted communication delay data at the next moment.
The beneficial technical effects of the invention are as follows:
establishing a vehicle-mounted communication delay mixed model based on a joint probability distribution function, aiming at different driving scenes and considering various influence factors, obtaining the vehicle-mounted communication delay mixed model under different traffic environments by utilizing vehicle-mounted communication delay data under different driving scenes through an iteration method, so that the mixed model can adapt to random traffic environments and is more suitable for application in actual traffic; under the same driving scene, vehicle-mounted communication delay data at the next moment can be obtained by predicting the corresponding hybrid model, so that the V2X can be predicted in advance and favorable data support is provided.
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Fig. 1 is a flowchart of a modeling method for vehicle-mounted communication delay provided by the present application.
Detailed Description
The following further describes the embodiments of the present invention with reference to the drawings.
As shown in fig. 1, a modeling method for vehicle-mounted communication delay includes the following steps:
step 1: and establishing a vehicle-mounted communication delay mixed model based on the joint probability distribution function.
The expression of the hybrid model is:
Figure BDA0003173124700000041
where f (x) is the joint probability distribution function, πkAre mixed weights, and
Figure BDA0003173124700000042
fk(. is) a probability distribution function of model k, θkThe data are parameters of probability distribution functions, K is the number of the probability distribution functions, and x is vehicle-mounted communication delay data.
The probability distribution function includes an exponential distribution function, a normal distribution function, and a lognormal distribution function, and:
1) the expression of the exponential distribution function is:
fexp(x|λ)＝λe-λxwherein λ is a scaling parameter;
2) the expression of the normal distribution function is:
Figure BDA0003173124700000043
wherein mu1、σ1Respectively representing the mean and variance of normal distribution;
3) the expression of the lognormal distribution function is:
Figure BDA0003173124700000044
wherein mu2、σ2Mean and variance of the lognormal distribution are respectively represented.
Step 2: vehicle-mounted communication delay data are respectively collected under different driving scenes and are used as sample sets under all the scenes.
Different driving scenes comprise various influence factors, namely sampling starting time T, position P changing along with time, traffic condition TF and weather W, wherein:
1) the position P is the longitude and latitude coordinates of the intersection.
2) The traffic condition TF refers to the congestion level of the current traffic, wherein 0-10 congested vehicles are set as level 1, 10-20 congested vehicles are set as level 2, 20-40 congested vehicles are set as level 3, 40-70 congested vehicles are set as level 4, 70-100 congested vehicles are set as level 5, and more than 100 congested vehicles are set as level 6.
Optionally, the traffic congestion level may be obtained by road side equipment, such as a radar-vision integration machine.
3) The weather is W0 in sunny days, the light rain is W1, the medium rain is W2, the heavy rain is W3, the light snow is W4, the heavy snow is W5, and the haze is W6.
The method comprises the following specific steps:
step 21: the influence factors under each driving scene are expressed as [ P, T, TF, W ] in an array form, such as an array [31.568,120.299,10:00,2, W1], and the influence factors are expressed as intersections with latitudes 31.568 and longitudes 120.299 in the current driving scene, the sampling starting time is 10 am, the intersection congestion level is 2, and the current weather is light rain.
Step 22: and under each driving scene, starting to acquire vehicle-mounted communication delay data according to the T corresponding to the array, and recording the changed influence factor array and the corresponding vehicle-mounted communication delay data in a set sampling time period.
And step 3: initial parameters are set for each distribution of the hybrid model.
Comparative example parameter λ, mean and variance μ of normal distribution1、σ1Mean and variance μ of lognormal distribution2、σ2And performing initialization setting.
And 4, step 4: and substituting the vehicle-mounted communication delay data in the ith driving scene into the mixing model to estimate the mixing weight of each distribution.
And respectively calculating the probability of the sample set under the ith driving scene under each distribution function according to the initial parameters of each distribution, and taking the probability as the mixing weight of each distribution function.
And 5: and re-estimating the parameters of each distribution according to the vehicle-mounted communication delay data and the mixing weight of the mixing model in the ith driving scene.
Assuming that the parameters of the three distribution functions are unknown, estimating the parameters of the three distribution functions by adopting a maximum likelihood function through a sample set under the ith driving scene, wherein the maximum likelihood function of the hybrid model is as follows:
Figure BDA0003173124700000051
and m is the number of vehicle-mounted communication delay data in the ith driving scene, namely the number of samples.
And respectively solving partial derivatives of the parameters to be solved, so that the partial derivatives are zero, and estimating the value of each parameter to be solved. Taking the proportional parameter λ as an example, let:
Figure BDA0003173124700000052
the value of the scaling parameter lambda can be estimated, and so on for the other parameters.
Step 6: and substituting the vehicle-mounted communication delay data in the ith driving scene into the mixed model with the updated parameters to re-estimate the mixed weight of each distribution.
And respectively calculating the probability of the sample set under the ith driving scene under each distribution function according to the updating parameters of each distribution, and taking the probability as the updating mixing weight of each distribution function.
And 5, re-executing the step until the mixing weight and the parameter change obtained by the two iterations are smaller than a preset value, and obtaining a mixing model under the ith driving scene.
And 7: and (5) making i equal to i +1, and re-executing the step 4 until hybrid models in all driving scenes are obtained.
