CN111785023A - Vehicle collision risk early warning method and system - Google Patents
Vehicle collision risk early warning method and system Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
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- G08G1/00—Traffic control systems for road vehicles
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- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
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- G—PHYSICS
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- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
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- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
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- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096766—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission
- G08G1/096775—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission where the origin of the information is a central station
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
- G08G1/164—Centralised systems, e.g. external to vehicles
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- G—PHYSICS
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- G08G1/166—Anti-collision systems for active traffic, e.g. moving vehicles, pedestrians, bikes
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Abstract
The invention discloses a vehicle collision risk early warning method and a vehicle collision risk early warning system, which comprise the steps of constructing a collision risk evaluation sample according to a historical traffic accident sample, and dividing collision risk grades; performing subjective weighting and objective weighting on the collision indexes in the collision risk assessment sample according to a Delphi method and an entropy weight method; taking Nash equilibrium as a coordination target, combining a subjective weight and an objective weight of the collision index by adopting a game theory method, and weighting the collision index according to the obtained optimal weight; and calculating the collision risk of the multi-source traffic data of the vehicle to be predicted according to the weighted collision index, and comparing the obtained collision risk value with the collision risk evaluation sample to obtain the risk level. The method supports calculation of collision danger situation under severe traffic environment, can evaluate collision risk by fusing multiple factors related to driving safety under complex environment, and can provide more scientific and accurate driving decision for intelligent networked automobiles.
Description
Technical Field
The invention relates to the technical field of intelligent traffic, in particular to a vehicle collision risk early warning method and system.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
With the rapid increase of the quantity of automobiles, safety, energy conservation and environmental protection are the constant subjects for ensuring the healthy development of the automobile industry. The timely and reliable judgment of the collision risk of the vehicle is an important work for road traffic safety. Through investigation and research on traffic accidents, the existing collision early warning equipment still has a plurality of limitations in the driving process;
the inventor finds that most information sources in the identification of the dangerous situation of the automobile in the traditional Intelligent Transportation System (ITS) depend on vehicle-mounted equipment such as a vehicle-mounted camera and a laser radar, but the accuracy of the vehicle-mounted equipment for data acquisition is obviously influenced in severe weather such as rain, fog and snow; the judgment standard of the existing early warning system is single, the inter-vehicle distance and the Time To Collision (TTC) are mainly used as indexes, the dynamic change of information of a driver, a vehicle, a road and the environment is ignored, the flexibility and the maneuverability are lacked, the recognition rate is low, the information is not comprehensively mastered, the situation of false alarm is easy to occur, and therefore the trouble is caused to the driver.
Disclosure of Invention
In order to solve the problems, the invention provides a vehicle collision risk early warning method and a vehicle collision risk early warning system, which combine subjective weight evaluation and objective weight by using a game theory method, have both subjectivity and objectivity, support the calculation of collision risk situation under severe traffic environment, and can evaluate collision risk by fusing multiple factors related to driving safety under complex environment, thereby providing scientific and accurate driving decision for intelligent networked automobiles.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the invention provides a vehicle collision risk early warning method, which comprises the following steps:
constructing a collision risk evaluation sample according to the historical traffic accident sample, and dividing collision risk grades;
performing subjective weighting and objective weighting on the collision indexes in the collision risk assessment sample according to a Delphi method and an entropy weight method;
taking Nash equilibrium as a coordination target, combining a subjective weight and an objective weight of the collision index by adopting a game theory method, and weighting the collision index according to the obtained optimal weight;
and calculating the collision risk of the multi-source traffic data of the vehicle to be predicted according to the weighted collision index, and comparing the obtained collision risk value with the collision risk evaluation sample to obtain the collision risk grade.
In a second aspect, the present invention provides a vehicle collision risk early warning system, comprising:
the sample construction module is used for constructing a collision risk evaluation sample according to the historical traffic accident sample and dividing collision risk grades;
the weighting module is used for respectively carrying out subjective weighting and objective weighting on the collision indexes in the collision risk assessment sample according to a Delphi method and an entropy weight method;
the balance module is used for taking Nash balance as a coordination target, combining the subjective weight and the objective weight of the collision index by adopting a game theory method, and weighting the collision index according to the obtained optimal weight;
and the prediction module is used for calculating the collision risk of the multi-source traffic data of the vehicle to be predicted according to the weighted collision indexes, and comparing the obtained collision risk value with the collision risk evaluation sample to obtain a collision risk grade.
