CN102320280A - Automatic alarm method for preventing front crash of vehicles at turning - Google Patents

Abstract

The invention discloses an automatic alarm method for preventing front crash of vehicles at a turning, comprising the following steps of: collecting coordinate data via an onboard GPS (Global Position System) device and providing the coordinate data to a filter unit for filtering treatment; calculating a road discrete curvature value according to the filtered data by a curvature calculation unit; judging whether a vehicle runs at the turning or not according to the road discrete curvature value by a turning identification unit; calculating a safe following distance according to a vehicle speed of the current vehicle, a relative distance between the current vehicle and a front vehicle and relative vehicle speed information provided by an onboard CAN (Controller Area Network) bus by a safe following distance calculating module if the current vehicle runs at the turning; calculating a following time interval and a collision avoidance time by a THW and TTC calculation module; calculating a risk state prediction value through a risk state prediction function based on the above parameters by a risk state prediction module; comparing the risk state prediction value with a pre-determined vehicle front crash alarm threshold by an alarm module; and sending a warning prompt to prompt the driver to reduce the speed by the alarm module if the risk state prediction value is equal to or greater than the vehicle front crash alarm threshold. The automatic alarm method can be widely arranged on all kinds of vehicles so that the driving safety of the vehicle is effectively improved.

Description

A kind of automatic alarm method of hitting before the bend vehicle of preventing

Technical field

The present invention relates to a kind of collision prevention of vehicle automatic alarm method, particularly about a kind of automatic alarm method of hitting before the bend vehicle of preventing.

Background technology

Along with the continuous growth of automobile pollution with the number of driving, road traffic accident more and more receives the concern of society.According to road traffic accident statistics in 2009, the traffic accident when occurring in negotiation of bends accounted for more than 10% of total number of accident, and wherein the accident more than 90% is caused by human elements such as driver fatigue, carelessness, errors in judgement.

Important component part as drive assist system; Hit the hour of danger that alerting automatic telling status can hit before the vehicle and send alarm message before vehicle takes place; Remind chaufeur to take appropriate measures avoiding taking place rear-end impact, thereby can improve the driving safety of vehicle effectively.Alarm method is the core technology of hitting before the vehicle in the alerting automatic telling status, has determined the control logic of system, is related to the opportunity that system reports to the police, thereby directly influences the safety performance of system.Existing alarm method mainly is based on the safety distance model, like fixing headway model, fixedly spacing model, kinematics model, numerical model etc., or utilizes artificial intelligence theory etc. to design.Method based on the safety distance pattern layout is comparatively simple, but does not often meet the operating habit of chaufeur; Utilize the artificial intelligence theory to wait the design-calculated method then to be difficult to set up pilot model accurately, and hit in the alerting automatic telling status before being difficult to be applied to actual vehicle.The more important thing is; Existing alarm method mainly is to the straight-line travelling operating mode; Rather than design to the negotiation of bends operating mode; And chaufeur driving behavior characteristic influences design-calculated when seldom considering negotiation of bends, and therefore existing alarm method is relatively poor to the comformability of negotiation of bends operating mode and chaufeur.

Summary of the invention

To the problems referred to above, the purpose of this invention is to provide and a kind ofly can and have better comformability to the negotiation of bends operating mode to chaufeur, have the automatic alarm method that hits before the bend vehicle of preventing of high accuracy simultaneously.

For realizing above-mentioned purpose; The present invention takes following technical scheme: a kind of automatic alarm method of hitting before the bend vehicle of preventing; It may further comprise the steps: 1) in the original vehicle control syetem of vehicle, add and one comprise the road curvature computing module, with the information processing part of car Calculation of Safety Distance module, THW and TTC computing module; And one comprise that risk status estimates the algorithm design part of module and alarm module, and said road curvature computing module comprises filter unit, curvature calculating unit and bend diagnosis unit; 2) utilize original vehicle GPS equipment collection vehicle location information, and the path coordinate data that will characterize vehicle position information offer filter unit and carry out Filtering Processing; 3) the curvature calculating unit is according to filtered path coordinate data computation road discrete curvature value; 4) the bend diagnosis unit judge according to the road discrete curvature value of step 3) gained whether vehicle goes on the bend: if vehicle does not go on bend, return step 2) restart the vehicle position information collection of a new round; If vehicle ' on bend, then gets into next step; 5) with car Calculation of Safety Distance module according to original vehicle-mounted CAN bus provide from the car speed of a motor vehicle, from the relative distance of car and front truck and relatively speed information calculate and follow car safety distance d _w, computing formula is following:

$d_w=\mathrm{v\&tau;}+\frac{v^2}{2a}-\frac{{v_{\mathrm{<figure-callout\; id="2"\; label="f"\; filenames="HDA0000072805300000031.png,HDA0000072805300000041.png"\; state="\{\{state\}\}">f</figure-callout>}}}^2}{2a_f}+{\mathrm{<figure-callout\; id="0"\; label="d"\; filenames="HDA0000072805300000012.png,HDA0000072805300000031.png"\; state="\{\{state\}\}">d</figure-callout>}}_0$

Wherein, τ is the chaufeur brake response time, promptly brakes the time of being experienced from the car braking from front truck, and v is from the car speed of a motor vehicle, v _fBe the front truck speed of a motor vehicle, a is from car deceleration/decel, a _fBe the front truck deceleration/decel, and under limiting condition, think and all do uniformly retarded motion from car and front truck, a=a with the maximum deceleration that traction was allowed _f=ug, u are road-adhesion coefficient, and g is an acceleration due to gravity, d ₀Be the relative distance after car and front truck all stop; 6) THW and TTC computing module according to original vehicle-mounted CAN bus provide from the car speed of a motor vehicle, from the relative distance of car and front truck and when speed information calculates with car relatively apart from THW and collision avoidance time T TC, computing formula is following:

