CN109242227A - The driving risk and assessment models of car steering behavior - Google Patents
The driving risk and assessment models of car steering behavior Download PDFInfo
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- CN109242227A CN109242227A CN201710554357.8A CN201710554357A CN109242227A CN 109242227 A CN109242227 A CN 109242227A CN 201710554357 A CN201710554357 A CN 201710554357A CN 109242227 A CN109242227 A CN 109242227A
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Abstract
It is confirmed according to related theoretical research, in the traffic conflict model that kinds of risks factor (road, vehicle, environment) induces, dangerous driving behavior is the main inducing of traffic accident.In practice, how by analysis driving behavior, the driving risk of assessment driver, the generation for improving driving habit for driver, reducing traffic accident and break in traffic rules and regulations is had a very important significance.The invention discloses the driving behavior risk evaluation models based on driving event.There is different driving risks for different types of driving event, the model has determined corresponding driving Risk rated ratio, and then analyzed by the driving behavior to driver, assessment driver triggers the risk of traffic accident and traffic violations event, calculates the numeralization assessment result of driving risk.
Description
Technical field
The present invention establishes driving behavior risk evaluation model, is analyzed by the driving behavior to driver, assessment
Driver triggers the risk of traffic accident and traffic violations event, and provides numeralization assessment result.
Background technique
China Today, traffic safety problem very severe.2006, the whole nation occurred road traffic accident 450 254 altogether,
It causes 98 738 people death, 469 911 people injured, directly then produces 18.8 hundred million yuan of loss.Traffic accident is then produced to the life of the people
Cause massive losses.The influence that the generation of traffic accident be unable to do without automobile, people is mainly driver, environment, but driver
Behavior has an important influence the safety and reliability of Traffic Systems.Many scholars think, the good driving of driver
Habit and with behavioural characteristic will be improve vehicle active safety and improve difficult traffic situations one of effective way.According to
Chinese transportation accident statistics are shown within 2011, in 97000 road traffic accidents, it is relevant to driving behavior factor just
91.85% is accounted for, the generation and driving behavior of road traffic accident there are highlights correlations.Theoretical research also confirms that kinds of risks factor
The dangerous driving behavior in traffic conflict model that (road, vehicle, environment) induces is the main inducing of traffic accident.It is practical
In, how by analysis driving behavior, the driving risk of assessment driver, driving habit is improved for driver, reduces traffic
The generation of accident and break in traffic rules and regulations, has a very important significance.
By the end of currently, people, which more combine in extraneous factor, influences angle, analysis driver to the driving behavior of driver
Driving behavior, the not influence of the driving habit to driver itself and behavior to driving risk.Driving wind described here
Danger refers to that the driving behavior derived from driver itself is improper, leads to the risk of traffic accident and traffic violations.
Summary of the invention
The present invention establishes driving behavior risk evaluation model
It is analyzed by the driving behavior to driver, assessment driver triggers the wind of traffic accident and traffic violations event
Danger, and numeralization assessment result is provided.
The definition of driving event:
" driving event " refer to when driving " the driving over the speed limit " of vehicle, " anxious to accelerate ", " anxious to slow down ", " vehicle collision ",
" electric voltage exception ", " cold vehicle is run at high speed ", " fault trip ", " preheating time is too long ", " revolving speed is abnormal ", " free time mistake
It is long ", the events such as " water temperature is abnormal " and " fatigue driving ".
In practice, different types of driving event has different driving risks, for example " driving over the speed limit " causes vehicle
Traffic faults and the probability of break in traffic rules and regulations increase." free time is too long ", " preheating time is too long ", " cold vehicle is run at high speed " then can
It is not to cause risk of driving a vehicle, but will affect vehicle driving efficiency.Meanwhile " revolving speed abnormal ", " electric voltage exception ", " water temperature is abnormal " and
" fault trip " reflects the driving in vehicle abnormality, the risk being easy to produce.
