CN112991685A - Traffic system risk assessment and early warning method considering fatigue state influence of driver - Google Patents
Traffic system risk assessment and early warning method considering fatigue state influence of driver Download PDFInfo
- Publication number
- CN112991685A CN112991685A CN202110185258.3A CN202110185258A CN112991685A CN 112991685 A CN112991685 A CN 112991685A CN 202110185258 A CN202110185258 A CN 202110185258A CN 112991685 A CN112991685 A CN 112991685A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- driver
- data
- vehicles
- state score
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/06—Alarms for ensuring the safety of persons indicating a condition of sleep, e.g. anti-dozing alarms
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- 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
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Emergency Management (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a traffic system risk assessment and early warning method considering the influence of fatigue states of drivers, which comprises the following steps: s1, collecting vehicle running data, and calculating to obtain a vehicle state score; s2, collecting eyelid closing data of the driver, and calculating to obtain a fatigue state score of the driver; s3, collecting regional environment data, and calculating to obtain a regional running state score; and S4, performing normalization processing and weighting on the vehicle state score, the driver fatigue state score and the region running state score, calculating to obtain traffic system risk information, and generating and outputting early warning information according to the traffic system risk information. The invention fuses the fatigue state of the driver with the vehicle state, the weather, the wind power, the other vehicle running states and other regional running states, thereby realizing the evaluation and early warning of the regional traffic system running risk considering the influence of the fatigue state of the driver.
Description
Technical Field
The invention relates to the field of traffic system operation risks, in particular to a traffic system risk assessment and early warning method considering the influence of fatigue states of drivers.
Background
A large number of traffic accident statistics data show that people are the most important factors influencing the operation risk of a traffic system. In the analysis of a large number of traffic system operation risks, the vehicle operation of a driver is usually considered, and the operation state of a driving area and the fatigue state of the driver are ignored, so that the traffic system operation risk assessment is not accurate and comprehensive, and the actual application efficiency is low.
Disclosure of Invention
The invention aims to provide a traffic system risk assessment and early warning method considering the influence of the fatigue state of a driver, and aims to fuse the fatigue state of the driver with the vehicle state, the weather, the wind power, the running conditions of other vehicles and other regional running conditions, so that the evaluation and early warning of the running risk of the regional traffic system considering the influence of the fatigue state of the driver are realized.
In order to solve the technical problems, the technical scheme of the invention is as follows: the traffic system risk assessment and early warning method considering the influence of the fatigue state of a driver comprises the following steps:
s1, collecting vehicle running data, and calculating to obtain a vehicle state score, wherein the vehicle running data at least comprises the real-time speed and acceleration of the vehicle and the angular acceleration of a steering wheel;
s2, acquiring eyelid closure data of the driver, and calculating to obtain a fatigue state score of the driver, wherein the eyelid closure data of the driver at least comprise the percentage of the time of the eye closure degree exceeding a preset closure degree threshold value in unit time to the total time;
s3, collecting regional environment data, and calculating to obtain a regional running state score, wherein the regional environment data at least comprise visibility, precipitation and crosswind data of regional environment;
and S4, performing normalization processing and weighting on the vehicle state score, the driver fatigue state score and the region running state score, calculating to obtain traffic system risk information, and generating and outputting early warning information according to the traffic system risk information.
Further, the S1 specifically includes:
s1.1, acquiring vehicle running data through a vehicle-mounted sensor;
s1.2, carrying out normalization processing on vehicle running data;
s1.3, weighting the vehicle driving data after the normalization processing by adopting an integrated weighting method based on an integrated analytic hierarchy process and an entropy weight method;
and S1.4, calculating to obtain a vehicle state score according to the weighted vehicle running data.
Further, the S2 specifically includes:
s2.1, acquiring eyelid closing data of a driver through a driver state monitor, and calculating to obtain PERCLOS data;
s2.2, carrying out normalization processing on the PERCLOS data, and calculating to obtain the fatigue state score of the driver.