$\mathrm{THW}=\frac{d}{v},$ $\mathrm{TTC}=\frac{d}{v_r}$

Wherein, d is the relative distance from car and front truck, and v is from the car speed of a motor vehicle, v _rBe relative velocity from car and front truck; 7) risk status estimate module according to step 5) obtain with car safety distance and step 6) obtain with car the time distance and collision avoidance time, the driving behavior characteristic of chaufeur when taking into account negotiation of bends simultaneously, the employing risk status is estimated function, its expression formula is following:

$R=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{P}_i\&times;G_i$

Calculate the risk status discreet value R that hits before vehicle takes place when negotiation of bends, wherein, n is the quantity of the hazard event that causes hitting before the vehicle generation, G _iBe the weighted value of hazard event, P _iProbability for the hazard event generation; 8) hitting alarm threshold value before the vehicle that preset alarm module risk status discreet value that step 7) is obtained and its inside compares: if the risk status discreet value is hit alarm threshold value before less than vehicle, return step 2) restart the vehicle position information collection of a new round; If the risk status discreet value is hit alarm threshold value before more than or equal to vehicle, alarm module is made alarm, reminds chaufeur to slow down; At this moment, if chaufeur has been taked brake measure, flow process finishes; Otherwise continue to make alarm.

Curvature calculating unit in the said step 3) adopts based on the method for the adaptive neighborhood window growth of fixed thickness and calculates the road discrete curvature; For any 1 p on the discrete curve point set _i, near neighborhood window Nw of a definite finite length it, the thickness that defines this neighborhood window is c, calculation expression is following:

c＝|hcos(arc?tanS)|

Wherein, h is the thickness of neighborhood window, and S is for connecting the neighborhood window slope of the straight line of two points from beginning to end; For forward direction neighborhood window Nwf, h corresponds to h _f, S corresponds to S _f, c corresponds to c _fTo neighborhood window Nwr, h corresponds to h for the back _r, S corresponds to S _r, c corresponds to c _rWhen the adaptive neighborhood window is grown, to a p _iChosen following regulation: i>=N, conditions must be fulfilled for N: carry out the neighborhood window backward when growing to first from N point, the thickness of neighborhood window must be more than or equal to the initial value c of a predetermined fixed ₀But if the curve that discrete point set is portrayed is a closed curve, N chooses arbitrarily; In addition, in adaptive neighborhood window when growth, also must be with the thickness of the neighborhood window constraint condition as the growth of neighborhood window, makes the height of neighborhood window to change with the variation of the tangential direction of discrete curve; Growth course based on the adaptive neighborhood window of fixed thickness is following: 1) initialization, the thickness c of selected neighborhood window ₀2) from p _iThe point beginning grows linear portion p downwards _ip _I+1, calculate this linear portion slope S _fReach the height h of forward direction neighborhood window Nwf this moment _fAnd the thickness c of this moment _f: if c _f＜c ₀, then forward direction neighborhood window Nwf continues to next abutment points growth; If c _f>=c ₀, then forward direction neighborhood window Nwf stops growing, and gets into step 3); 3) from p _iPoint begins upwards a bit to grow linear portion p _ip _I-1, calculate this linear portion slope S _rAnd back at this moment height h to neighborhood window Nwr _rAnd the thickness c of this moment _r: if c _r＜c ₀, then upwards abutment points growth is continued to neighborhood window Nwr in the back; If c _r>=c ₀, then the back stops growing to neighborhood window Nwr; Through the process of above-mentioned iteration, for concentrated any 1 p of discrete curve point _i, i>=N, the neighborhood window Nwf that grows forward is by discrete point set p _i, p _I+1..., p _I+fForm, its corresponding point coordinate is (x _i, y _i) ..., (x _I+f, y _I+f); The neighborhood window Nwr that grows backward is by discrete point set p _i, p _I-1..., p _I-rForm, its corresponding point coordinate is (x _i, y _i) ..., (x _I-r, y _I-r), for concentrated any 1 p of discrete curve point _i, i>=N is with the curve arc long l of its neighborhood window process of growing forward _fThe curve arc long l of the neighborhood window process of growing backward _rCarry out approximate calculation with the Euclidean distance of corresponding neighborhood window edge point respectively, that is:

$l_f=\sqrt{{(x_{i+f}-x_i)}^2+{(y_{i+f}-y_i)}^2}$

$l_r=\sqrt{{(x_{i-r}-x_i)}^2+{(y_{i-r}-y_i)}^2}$

To pass through the straight line p of neighborhood window edge point _ip _I+f, p _I-rp _iBe approximately the tangent line of the curve of this neighborhood window process, make θ _f=arctan (S _f), θ _r=arctan (S _r), then according to the defined formula of curvature at continuous space, some p _iThe Curvature Estimation value ρ (p at place _i) be:

$\mathrm{\&rho;}\left(p_i\right)=\frac{1}{2}|{\mathrm{\&theta;}}_f-{\mathrm{\&theta;}}_r|(\frac{1}{l_f}+\frac{1}{l_r})$

The module of estimating risk status in the said step 8) adopts risk status to estimate function, and its expression formula is following:

$R=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{P}_i\&times;G_i$

When calculating the risk status discreet value of hitting before vehicle takes place when negotiation of bends, to the hazard event that hits before causing vehicle to take place adopt chaufeur bend following distance, distance and collision avoidance time three parameters are described when following car, the n value is 3.

Risk status in the said step 8) is estimated module and is being adopted risk status to estimate function, and its expression formula is following:

$R=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{P}_i\&times;G_i$

When calculating the risk status discreet value of hitting before vehicle takes place when negotiation of bends, utilize the weighted value G of adaptive weighted blending algorithm to the hazard event that causes hitting before the vehicle generation _iCarrying out optimum distributes in real time.