The representation and its weight of driving event
Driving behavior proposed by the present invention based on " driving event " is driven a vehicle risk method, need for different driving event and
Its close relation between driving risk, invention defines the magnitude expression of driving event and its in driving risk
Weight in assessment.
Assuming that specified car owner is interior in " specified time range ", a shared n times driving recording, wherein n-th of driving recording is corresponding
Driving behavior symbol indicate and its weight, the weight be the present invention according to different driving events to driving risk influence size and
Fixed, they are a kind of relative weighting, and visual actual needs is adjusted in practice.
Chinese Traffic Administration Bureau of Ministry of Public Security statistics display, Kuomintang-Communist generation road traffic accident 378781 rises in 2006, causes 89455 altogether
People is dead.In the road traffic accidents occurred in 2006, vehicle driver traffic offence causes death toll to be 76350 in violation of rules and regulations
People.Wherein, it drives over the speed limit, fatigue driving, drive when intoxicated as the principal element of accident.Therefore, in the weight of driving event, surpass
Speed traveling, the weight of fatigue driving are relatively high.
The symbol of table row vehicle event indicates and its weight definition
| Serial number | Driving behavior type | Linear module | Symbol | Weight symbol | Weighted value | Remarks explanation |
| 1 | It drives over the speed limit | % | s1(n) | w1 | 30% | Hypervelocity accounting in unit mileage |
| 2 | It is anxious to accelerate | n/100km | s2(n) | w2 | 15% | Anxious acceleration times in unit mileage |
| 3 | It is anxious to slow down | n/100km | s3(n) | w3 | 15% | Anxious deceleration number in unit mileage |
| 4 | Collision | n/100km | s4(n) | w4 | 100% | Collision frequency in unit mileage |
| 5 | Electric voltage exception | km | s5(n) | w5 | 6% | Electric voltage exception total kilometrage |
| 6 | Cold vehicle is run at high speed | km | s6(n) | w6 | 2% | Cold overall height speed mileage travelled |
| 7 | Failure mileage | km | s7(n) | w7 | 6% | Failure mileage |
| 8 | Preheating time is too long | mm | s8(n) | w8 | 2% | Hot overlong time |
| 9 | Revolving speed is abnormal | km | s9(n) | w9 | 6% | Revolving speed exception mileage |
| 10 | Free time is too long | mm | s10(n) | w10 | 2% | Free time |
| 11 | Fatigue driving | % | s11(n) | w11 | 6% | Fatigue driving |
Remarks:
(1) % indicates that current driving behavior mileage accounting participates in driving behavior risk assessment；
(2) n/100km indicates that current driving behavior participates in driving behavior risk assessment with hundred kilometers of numbers；
(3) km indicates that current driving behavior participates in driving behavior risk assessment with corresponding mileage travelled；
(4) mm indicates that current driving behavior participates in driving behavior risk assessment with corresponding time span；
The magnitude method for normalizing of driving event
At the appointed time in range, driver is there may be multiple driving recordings, the driving event quantizating index that goes out given in table 1
It is assessed for each driving recording, it is corresponding with each driving recording.In order to assess the row within the scope of specified time
Vehicle risk, present invention needs are further processed the driving event index of each driving recording in the time range.It is practical
On, there is N number of driving recording within the scope of specified time, then there must be N number of speeding index value, there are N number of other driving events
Index.In this way, we will obtain a sample sequence, the sequence it is more steady, then can more reflect that driver drives vehicle is accustomed to, instead
It, cannot more reflect the habit of driver.
By the driving event of n times driving recording, the present invention is used to find the Rule section of these data, is driven a vehicle and is remembered using n times
The driving event of record quantifies the siding-to-siding block length of average value and its quantized value to be normalized.Refer to by n times driving event quantization
Marking the index value after normalizing is
Wherein i=1,2 ..., N, and siExpression symbol as described in table 1.By the formula, the present invention be can get for assessing row
Each driving event of vehicle risk.