In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored on the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method of the first aspect is performed.
In a fourth aspect, the present invention provides a computer readable storage medium for storing computer instructions which, when executed by a processor, perform the method of the first aspect.
Compared with the prior art, the invention has the beneficial effects that:
the method is based on the existing vehicle networking communication technology, comprehensively considers the driving state of a driver, the driving strategy of a vehicle, the environment of a road and the weather condition, establishes a mathematical model for collision risk situation assessment on the basis, outputs a computation result from collision risk situation computation equipment in real time, displays the collision risk situation assessment result of a vehicle and a traffic participant in a graphical mode through result quantification, and sends alarm information by using an alarm terminal to remind the driver to immediately take measures or make an intelligent networked vehicle to carry out strategy planning again. The method and the system not only comprehensively consider main factors influencing the road traffic safety, but also calculate the collision risk situation of the own vehicle and the traffic participants in real time, ensure that a driver can reliably master the current driving safety state in real time, and improve the road traffic safety.
The invention not only can display the current driving environment information of the intelligent networked automobile in real time, but also can evaluate the situation of surrounding traffic participants, early warn collision risks in time, utilize graphical display and alarm terminal equipment matched with the graphical display to early warn the intelligent networked automobile or a driver in time, correct the current dangerous driving behaviors, and further adopt a new driving strategy, thereby improving the driving safety of the intelligent networked automobile.
The method scientifically combines the subjective weight evaluation and the objective weight by using a game theory method, has both subjectivity and objectivity, supports calculation of collision danger situation in severe traffic environment, can evaluate collision risk by fusing multiple factors related to driving safety in a complex environment, and can provide more scientific and accurate driving decision for the intelligent networked automobile.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
Fig. 1 is a flowchart of a vehicle collision risk early warning method provided in embodiment 1 of the present invention.
The specific implementation mode is as follows:
the invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example 1
As shown in fig. 1, the present embodiment provides a vehicle collision risk early warning method, including:
s1: constructing a collision risk evaluation sample according to the historical traffic accident sample, and dividing collision risk grades;
s2: performing subjective weighting and objective weighting on the collision indexes in the collision risk assessment sample according to a Delphi method and an entropy weight method;
s3: taking Nash equilibrium as a coordination target, combining a subjective weight and an objective weight of the collision index by adopting a game theory method, and weighting the collision index according to the obtained optimal weight;
s4: and calculating the collision risk of the multi-source traffic data of the vehicle to be predicted according to the weighted collision index, and comparing the obtained collision risk value with the collision risk evaluation sample to obtain the collision risk grade.
In step S1, the collision risk level is classified into a high risk level, a medium risk level and a low risk level according to the braking acceleration value of the driver in an emergency;
presetting a first threshold value and a second threshold value, and if the braking deceleration of the automobile is greater than the first threshold value, considering that the current driving risk degree is greater, namely the high-risk driving situation;
if the braking deceleration of the automobile is between the first threshold and the second threshold, the current driving is considered to be in a critical safety state, namely a dangerous driving situation;
if the braking deceleration of the automobile is smaller than a second threshold value, the current driving risk degree is considered to be small, namely the low-risk driving situation;
in the present embodiment, if the vehicle braking deceleration is greater than 5m/s2In time, the situation is a high-risk driving situation; if the braking deceleration of the vehicle is [ 2-5 ]]m/s2If so, the vehicle is in a middle risk driving situation; if the braking deceleration of the automobile is less than 2m/s2And in time, the running situation is low risk.