Alarm module in the said step 8) hits the risk status estimation results before utilizing the bend vehicle, and consider time of driver's reaction the safe clearance that should reserve, confirm to hit alarm threshold value before the bend vehicle.

Hit alarm threshold value before the said bend vehicle and be divided into two grades: elementary alarm threshold value and senior alarm threshold value.

The present invention is owing to take above technical scheme; It has the following advantages: 1, the present invention is owing to adopt Kalman filtering algorithm that the path coordinate data of GPS equipment collection are carried out Filtering Processing; Reduced the influence of gps system random error effectively, therefore made the road discrete curvature value of calculating based on these coordinate datas comparatively accurate.2, the present invention has improved the precision that curvature is calculated owing to adopted a kind of discrete curvature method of calculating of growing based on the adaptive neighborhood window of fixed thickness.3, the present invention estimates function owing to introduced risk status; And adopting adaptive weighted blending algorithm that the weighted value of the hazard event that hits before can causing vehicle to take place has been carried out optimum real-time distribution, the precarious position of hitting before therefore can taking place the bend vehicle estimated comparatively exactly.4, the present invention has been owing to combined vehicle movement to learn model and from car and front truck state, the driving behavior characteristic of chaufeur when having considered negotiation of bends simultaneously, so improved the comformability of automatic alarm method to bend operating mode and chaufeur.5, the present invention can be widely used on various types of vehicles, improves the driving safety of vehicle effectively.

Description of drawings

Fig. 1 is that structure of the present invention is formed scheme drawing

Fig. 2 is based on the drawn road curve comparison diagram of gps coordinate data before and after the Kalman filtering

Fig. 3 is the definition scheme drawing of curvature at continuous space

Fig. 4 is that the discrete curvature that the present invention is based on tangential direction is calculated scheme drawing

Fig. 5 is a parabolic curvature result of calculation comparison diagram of the present invention

Fig. 6 is that the present invention follows car Calculation of Safety Distance scheme drawing

Fig. 7 is that bend is near the THW cumulative frequency distribution graph constantly of chaufeur release the accelerator pedal under run rider's condition

Fig. 8 is that bend is near the TTC cumulative frequency distribution graph constantly of chaufeur release the accelerator pedal under run rider's condition

Fig. 9 is a workflow scheme drawing of the present invention

The specific embodiment

Below in conjunction with accompanying drawing and embodiment the present invention is carried out detailed description.

As shown in Figure 1, the present invention is made up of information acquisition part 1, information processing part 2 and algorithm design part 3.

Information acquisition part 1 of the present invention comprises vehicle GPS (Global Positioning System, global positioning system) equipment 11 and vehicle-mounted CAN (Controller Area Network) bus 12.GPS equipment 11 is responsible for gathering the path coordinate data that characterize vehicle position information; Vehicle-mounted CAN bus 12 is responsible for providing from the car speed of a motor vehicle, the relative distance from car and front truck, relative speed information.

Information processing part 2 of the present invention comprises road curvature computing module 21, with car Calculation of Safety Distance module 22, THW (Time Headway, during apart from) and TTC (Time to Collision, collision avoidance time) computing module 23 with car.Wherein, road curvature computing module 21 is made up of filter unit 211, curvature calculating unit 212 and bend diagnosis unit 213.

The function of the filter unit 211 of road curvature computing module 21 is that the coordinate data that GPS equipment 11 provides is carried out Kalman filtering; Reducing of the influence of all kinds of random errors, thereby improve the precision of the road discrete curvature of calculating based on these coordinate datas to these coordinate datas.Say from physical significance; In the gps coordinate system; These three physical quantitys of position, speed and acceleration/accel do not have positive connection each other at the component of the x of road direction and y direction; Be mutual decoupling zero, therefore can equation of state be decomposed into x direction equation and y direction equation, and two equations have identical structure.With the x direction is example, and the discrete state equations of x direction is as follows with the measurement equation:

Wherein, X _kBe discrete state matrix of variables, X _k=[x _k, v _Xk, a _Xk, ε _Xk] ^T, each element in this matrix is represented total positional error that cause in the x direction at component and each error source of x direction position, speed, these three physical quantitys of acceleration/accel respectively; Z _kBe observational variable matrix, Z _k=[x _k].K=1,2 ... N _c, N _cFor coordinate acquisition is counted.

is state-transition matrix, and its representation is:

T is the sampling time, sets T=1/30s; τ ₁, τ ₂Be time constant, set τ ₁=1s, τ ₂=0.01s; E is an exponential constant; H is for measuring transfer matrix, H=[1 00 1]; W _kBe the process noise matrix; N _kFor measuring noise matrix.

Following based on the optimum calculation expression of the road x direction parameter of Kalman filter:

${\hat{X}}_k={\hat{X}}_k^{-}+K_k(Z_k-H_k{\hat{X}}_k^{-})$

In the following formula,

Be X _kThe best guess value,

Be X _kPriori state estimation value, K _kBe kalman gain matrix, H _k=H=[1 00 1], recursive process is following:

$K_k=P_k^{-}{H}_k^T{(H_k{P}_k^{-}{H}_k^T+B_k)}^{-1}$

$P_k=(I-K_k{H}_k)P_k^{-}$

Wherein, P _kBe best guess value error covariance matrix,

Be priori estimates error covariance matrix, Q _kBe process noise covariance matrix, B _kFor measuring noise covariance matrix, I is an identity matrix.

