CN110588658B - Method for detecting risk level of driver based on comprehensive model - Google Patents

Method for detecting risk level of driver based on comprehensive model Download PDF

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CN110588658B
CN110588658B CN201910920173.8A CN201910920173A CN110588658B CN 110588658 B CN110588658 B CN 110588658B CN 201910920173 A CN201910920173 A CN 201910920173A CN 110588658 B CN110588658 B CN 110588658B
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牛世峰
董兆晨
郑佳红
付锐
郭应时
袁伟
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Changan University
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
    • B60W40/09—Driving style or behaviour
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/107—Longitudinal acceleration
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B60W2050/0028—Mathematical models, e.g. for simulation
    • B60W2050/0029—Mathematical model of the driver
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed

Abstract

The invention discloses a method for detecting the risk level of a driver based on a comprehensive model, which judges the driving behavior and classifies the risk level of the driver by acquiring the speed data of a vehicle and different alarm types of the driver, identifies the risk level of the driver by using the trained comprehensive model, and judges the risk level of the driver by using the identification result. The invention is mainly used for a safety management system of a transportation enterprise, and when the driver is identified to be a high-risk driver, corresponding management training measures can be taken to improve the safety of the driver. The practicability of the invention can reduce the casualties and property loss caused by traffic accidents and improve the overall safety of the traffic system.