In step S2, the subjectively assigning includes:
(1) forming an expert group, and determining the number of the experts to be 10 according to the size of the weighted subject of the collision risk assessment index and the width of the related area;
(2) issues to be granted and related requirements are presented to all experts, and all background material and the like relating to the issues are attached;
(3) each expert puts forward own opinions on the car collision risk empowerment according to the received materials;
(4) gathering the first empowerment results of each expert, listing the empowerment results into a chart, comparing the chart and distributing the chart to each expert, so that the experts compare different opinions of the experts and others, and modify the opinions and judgment of the experts; or the opinions of each expert are collated or other experts with higher identities are requested to comment, and then the opinions are sent to each expert so as to modify the own opinions after being referred;
(5) collecting the modification opinions of all the experts, gathering the modification opinions, and distributing the modification opinions to each expert again so as to carry out second modification; collecting opinions and feeding back information for experts one by one is a main link of the Delphi method;
the collection of opinions and information feedback generally needs three or four rounds, when the feedback is carried out to the experts, only various opinions are given, but the specific names of the experts which issue various opinions are not described, and the process is repeated until each expert does not change own opinions any more.
(6) And comprehensively processing the empowerment result of the expert.
In step S2, the objective weighting includes:
the entropy weight method is to determine objective weight according to the index variability; if the information entropy E of a certain indexjThe smaller the variation degree of the index value, the larger the amount of information provided, the larger the effect that can be played in the comprehensive evaluation, and the larger the weight; in contrast, the information entropy E of a certain indexjThe larger the index value, the smaller the degree of variation of the index value, the smaller the amount of information to be provided, the smaller the effect to be exerted in the comprehensive evaluation, and the smaller the weight.
Entropy weight method weighting step:
(1) data normalization:
carrying out standardized processing on data of the evaluation indexes of the automobile collision danger situation; suppose that k evaluation indexes X are given1,X2,X3,…,XkWherein X isi＝{x1,x2,x3,…,xnSuppose that the value normalized for each evaluation index data is Y1,Y2,…,YkThen the normalized values are:
(2) solving the information entropy of each index:
information entropy e for determining evaluation index of automobile collision danger situation by using entropy weight methodj：
In the formula: k is 1/ln m is the adjustment coefficient.
(3) According to the calculation formula of the information entropy, calculating the evaluation index of each automobile collision danger situationTarget weight wj：
In the step S3, the subjective weighting method can reflect the will of each decision-making expert, but different experts have different tendencies for evaluation indexes of different automobile collision danger situations, which results in a relatively large randomness of evaluation results; the objective weighting method carries out scientific calculation according to the data change of the evaluation decision information table, has stronger mathematical theoretical basis, but has less information of the evaluation decision table of the automobile collision danger situation, and the calculation result may not completely conform to the reality; therefore, in order to fully combine the advantages of the subjective evaluation and the objective evaluation, a game theory method is introduced to combine the subjective evaluation and the objective evaluation, and a mathematic planning method is utilized to enable the conflict between the subjective evaluation and the objective evaluation to use Nash equilibrium as a coordination target, so that the consistency and the compromise between different decision makers are searched. The calculation process is as follows:
(1) vector linear combination: and if L methods are used for determining the combined weight of the automobile collision danger situations, then:
in the formula: u. ofkA constructed set of basis weight vectors;
the weight coefficients are linearly combined.
(2) Determining an optimized combination coefficient:
to determine the optimal combination coefficient, u and u can be minimizedkAnd (3) optimizing L weight combination coefficients in the formula to obtain the optimal weight value in u according to the dispersion, wherein an objective function is set as:
the linear system of equations for the first derivative condition optimized by the above equation is, according to the matrix differential properties:
(3) and (3) calculating combination weight:
to pair
After normalization processing, determining the weighting u of the automobile collision danger situation evaluation index combination weighting according to the following formula:
therefore, the combined weighting method based on the game theory scientifically combines the automobile collision danger situation index weights calculated by the Delphi method and the entropy weight method, so that more balanced comprehensive weights are obtained.
In step S4, the collection of the multi-source traffic data of the vehicle to be predicted is completed by a collection device, in this embodiment, the multi-source information collection device is composed of a vehicle-mounted camera, a GPS, a wireless communication technology, a roadside device, and an electronic control unit;
specifically, the vehicle-mounted camera is used for collecting weather visibility information;
GPS and vehicle-mounted communication technologies are mainly used for determining the position and driving state of a vehicle, including vehicle speed and acceleration;
the road side equipment is mainly used for acquiring the road slippery degree of the road section;
the electric control unit is used for collecting intelligent networking automobile information, including the running time of the automobile, whether faults exist, the current driving behavior and the like.