Given initial state vector X ₀=[0 00 0] ^T

Initial best guess value error covariance matrix $P_{}$ ${}_0=\left[\begin{array}{cccc}0& 0& 0& 0\\ 0& 0& 0& 0\\ 0& 0& 0& 0\\ 0& 0& 0& 0\end{array}\right];$ $Q_k=\left[\begin{array}{cccc}0& 0& 0& 0\\ 0& 0& 0& 0\\ 0& 0& {{\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="HDA0000072805300000041.png"\; state="\{\{state\}\}">s</figure-callout>}}_1}^2& 0\\ 0& 0& 0& {{\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000072805300000031.png,HDA0000072805300000041.png"\; state="\{\{state\}\}">s</figure-callout>}}_2}^2\end{array}\right];$

B _k=[s ₃ ²], s wherein ₁ ², s ₂ ²Be state of the system variance, s ₃ ²Be the systematic survey variance, their value is respectively: s ₁ ²=0.01; s ₂ ²=0.1; s ₃ ²=10 ²

According to above-mentioned Kalman filtering recursive algorithm the coordinate data of the x of gps coordinate system direction is carried out filtering, adopt same procedure that the coordinate data of y direction is handled simultaneously.As shown in Figure 2, with comparing based on the drawn road curve of gps coordinate data before and after the filtering, can know that Kalman filtering can reduce the influence of the path coordinate data that all kinds of random errors are gathered GPS equipment 11 effectively.

The function of the curvature calculating unit 212 of road curvature computing module 21 is based on through the path coordinate The data after the Kalman filtering processing and calculates road discrete curvature value based on the method for the adaptive neighborhood window growth of fixed thickness, and the result is offered bend diagnosis unit 213.

As shown in Figure 3, curvature defines as follows in continuous space: make p _iAnd p _jBe the point on the curve 1, δ is the tangent line forward angle on these two points, then p _iCurvature ρ (p on the point _i) may be defined as and work as | p _ip _j| → 0 o'clock, δ and | p _ip _j| the ratiometric limit, that is:

$\mathrm{\&rho;}\left(p_i\right)=\underset{\left|p_i{p}_j\right|\&RightArrow;0}{\lim }\frac{\mathrm{\&delta;}}{\left|p_i{p}_j\right|}$

As shown in Figure 4, realize the local curvature's calculating on the discrete curve point set in order to utilize following formula, for any 1 p on the discrete curve point set _i, at first need confirm the neighborhood window Nw of a finite length, carry out tangent directional angle and arc length then on the finite point set in Nw and calculate, and utilize computing value to carry out curvature and calculate.Its key point is confirming of neighborhood length of window and tangential direction.More existing research techniquies are usually with the constraint condition of neighborhood window maximum height as the growth of neighborhood window.Though these class methods are calculated simple, relatively more responsive to the variation of curve tangential direction, can't guarantee in a big way, to have higher design accuracy.For this reason; Propose a kind of adaptive discrete curvature method of calculating among the present invention,, make the length of neighborhood window under this constraint condition, can change along with the variation of tangential direction through the fixing thickness of neighborhood window; To improve the adaptive capacity that tangential direction is changed, improve the precision that curvature is calculated.

Suppose straight line p _ip _I+fEquation be y=S _fX+b _f, S wherein _fBe straight line p _ip _I+fSlope, b _fBe straight line p _ip _I+fWith the ordinate of y axle intersection point, the height h of forward direction neighborhood window Nwf then _fDefine as follows:

h _f＝max|y _j-(S _fx _j+b _f)|，j＝i，i+1，...，i+f

Wherein, (x _j, y _j), j=i, i+1 ..., i+f is respectively discrete point p _i, p _I+1..., p _I+fCoordinate.The back is to the height h of neighborhood window Nwr _rCalculating formula and following formula similar.As can beappreciated from fig. 4, even h _fEqual h _r, because straight line p _ip _I+fSlope greater than straight line p _ip _I-rSlope, so straight line p _ip _I+fWith the maximum absolute distance of discrete point in the Nwf will be less than straight line p _ip _I-rWith the maximum absolute distance of discrete point in the Nwr, this moment, in fact the growth of neighborhood window Nwf and Nwr had different accuracy standards, the foundation that therefore can not the height of neighborhood window be grown as the neighborhood window.Therefore, the thickness c of definition neighborhood window is:

c＝|hcos(arc?tanS)|

In the following formula, S is for connecting the neighborhood window slope of the straight line of two points from beginning to end, and for forward direction neighborhood window Nwf, S corresponds to S _f, to neighborhood window Nwr, S corresponds to S for the back _rH is the height of neighborhood window, and for forward direction neighborhood window Nwf, h corresponds to h _f, to neighborhood window Nwr, h corresponds to h for the back _rCan find out from following formula, when the fixed thickness of neighborhood window is some initial value c ₀The time; Increase along with straight slope; The height of neighborhood window also can guarantee so no matter how the discrete curve tangential direction changes, and the growth of neighborhood window all has equal accuracy requirement along with increase; Therefore growing based on the neighborhood window of fixed thickness, variation has certain adaptivity to tangential direction, can improve the ability that curvature is calculated greatly.

For concentrated any 1 p of discrete curve point _i, i>=N, N chooses that conditions must be fulfilled: carry out the neighborhood window backward when growing to first from N point, the thickness c>=c of neighborhood window ₀But if the curve that discrete point set is portrayed is a closed curve, N chooses arbitrarily.Growth course based on the adaptive neighborhood window of fixed thickness is following:

1) initialization, the thickness c of selected neighborhood window ₀

2) from p _iThe point beginning grows linear portion p downwards _ip _I+1, calculate this linear portion slope S _fReach the height h of neighborhood window Nwf this moment _fAnd the thickness c of this moment

If c _f＜c ₀, then forward direction neighborhood window Nwf continues to next abutment points growth;

If c _f>=c ₀, then forward direction neighborhood window Nwf stops growing, and gets into step 3);

3) from p _iPoint begins upwards a bit to grow linear portion p _ip _I-1, calculate this linear portion slope S _rReach the height h of neighborhood window Nwr this moment _rAnd the thickness c of this moment _r:

If c _r＜c ₀, then upwards abutment points growth is continued to neighborhood window Nwr in the back;

If c _r>=c ₀, then the back stops growing to neighborhood window Nwr.