Further, the S3 specifically includes:
s3.1, acquiring visibility, precipitation and crosswind data of the regional environment through a vehicle-mounted weather information collector;
s3.2, acquiring the transverse distance between the driving vehicle and other vehicles, the distance between the driving vehicle and other vehicles in front and the relative speed between the driving vehicle and other vehicles in front through a laser radar sensor;
s3.3, performing normalization processing on the regional environment data, the transverse distance between the driving vehicle and other vehicles, the distance between the driving vehicle and other vehicles in front and the relative speed between the driving vehicle and other vehicles in front;
s3.4, weighting the normalized regional environment data, the transverse distance between the driving vehicle and other vehicles, the distance between the driving vehicle and other vehicles in front and the relative speed between the driving vehicle and other vehicles in front by adopting an integrated weighting method based on an integrated analytic hierarchy process and an entropy weight method;
and S3.5, calculating the score of the regional running state according to the weighted regional environment data, the transverse distance between the driving vehicle and other vehicles, the distance between the driving vehicle and other vehicles in front and the relative speed between the driving vehicle and other vehicles in front.
Further, the S4 specifically includes:
s4.1, carrying out normalization processing on the vehicle state score, the driver fatigue state score and the region running state score;
s4.2, assigning weights to the vehicle state score, the driver fatigue state score and the region running state score after the normalization processing;
s4.3, calculating to obtain traffic system risk information according to the weighted vehicle state score, the fatigue state score of the driver and the regional running state score, and building a traffic system running risk evaluation model;
s4.4, determining a threshold value according to the traffic system operation risk evaluation model and multiple groups of historical accident data, wherein the historical accident data are multiple groups of traffic system risk information when traffic risk accidents occur;
and S4.5, comparing the risk information of the traffic system with a threshold value, generating corresponding early warning information and outputting the early warning information.
The invention also includes a traffic system risk assessment and early warning system considering the influence of the fatigue state of the driver, comprising:
the vehicle state sensing module is used for acquiring vehicle running data and calculating to obtain a vehicle state score;
the driver fatigue state sensing module is used for collecting eyelid closure data of a driver and calculating to obtain a driver fatigue state score;
the regional running state sensing module is used for acquiring regional environment data and calculating a regional running state score;
and the risk evaluation and early warning module is used for carrying out normalization processing and weighting on the vehicle state score, the driver fatigue state score and the region running state score, calculating to obtain traffic system risk information, and generating and outputting early warning information according to the traffic system risk information.
Further, the specific working mode of the vehicle state sensing module is as follows:
collecting vehicle driving data through a vehicle-mounted sensor;
carrying out normalization processing on the vehicle running data;
weighting the vehicle driving data after the normalization processing by adopting an integrated weighting method based on an integrated analytic hierarchy process and an entropy weight method;
and calculating to obtain the vehicle state score according to the weighted vehicle running data.
Further, the specific working mode of the driver fatigue state perception module is as follows:
acquiring eyelid closure data of a driver through a driver state monitor, and calculating to obtain PERCLOS data;
and carrying out normalization processing on the PERCLOS data, and calculating to obtain the fatigue state score of the driver.
Further, the specific working mode of the regional operation state sensing module is as follows:
the visibility, the precipitation and the crosswind data of the regional environment are acquired through a vehicle-mounted weather information collector;
acquiring the transverse distance between the driving vehicle and other vehicles, the distance between the driving vehicle and other vehicles in front and the relative speed between the driving vehicle and other vehicles in front through a laser radar sensor;
normalizing the regional environment data, the transverse distance between the driving vehicle and other vehicles, the distance between the driving vehicle and other vehicles in front and the relative speed between the driving vehicle and other vehicles in front;
weighting the normalized regional environment data, the transverse distance between the driving vehicle and other vehicles, the distance between the driving vehicle and other vehicles in front and the relative speed between the driving vehicle and other vehicles in front by adopting an integrated weighting method based on an integrated analytic hierarchy process and an entropy weight method;
and calculating the score of the regional running state according to the weighted regional environment data, the transverse distance between the driving vehicle and other vehicles, the distance between the driving vehicle and other vehicles in front and the relative speed between the driving vehicle and other vehicles in front.