In this embodiment, the collected multi-source traffic data is subjected to quantization processing by a data processing module, and the data processing module comprises a communication module and a decoding module;
the digital signals are converted into digital signals through a communication module NMEA.0183, and the positions, speeds, weather visibility and road conditions of the vehicles to be predicted and the surrounding vehicles are obtained after the digital signals are decoded by a decoding module.
In the step S4, a TOPSIS approach to ideal solution sorting is adopted to solve, a positive ideal solution set is set as a solution with the most threat to driving safety in each evaluation index in the sample, a negative ideal solution set is a solution with the least threat to driving safety for each evaluation index, the collision risk of the multi-source traffic data of the vehicle to be predicted is calculated according to the weighted collision index, and a relative closeness evaluation safety grade is calculated, wherein the relative closeness is:
wherein the content of the first and second substances,ithe larger the value, the higher the risk of a collision of the vehicle.
The euclidean distances from each collision risk evaluation index to the positive ideal point are respectively.
And evaluating Euclidean distance from each collision risk evaluation index to the negative ideal point.
And similarly, calculating the collision risk of the collision risk evaluation sample by adopting an approximate ideal solution ordering TOPSIS method according to the weighted collision indexes to obtain a sample collision risk which is the closest to the collision risk of the vehicle to be predicted, wherein the risk grade of the sample is the collision risk grade of the vehicle to be predicted.
In the embodiment, the information, weather conditions, road environment and collision danger level of the intelligent networked vehicles and the traffic vehicles are displayed on a display screen; the alarm terminal equipment determines whether to alarm or not according to the collision danger level displayed on the display screen, the alarm terminal equipment is divided into different alarm levels according to the collision risk level, and when the collision danger level is high, the loudspeaker continuously rings until danger is relieved; when the collision danger level is middle, the loudspeaker intermittently emits the sound of dripping; when there is no collision danger temporarily, the loudspeaker does not sound;
in the present embodiment, the results of the two-vehicle crash situation simulation and calculated crash are given, as shown in table 1:
TABLE 1
In the table above, six cases correspond to six encountered situations. And evaluating the collision danger level by comparing the closeness value with the closeness degree of the decision sample according to the position relation between the intelligent internet automobile and the traffic vehicle and the information of weather and road environment.
In the existing ADAS, the distance between two vehicles and the TTC are mainly used to determine whether to avoid collision; when two vehicles run in the same lane, if the weather is bad, the visibility is low, the road surface is wet and slippery, and the existing collision early warning system cannot be applied to a dynamic and variable environment; in severe weather, if the safe distance between two vehicles still adopts the safe distance obtained in good weather, the safe distance is obviously inaccurate;
taking examples 5 and 6 in the above table as examples, the vehicle information of examples 5 and 6 is consistent, and if the distance between two vehicles is taken as a judgment basis, the conventional collision danger situation early warning system will regard the vehicle information as a medium level; in practice, however, in the case of low weather visibility and very slippery roads, it is still very dangerous to change lanes at higher speeds in order to avoid collisions with the preceding vehicles; at the moment, the driver can neglect the severity of the situation due to the medium level of the dangerous situation misreported by the common collision early warning system, the collision dangerous situation is evaluated to be high level by the algorithm under the environment, and the driver pays enough attention by continuous buzzing, so that the method can evaluate the collision risk by fusing multiple factors related to driving safety under the complex environment, has higher scientificity and accuracy, and can provide more accurate decision for the intelligent internet automobile anti-collision system.
Example 2
The embodiment provides a vehicle collision risk early warning system, includes:
the sample construction module is used for constructing a collision risk evaluation sample according to the historical traffic accident sample and dividing collision risk grades;
the weighting module is used for respectively carrying out subjective weighting and objective weighting on the collision indexes in the collision risk assessment sample according to a Delphi method and an entropy weight method;
the balance module is used for taking Nash balance as a coordination target, combining the subjective weight and the objective weight of the collision index by adopting a game theory method, and weighting the collision index according to the obtained optimal weight;
and the prediction module is used for calculating the collision risk of the multi-source traffic data of the vehicle to be predicted according to the weighted collision indexes, and comparing the obtained collision risk value with the collision risk evaluation sample to obtain the risk level.