Through the process of above iteration, for concentrated any 1 p of discrete curve point _i, i>=N, the neighborhood window Nwf that grows forward is by discrete point set p _i, p _I+1..., p _I+fForm, its corresponding point coordinate is (x _i, y _i) ..., (x _I+f, y _I+f); The neighborhood window Nwr that grows backward is by discrete point set p _i, p _I-1..., p _I-rForm, its corresponding point coordinate is (x _i, y _i) ..., (x _I-r, y _I-r).

For concentrated any 1 p of discrete curve point _i, i>=N is with the curve arc long l of its neighborhood window process of growing forward _fThe curve arc long l of the neighborhood window process of growing backward _rCarry out approximate calculation with the Euclidean distance of corresponding neighborhood window edge point respectively, that is:

$l_f=\sqrt{{(x_{i+f}-x_i)}^2+{(y_{i+f}-y_i)}^2}$

$l_r=\sqrt{{(x_{i-r}-x_i)}^2+{(y_{i-r}-y_i)}^2}$

To pass through the straight line p of neighborhood window edge point _ip _I+f, p _I-rp _iBe approximately the tangent line of the curve of this neighborhood window process, make θ _f=arctan (S _f), θ _r=arctan (S _r), then according to the defined formula of curvature at continuous space, some p _iThe curvature computing value at place is:

$\mathrm{\&rho;}\left(p_i\right)=\frac{1}{2}|{\mathrm{\&theta;}}_f-{\mathrm{\&theta;}}_r|(\frac{1}{l_f}+\frac{1}{l_r})$

As shown in Figure 5, verify for validity the discrete curvature method of calculating of above-mentioned adaptive neighborhood window growth based on fixed thickness, choose the parabola that is shown below as subjects.

y＝0.5x ²+2x+1

According to the curvature method of calculating, this parabolical theoretical curvature expression formula is:

$\mathrm{\&rho;}\left(x\right)=\left|\frac{y^{\&prime;\&prime;}}{{(1+y^{\&prime;2})}^{3/2}}\right|=\left|\frac{1}{{[1+{(x+2)}^2]}^{3/2}}\right|$

The parabola expression formula is carried out discretization; Obtain one group of discrete point; With the discrete curvature method of calculating that proposes among the discrete curvature method of calculating of standing height and the present invention it is carried out discrete curvature respectively and calculate, utilize the expression formula shown in the following formula that its theoretical curvature is calculated simultaneously based on the adaptive neighborhood window growth of fixed thickness.When choosing suitable height threshold and thickness threshold value; The discrete curvature method of calculating based on the adaptive neighborhood window growth of fixed thickness that proposes based on the discrete curvature method of calculating of standing height and the present invention all has higher precision; The error of curvature that calculates all can remain in 5%, but the overall precision of the discrete curvature method of calculating that the adaptive neighborhood window based on fixed thickness that the present invention proposes is grown is apparently higher than the former.In addition, because the height of neighborhood window can not reflect the realistic accuracy of field window growth, therefore when the constraint condition of growing as the neighborhood window with standing height, its error of curvature that calculates presents the ccasual variation tendency; And based on the discrete curvature method of calculating of the adaptive neighborhood window of fixed thickness growth because the growth precision of neighborhood window remains constant; Therefore its error of curvature variation tendency is obvious; The actual curvature that is curve is big more; The error of its computing curvature is big more, and the predictable characteristics of this error variation tendency of adaptive discrete curvature method of calculating based on fixed thickness help the utilization of some other error compensating method.

The function of the bend diagnosis unit 213 of road curvature computing module 21 is to converse corresponding road curvature radius through the road discrete curvature value that curvature calculating unit 212 is exported, and judges according to its size whether vehicle goes on bend.

$r\left(p_i\right)=\frac{1}{\mathrm{\&rho;}\left(p_i\right)}$

Wherein, r is the road curvature radius.

Confirm that the criterion of vehicle ' on bend is following:

r≤2000m

Information processing part 2 with the function of car Calculation of Safety Distance module 22 be vehicle-mounted CAN bus 12 according to information acquisition part 1 provide from the car speed of a motor vehicle, from the relative distance of car and front truck, the speed information calculating safety distance that should keep from car and front truck relatively for fear of car to car impact.

As shown in Figure 6, from car near the front truck process in, suppose from the car chaufeur at t ₀Constantly find the front truck braking, and at t ₁Start braking constantly, and at t ₂Two cars are all static constantly.The τ during this period of time that brakes from car braking process from front truck is called the chaufeur brake response time, supposes at the uniform velocity to go with speed v in time τ from car, does uniformly retarded motion from car afterwards, is a from the car deceleration/decel; Front truck is with rate of onset v _fDo uniformly retarded motion, the front truck deceleration/decel is a _fDistance from car and front truck during parking is d ₀, then at t ₀The safety distance d that constantly, should keep from car and front truck for fear of car to car impact _w, promptly with the car safety distance computing formula following:

$d_w=\mathrm{v\&tau;}+\frac{{\mathrm{<figure-callout\; id="2"\; label="v"\; filenames="HDA0000072805300000031.png,HDA0000072805300000041.png"\; state="\{\{state\}\}">v</figure-callout>}}^2}{2a}-\frac{{{\mathrm{<figure-callout\; id="2"\; label="v\; f"\; filenames="HDA0000072805300000031.png,HDA0000072805300000041.png"\; state="\{\{state\}\}">v</figure-callout>}}_f}^2}{2a_f}+{\mathrm{<figure-callout\; id="0"\; label="d"\; filenames="HDA0000072805300000012.png,HDA0000072805300000031.png"\; state="\{\{state\}\}">d</figure-callout>}}_0$

Simultaneously, think that under limiting condition two cars all slow down with the maximum deceleration that traction was allowed, and two car deceleration/decels are equal, establish its expression formula to be:

a＝a _f＝ug

In the following formula, u is a road-adhesion coefficient, and g is an acceleration due to gravity.