Further, the specific working mode of the risk assessment and early warning module is as follows:
carrying out normalization processing on the vehicle state score, the driver fatigue state score and the region running state score;
assigning weights to the vehicle state score, the driver fatigue state score and the region running state score after the normalization processing;
calculating to obtain traffic system risk information according to the weighted vehicle state score, the fatigue state score of the driver and the regional operation state score, and building a traffic system operation risk evaluation model;
determining a threshold value according to a traffic system operation risk evaluation model and multiple groups of historical accident data, wherein the historical accident data are multiple groups of traffic system risk information when traffic risk accidents occur;
and comparing the risk information of the traffic system with a threshold value, generating corresponding early warning information and outputting the early warning information.
Compared with the prior art, the invention has the beneficial effects that:
the invention integrates the fatigue state of the driver with the operation risk evaluation of the traditional traffic system, considers the common dangerous driving state of fatigue driving, and can solve the operation risk problem caused by the fatigue of the driver in the regional traffic system, compared with the prior art, the invention has the following advantages: (1) the fatigue state of a driver is considered to enter the operation risk of a traffic system; (2) and (4) evaluating the running risk of the traffic system in the running state of the specific area by considering the fatigue state of the driver, the vehicle state and the running state of the area.
Drawings
FIG. 1 is a flow chart of a method of an embodiment of the present invention;
FIG. 2 is a system block diagram of an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1 and 2, the system of the invention comprises a vehicle state sensing module, a driver fatigue state sensing module, a regional operation state sensing module and a risk assessment and early warning module; the specific working process of the vehicle state sensing module comprises the following steps:
s1.1, collecting vehicle running data in real time
The vehicle driving data collected in the invention comprises: vehicle real-time position, velocity, acceleration, steering wheel angular acceleration data.
Vehicle real time position PiThe vehicle GPS positioning is obtained, and the vehicle GPS positioning comprises the longitude and the latitude of the position of the vehicle, and the positioning precision is 1 meter.
Speed StI.e. the vehicle speed at the present moment in kilometers per hour.
Acceleration S of vehicle2 tThe acquisition frequency is 1 time per 1 second for the real-time acceleration of the vehicle.
Angular acceleration W of steering wheel2 tI.e. the change of angular velocity of the steering wheel in unit time, the acquisition frequency is 1 second and 1 time.
S1.2, vehicle driving data normalization
For the vehicle state data collected by module a: real-time vehicle speed StReal-time acceleration S2 tAngular acceleration W of steering wheel2 tAnd carrying out normalization processing, wherein the formula is as follows:
wherein X' is normalized data, X is original data, Xmax、XminRespectively, the maximum and minimum values of the original data set. Obtaining the normalized real-time vehicle speed S'tReal-time acceleration S2′tAngular acceleration W of steering wheel2′t。
And S1.3, weighting each normalized index.
The invention adopts an integrated weighting method to carry out weight analysis on the basis of an analytic hierarchy process and an entropy weight method.
1. Determining weights by analytic hierarchy process
The invention adopts a 1-9 scale method in an analytic hierarchy process to define a judgment matrix: a. theI＝(aij)n×nAnd calculating a weight coefficient by adopting a feature vector method, then carrying out consistency test, and determining the correctness of weight distribution through a consistency index CI. When in use
If the matrix is uniform (RI is an average random uniformity index), the uniformity of the judgment matrix may be considered to be within an acceptable range, otherwise, the matrix is revised again.
2. Determination of weights by entropy weight method
The entropy weight method judges the weight according to the discrete degree of the index, and sets a decision matrix as xB＝(xij)m×nWherein x isijThe j-th evaluation index in the i-th evaluation range is the evaluation index m which is 3.
First, the index x is calculatedjProbability at the ith evaluation interval:
and then, calculating the information entropy:
finally, obtaining the weight of the entropy weight:
wherein 1-hjIs an index xjCoefficient of variation of (a).
3. Integrated empowerment method
The final weight analysis is carried out on the basis of integrating the analytic hierarchy process and the entropy weight method, and the method has the advantages of the analytic hierarchy process and the entropy weight method. Determining index weight as a by subjective weighting method of analytic hierarchy processjWith an index weight of q determined by the entropy weight methodjGenerating final weight of omega using integrated weighting method of synthesis normalizationj。
The calculation formula is as follows:
s1.4, calculating the comprehensive score of the vehicle state, wherein the calculation formula is as follows:
wherein, x'jValue is S't，S2′t，W2′t；ωjFor which the corresponding weight is.
