CN105160431B - A kind of security effectiveness prediction technique of pair of Shape Of Things To Come driver's auxiliary system - Google Patents
A kind of security effectiveness prediction technique of pair of Shape Of Things To Come driver's auxiliary system Download PDFInfo
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- CN105160431B CN105160431B CN201510573713.1A CN201510573713A CN105160431B CN 105160431 B CN105160431 B CN 105160431B CN 201510573713 A CN201510573713 A CN 201510573713A CN 105160431 B CN105160431 B CN 105160431B
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Abstract
The present invention discloses the security effectiveness prediction technique of a kind of pair of Shape Of Things To Come driver's auxiliary system, includes the following steps: the vehicle group that similar stiffness 1) is determined based on vehicle deformation degree；2) severity of injuries grade-ASC and correlation function are determined；3) the representative impact velocity of ASC and the relationship of damage risk probability are determined；4) the representational accident scene of ASC is determined；5) there is security effectiveness evaluation of the vehicle of auxiliary system in representative accident scene；By evaluation result, learn whether auxiliary system is effective to vehicle safety is improved.The prediction technique only relies on accurate deformation extent information in incident database, it inquires into and how prospect predicts driving assistance system validity using deformation extent, compared to the method that tradition utilizes velocity information, more casualty datas can be obtained and supported, more fully evaluation driver assistance system.
Description
Technical field
The present invention relates to vehicle safety technologies, and in particular to a kind of pair of Shape Of Things To Come driver assistance system security effectiveness is pre-
The method of survey.
Background technique
In recent years, with the development of electron controls technology, the driving assistance system function of automobile, such as adaptive cruise system
System (Adaptive Cruise Control, abbreviation ACC) and lane departure warning (Lane Departure Warning, abbreviation
LDW), largely being introduced, the purpose of these systems is attempt to reduce accident harm by intervening, in Shape Of Things To Come, these
Driver's auxiliary system will be widely used.
Passive security technology, such as safety belt and air bag, NCAP (New car different in the whole world
The safe criticism association of assessment programme new car) testing experiment in show its performance.From (the Europe Euro NCAP
The safe criticism association of continent new car) set up beginning so far, the improvement of road safety is played in these passive security performance evaluation tests
Important role.If relevant departments set up the efficiency assay for driving assistance system function, vehicle driver
Also it can gradually recognize the importance of driving assistance system, and safety between different brands can be compared when buying vehicle
Energy.But compared to existing passive security technology, there is presently no the security effectivenesses for this kind of system in future being widely recognized as
Method is effectively predicted.
From the angle of enterprise's technology development, major motor corporation's investment substantial contribution and research staff assist driving
The exploitation of systems technology, motor corporation have in face of such a fact, it is generally the case that various technologies are only used in product
It can be just verified after putting into market for a period of time, and almost each motor corporation is intended to producing safe practice effect
Product are verified before entering market, facilitate the determination of corporate investment and R&D direction in this way, while can be directed to certain productions
Evaluation effect of the product under the traffic condition of different regions improves the technology for local market.And there is presently no people
It is proposed the prediction to Shape Of Things To Come auxiliary system security effectiveness.
Summary of the invention
In order to solve the problems, such as the driving assistance system validity predicted distortion based on velocity information in incident database, this
Invention provides a kind of security effectiveness prediction technique to Shape Of Things To Come driver's auxiliary system based on vehicle deformation degree.
Bring vehicle safety improves degree after this method can go out the following a certain driving assistance system technical application with quantitative forecast.
To achieve the above object, the present invention takes following technical scheme: a kind of couple of Shape Of Things To Come driver assists system
The security effectiveness prediction technique of system, it is characterised in that: this is a kind of prediction technique based on vehicle deformation degree, the method
Implementation includes the following steps:
1) the vehicle group of similar stiffness is determined based on vehicle deformation degree；
2) severity of injuries grade in the vehicle group of similar stiffness is determined --- ASC and associated function；
3) the damage risk probability P [MAISx+ | ASC] under the representative impact velocity vk and the ASC of every ASC is determined
Relationship；
4) the representational accident scene of every ASC is determined；
5) there is security effectiveness evaluation of the vehicle of auxiliary system in representative accident scene；By evaluation result, obtain
Know whether auxiliary system is effective to vehicle safety is improved.