The function of the THW of information processing part 2 and TTC computing module 23 be according to vehicle-mounted CAN bus 12 provide from the car speed of a motor vehicle, from the relative distance of car and front truck, distance and collision avoidance time when speed information calculates with car relatively.

Following during with car apart from the computing formula of THW and collision avoidance time T TC:

$\mathrm{THW}=\frac{d}{v},$ $\mathrm{TTC}=\frac{d}{v_r}$

Wherein, d is from car and leading vehicle distance, and v is from the car speed of a motor vehicle, v _rBe relative velocity from car and front truck.

In addition; Like Fig. 7, shown in Figure 8; In order to obtain chaufeur driving behavior characteristic during near front truck when bend is driven; The present invention handles and analyzes through the bend chaufeur real vehicle observed data to 18 chaufeurs, obtained chaufeur bend near the front truck operating mode under during release the accelerator pedal with car the time distance and collision avoidance accumulated time frequency distribution situation.Because preventing the auto alarm function of hitting before the vehicle is to emergency situation; Should not cause interference to the normal running of chaufeur; And 5% frequency cooresponding when the car distance be the most urgent interval of its situation with the value of collision avoidance time, therefore select the cooresponding secure threshold parameter THW of chaufeur driving behavior characteristic when distance and the value of collision avoidance time are as the sign negotiation of bends during with car of each distribution graph frequency 5% _wAnd TTc _w:

THW _w＝0.5s，TTC _w＝5s

Algorithm design part 3 of the present invention comprises that risk status estimates module 31 and alarm module 32.The function that risk status is estimated module 31 is to adopt risk status to estimate function; In conjunction with by provide with car Calculation of Safety Distance module 22 with the car safety distance with THW and TTC computing module 23 provide with car the time secure threshold parameter of chaufeur driving behavior characteristic when distance and collision avoidance time and sign negotiation of bends; The possibility of hitting before vehicle taken place when the negotiation of bends is estimated output risk status discreet value R.

It is following that risk status is estimated function expression:

$R=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{P}_i\&times;G_i$

Wherein, n is the quantity of the hazard event that hits before causing vehicle to take place, G _iBe the weighted value of hazard event, promptly the degree of tortuosity that the back can cause, P take place in hazard event _iProbability for the hazard event generation.In the present invention, because hazard event is to describe through these three parameters of chaufeur bend following distance, THW and TTC, so n=3, following formula can be changed into:

R＝p ₁×G ₁+P ₂×G ₂+P ₃×G ₃

For confirming the value of risk status discreet value R, at first need calculate three hazard event probability of occurrence P ₁, P ₂, P ₃, calculation expression is following:

${\mathrm{<figure-callout\; id="1"\; label="P"\; filenames="HDA0000072805300000041.png"\; state="\{\{state\}\}">P</figure-callout>}}_1=\left\{\begin{array}{cc}\frac{d_w}{d}& d>d_w\\ 1& d\&le;{\mathrm{<figure-callout\; id="2"\; label="d\; w\; P"\; filenames="HDA0000072805300000031.png,HDA0000072805300000041.png"\; state="\{\{state\}\}">d</figure-callout>}}_w& \\ \end{array},\right.$ $P_{}$ ${}_2=\left\{\begin{array}{cc}\frac{{\mathrm{THW}}_w}{\mathrm{THW}}& \mathrm{THW}>{\mathrm{THW}}_{\mathrm{<figure-callout\; id="1"\; label="w"\; filenames="HDA0000072805300000041.png"\; state="\{\{state\}\}">w</figure-callout>}}\\ 1& \mathrm{THW}\&le;{\mathrm{<figure-callout\; id="3"\; label="THW\; w\; P"\; filenames="HDA0000072805300000041.png,HDA0000072805300000042.png"\; state="\{\{state\}\}">THW</figure-callout>}}_w& \\ \end{array},\right.$ $P_{}$ ${}_3=\left\{\begin{array}{cc}\frac{{\mathrm{TTC}}_w}{\mathrm{TTC}}& \mathrm{TTC}>{\mathrm{<figure-callout\; id="1"\; label="TTC\; w"\; filenames="HDA0000072805300000041.png"\; state="\{\{state\}\}">TTC</figure-callout>}}_w\\ 1& 0<\mathrm{TTC}\&le;{\mathrm{<figure-callout\; id="0"\; label="TTC\; w"\; filenames="HDA0000072805300000012.png,HDA0000072805300000031.png"\; state="\{\{state\}\}">TTC</figure-callout>}}_w\\ 0& \mathrm{TTC}\&le;0\end{array}\right.$

For confirming the value of risk status discreet value R, also need the weighted value G of three hazard events ₁, G ₂And G ₃, the present invention adopts adaptive weighted blending algorithm that the weighted value of these three hazard events is carried out optimum distribution in real time.Adaptive weighted blending algorithm can based on the size of each measured value, be sought corresponding with it optimal weights value through adaptive mode satisfying under the minimum condition of population variance.