The specific working process of the driver fatigue state perception module comprises the following steps:
s2.1, PERCLOS data calculation
Collecting eyelid closing data of a driver through DSM (driver State monitor) to calculate PERCLOStThe data refers to the percentage of the total time that the degree of eye closure exceeds a certain closure value (70 percent and 80 percent) in a unit time, the invention selects P70, and refers to the percentage of the time that the eye closure degree exceeds a certain closure value (70 percent and 80 percent) in a certain time, and the percentage of the time that the eye closure degree occupies in a certain time is counted.
S2.2, PERCLOS data normalization
PERCLOS FOR DRIVER FOR DSM COLLECTIONtThe data was normalized to obtain ScoreperclosThe process is the same as step a 3.
The specific working process of the regional operation state sensing module comprises the following steps:
and S3.1, sensing the weather condition. Visibility, precipitation and crosswind data in the area are acquired through a vehicle-mounted weather information collector.
Visibility DiThe maximum distance in meters that a person with normal vision can recognize a target object from the background.
Precipitation amount CiThe unit of the precipitation of the area where the vehicle is located is millimeter.
Crosswind FiThe unit of the crosswind in the invention is meter/second, and the crosswind meets the vehicle in the driving process in the area where the vehicle is located.
And S3.2, sensing the motion state of other vehicles in the domain. Acquiring the distance between the vehicle and the front vehicle and the transverse distance between the vehicle and other vehicles through sensors such as a laser radar and the like; and the relative speed of the vehicle and the preceding vehicle.
Front vehicle distance LiI.e. the distance of the vehicle head from the vehicle tail in front, in meters.
Transverse distance H between vehicle and other vehiclesiIn the present invention, the lateral distance when the vehicle runs side by side with other running vehicles is 0 when the distance is more than 140 cm; when the distance between the two vehicles is more than 0 cm and less than or equal to 140 cm, the distance is recorded as a unit of cm.
Relative speed V between vehicle and front vehicleiIn kilometers per hour.
S3.3, visibility D obtained for the module CiPrecipitation amount CiCrosswind FiFront vehicle distance LiTransverse distance HiRelative speed V between vehicle and front vehicleiThe data is normalized in the same manner as in step a 2. Obtaining the normalized visibility D'iAnd precipitation amount of C'iAnd transverse wind F'iAnd a vehicle distance L 'ahead'iTransverse spacing H'iAnd relative speed V 'between vehicle and front vehicle'i
And S3.4, weighting each normalized index, wherein the processing process is the same as that in the step A3.
S3.5, calculating the comprehensive score of the running state of the region according to the following calculation formula
Wherein, x'jValue is D'i，C′i，F′i，L′i，H′i，V′i；ωjFor which the corresponding weight is.
The specific working process of the risk assessment and early warning module comprises the following steps:
and S4.1, normalizing the vehicle state score, the driver fatigue state score and the region running state score. Obtaining a normalized vehicle state Score of'carDriver fatigue status Score of'perclosRegion running State Score'areaThe process is the same as step a 3.
S4.2, according to the conventional research, the normalized vehicle state Score is'carDriver fatigue status Score of'perclosRegion running State Score'areaGiving weight to obtain Score'carCorresponding weight ωcar，Score′perclosCorresponding weight ωperclos，Score′areaCorresponding weight ωarea。
S4.3, calculating the running risk of the traffic system, wherein the formula is as follows:
wherein R is the traffic system operational risk, Scorej' is normalized State score, ωjAre the corresponding weights.
And S4.4, inputting a large amount of historical accident data to obtain a traffic system risk early warning threshold value.
Real-time vehicle speed S in a large amount of historical accident datatReal-time acceleration S2 tAngular acceleration W of steering wheel2 tPERCLOS of drivertVisibility DiPrecipitation amount CiCrosswind FiFront vehicle distance LiTransverse distance HiRelative speed V between vehicle and front vehicleiThe input is sent to step D3 to calculate the risk R of the traffic system running for each accidentiUsing maximum likelihood estimation of RiAnd fitting to obtain a longitudinal intercept k of the fitting function, wherein k is the risk threshold.