The implementation method of specific each step are as follows:
1, the method for determining the vehicle group of similar stiffness is: vehicle deformation degree is obtained from the NCAP test result of vehicle,
After the vehicle deformation level data for obtaining history, by clustering, rigidity of the vehicle rank, the vehicle under same rank are divided
Form the vehicle group of similar stiffness.
2, severity of injuries grade in the vehicle group of similar stiffness is determined --- the method for ASC and associated function
It is:
I determines description thing according to vehicle deformation level data, the type of colliding object, collision mode, the angle of collision
Therefore the ASC of scene；
II is after having determined all ASC, and then the probability P [ASC] of every level-one ASC occurs for determination and at each ASC
Damage risk probability P [MAISx+ | ASC], MAISx+ indicates to reach x grades or more of damage in casualty loss result；
III is in entire accident sample range, and under all ASC, damage reaches MAISx+ grades of the sum of risk probability are as follows:
3, the damage risk probability P [MAISx+ | ASC] under the representative impact velocity vk and the ASC of every ASC is determined
The method of relationship is:
For every ASC, a representative impact velocity vk can be determined by vehicle manufacturers；
For all ASC, vk and P [ASC], P [MAISx+ | ASC] these parameters, vk can be established by regression analysis
Relationship between P [MAISx+ | ASC].
4, the method for determining the representational accident scene of every ASC is:
I describes the corresponding accident scene of a certain ASC, include the object collided, the mode of collision, collision angle
Degree and the initial movement velocity and braking distance of accident, it is available in the database, it is obtained using these data initial
Accident scene；
Then we are emulated II using these primary datas, and by amendment gradually, so that the speed of collision moment
Degree gradually coincide with representative impact velocity vk, so far, describes the representative accident scene of this ASC and is known that.
5, having the method for security effectiveness assessment of the vehicle of auxiliary system in representative accident scene is:
By plus safety assisting system vehicle application into each identified representative accident scene, these generations
New one by one vk and corresponding new ASC can be measured in table accident scene, at this new ASC, and can determine that P
[ASCThere is system] and P [MAISx+ | ASCThere is system] and P [MAISx+There is system],
There is the function obtained under system and without the function obtained under system, the vehicle for having auxiliary system just can be evaluated using following formula
Security effectiveness:
Wherein, P [MAISx+There is system] indicate under auxiliary system each ASC damage reach MAISx+ grades risk probability it
With P [ASCWithout system] indicate that each ASC damage under no auxiliary system reaches MAISx+ grades of the sum of risk probability,
If the result of the calculating formula is positive, illustrate that auxiliary system has good result to vehicle safety is improved, as a result
It is negative and illustrates auxiliary system to raising vehicle safety without good result.
Proposed by the present invention is a kind of pre- to Shape Of Things To Come driver assistance system safety based on vehicle deformation degree
Survey method establishes the grade by introducing severity of injuries grade ASC (accident severity classes) concept
With the functional relation of degree of injury, impact velocity, come predict the following driver assistance system application for automotive safety efficiency
Whole improvement degree.The prediction technique only relies on accurate deformation extent information in incident database, inquires into and how is prospect
Driving assistance system validity is predicted using deformation extent, compared to the method that tradition utilizes velocity information, can be obtained more
Casualty data support, more fully evaluation driver assistance system.
Detailed description of the invention
Fig. 1 is the vk (ASC) established using the Return Law and the relational graph of P [MAISx+ | ASC].
Specific embodiment
The present invention is described in detail below with reference to the accompanying drawings and embodiments, wherein attached drawing and the embodiment of the present invention one
It rises for illustrating the present invention, but it will be appreciated by those skilled in the art that, following embodiment is not to technical solution of the present invention
The unique restriction made, all any equivalents or change done under technical solution of the present invention Spirit Essence are regarded as
It belongs to the scope of protection of the present invention.
It is known that in the past few decades automobile since the requirement body stiffness that Euro-NCAP is tested increases.Automobile it is rigid
Degree can be characterized in certain degree by vehicle deformation index (vehicle deformation index, VDI6), i.e., by vehicle
Front end or rear end be divided into 5 parts, and use the deformation extent after the collision of this measurement vehicle.So being referred to based on vehicle deformation
The evaluation method of the automotive safety enhancing efficiency of number (VDI6), this is advantageous not only for casualty data, and more can provide reliably has
The database of the vehicle deformation index of effect evaluation vehicle safety efficiency.It means that the biggish vehicle of rigidity, up to the present,
It is safer automobile.
