CN106250637A - Automobile safety system parameter optimization method based on micro-Traffic Flow Simulation Models - Google Patents

Automobile safety system parameter optimization method based on micro-Traffic Flow Simulation Models Download PDF

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CN106250637A
CN106250637A CN201610632964.7A CN201610632964A CN106250637A CN 106250637 A CN106250637 A CN 106250637A CN 201610632964 A CN201610632964 A CN 201610632964A CN 106250637 A CN106250637 A CN 106250637A
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CN106250637B (en
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罗禹贡
陈龙
李克强
边明远
张书玮
秦兆博
解来卿
罗剑
张东好
连小珉
王建强
杨殿阁
郑四发
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Tsinghua University
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
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    • G06F30/15—Vehicle, aircraft or watercraft design
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]

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Abstract

The present invention discloses a kind of automobile safety system parameter optimization method based on micro-Traffic Flow Simulation Models, it is adaptable to car load factory reasonable selection control system parameter is to obtain optimum effect.It sets up the micro-traffic model being made up of many cars, by changing the parameter of different control system, by long random simulation, find the parameter of setting and the relation of occupant injury risk, setting up majorized function, recycling modern optimization method solves this function, obtains control system parameter.Present invention only requires nature driving data, it is not necessary to substantial amounts of casualty data so that the method application is convenient.