And 8: and under each driving scene, inputting a corresponding sample set into the hybrid model and predicting to obtain vehicle-mounted communication delay data at the next moment.
What has been described above is only a preferred embodiment of the present application, and the present invention is not limited to the above embodiment. It is to be understood that other modifications and variations directly derivable or suggested by those skilled in the art without departing from the spirit and concept of the present invention are to be considered as included within the scope of the present invention.

Claims (8)

1. A vehicle-mounted communication delay modeling method is characterized by comprising the following steps:
establishing a vehicle-mounted communication delay mixed model based on a joint probability distribution function;
respectively acquiring vehicle-mounted communication delay data in different driving scenes;
setting initial parameters for each distribution of the hybrid model;
substituting the vehicle-mounted communication delay data in the ith driving scene into the hybrid model to estimate the hybrid weight of each distribution;
re-estimating the distributed parameters according to the vehicle-mounted communication delay data in the ith driving scene and the mixing weight of the mixing model;
substituting the vehicle-mounted communication delay data in the ith driving scene into the mixed model with updated parameters to re-estimate the mixed weight of each distribution;
re-estimating the distributed parameters according to the vehicle-mounted communication delay data and the mixed weight of the mixed model in the ith driving scene until the mixed weight and parameter change obtained by two iterations are smaller than a preset value, and obtaining the mixed model in the ith driving scene;
and substituting the vehicle-mounted communication delay data in the ith driving scene into the hybrid model to estimate the distributed hybrid weights until the hybrid models in all driving scenes are obtained, wherein i is i + 1.
2. The vehicle-mounted communication delay modeling method according to claim 1, wherein the expression of the hybrid model is:
Figure FDA0003173124690000011
wherein f (x) is the joint probability distribution function, πkIs the mixing weight, and
Figure FDA0003173124690000012
fk(. is) a probability distribution function of model k, θkIs the parameter of the probability distribution function, K is the number of the probability distribution function, and x is the vehicle-mounted communication delay data.
3. The vehicle-mounted communication delay modeling method according to claim 1, wherein estimating the mixing weight of each distribution comprises:
and respectively calculating the probability of the vehicle-mounted communication delay data under the ith driving scene under each distribution function according to the initial parameters or the updated parameters of each distribution, and taking the probability as the mixed weight of each distribution function.
4. The vehicle-mounted communication delay modeling method according to claim 1, wherein the different driving scenarios include a plurality of influence factors, namely a sampling start time T, a position P which changes with time, a traffic condition TF and weather W;
the position P is a longitude and latitude coordinate of the intersection;
the traffic condition TF refers to the congestion level of the current traffic, and is set with 0-10 congested vehicles as level 1, 10-20 congested vehicles as level 2, 20-40 congested vehicles as level 3, 40-70 congested vehicles as level 4, 70-100 congested vehicles as level 5 and more than 100 congested vehicles as level 6;
setting the weather as W0, the light rain as W1, the medium rain as W2, the heavy rain as W3, the light snow as W4, the heavy snow as W5 and the haze as W6;
the vehicle-mounted communication delay data are respectively collected under different driving scenes, and the method comprises the following steps:
expressing the influence factors under each driving scene as [ P, T, TF, W ] in an array form;
and under each driving scene, starting to acquire vehicle-mounted communication delay data according to the T corresponding to the array, and recording the changed influence factor array and the corresponding vehicle-mounted communication delay data in a set sampling time period.
5. The vehicle-mounted communication delay modeling method according to claim 2, wherein the probability distribution function includes an exponential distribution function, a normal distribution function, and a lognormal distribution function;
the expression of the exponential distribution function is:
fexp(x|λ)＝λe-λxwherein λ is a scaling parameter;
the expression of the normal distribution function is as follows:
Figure FDA0003173124690000021
wherein mu1、σ1Respectively representing the mean and variance of normal distribution;
the expression of the lognormal distribution function is as follows:
Figure FDA0003173124690000022
wherein mu2、σ2Mean and variance of the lognormal distribution are respectively represented.
6. The vehicle-mounted communication delay modeling method according to claim 5, wherein re-estimating the parameters of each distribution comprises:
assuming that the parameters of the three distribution functions are unknown, estimating the parameters of the three distribution functions by adopting a maximum likelihood function through vehicle-mounted communication delay data in the ith driving scene, wherein the maximum likelihood function of the hybrid model is as follows:
Figure FDA0003173124690000023
wherein m is the number of vehicle-mounted communication delay data in the ith driving scene;
and respectively solving partial derivatives of the parameters to be solved, so that the partial derivatives are zero, and estimating the value of each parameter to be solved.
7. The vehicle-mounted communication delay modeling method according to claim 5, wherein the setting of the initial parameters for each distribution of the hybrid model includes:
and initializing and setting the proportional parameters, the mean and variance of normal distribution and the mean and variance of log-normal distribution.
8. The vehicle-mounted communication delay modeling method according to any one of claims 1-7, wherein after obtaining the hybrid model under all driving scenarios, the method further comprises:
and under each driving scene, inputting corresponding vehicle-mounted communication delay data into the hybrid model and predicting to obtain the vehicle-mounted communication delay data at the next moment.
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