We can use VDI6 to evaluate accident (Crash) severity, but the measurement that VDI6 is not severity of injuries refers to
Mark, because it depends on the rigidity of automobile.But for the automobile of approximate rigidity, it is regarded as the function of severity of injuries.
As long as accident cannot avoid completely, security effectiveness can subtracting by the probability of damage or the death of certain accidents
It is few to assess.This can be by estimated service life and without using MAIS (the Maximum Abbreviated Injury under auxiliary system
Scale, maximum Abbreviated injury scale) probability obtains.
Below, we summarize it is a kind of based on vehicle deformation degree to Shape Of Things To Come driver assistance system security effectiveness
Prediction technique.It will realize the security effectiveness of estimation driver assistance system under conditions of no velocity information.This research
It will be carried out according to the data of such as vehicle deformation index (VDI6), this method implementation steps include the following:
1. determining the vehicle group of similar stiffness based on vehicle deformation degree
2. determining severity of injuries grade --- ASC (accident severity classes) and corresponding function
3. determining the relationship of the representative impact velocity vk and P [MAISx+ | ASC] of every ASC
4. determining the representational accident scene of every ASC
5. security effectiveness estimation of the auxiliary system in representative accident scene
It describes one by one below:
1. determining the vehicle group of similar stiffness based on vehicle deformation degree
The implementation of this method (security effectiveness of the Shape Of Things To Come driver assistance system based on VDI6 is predicted) is selected in phase
(alternatively referred to as same level rigidity vehicle) is carried out like vehicle group under rigidity, will be comparable in this way.The vehicle of different-stiffness rank
, it can discuss with the method grouping, be the same in subsequent step.
The rank of rigidity of the vehicle affects the safety of vehicle, it would be desirable to for the security effectiveness of the vehicle of different-stiffness
The rank that rigidity is distinguished if comprehensive evaluation is carried out, this can just carry out effective evaluation between different-stiffness rank,
It is namely based on the security effectiveness effective evaluation of different distortion degree.Vehicle deformation degree can be obtained from the NCAP test result of vehicle
It arrives, after obtaining the vehicle deformation level data of history, passes through clustering, so that it may is reasonable to divide rigidity of the vehicle rank.
It may include multiple vehicles under same rank.This defines the vehicle groups of similar stiffness.
2. determining severity of injuries grade --- ASC and corresponding function
After the data based on vehicle deformation degree have determined similar stiffness vehicle group, next we will determine this
Severity of injuries grade-ASC and two function of class vehicle group.
It is concluded that vehicle deformation index (VDI6) forecast of distribution lesion capability is better than the ability of velocity variations,
VDI6 is distributed a dependent variable for being likely to become accident (Crash) severity function.But in addition to this, ASC depends not only upon VDI6, it
Also need additional parameter: the type (such as car, truck, electric pole, pedestrian, bicycle) of colliding object, collision mode are (such as
Head-on crash, side collision, rear collision, overturning), the angle, etc. of collision.According to these parameters, accident scene is described
One ASC is assured that these parameters are recorded in incident database, by these parameters we it is known that
ASC.The accident scene specifically considered depends on evaluated security system.
In order to realize assessment this target of driver assistance system, after determining ASC, we also need two functions,
The probability P [ASC] of every ASC grade and the damage risk probability P [MAISx+ | ASC] at each ASC occurs.P [ASC] table
Show the share (probability) that every ASC grade accounts in the accident scene of all considerations, and P [MAISx+ | ASC] it indicates in this ASC etc.
Damage reaches the risk of MAISx+ grades (damage reaches x grades and the above rank), i.e., the casualty loss result at this ASC in grade accident
The shared share of middle MAISx+ grades of damage.
If the two probability functions exist, the risk probability of MAISx+ can utilize number in entire accident sample range
The calculation method of reason statistics obtains, and such as following formula, i.e., all ASC rank damages reach MAISx+ grades of the sum of risk probability.
3. determining the relationship of every ASC representative impact velocity vk (ASC) and P [MAISx+ | ASC]
For every ASC, a representative speed is determined, this can be determined by vehicle manufacturers.Its result
It is that each ASC can have corresponding impact velocity vk (ASC).So, for all ASC, vk (ASC) and P [ASC], P
[MAISx+ | ASC] all becomes known quantity, it is possible to establish between vk (ASC) and P [MAISx+ | ASC] by regression analysis
Relationship, process such as Fig. 1, each point represents vk under an ASC and corresponding P [MAISx+ | ASC] in figure.