Description

Automobile safety system parameter optimization method based on micro-Traffic Flow Simulation Models
Technical field
The present invention relates to a kind of automobile safety system parameter optimization method based on micro-Traffic Flow Simulation Models, especially with regard to A kind of security parameter optimization method of automobile emergency anti-collision system.
Background technology
Along with the development of electron controls technology, modern vehicle is the most intelligent.And vehicle intellectualized needs one are longer Course, thus causes traffic to there is intelligent vehicle and non intelligentization automobile mixes the situation of traveling.In order to adapt to this Complicated case, whole-car firm needs automobile safety system is optimized design, with obtain minimum contingency occurrence probability or Occupant injury risk probability.And due to the more difficult acquisition of casualty data, and limited amount, therefore in the past by accident reconstruction data The optimization method emulated again can not be suitable for the quick application demand of intellectual technology.
Along with the continuous progress of data acquisition means, the collection difficulty of natural driving data constantly reduces, and this results in and drives The person of sailing drives the most deep of study mechanism.Some scholars have applied existing driver mechanism to set up micro-phantom Evaluate the security status of traffic environment.Given this background, can combine driver mechanism and automobile safety system, to examine Examine the different parameter that controls and set the impact of the safe coefficient on traffic environment.
Summary of the invention
For above-mentioned analysis, it is an object of the invention to provide a kind of automobile safety system based on micro-Traffic Flow Simulation Models ginseng Number optimization method.The method can utilize existing pilot model that system leaved for development is optimized design to be developed System be obtained in that more preferable effect (relatively low accident rate, good riding comfort).
For achieving the above object, the present invention takes techniques below scheme:
A kind of automobile safety system parameter optimization method based on micro-Traffic Flow Simulation Models, is carried out as follows:
1) the micro-traffic model being made up of many cars is set up；
2) change the parameter of different control systems in same car, by long accident random simulation, find difference The parameter to be optimized of secondary setting and the relation of occupant injury risk in accident；
3) set up the majorized function of described parameter to be optimized, utilize modern optimization method to solve this function, obtain optimum Control system parameter.
1) in, the method setting up the micro-traffic model being made up of many cars is:
Number of vehicles is at least two cars, if first car is freely to drive, other are with sailing the vehicle status parameters of vehicle The car-following model with faulty operation mechanism proposed by University of Michigan determines.
2) in, find the relational approach of the parameter to be optimized and occupant injury risk set in control system as:
A) assume the vehicle that n-th car is control to be optimized, then corresponding amendment 1) in the car-following model of n-th car, n ＞ 1；
B) one group of parameter to be optimized is set for this car, by long-time emulation, record each time n-th car with (n-1)th or (n+1)th car have an accident before and after the variable information A with occupant injury risk existence function relation, described change Amount information joins with relating to parameters to be optimized；
C) utilize the variable information A having an accident each time recorded, obtain according to the data matching in incident database Go out collision occupant injury risk and the relation of this variable information each time, be designated as:
P (MAISX+)=f1(A)
P (MAISX+) " " occupant injury is the risk probability of more than X level in expression；
D) the occupant injury risk of all accidents is sued for peace, and travel divided by this vehicle to be optimized in simulation time Total distance, obtained being related to the damage risk of the unit operating range under this parameter to be optimized accordingly:
P d - x y ( M A I S X + ) = Σ P ( M A I S X + ) D
P in formulad-xy(MAIS X+) is the above damage risk of X level of unit operating range, and D is vehicle row in simulation time The total distance sailed, x, y represent parameter to be optimized, according to Pd-xyThe size of (MAIS X+) is it is known which parameter is desirable 's；
E) repeat b)-d) simulation calculation, for different parameters optimization to be optimized, record P respectivelyd-xy(MAIS X +) and the parameter to be optimized of correspondence；
F) data fitting method is utilized to set up the relation of all parameters to be optimized and unified occupant injury risk:
PAlways(MAIS X+)=f2(X,Y)
P in formulaAlways(MAIS X+) represents the unified occupant injury risk containing all parameters to be optimized, and X, Y represent all Parameter to be optimized, f2Characterize the functional relationship between the above damage risk of X level of unit operating range and parameter to be optimized.
3) in, the majorized function setting up described parameter to be optimized is:
(x in above formula1,x2) represent the interval of X, (y1,y2) representing the interval of Y, concrete interval value is by designing Person sets.
This majorized function implication is, under meeting constraints, to make minf2(X, Y) is minimum, namely occupant injury risk PAlwaysWhen (MAIS X+) minimizes, acquired control parameter is optimized parameter.
The modern optimization method utilized can be interior point method or steepest Decent Gradient Methods etc..
Due to the fact that and take above technical scheme, it has the advantage that it sets up the micro-traffic being made up of many cars Model, by changing the parameter of each vehicle difference control system, through long random simulation, find the parameter of setting with The relation of occupant injury risk, sets up majorized function, utilizes modern optimization method to solve this function, obtains optimal control system Parameter.Obtain convenient due to the driver operational data under naturally driving and sample size is big, so micro-Traffic Flow Simulation Models is built Cube just and expansion is strong, the security system being suitable for multi-form models.The present invention with existing by accident reconstruction data are entered The optimization method that row emulates again is compared, and not only applies conveniently, and has expansibility, can be applicable to the system of non intelligent degree Optimize.
Detailed description of the invention
Use the automobile safety system parameter optimization method based on micro-Traffic Flow Simulation Models of the present invention, generally include following Step:
1, the micro-traffic model being made up of many cars is set up；
2, change the parameter of same car difference control system, by long random simulation, find the parameter of setting with The relation of occupant injury risk；