Description

Method for detecting risk level of driver based on comprehensive model
Technical Field
The invention belongs to the field of traffic safety, and particularly relates to a method for detecting a risk level of a driver based on a comprehensive model.
Background
With the rapid development of national economy and the acceleration of urbanization process, the motor vehicle ownership and road traffic volume in China are rapidly increased, and the problem of traffic accidents is increasingly prominent. Research has shown that, the driver factors are the main causes of traffic accidents, and the drivers have different driving risk levels and different contributions to traffic accidents, so that the drivers with low risk levels may cause less or even avoid traffic accidents, and the drivers with higher risk levels may cause more serious traffic accidents. Therefore, it is important to intensively study a driver risk level identification method.
At present, existing achievements mainly aim at the assessment of the risk level of the driver for research, and few researches aim at the identification of the risk level of the driver, so that the requirements of traffic safety management cannot be met.
Disclosure of Invention
The invention aims to overcome the defects and provide a method for detecting the risk level of a driver based on a comprehensive model, which can identify the risk level of the driver according to different driving behaviors and provide reference for reducing the occurrence of traffic accidents.
In order to achieve the above object, the present invention comprises the steps of:
acquiring speed data of a driver to obtain a driving behavior sequence and a driver risk level;
repeatedly and iteratively training the comprehensive model, and determining each parameter of the model to obtain the comprehensive model;
step three, detecting a driving behavior sequence to be recognized by using the trained comprehensive model;
and step four, counting the detection results in a preset time period to obtain a final risk grade judgment result under the preset confidence level of the driver.
In the first step, acceleration data of the vehicle is calculated according to the speed data, and the calculation method of the acceleration data comprises the following steps:
Figure BDA0002217310020000021
vtvelocity at time t, vt-1Speed at time t-1, Δ t being the time interval, obtained from GPS data of the vehicle, atIs the average acceleration over time t.
In the first step, when the driving behavior is determined, firstly, the acceleration information of a driver is analyzed according to the speed data of the driver, then the driver behavior is divided into a plurality of types according to the acceleration information, the alarming times of the driver are collected according to an alarming system arranged in a vehicle, and the risk level of the driver is clustered through k-means.
In the first step, the established comprehensive model comprises the following steps:
the number of hidden states is N, and the set of hidden states is Q ═ Q1,q2,q3Corresponding to a low risk level, a medium risk level, a high risk level, respectively;
the number of observation states M, the set of observation states is V ═ V1,v2,…v5Corresponding to fast deceleration, slow deceleration, normal driving, slow acceleration and fast acceleration respectively;
hidden state sequence is i1,i2,...iTObservation sequence is o1,o2,...oTAnd T is the length of the driving behavior sequence;
output probability matrix B, B ═ Bj(ok)＝P(ot＝vk|it＝qj) I is more than or equal to 1 and less than or equal to 3, k is more than or equal to 1 and less than or equal to 5, and the hidden state at the time t is qjThe observed value generated is vkMatrix B reflects the relation between the hidden state and the observed value;
the probability matrix C of the transition of the observed values,
Figure BDA0002217310020000022
representing a risk rating of qjIs observed by vkConversion to vlJ ═ 1,2, 3;
initial state distribution probability of pi ═ pii}，1≤i≤3,
Figure BDA0002217310020000023
In the second step, the method for training the stable comprehensive model comprises the following steps: and taking the driving behavior of the driver with each risk level as the input of the comprehensive model, and solving the model parameters of the comprehensive model.
In the third step, the driving behavior sequence is identified by utilizing the trained comprehensive model, and the output result is
Figure BDA0002217310020000024
Selecting the risk grade corresponding to the comprehensive model with the maximum probability as the recognition result of the driving behavior sequence, wherein P isiIs a hidden state i at the time t under the condition of known observation value sequence (driving behavior sequence) O and model parameter lambdatIs a risk class qiThe probability of (d); alpha is alphat(i) Is the forward probability, beta, of the state at time tt(i) Refers to the backward probability of the i state at time t.
The concrete method of the step four is as follows:
firstly, judging the risk level of a driver in a short time period according to the driving behavior sequence recognition result with the length of T, wherein the judgment method comprises the following steps:
Figure BDA0002217310020000031
wherein the content of the first and second substances,
Figure BDA0002217310020000032
for the recognition of the risk level of the driver in a short time,
Figure BDA0002217310020000033
respectively representing a low risk level, a medium risk level and a high risk level, P1、P2、P3Respectively representing the probabilities of identifying the driving behavior sequence to be tested as a low risk level, a medium risk level and a high risk level through a comprehensive model;
and secondly, calculating the number of the three risk levels in a long time according to the judgment result of the risk level of the driver in a short time period, wherein the calculation formula is as follows:
Figure BDA0002217310020000034
n1 is the number of sequences identified as low risk levels over a long period of time, n2 is the number of sequences identified as medium risk levels over a long period of time, n3 is the number of sequences identified as high risk levels over a long period of time, n is the total, xjUsed as a count;
thirdly, calculating the proportion of each risk grade according to the number of the three risk grades in a long time, wherein the calculation formula is as follows:
Figure BDA0002217310020000035
k1、k2、k3respectively representing the recognition proportions of a low risk grade, a medium risk grade and a high risk grade in a long time period;
fourthly, expressing the uncertainty of the random variable by using the information entropy, and calculating the formula as follows:
Figure BDA0002217310020000041
c is 1-H and represents the confidence coefficient of the risk level of the driver, and the higher the C value is, the higher the confidence coefficient is;
fifthly, under the condition that the confidence coefficient is C, the method for judging the risk level of the driver in a long time period is as follows:
Figure BDA0002217310020000042
Figure BDA0002217310020000043
indicating the driver's long-term risk level recognition result, I1、I2、I3Respectively representing a low risk level, a medium risk level and a high risk level.
Compared with the prior art, the method and the device have the advantages that the driving behavior is judged and the risk level of the driver is classified by acquiring the speed data of the vehicle and different alarm types of the driver, the risk level of the driver is identified by using the trained comprehensive model, and then the risk level of the driver is judged by using the identification result. The invention is mainly used for a safety management system of a transportation enterprise, and when the driver is identified to be a high-risk driver, corresponding management training measures can be taken to improve the safety of the driver. The practicability of the invention can reduce the casualties and property loss caused by traffic accidents and improve the overall safety of the traffic system.