4. determining every its representative accident scene of an ASC
Mode, the angle of collision of the object, collision that are collided, all in step 2 it is known that so for a certain ASC phase
Corresponding accident scene, parameter unknown at present just mainly include movement velocity and braking distance.Due in incident database
There are initial velocity, the braking distance of accident, so we, which can use this data, obtains initial incident scene；Then we are sharp
Emulated with these primary datas, and by amendment gradually, can make the speed of collision moment in gradually step 3 really
Fixed vk (ASC) coincide.It coincide so far, describes the more accurate accident scene of this ASC and be known that.
The advantages of this way is that this representative accident can (i.e. we be defined above just with a hypothetical accident before this
Beginning accident scene), the speed of this representative accident can be determined by engineering judgement or manufacturer.So hypothetical accident can
With reliability with higher, and the number of emulation is reduced.Since the accident quantity to be emulated reduces, sexual behavior is represented
Therefore deep simulation calculating can be carried out, this method need to only emulate an accident in each accident group, rather than picture
Pervious method will be to each Failure Simulation.
5. security effectiveness estimation of the auxiliary system in representative accident scene
Under above by the determining corresponding accident scene of this ASC of emulation, we add safety assisting system, utilize
This accident scene can measure a new vk and corresponding new ASC, this be exactly recalculate having safe auxiliary
The ASC of accident scene under system.
We measure one using accident scene plus safety assisting system under the accident scene of all ASC determined
Each and every one new vk and corresponding new ASC, this is exactly all accident scenes in the case where there is safety assisting system recalculated
ASC.The ASC under all scenes is counted again later, as the method for step 2 description, correspondingly, there is safety auxiliary
Two functions-P [ASC under auxiliary systemThere is system] and P [MAISx+ | ASCThere is system] all can determine and P [MAISx+There is system] can also
It determines.
After collision after the sum for having obtained not installing safety assisting system has auxiliary system intervention, can with analogy we
The calculating process based on vehicle deformation degree utilized in front, the efficiency evaluation for reducing damage can state are as follows:
By this efficiency evaluation as a result, just can know that whether auxiliary system is effective to vehicle safety is improved, as a result
It is positive and illustrates that auxiliary system is effective to vehicle safety is improved, be as a result negative and illustrate auxiliary system to raising vehicle safety without good
Good effect.
Conclusion
A kind of validity prediction side to Shape Of Things To Come driving assistance system based on vehicle deformation degree is outlined herein
Method.By introducing using deformation extent as the severity of injuries grade ASC concept of dependent variable and the grade and degree of injury, touching
The functional relation of speed is hit, to estimate whole improvement degree of the application for automotive safety efficiency of driver assistance system.Always
Knot can be concluded that herein
(1) it is serious compared to the velocity information in incident database to be more suitable as evaluation accident for vehicle deformation degree information
The index of degree.
(2) method proposed can have more accident numbers due to the velocity information independent of accident traffic participant
Statistics calculating is carried out according to amount, to more all-sidedly and accurately evaluate the security effectiveness of driving assistance system.
(3) data used in the method are all to bow one's head available, and vehicle manufacturers can provide the speed of different collision situations
Spend information；NCAP detection can provide the testing result of automobile, to estimate the rigidity of vehicle；Accident statistics person can provide thing
Therefore data, including vehicle deformation index.