3, set up majorized function, utilize modern optimization method to solve this function, obtain optimal control system parameter.
In above step 1, the method for building up of micro-traffic model of many car compositions is:
When setting up model, number of vehicles is without determining especially, designer the scene considered determines.Such as, designer It is only concerned the car collision in face in front of not, this model the most only two cars；Rear car is not allowed to hit if also kept in mind, just design three The model of car.
1) first car (being defined as a car) is set as freely driving；The state parameter characterizing this car includes acceleration a1(t)、 Speed v1(t), operating range X1T (), is all the function of time t；
Then the relation in three parameters of k+1 moment is as follows:
v1(k+1)=v1(k)+Ts·a1(k)
X 1 ( k + 1 ) = T s · v 1 ( k ) + 1 2 T s 2 · a 1 ( k )
In above formula, k is sampling instant, and TsFor simulation step length, time quantum；a1(k+1) value distribution meets normal distribution, Be average be a1K (), variance is v1The function of (k).
2) and other with sail the vehicle status parameters of vehicle by University of Michigan propose with faulty operation mechanism with Vehicle model (H.Yang, H.Peng, T.J.Gordon, and D.Leblanc, " Development and Validation of an Errorable Car-Following Driver Model,”2008American Control Conference, Pp.3927 3932, Jun.2008.) determine.This rear car car-following model is determined that by University of Michigan.
In step 2, by changing the different control system parameters of same car, long-time random simulation, setting is found Parameter and the relation of occupant injury risk.The kind of the parameter of control system can determine, as hereafter according to Vehicular system design In TTC threshold value, severity of braking be all two indispensable parameters of emergency braking system, optimizable parameter.
1) assume that n-th car is by driver and the vehicle of control to be optimized, then the in corresponding amendment step 1 The car-following model of n car, n ＞ 1.
Corresponding amendment becomes the original of this car into control system to be studied (certain in change system with vehicle control exactly A little parameters) control.Such as study the control strategy of urgent anti-collision system, then controlled vehicle under non-emergent operating mode be then by Drive and control with car, if the precarious position of reaching, be switched to emergency brake operations.
2) for the setting of one group of control parameter, it is by long-time emulation, records n-th car and (n-1)th each time Car collide before and after and the variable information of damage risk existence function relation, or n-th car and (n+1)th car send out Give birth to before and after colliding and the variable information of damage risk existence function relation.
As, as a example by emergency braking system, its key control parameter is TTC threshold value and severity of braking, by long-time imitative Very, the speed variable quantity of n-th car before and after the collision moment of n-th car and (n-1)th car each time and n-th car are recorded Speed variable quantity with n-th car before and after the collision moment of (n+1)th car；
3) utilizing the variable information A collided each time recorded, the Fitting Calculation collides occupant injury wind each time Danger and the relation of this variable information, be designated as:
P (MAISX+)=f1(A)
In formula, MAIS X+ is that occupant's maximum damages deciding grade and level for damage more than X level, P (MAISX+) " expression " occupant injury For risk probability more than X level, this functional relationship is to need to draw according to the data matching in incident database, is not one Individual fixing relation.
As: by upper example, utilize the speed variable quantity each time recorded, calculate collision occupant injury wind every time in conjunction with following formula Danger；
P (MAIS2+)=f1(Δv)
P (MAIS2+) " " occupant injury is the risk probability of more than 2 grades, and Δ v is car before and after vehicle collision to be optimized in expression Speed variable quantity, f1Characterize the functional relationship between occupant injury risk and speed variable quantity, and speed variable quantity can correspondence mappings Go out TTC threshold value and the severity of braking of dynamically change.
4) damage risk of all collisions is sued for peace, and (be exactly this divided by this controlled vehicle in simulation time Vehicle to be optimized) total distance of travelling, obtain being related under this control parameter (such as TTC threshold value and severity of braking) sets accordingly The damage risk of unit operating range:
P d - x y ( M A I S X + ) = Σ P ( M A I S X + ) D
P in formulad-xy(MAIS X+) is the above damage risk of X level of unit operating range, and D is vehicle row in simulation time The total distance sailed, x, y represent two control parameters (being TTC threshold value and severity of braking such as represent in embodiment) of optimization, are The most dynamically amount of change, the possible only one of which of the control number of parameters of optimization, it is also possible to have two or more.
5) for different optimal control parameters, carry out 2 respectively)-4) simulation calculation, record all of Pd-xy (MAIS X+)。
6) data fitting method is utilized to obtain x, y and Pd-xyThe functional relationship of (MAIS X+), the ginseng i.e. set in step 2 Number and the relation of occupant injury risk.
Pd-xy(MAIS X+)=f2(x,y)
F in formula2Characterize the functional relationship between the above damage risk of X level of unit operating range and x, y.
In step 3, first setting up majorized function, recycling modern optimization method solves this function, obtains optimum control The detailed process of systematic parameter is as follows:
1) majorized function is set up as follows:
(x in above formula1,x2) represent the interval of x, (y1,y2) representing the interval of y, concrete interval value is by designing Person sets.
This majorized function implication is, under meeting constraints, to make minf2(x, y) minimum, namely damage risk Pd-xy When (MAIS X+) minimizes, (x y) is optimized parameter to acquired control parameter.
Parameters optimization in above-mentioned majorized function is not unique, can be determined voluntarily by car load factory and driver.Meanwhile, this To minimize damage risk as optimization aim in bright, also it be only used as example, other purpose optimal methods can be used completely.
2) utilize modern optimization method (such as interior point method, steepest Decent Gradient Methods) to solve this function, obtain optimum control Systematic parameter.
The various embodiments described above are merely to illustrate the present invention, and wherein the enforcement step etc. of method all can be varied from, Every equivalents carried out on the basis of technical solution of the present invention and improvement, the most should not get rid of in protection scope of the present invention Outside.