And S4.5, if the traffic system operation risk is larger than a threshold k, sending the early warning information that the vehicle collision risk exists in the area and the vehicle is required to be driven carefully to a road side unit through the vehicle-mounted unit, and distributing the vehicle to all vehicles in the area through the road side unit.
The above embodiments are only used for illustrating the design idea and features of the present invention, and the purpose of the present invention is to enable those skilled in the art to understand the content of the present invention and implement the present invention accordingly, and the protection scope of the present invention is not limited to the above embodiments. Therefore, all equivalent changes and modifications made in accordance with the principles and concepts disclosed herein are intended to be included within the scope of the present invention.
Claims (10)
1. The traffic system risk assessment and early warning method considering the influence of the fatigue state of a driver is characterized by comprising the following steps of:
s1, collecting vehicle running data, and calculating to obtain a vehicle state score, wherein the vehicle running data at least comprises the real-time speed and acceleration of the vehicle and the angular acceleration of a steering wheel;
s2, acquiring eyelid closure data of the driver, and calculating to obtain a fatigue state score of the driver, wherein the eyelid closure data of the driver at least comprise the percentage of the time of the eye closure degree exceeding a preset closure degree threshold value in unit time to the total time;
s3, collecting regional environment data, and calculating to obtain a regional running state score, wherein the regional environment data at least comprise visibility, precipitation and crosswind data of regional environment;
and S4, performing normalization processing and weighting on the vehicle state score, the driver fatigue state score and the region running state score, calculating to obtain traffic system risk information, and generating and outputting early warning information according to the traffic system risk information.
2. The method for risk assessment and early warning of a transportation system considering the influence of fatigue state of a driver as claimed in claim 1, wherein said S1 is specifically:
s1.1, acquiring vehicle running data through a vehicle-mounted sensor;
s1.2, carrying out normalization processing on vehicle running data;
s1.3, weighting the vehicle driving data after the normalization processing by adopting an integrated weighting method based on an integrated analytic hierarchy process and an entropy weight method;
and S1.4, calculating to obtain a vehicle state score according to the weighted vehicle running data.
3. The method for risk assessment and early warning of a transportation system considering the influence of fatigue state of a driver as claimed in claim 1, wherein said S2 is specifically:
s2.1, acquiring eyelid closing data of a driver through a driver state monitor, and calculating to obtain PERCLOS data;
s2.2, carrying out normalization processing on the PERCLOS data, and calculating to obtain the fatigue state score of the driver.
4. The method for risk assessment and early warning of a transportation system considering the influence of fatigue state of a driver as claimed in claim 1, wherein said S3 is specifically:
s3.1, acquiring visibility, precipitation and crosswind data of the regional environment through a vehicle-mounted weather information collector;
s3.2, acquiring the transverse distance between the driving vehicle and other vehicles, the distance between the driving vehicle and other vehicles in front and the relative speed between the driving vehicle and other vehicles in front through a laser radar sensor;
s3.3, performing normalization processing on the regional environment data, the transverse distance between the driving vehicle and other vehicles, the distance between the driving vehicle and other vehicles in front and the relative speed between the driving vehicle and other vehicles in front;
s3.4, weighting the normalized regional environment data, the transverse distance between the driving vehicle and other vehicles, the distance between the driving vehicle and other vehicles in front and the relative speed between the driving vehicle and other vehicles in front by adopting an integrated weighting method based on an integrated analytic hierarchy process and an entropy weight method;
and S3.5, calculating the score of the regional running state according to the weighted regional environment data, the transverse distance between the driving vehicle and other vehicles, the distance between the driving vehicle and other vehicles in front and the relative speed between the driving vehicle and other vehicles in front.