Claims (4)
1. the security effectiveness prediction technique of a kind of pair of vehicle driver's auxiliary system, it is characterised in that: this is that one kind is based on
The implementation of the prediction technique of vehicle deformation degree, the method includes the following steps:
1) the vehicle group of similar stiffness is determined based on vehicle deformation degree；
2) severity of injuries grade in the vehicle group of similar stiffness is determined --- ASC and associated function；
3) pass of the damage risk probability P [MAISx+ | ASC] under the representative impact velocity vk and the ASC of every ASC is determined
System；
4) the representational accident scene of every ASC is determined；
5) there is security effectiveness evaluation of the vehicle of auxiliary system in representative accident scene, by evaluation result, learn auxiliary
Whether auxiliary system is effective to vehicle safety is improved；
Wherein it is determined that severity of injuries grade in the vehicle group of similar stiffness --- ASC and associated functional based method
It is:
I determines description accident field according to vehicle deformation level data, the type of colliding object, collision mode, the angle of collision
The ASC of scape；
II determines the probability P [ASC] that every level-one ASC occurs and the damage at each ASC after all ASC have been determined
The dangerous probability P of cold [MAISx+ | ASC], MAISx+ indicates to reach x grades or more of damage in casualty loss result；
III is in entire accident sample range, and under all ASC, damage reaches MAISx+ grades of the sum of risk probability are as follows:
Wherein, the method for security effectiveness evaluation of the vehicle of auxiliary system in representative accident scene is:
I is by plus the vehicle application of safety assisting system, into each identified representative accident scene, these are represented
New one by one vk and corresponding new ASC can be measured in property accident scene, at new ASC, determines P [ASCThere is system]、P
[MAISx+|ASCThere is system] and P [MAISx+There is system], P [ASCThere is system] refer to there is the probability that each grade of ASC occurs under auxiliary system, P
[MAISx+|ASCThere is system] refer to the damage risk probability for there are the ASC at different levels under auxiliary system, P [MAISx+There is system] refer to all grades of ASC
Under, damage reaches MAISx+ grades of the sum of risk probability, i.e.,
II calculates the damage risk probability of the ASC at different levels of the vehicle of no auxiliary system in the same way；
III will have the probability function obtained under auxiliary system and without the probability function obtained under auxiliary system, be evaluated using following formula
There is the security effectiveness of the vehicle of auxiliary system:
Wherein, P [MAISx+Without system] refer to that the ASC at different levels damage under no auxiliary system reaches MAISx+ grades of the sum of risk probability；Such as
The result of calculating formula described in fruit is positive, and illustrates that auxiliary system has good result to vehicle safety is improved, being as a result negative, it is auxiliary to illustrate
Auxiliary system is to raising vehicle safety without good result.
2. the security effectiveness prediction technique according to claim 1 to vehicle driver's auxiliary system, feature exist
In: the method for determining the vehicle group of similar stiffness is: obtaining vehicle deformation degree from the NCAP test result of vehicle, is gone through
After the vehicle deformation level data of history, by clustering, rigidity of the vehicle rank is divided, the vehicle composition under same rank is similar
The vehicle group of rigidity.
3. the security effectiveness prediction technique according to claim 1 to vehicle driver's auxiliary system, feature exist
In: determine the relationship of the damage risk probability P [MAISx+ | ASC] under the representative impact velocity vk and the ASC of every ASC
Method is:
For every ASC, a representative impact velocity vk is determined by vehicle manufacturers；
For all ASC, vk and P [ASC], P [MAISx+ | ASC] these parameters, vk and P are established by regression analysis
Relationship between [MAISx+ | ASC].
4. the security effectiveness prediction technique according to claim 1 to vehicle driver's auxiliary system, feature exist
In: the method for determining the representational accident scene of every ASC is:
I describes the corresponding accident scene of a certain ASC, include the object collided, the mode of collision, the angle of collision, with
And movement velocity and braking distance that accident is initial, it is available in the database, initial accident is obtained using these data
Scene；
Then II is emulated using these primary datas, and by amendment gradually so that the speed of collision moment gradually with
Representative impact velocity vk coincide, and so far, describes the representative accident scene of this ASC and is known that.
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| CN106250637B (en) * | 2016-08-04 | 2019-04-16 | 清华大学 | Automobile safety system parameter optimization method based on micro- Traffic Flow Simulation Models |
| US10730526B2 (en) * | 2017-07-14 | 2020-08-04 | Ccc Information Services Inc. | Driver assist design analysis system |
| DE102017223005A1 (en) * | 2017-12-18 | 2019-06-19 | Robert Bosch Gmbh | Method and device for providing injury information about an injury to an unprotected road user in a collision with a vehicle |
| CN111735639B (en) * | 2020-05-26 | 2022-03-22 | 清华大学苏州汽车研究院（相城） | Automatic driving scene minimum set generation method for intelligent networked automobile demonstration area |
| CN112287300A (en) * | 2020-10-23 | 2021-01-29 | 北京嘀嘀无限科技发展有限公司 | Data processing method and device, server and storage medium |
| CN116881778B (en) * | 2023-09-06 | 2023-11-28 | 交通运输部公路科学研究所 | Intelligent auxiliary teaching system safety early warning and protection function testing system and method |
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