Claims (5)

1. automobile safety system parameter optimization method based on micro-Traffic Flow Simulation Models, it is characterised in that carry out as follows:
1) the micro-traffic model being made up of many cars is set up；
2) change the parameter of different control systems in same car, by long accident random simulation, find not homogeneous to set Fixed parameter to be optimized and the relation of occupant injury risk in accident；
3) set up the majorized function of described parameter to be optimized, utilize modern optimization method to solve this function, obtain optimum control Systematic parameter.
Automobile safety system parameter optimization method based on micro-Traffic Flow Simulation Models the most according to claim 1, its feature It is,
1) in, the method setting up the micro-traffic model being made up of many cars is:
Number of vehicles is at least two cars, if first car is freely to drive, other are with sailing the vehicle status parameters of vehicle by close The car-following model with faulty operation mechanism that Xi Gen university proposes determines.
Automobile safety system parameter optimization method based on micro-Traffic Flow Simulation Models the most according to claim 1, its feature It is,
2) in, find the relational approach of the parameter to be optimized and occupant injury risk set in control system as:
A) assume the vehicle that n-th car is control to be optimized, then corresponding amendment 1) in the car-following model of n-th car, n ＞ 1；
B) one group of parameter to be optimized is set for this car, by long-time emulation, record each time n-th car and n-th- 1 or (n+1)th car have an accident before and after the variable information A with occupant injury risk existence function relation, described variable is believed Breath and relating to parameters to be optimized connection；
C) utilize the variable information A having an accident each time recorded, draw often according to the data matching in incident database Primary collision occupant injury risk and the relation of this variable information, be designated as:
P (MAISX+)=f1(A)
P (MAISX+) " " occupant injury is the risk probability of more than X level in expression；
D) the occupant injury risk of all accidents is sued for peace, and divided by simulation time this vehicle to be optimized travel total Distance, has obtained being related to the damage risk of the unit operating range under this parameter to be optimized accordingly:
P d - x y ( M A I S X + ) = Σ P ( M A I S X + ) D
P in formulad-xy(MAIS X+) is the above damage risk of X level of unit operating range, D be in simulation time vehicle travel total Distance, x, y represent parameter to be optimized, according to Pd-xyThe size of (MAIS X+) determines that parameter is the most desirable；
E) repeat b)-d) simulation calculation, for different parameters optimization to be optimized, record P respectivelyd-xy(MAIS X+) and right The parameter to be optimized answered；
F) data fitting method is utilized to set up the relation of all parameters to be optimized and unified occupant injury risk:
PAlways(MAIS X+)=f2(X,Y)
P in formulaAlways(MAIS X+) represents the unified occupant injury risk containing all parameters to be optimized, and X, Y represent needed excellent The parameter changed, f2Characterize the functional relationship between the above damage risk of X level of unit operating range and parameter to be optimized.
Automobile safety system parameter optimization method based on micro-Traffic Flow Simulation Models the most according to claim 1, its feature It is,
3) in, the majorized function setting up described parameter to be optimized is:
(x in above formula1,x2) represent the interval of X, (y1,y2) representing the interval of Y, concrete interval value is set by designer Fixed；
Under meeting constraints, minf2(X, Y) is minimum, namely occupant injury risk PAlwaysWhen (MAIS X+) minimizes, institute The control parameter obtained is optimized parameter.
5., according to the automobile safety system parameter optimization method based on micro-Traffic Flow Simulation Models described in claim 1 or 4, it is special Levying and be, the modern optimization method of utilization is interior point method or steepest Decent Gradient Methods.
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