5. The method for risk assessment and early warning of a transportation system considering the influence of fatigue state of a driver as claimed in claim 1, wherein said S4 is specifically:
s4.1, carrying out normalization processing on the vehicle state score, the driver fatigue state score and the region running state score;
s4.2, assigning weights to the vehicle state score, the driver fatigue state score and the region running state score after the normalization processing;
s4.3, calculating to obtain traffic system risk information according to the weighted vehicle state score, the fatigue state score of the driver and the regional running state score, and building a traffic system running risk evaluation model;
s4.4, determining a threshold value according to the traffic system operation risk evaluation model and multiple groups of historical accident data, wherein the historical accident data are multiple groups of traffic system risk information when traffic risk accidents occur;
and S4.5, comparing the risk information of the traffic system with a threshold value, generating corresponding early warning information and outputting the early warning information.
6. A system for utilizing the method of claim 1 for risk assessment and warning of a transportation system that considers the effects of driver fatigue, comprising:
the vehicle state sensing module is used for acquiring vehicle running data and calculating to obtain a vehicle state score;
the driver fatigue state sensing module is used for collecting eyelid closure data of a driver and calculating to obtain a driver fatigue state score;
the regional running state sensing module is used for acquiring regional environment data and calculating a regional running state score;
and the risk evaluation and early warning module is used for carrying out normalization processing and weighting on the vehicle state score, the driver fatigue state score and the region running state score, calculating to obtain traffic system risk information, and generating and outputting early warning information according to the traffic system risk information.
7. The system of claim 6, wherein the vehicle status sensing module operates in a specific manner:
collecting vehicle driving data through a vehicle-mounted sensor;
carrying out normalization processing on the vehicle running data;
weighting the vehicle driving data after the normalization processing by adopting an integrated weighting method based on an integrated analytic hierarchy process and an entropy weight method;
and calculating to obtain the vehicle state score according to the weighted vehicle running data.
8. The system according to claim 6, wherein the driver fatigue state perception module works in a specific way:
acquiring eyelid closure data of a driver through a driver state monitor, and calculating to obtain PERCLOS data; and carrying out normalization processing on the PERCLOS data, and calculating to obtain the fatigue state score of the driver.
9. The system according to claim 6, wherein the operation mode of the regional operation status sensing module is as follows:
the visibility, the precipitation and the crosswind data of the regional environment are acquired through a vehicle-mounted weather information collector;
acquiring the transverse distance between the driving vehicle and other vehicles, the distance between the driving vehicle and other vehicles in front and the relative speed between the driving vehicle and other vehicles in front through a laser radar sensor;
normalizing the regional environment data, the transverse distance between the driving vehicle and other vehicles, the distance between the driving vehicle and other vehicles in front and the relative speed between the driving vehicle and other vehicles in front;
weighting the normalized regional environment data, the transverse distance between the driving vehicle and other vehicles, the distance between the driving vehicle and other vehicles in front and the relative speed between the driving vehicle and other vehicles in front by adopting an integrated weighting method based on an integrated analytic hierarchy process and an entropy weight method;
and calculating the score of the regional running state according to the weighted regional environment data, the transverse distance between the driving vehicle and other vehicles, the distance between the driving vehicle and other vehicles in front and the relative speed between the driving vehicle and other vehicles in front.
10. The system of claim 6, wherein the risk assessment and pre-warning module operates in a specific manner:
carrying out normalization processing on the vehicle state score, the driver fatigue state score and the region running state score;