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FIG. 1 is a diagram of an integrated model of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Referring to fig. 1, the present invention first designs an acceleration index by analyzing speed data of each driver, and identifies a driving behavior sequence of the driver by using a characteristic index. And then, dividing the drivers into low-risk level drivers, medium-risk level drivers and high-risk level drivers by using the acquired alarm type of each driver and adopting a K-Means mean clustering method. The classification of the risk level of the driver is the basis of the identification of the risk level of the driver, and whether the classification is reasonable or not directly determines the success or failure of the identification algorithm. And training the comprehensive model by using partial data, and then identifying the driving behavior sequence.
The driving behaviors comprise 5 types of rapid deceleration, slow deceleration, normal driving, slow acceleration and rapid acceleration.
1. Calculating the acceleration of the vehicle;
the vehicle acceleration is an average acceleration of the vehicle in a short time, and is specifically represented by formula (1).
Figure BDA0002217310020000051
In the formula: v. oftVelocity at time t, vt-1Speed at time t-1, Δ t being the time interval, obtained from GPS data of the vehicle, atIs the average acceleration over time t.
2. Determining driving behaviors and a risk level of a driver;
the acceleration threshold for distinguishing different driving behaviors is not yet specified, and the acceleration determination and discrimination method calculated according to the formula (1) is as follows:
Figure BDA0002217310020000052
atand 1,2,3, 4 and 5 represent fast deceleration, slow deceleration, normal driving, slow acceleration and fast acceleration respectively for the average acceleration in the period t. The specific determination method is to take 30% of acceleration and deceleration and 70% of acceleration and deceleration as threshold values in the acceleration sequence from large to small.
Determining the risk level of the driver mainly divides the drivers into low-risk level drivers, middle-risk level drivers and high-risk level drivers by using a K-Means clustering method for different driving alarm types.
3. Detecting a driver risk level based on the comprehensive model;
firstly, a stable comprehensive model is trained, and the specific method is that the driving behaviors of drivers with all risk levels are used as the input of the comprehensive model, the model parameters of the comprehensive model are solved, wherein lambda is { B, C, pi }, B is an output matrix, C is an observed value transfer matrix, and pi is an initial state matrix.
Then, the trained model is used for identifying the driving behavior sequence, and the output result is as follows:
Figure BDA0002217310020000053
Figure BDA0002217310020000054
and selecting the risk grade corresponding to the comprehensive model with the maximum probability as the recognition result of the driving behavior sequence.
The method is characterized in that the detection result is counted in a certain time period to obtain a final risk grade judgment result of the driver under a certain confidence coefficient, and the method comprises the following steps:
judging the risk level of the driver in a short time period according to the driving behavior sequence recognition result with the length of T, wherein the judgment method is as follows (2):
Figure BDA0002217310020000061
in the formula (I), the compound is shown in the specification,
Figure BDA0002217310020000062
to representThe result of the recognition of the risk level of the driver in a short time,
Figure BDA0002217310020000063
respectively representing a low risk level, a medium risk level and a high risk level, P1、P2、P3Respectively representing the probability of identifying the driving behavior sequence to be tested as a low risk level, a medium risk level and a high risk level through the comprehensive model.
Secondly, calculating the number of the three risk levels in a long time according to the judgment result of the risk level of the driver in a short time period, wherein the calculation formula is as follows (3):
Figure BDA0002217310020000064
where n1 is the number of sequences identified as low risk levels over a long period of time, n2 is the number of sequences identified as medium risk levels over a long period of time, n3 is the number of sequences identified as high risk levels over a long period of time, n is the total,
Figure BDA0002217310020000065
is the recognition result of the risk level of the driver in a medium-short time, xjAnd is used only as a count.
Thirdly, calculating the proportion of each risk grade according to the number of the three risk grades in a long time, wherein the calculation formula is as shown in formula (4):
Figure BDA0002217310020000066
in the formula, k1、k2、k3Respectively representing the recognition proportions of a low risk level, a medium risk level and a high risk level in a long time period.
Fourthly, expressing the uncertainty of the random variable by using the information entropy, wherein the calculation formula is as shown in the formula (5):
Figure BDA0002217310020000067
let C be 1-H, which represents the confidence in the driver risk level, with higher C values giving higher confidence.
And fifthly, under the condition that the confidence is C, judging the risk level of the driver in a long time period according to the formula (6):
Figure BDA0002217310020000068
in the formula (I), the compound is shown in the specification,
Figure BDA0002217310020000071
indicating the driver's long-term risk level recognition result, I1、I2、I3Respectively representing a low risk level, a medium risk level and a high risk level.
Whether the identification result of the risk grade of the driver is accurate or not is possibly related to the selection of a plurality of factors, so that the applicant carries out data analysis on the division of the driving behaviors and the classification of the risk grade based on the driving actual measurement data of the driver (n is greater than 30), and researches show that when the length of the detection sequence is 8, the detection result is relatively stable, so that when the model is trained and detected, the length of the selected driving behavior sequence is 8. In the identification considering the risk level of the driver over a long period of time, the present invention selects a period of time as long as 24 hours. The final recognition result is used as a reference according to the given confidence C value.
The method provided by the invention has the advantages that the GPS data of the vehicle and different types of alarms of the driver are collected in real time, the comprehensive model is trained and the driving behavior sequence is detected by utilizing the model through the division of the driving behavior and the classification of the risk grade, the inaccuracy and instability of the detection result based on a short period of time are overcome, the risk grade of the driver is considered through the detection of a long period of time, and the corresponding confidence coefficient is given to the final recognition result. The invention is mainly used for a vehicle safety system and a transportation enterprise safety management system, the hidden danger degrees brought to traffic safety by drivers with different risk levels are different, and the transportation enterprise can reasonably carry out batch, classification and education with different degrees on the management level of the drivers, thereby reducing the accident frequency of operating vehicles, bringing convenience for the management of the transportation enterprise and improving the operation benefit of the transportation enterprise. In addition, the invention can give reasonable advice to the driving state of the driver when the driving state fluctuates for a long time, thereby improving the safety of the driver. The practicability of the invention can reduce the casualties and property loss caused by traffic accidents and improve the overall safety of the traffic system.