If three probability observed reading P that hazard event takes place ₁, P ₂, P ₃Variance be respectively σ ₁ ², σ ₂ ², σ ₃ ², establish them and be mutually independent, and calculate for not having partially; The weighted value of each hazard event is respectively G ₁, G ₂, G ₃And corresponding probability observed reading P ₁, P ₂, P ₃Carry out weighting fusion, after the fusion

Value satisfies following formula:

$\left\{\begin{array}{c}\hat{P}=\underset{i=1}{\overset{3}{\mathrm{\&Sigma;}}}{G}_i{P}_i\\ \underset{i=1}{\overset{3}{\mathrm{\&Sigma;}}}{G}_i=1\end{array}\right.$

According to adaptive weighted blending algorithm, satisfy total square deviation hour pairing optimal weights value G _i ^*Calculating formula is following:

$G_i^{*}=\frac{1}{{\mathrm{\&sigma;}}_i^2\underset{i=1}{\overset{3}{\mathrm{\&Sigma;}}}\frac{1}{{\mathrm{\&sigma;}}_i^2}}(i=\mathrm{1,2,3})$

Wherein, variances sigma _i ²Can obtain through computes:

σ _i ²＝E[e _i ²]＝q _ii-q _ij(i≠j；i，j＝1，2，3)

In the following formula, q _IiBe P _iAuto-covariance function, q _IjBe P _i, P _jCross covariance function.If measuring number of times is k, then the expression formula of their time domain computing value is following:

$q_{\mathrm{ii}}\left(k\right)=\frac{1}{k-1}{q}_{\mathrm{ii}}(k-1)+\frac{1}{k}{P}_i\left(k\right)P_i\left(k\right)$

$q_{\mathrm{ij}}\left(k\right)=\frac{1}{k-1}{q}_{\mathrm{ij}}(k-1)+\frac{1}{k}{P}_i\left(k\right)P_j\left(k\right)$

$(i\&NotEqual;j;i,j=\mathrm{1,2,3})$

As stated, utilize the observed reading of hazard event probability of occurrence to try to achieve q _IiAnd q _IjThe time domain computing value, thereby calculate the variances sigma of each observed reading _i ²Utilize σ then _i ²Obtain the corresponding optimal weights value of each hazard event G _i ^*

The probability observed reading P that will take place according to three hazard events that said method is confirmed ₁, P ₂, P ₃With corresponding optimal weights value

The substitution risk status is estimated function expression and is carried out weighting fusion, promptly can draw to hit risk status discreet value R before the present invention judges the required bend vehicle of auto alarm, and its expression formula is as follows:

$R={\mathrm{<figure-callout\; id="1"\; label="P"\; filenames="HDA0000072805300000041.png"\; state="\{\{state\}\}">P</figure-callout>}}_1\&times;{\mathrm{<figure-callout\; id="1"\; label="G"\; filenames="HDA0000072805300000041.png"\; state="\{\{state\}\}">G</figure-callout>}}_1^{*}+{\mathrm{<figure-callout\; id="2"\; label="P"\; filenames="HDA0000072805300000031.png,HDA0000072805300000041.png"\; state="\{\{state\}\}">P</figure-callout>}}_2\&times;{\mathrm{<figure-callout\; id="2"\; label="G"\; filenames="HDA0000072805300000031.png,HDA0000072805300000041.png"\; state="\{\{state\}\}">G</figure-callout>}}_2^{*}+{\mathrm{<figure-callout\; id="2"\; label="P"\; filenames="HDA0000072805300000031.png,HDA0000072805300000041.png"\; state="\{\{state\}\}">P</figure-callout>}}_2\&times;{\mathrm{<figure-callout\; id="2"\; label="G"\; filenames="HDA0000072805300000031.png,HDA0000072805300000041.png"\; state="\{\{state\}\}">G</figure-callout>}}_2^{*}$

The function of the alarm module 32 of algorithm design part 3 is risk status to be estimated hit alarm threshold value before the vehicle that risk status discreet value R that module 31 provides and its inside presets and compare, and whether decision makes alarm, and its concrete rule is as follows:

In this alarm rule,, hit alarm threshold value before the vehicle that presets and be made as 0.8 owing to the safe clearance of considering that time of driver's reaction is reserved.

The present invention can also be divided into two grades with alarm threshold value: elementary alarm threshold value 0.8 and senior alarm threshold value 1.0.If risk status discreet value R less than elementary alarm threshold value 0.8, does not then report to the police; If risk status discreet value R is more than or equal to elementary alarm threshold value 0.8 and less than senior alarm threshold value 1.0, alarm is employed in the mode of display text information on original vehicle fluorescent screen and reports to the police; If risk status discreet value R more than or equal to senior alarm threshold value 1.0, then also sends voice suggestion and reports to the police in display text information.

Like Fig. 1, shown in Figure 9; Workflow of the present invention is to utilize the vehicle GPS equipment 11 collection vehicle location informations of information acquisition part 1; The filter unit 211 that the path coordinate data that characterize vehicle position information is offered the road curvature computing module 21 of information processing part 2 carries out the Kalman filtering processing; Curvature calculating unit 212 calculates the road discrete curvature based on the method for the adaptive neighborhood window growth of fixed thickness according to the utilization of filtered path coordinate data, and output road discrete curvature value is given bend diagnosis unit 213; Bend diagnosis unit 213 judge according to the road discrete curvature value of input whether vehicle goes on the bend: if vehicle does not go on bend, return the vehicle position information collection that section start restarts a new round; If vehicle ' on bend, is then gathered from the car speed of a motor vehicle, from the relative distance and the relative speed information of car and front truck from the vehicle-mounted CAN bus of information acquisition part 1 12; According to these information processing sections 2 with car Calculation of Safety Distance module 22 calculate with the car safety distance, distance and collision avoidance time when THW and TTC computing module 23 calculate with car; And the risk status that The above results all imports algorithm design part 3 estimated module 31; Risk status is estimated module 31 introducing risk status and is estimated function; And utilize adaptive weighted blending algorithm that the weighted value of three hazard events that hit before can causing vehicle to take place is carried out optimum distribution in real time; Hit the risk status discreet value before after weighting fusion, calculating the bend vehicle; And export to alarm module 32; Hitting alarm threshold value before the vehicle that alarm module 32 presets risk status discreet value and its inside compares: if the risk status discreet value is hit alarm threshold value before less than vehicle, represent that then vehicle is in a safe condition, flow process finishes to return step 2) restart the vehicle position information collection of a new round; If the risk status discreet value is hit alarm threshold value before more than or equal to vehicle, represent that then vehicle is in the precarious position of hitting before being about to take place, hit alarm before sending the bend vehicle, remind chaufeur to slow down; At this moment, if chaufeur has been taked brake measure, flow process finishes; Otherwise continue to send alarm.