assigning weights to the vehicle state score, the driver fatigue state score and the region running state score after the normalization processing;
calculating to obtain traffic system risk information according to the weighted vehicle state score, the fatigue state score of the driver and the regional operation state score, and building a traffic system operation risk evaluation model;
determining a threshold value according to a traffic system operation risk evaluation model and multiple groups of historical accident data, wherein the historical accident data are multiple groups of traffic system risk information when traffic risk accidents occur;
and comparing the risk information of the traffic system with a threshold value, generating corresponding early warning information and outputting the early warning information.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110185258.3A CN112991685A (en) | 2021-02-10 | 2021-02-10 | Traffic system risk assessment and early warning method considering fatigue state influence of driver |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110185258.3A CN112991685A (en) | 2021-02-10 | 2021-02-10 | Traffic system risk assessment and early warning method considering fatigue state influence of driver |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112991685A true CN112991685A (en) | 2021-06-18 |
Family
ID=76393089
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110185258.3A Pending CN112991685A (en) | 2021-02-10 | 2021-02-10 | Traffic system risk assessment and early warning method considering fatigue state influence of driver |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112991685A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113624514A (en) * | 2021-08-17 | 2021-11-09 | 中国汽车技术研究中心有限公司 | Test method, system, electronic device and medium for driver state monitoring product |
| CN113822574A (en) * | 2021-09-23 | 2021-12-21 | 佳都科技集团股份有限公司 | Vehicle risk assessment method and device based on analytic hierarchy process and entropy weight method |
| CN115782905A (en) * | 2023-01-31 | 2023-03-14 | 北京航空航天大学 | Automatic driving vehicle driving safety degree quantification system |
| CN116453345A (en) * | 2023-06-13 | 2023-07-18 | 吉林大学 | Bus driving safety early warning method and system based on driving risk feedback |
| CN116579619A (en) * | 2023-07-13 | 2023-08-11 | 万联易达物流科技有限公司 | Air control method and system for freight bill |
| CN118247773A (en) * | 2024-03-21 | 2024-06-25 | 北京白龙马云行科技有限公司 | Risk driver identification method and system based on different SP driver behaviors |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2015219736A (en) * | 2014-05-19 | 2015-12-07 | 東芝アルパイン・オートモティブテクノロジー株式会社 | Drive support device |
| CN106530829A (en) * | 2016-12-07 | 2017-03-22 | 深圳市元征科技股份有限公司 | Driver state broadcasting method and device |
| CN106816020A (en) * | 2015-12-02 | 2017-06-09 | 大陆汽车投资（上海）有限公司 | Traffic accident information processing method based on data analysis |
| CN107618512A (en) * | 2017-08-23 | 2018-01-23 | 清华大学 | Driving behavior safe evaluation method based on people's car environment multi-data source |
| CN108230616A (en) * | 2018-02-02 | 2018-06-29 | 辽宁友邦网络科技有限公司 | A kind of dangerous driving identification alarming method and system |
| CN108446600A (en) * | 2018-02-27 | 2018-08-24 | 上海汽车集团股份有限公司 | A kind of vehicle driver's fatigue monitoring early warning system and method |
| CN110316198A (en) * | 2019-07-29 | 2019-10-11 | 竹旭 | A kind of safe-guard system and operation method for highway speed-raising |
| CN110807930A (en) * | 2019-11-07 | 2020-02-18 | 中国联合网络通信集团有限公司 | Dangerous vehicle early warning method and device |
| CN111062240A (en) * | 2019-10-16 | 2020-04-24 | 中国平安财产保险股份有限公司 | Method and device for monitoring automobile driving safety, computer equipment and storage medium |
| KR20200095383A (en) * | 2019-01-31 | 2020-08-10 | 주식회사 스트라드비젼 | Method and device for alerting abnormal driver situation detected by using humans' status recognition via v2v connection |
| CN112216066A (en) * | 2020-08-25 | 2021-01-12 | 南京市德赛西威汽车电子有限公司 | Fatigue driving traffic guiding method based on V2X |
-
2021
- 2021-02-10 CN CN202110185258.3A patent/CN112991685A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2015219736A (en) * | 2014-05-19 | 2015-12-07 | 東芝アルパイン・オートモティブテクノロジー株式会社 | Drive support device |
| CN106816020A (en) * | 2015-12-02 | 2017-06-09 | 大陆汽车投资（上海）有限公司 | Traffic accident information processing method based on data analysis |
| CN106530829A (en) * | 2016-12-07 | 2017-03-22 | 深圳市元征科技股份有限公司 | Driver state broadcasting method and device |
| CN107618512A (en) * | 2017-08-23 | 2018-01-23 | 清华大学 | Driving behavior safe evaluation method based on people's car environment multi-data source |