It should be noted here that the above modules correspond to steps S1 to S4 in embodiment 1, and the above modules are the same as the examples and application scenarios realized by the corresponding steps, but are not limited to the disclosure of embodiment 1. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer-executable instructions.
In further embodiments, there is also provided:
an electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions when executed by the processor performing the method of embodiment 1. For brevity, no further description is provided herein.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
A computer readable storage medium storing computer instructions which, when executed by a processor, perform the method described in embodiment 1.
The method in embodiment 1 may be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. A vehicle collision risk early warning method is characterized by comprising the following steps:
constructing a collision risk evaluation sample according to the historical traffic accident sample, and dividing collision risk grades;
performing subjective weighting and objective weighting on the collision indexes in the collision risk assessment sample according to a Delphi method and an entropy weight method;
taking Nash equilibrium as a coordination target, combining a subjective weight and an objective weight of the collision index by adopting a game theory method, and weighting the collision index according to the obtained optimal weight;
and calculating the collision risk of the multi-source traffic data of the vehicle to be predicted according to the weighted collision index, and comparing the obtained collision risk value with the collision risk evaluation sample to obtain the collision risk grade.
2. A vehicle collision risk early warning method as claimed in claim 1, wherein the collision risk level defines a risk level according to the braking acceleration value of the driver in an emergency, and the collision risk level is divided into a high risk, a medium risk and a low risk.
3. The vehicle collision risk early warning method according to claim 2, wherein a first threshold value and a second threshold value are preset, and if the braking acceleration value is greater than the first threshold value, the vehicle collision risk early warning method is judged to be high risk;
if the braking acceleration value is between the first threshold value and the second threshold value, judging the braking acceleration value to be a medium risk;
and if the braking acceleration value is smaller than the second threshold value, judging that the risk is low.
4. The vehicle collision risk early warning method according to claim 1, wherein the objective weighting includes:
carrying out standardization processing on the collision indexes;
solving the information entropy of each collision index by adopting an entropy weight method;
and calculating the weight of each collision index according to the information entropy.
5. The vehicle collision risk early warning method according to claim 1, wherein the combining the subjective weight and the objective weight of the collision index by using the game theory method comprises:
setting L collision index weight combinations, and optimizing weight combination coefficients by taking the deviation of the minimum combination weight and the basic weight as a target function;
and re-weighting the collision index weight combination according to the optimal weight combination coefficient.
6. The vehicle collision risk early warning method according to claim 1, characterized in that a TOPSIS method is adopted, a positive ideal solution set is set as a solution having the most threat to the collision risk in each collision index, a negative ideal solution set is a solution having the least threat to the collision risk in each collision index, the collision risk of the multi-source traffic data of the vehicle to be predicted is calculated according to the weighted collision index, and the relative closeness evaluation risk grade is calculated.
7. The vehicle collision risk early warning method according to claim 1, wherein the collection of the multi-source traffic data of the vehicle to be predicted is completed by collection equipment, and the collection equipment comprises a vehicle-mounted camera, a GPS, road side equipment and an electronic control unit;
the vehicle-mounted camera collects weather visibility information;
the GPS determines the position of the vehicle, the driving speed and the acceleration;
the road side equipment acquires the road slippery degree of a road section;
the running time, the existence of faults and the current driving behavior of the electric control unit vehicle.
8. A vehicle collision risk early warning system, comprising:
the sample construction module is used for constructing a collision risk evaluation sample according to the historical traffic accident sample and dividing collision risk grades;
the weighting module is used for respectively carrying out subjective weighting and objective weighting on the collision indexes in the collision risk assessment sample according to a Delphi method and an entropy weight method;
the balance module is used for taking Nash balance as a coordination target, combining the subjective weight and the objective weight of the collision index by adopting a game theory method, and weighting the collision index according to the obtained optimal weight;
and the prediction module is used for calculating the collision risk of the multi-source traffic data of the vehicle to be predicted according to the weighted collision indexes, and comparing the obtained collision risk value with the collision risk evaluation sample to obtain a collision risk grade.
9. An electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions when executed by the processor performing the method of any of claims 1-7.
10. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the method of any one of claims 1 to 7.
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