The appraisal procedure for risk of driving a vehicle
According to the quantitative estimation of driving event nondimensionalization, in conjunction with the driving Risk rated ratio of driving event, the present invention is further used
Influence of following model evaluation driving events to driving risk.Here, present invention definition one is known as " driving risk index "
Concept, for portraying the size for risk of driving a vehicle caused by driving behavior.It defines within the scope of specified time and is sent out in driving recording
The weighted sum of raw driving event normalization average value, i.e.,
Wherein r is known as risk index of driving a vehicle, it is to assess to obtain according to the driving recording within the scope of specified time, reflects vehicle
Driver drives a vehicle risk size caused by this period driving behavior；siAnd wiThe numerical value for risk of driving a vehicle is bigger, and driving risk is most
Greatly, it more needs to introduce vehicle operator and its administrative department note that effectively supervised to vehicle operator, reduces
Driving risk.
Detailed description of the invention
Fig. 1 is driving behavior risk assessment flow chart
Specific embodiment
Driving risk assessment process
In practice, special user's automobile data recorder is needed, the driving behavior of user's driving conditions is detected.Common
User's automobile data recorder is generally based on the vehicle mounted failure detection device of OBD, which can pass through the gyroscope and GPS built in it
Locating module handles user's driving driving behavior in real time, detects the driving event in driving conditions, and it is transmitted to
System service platform, then a step is carried out by service platform and carries out driving behavior analysis, assess the driving wind in user's driving conditions
Dangerous index.
For driving risk assessment in practice, the present invention combines the application scenarios, devises the assessment meter of driving risk index
Calculating process will be deployed in the application scenarios as shown, driving risk evaluation module directly can be built by the calculation process
Service platform on, can be realized in particular time range user drive a vehicle risk assess.Therefore, row of the present invention
The process of vehicle risk assessment and steps are as follows:
(1) it inquires within the scope of specified time, the driving recording of vehicle, it is assumed here that one shares N number of driving recording, obtains each row
The duration of vehicle record, mileage, for quantifying the driving event in each driving recording.
(2) it is directed to each driving recording, judges whether there is the generation of driving event.If i-th of event of driving a vehicle, calculates i-th
Otherwise the quantization index value of a driving event sets 0 for the quantization index value of i-th of event of driving a vehicle.
(3) normalization average value for calculating each type of quantization index value, it is temporarily stored, and is receiving the process of coming
In, calculate driving risk index.
(4) the driving risk index of driver during this period of time is further calculated according to the calculation formula of driving risk assessment.
Claims (3)
1. a kind of driving behavior risk evaluation model, it is characterized in that being analyzed by the driving behavior to driver, assessment is driven
The person of sailing triggers the risk of traffic accident and traffic violations event, and provides numeralization assessment result.
2. a kind of driving behavior risk evaluation model based on driving event according to claim 1, feature are as follows
(1) for different driving event and its close relation between driving risk, the magnitude of driving event is defined
Expression and its weight in driving risk assessment.
(2) the magnitude method for normalizing for event of driving a vehicle, the index value after n times driving event quantizating index normalization are as follows:
Wherein i=1,2 ..., N can get each driving event for assessing driving risk.
(3) appraisal procedure for event of driving a vehicle, according to the quantitative estimation of driving event nondimensionalization, in conjunction with the driving wind of driving event
Dangerous weight, the further influence with following model evaluations driving event to driving risk.I.e.
Wherein r is known as risk index of driving a vehicle, it is to assess to obtain according to the driving recording within the scope of specified time, reflects vehicle
Driver drives a vehicle risk size caused by this period driving behavior；siAnd wiThe numerical value for risk of driving a vehicle is bigger, and driving risk is most
Greatly.
3. a kind of driving behavior risk assessment process based on driving event according to claim 2, it is characterised in that in detail
It is thin that steps are as follows:
(1) it inquires within the scope of specified time, the driving recording of vehicle, it is assumed here that one shares N number of driving recording, obtains each row
The duration of vehicle record, mileage, for quantifying the driving event in each driving recording.