| CN108230616A (en) * | 2018-02-02 | 2018-06-29 | 辽宁友邦网络科技有限公司 | A kind of dangerous driving identification alarming method and system |
| CN108446600A (en) * | 2018-02-27 | 2018-08-24 | 上海汽车集团股份有限公司 | A kind of vehicle driver's fatigue monitoring early warning system and method |
| KR20200095383A (en) * | 2019-01-31 | 2020-08-10 | 주식회사 스트라드비젼 | Method and device for alerting abnormal driver situation detected by using humans' status recognition via v2v connection |
| CN110316198A (en) * | 2019-07-29 | 2019-10-11 | 竹旭 | A kind of safe-guard system and operation method for highway speed-raising |
| CN111062240A (en) * | 2019-10-16 | 2020-04-24 | 中国平安财产保险股份有限公司 | Method and device for monitoring automobile driving safety, computer equipment and storage medium |
| CN110807930A (en) * | 2019-11-07 | 2020-02-18 | 中国联合网络通信集团有限公司 | Dangerous vehicle early warning method and device |
| CN112216066A (en) * | 2020-08-25 | 2021-01-12 | 南京市德赛西威汽车电子有限公司 | Fatigue driving traffic guiding method based on V2X |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113624514A (en) * | 2021-08-17 | 2021-11-09 | 中国汽车技术研究中心有限公司 | Test method, system, electronic device and medium for driver state monitoring product |
| CN113822574A (en) * | 2021-09-23 | 2021-12-21 | 佳都科技集团股份有限公司 | Vehicle risk assessment method and device based on analytic hierarchy process and entropy weight method |
| CN115782905A (en) * | 2023-01-31 | 2023-03-14 | 北京航空航天大学 | Automatic driving vehicle driving safety degree quantification system |
| CN116453345A (en) * | 2023-06-13 | 2023-07-18 | 吉林大学 | Bus driving safety early warning method and system based on driving risk feedback |
| CN116453345B (en) * | 2023-06-13 | 2023-08-18 | 吉林大学 | Bus driving safety early warning method and system based on driving risk feedback |
| CN116579619A (en) * | 2023-07-13 | 2023-08-11 | 万联易达物流科技有限公司 | Air control method and system for freight bill |
| CN116579619B (en) * | 2023-07-13 | 2023-09-22 | 万联易达物流科技有限公司 | Air control method and system for freight bill |
| CN118247773A (en) * | 2024-03-21 | 2024-06-25 | 北京白龙马云行科技有限公司 | Risk driver identification method and system based on different SP driver behaviors |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112991685A (en) | Traffic system risk assessment and early warning method considering fatigue state influence of driver | |
| EP3533681B1 (en) | Method for detecting safety of driving behavior, apparatus and storage medium | |
| CN107618512B (en) | Driving behavior safe evaluation method based on Human Automobile Environment multi-data source | |
| CN112071061B (en) | Vehicle service system based on cloud computing and data analysis | |
| CN109910904B (en) | Driving behavior and vehicle driving posture recognition system | |
| CN103221665B (en) | Driving support system and driving support managing device | |
| CN104527647B (en) | Monitoring and evaluation method of driving behavior risk degrees | |
| US9604648B2 (en) | Driver performance determination based on geolocation | |
| CN103871242B (en) | A kind of driving behavior overall evaluation system and method | |
| CN111785023A (en) | Vehicle collision risk early warning method and system | |
| CN110316198A (en) | A kind of safe-guard system and operation method for highway speed-raising | |
| CN105946860B (en) | A kind of bend speed prediction method for considering driving style | |
| CN106448265A (en) | Collecting method and device of driver's driving behavior data | |
| CN104691545A (en) | Adaptive vehicle anti-collision method | |
| CN105564436A (en) | Advanced driver assistance system | |
| CN110077398B (en) | Risk handling method for intelligent driving | |
| JP7354295B2 (en) | Apparatus and method for processing vehicle signals and calculating behavioral risk measures | |
| CN112884288B (en) | Highway rain and fog scene driving safety evaluation system | |
| CN104200687A (en) | Driver speed control behavior monitoring device and method | |
| CN114169444B (en) | Driving style classification method considering risk potential field distribution under vehicle following working condition | |
| CN106882141A (en) | Driving behavior detection method and device | |
| WO2016107876A1 (en) | Vehicular motion monitoring method | |
| CN114379559A (en) | Driving risk evaluation feature sketch method based on vehicle information acquisition system | |
| CN112356849A (en) | Early warning method and device for dangerous driving | |
| CN117125083B (en) | Vehicle following behavior risk quantification method considering driving style inclination |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210618 |
|
| RJ01 | Rejection of invention patent application after publication |