Claims (6)

1. A method for detecting a risk level of a driver based on a comprehensive model is characterized by comprising the following steps:
acquiring speed data of a driver to obtain a driving behavior sequence and a driver risk level;
repeatedly and iteratively training the comprehensive model, and determining each parameter of the model to obtain the comprehensive model; the comprehensive model comprises:
the number of hidden states is N, and the set of hidden states is Q ═ Q1,q2,q3Corresponding to a low risk level, a medium risk level, a high risk level, respectively;
the number of observation states M, the set of observation states is V ═ V1,v2,…v5Corresponding to fast deceleration, slow deceleration, normal driving, slow acceleration and fast acceleration respectively;
hidden state sequence is i1,i2,...iTObservation sequence is o1,o2,...oTAnd T is the length of the driving behavior sequence;
output probability matrix B, B ═ Bj(ok)＝P(ot＝vk|it＝qj) I is more than or equal to 1 and less than or equal to 3, k is more than or equal to 1 and less than or equal to 5, and the hidden state at the time t is qjThe observed value generated is vkMatrix B reflects the relation between the hidden state and the observed value;
the probability matrix C of the transition of the observed values,
Figure FDA0002756935210000011
to representRisk rating of qjIs observed by vkConversion to vlJ ═ 1,2, 3;
initial state distribution probability of pi ═ pii}，1≤i≤3,
Figure DEST_PATH_IMAGE001
；
Step three, detecting a driving behavior sequence to be recognized by using the trained comprehensive model;
and step four, counting the detection results in a preset time period to obtain a final risk grade judgment result under the preset confidence level of the driver.
2. The method for detecting the risk level of the driver based on the comprehensive model as claimed in claim 1, wherein in the step one, the acceleration data of the vehicle is calculated according to the speed data, and the calculation method of the acceleration data is as follows:
Figure FDA0002756935210000013
vtvelocity at time t, vt-1Speed at time t-1, Δ t being the time interval, obtained from GPS data of the vehicle, atIs the average acceleration over time t.
3. The method for detecting the risk level of the driver based on the comprehensive model as claimed in claim 1, wherein in the step one, when the driving behavior is determined, firstly, the acceleration information of the driver is analyzed according to the speed data of the driver, then, the driver behavior is divided into a plurality of types according to the acceleration information, the alarming times of the driver are collected according to an alarming system built in the vehicle, and the risk level of the driver is clustered through K-means.
4. The method for detecting the risk level of the driver based on the comprehensive model as claimed in claim 1, wherein in the second step, the method for training the stable comprehensive model comprises the following steps: and taking the driving behavior of the driver with each risk level as the input of the comprehensive model, and solving the model parameters of the comprehensive model.
5. The method for detecting the risk level of the driver based on the comprehensive model as claimed in claim 1, wherein in the third step, the driving behavior sequence is recognized by using the trained comprehensive model, and the output result is
Figure FDA0002756935210000021
Figure FDA0002756935210000022
Selecting the risk grade corresponding to the comprehensive model with the maximum probability as the recognition result of the driving behavior sequence, wherein P isiUnder the condition of a driving behavior sequence O and a model parameter lambda, a hidden state i at the time ttIs a risk class qiThe probability of (d); alpha is alphat(i) Is the forward probability, β, of the i state at time tt(i) Refers to the backward probability of the i state at time t.
6. The method for detecting the risk level of the driver based on the comprehensive model as claimed in claim 1, wherein the concrete method of the step four is as follows:
firstly, judging the risk level of a driver in a short time period according to the driving behavior sequence recognition result with the length of T, wherein the judgment method comprises the following steps:
Figure FDA0002756935210000023
wherein the content of the first and second substances,
Figure FDA0002756935210000024
for the recognition of the risk level of the driver in a short time,
Figure FDA0002756935210000025
respectively representing a low risk level, a medium risk level and a high risk level, P1、P2、P3Respectively representing the probabilities of identifying the driving behavior sequence to be tested as a low risk level, a medium risk level and a high risk level through a comprehensive model;
and secondly, calculating the number of the three risk levels in a long time according to the judgment result of the risk level of the driver in a short time period, wherein the calculation formula is as follows:
Figure FDA0002756935210000031
n1 is the number of sequences identified as low risk levels over a long period of time, n2 is the number of sequences identified as medium risk levels over a long period of time, n3 is the number of sequences identified as high risk levels over a long period of time, n is the total, xjUsed as a count;
thirdly, calculating the proportion of each risk grade according to the number of the three risk grades in a long time, wherein the calculation formula is as follows:
Figure FDA0002756935210000032
k1、k2、k3respectively representing the recognition proportions of a low risk grade, a medium risk grade and a high risk grade in a long time period;
fourthly, expressing the uncertainty of the random variable by using the information entropy, and calculating the formula as follows:
Figure FDA0002756935210000033
c is 1-H and represents the confidence coefficient of the risk level of the driver, and the higher the C value is, the higher the confidence coefficient is;
fifthly, under the condition that the confidence coefficient is C, the method for judging the risk level of the driver in a long time period is as follows:
Figure FDA0002756935210000034
Figure FDA0002756935210000035
indicating the driver's long-term risk level recognition result, I1、I2、I3Respectively representing a low risk level, a medium risk level and a high risk level.
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