(2) it is directed to each driving recording, judges whether there is the generation of driving event.If i-th of event of driving a vehicle, calculates i-th
Otherwise the quantization index value of a driving event sets 0 for the quantization index value of i-th of event of driving a vehicle.
(3) normalization average value for calculating each type of quantization index value, it is temporarily stored, and is receiving the process of coming
In, calculate driving risk index.
(4) the driving risk index of driver during this period of time is further calculated according to the calculation formula of driving risk assessment.
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Cited By (9)
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| CN110782125A (en) * | 2019-09-23 | 2020-02-11 | 同济大学 | Road safety risk degree evaluation method for automatically driving automobile |
| 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 |
| CN111652498A (en) * | 2020-05-29 | 2020-09-11 | 李欣蕊 | Automobile driving risk scoring system and method |
| CN112100857A (en) * | 2020-09-17 | 2020-12-18 | 燕山大学 | Risk assessment method for distracted driving behaviors |
| CN112862276A (en) * | 2021-01-26 | 2021-05-28 | 电子科技大学 | Vehicle networking platform and method for defining risk preference of driver by combining longitudinal direction and transverse direction |
| CN112989069A (en) * | 2021-05-10 | 2021-06-18 | 苏州博宇鑫交通科技有限公司 | Traffic violation analysis method based on knowledge graph and block chain |
| CN113673826A (en) * | 2021-07-20 | 2021-11-19 | 中国科学技术大学先进技术研究院 | Driving risk assessment method and system based on individual factors of driver |
| CN113887904A (en) * | 2021-09-23 | 2022-01-04 | 同济大学 | Freight vehicle driver risk grade assessment method based on analytic hierarchy process |
| CN114092267A (en) * | 2022-01-18 | 2022-02-25 | 成都车晓科技有限公司 | High-risk vehicle insurance customer car insurance evaluation method and system based on machine learning |
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Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110782125A (en) * | 2019-09-23 | 2020-02-11 | 同济大学 | Road safety risk degree evaluation method for automatically driving automobile |
| CN110782125B (en) * | 2019-09-23 | 2023-05-02 | 同济大学 | Road safety risk assessment method for automatic driving automobile |
| 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 |
| CN111062240B (en) * | 2019-10-16 | 2024-04-30 | 中国平安财产保险股份有限公司 | Monitoring method and device for automobile driving safety, computer equipment and storage medium |
| CN111652498A (en) * | 2020-05-29 | 2020-09-11 | 李欣蕊 | Automobile driving risk scoring system and method |
| CN112100857B (en) * | 2020-09-17 | 2022-04-12 | 燕山大学 | Risk assessment method for distracted driving behaviors |
| CN112100857A (en) * | 2020-09-17 | 2020-12-18 | 燕山大学 | Risk assessment method for distracted driving behaviors |
| CN112862276B (en) * | 2021-01-26 | 2023-04-28 | 电子科技大学 | Longitudinal and transverse combined Internet of vehicles device and method for defining risk preference of driver |
| CN112862276A (en) * | 2021-01-26 | 2021-05-28 | 电子科技大学 | Vehicle networking platform and method for defining risk preference of driver by combining longitudinal direction and transverse direction |
| CN112989069A (en) * | 2021-05-10 | 2021-06-18 | 苏州博宇鑫交通科技有限公司 | Traffic violation analysis method based on knowledge graph and block chain |
| CN113673826A (en) * | 2021-07-20 | 2021-11-19 | 中国科学技术大学先进技术研究院 | Driving risk assessment method and system based on individual factors of driver |
| CN113673826B (en) * | 2021-07-20 | 2023-06-02 | 中国科学技术大学先进技术研究院 | Driving risk assessment method and system based on individual factors of driver |
| CN113887904A (en) * | 2021-09-23 | 2022-01-04 | 同济大学 | Freight vehicle driver risk grade assessment method based on analytic hierarchy process |
| CN114092267A (en) * | 2022-01-18 | 2022-02-25 | 成都车晓科技有限公司 | High-risk vehicle insurance customer car insurance evaluation method and system based on machine learning |
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