The confirming of the method for calculating of road discrete curvature of the present invention, the hazard event that hits before causing the bend vehicle to take place, with and the distribution method of weighted value can change to some extent.On the basis of technical scheme of the present invention,, should not get rid of outside protection scope of the present invention improvement and the equivalents that indivedual methods are carried out.

Claims (10)

1. automatic alarm method that prevents to hit before the bend vehicle, it may further comprise the steps:

1) in the original vehicle control syetem of vehicle, add one comprise the road curvature computing module, with the information processing part of car Calculation of Safety Distance module, THW and TTC computing module; And one comprise that risk status estimates the algorithm design part of module and alarm module, and said road curvature computing module comprises filter unit, curvature calculating unit and bend diagnosis unit;

2) utilize original vehicle GPS equipment collection vehicle location information, and the path coordinate data that will characterize vehicle position information offer filter unit and carry out Filtering Processing;

3) the curvature calculating unit is according to filtered path coordinate data computation road discrete curvature value;

4) the bend diagnosis unit judge according to the road discrete curvature value of step 3) gained whether vehicle goes on bend:

If vehicle does not go on bend, return step 2) restart the vehicle position information collection of a new round;

If vehicle ' on bend, then gets into next step;

5) with car Calculation of Safety Distance module according to original vehicle-mounted CAN bus provide from the car speed of a motor vehicle, from the relative distance of car and front truck and relatively speed information calculate and follow car safety distance d _w, computing formula is following:

$d_w=\mathrm{v\&tau;}+\frac{v^2}{2a}-\frac{{v_f}^2}{2a_f}+d_0$

Wherein, τ is the chaufeur brake response time, promptly brakes the time of being experienced from the car braking from front truck, and v is from the car speed of a motor vehicle, v _fBe the front truck speed of a motor vehicle, a is from car deceleration/decel, a _fBe the front truck deceleration/decel, and under limiting condition, think and all do uniformly retarded motion with the maximum deceleration that traction was allowed from car and front truck,, a=a _f=ug, u are road-adhesion coefficient, and g is an acceleration due to gravity, d ₀Be the relative distance after car and front truck all stop;

6) THW and TTC computing module according to original vehicle-mounted CAN bus provide from the car speed of a motor vehicle, from the relative distance of car and front truck and when speed information calculates with car relatively apart from THW and collision avoidance time T TC, computing formula is following:

$\mathrm{THW}=\frac{d}{v},$ $\mathrm{TTC}=\frac{d}{v_r}$

Wherein, d is the relative distance from car and front truck, and v is from the car speed of a motor vehicle, v _rBe relative velocity from car and front truck;

7) risk status estimate module according to step 5) obtain with car safety distance and step 6) obtain with car the time distance and collision avoidance time, the driving behavior characteristic of chaufeur when taking into account negotiation of bends simultaneously, the employing risk status is estimated function, its expression formula is following:

$R=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{P}_i\&times;G_i$

Calculate the risk status discreet value R that hits before vehicle takes place when negotiation of bends, wherein, n is the quantity of the hazard event that causes hitting before the vehicle generation, G _iBe the weighted value of hazard event, P _iProbability for the hazard event generation;

8) hitting alarm threshold value before the vehicle that preset alarm module risk status discreet value that step 7) is obtained and its inside compares:

If the risk status discreet value is hit alarm threshold value before less than vehicle, return step 2) restart the vehicle position information collection of a new round;

If the risk status discreet value is hit alarm threshold value before more than or equal to vehicle, alarm module is made alarm, reminds chaufeur to slow down; At this moment, if chaufeur has been taked brake measure, flow process finishes; Otherwise continue to make alarm.

2. a kind of automatic alarm method of hitting before the bend vehicle of preventing as claimed in claim 1 is characterized in that: the curvature calculating unit in the said step 3) adopts based on the method for the adaptive neighborhood window growth of fixed thickness and calculates the road discrete curvature; For any 1 p on the discrete curve point set _i, near neighborhood window Nw of a definite finite length it, the thickness that defines this neighborhood window is c, calculation expression is following:

c＝|hcos(arctanS)|

Wherein, h is the thickness of neighborhood window, and S is for connecting the neighborhood window slope of the straight line of two points from beginning to end; For forward direction neighborhood window Nwf, h corresponds to h _f, S corresponds to S _f, c corresponds to c _fTo neighborhood window Nwr, h corresponds to h for the back _r, S corresponds to S _r, c corresponds to c _rWhen the adaptive neighborhood window is grown, to a p _iChosen following regulation: i>=N, conditions must be fulfilled for N: carry out the neighborhood window backward when growing to first from N point, the thickness of neighborhood window must be more than or equal to the initial value c of a predetermined fixed ₀But if the curve that discrete point set is portrayed is a closed curve, N chooses arbitrarily; In addition, in adaptive neighborhood window when growth, also must be with the thickness of the neighborhood window constraint condition as the growth of neighborhood window, makes the height of neighborhood window to change with the variation of the tangential direction of discrete curve; Growth course based on the adaptive neighborhood window of fixed thickness is following:

1) initialization, the thickness c of selected neighborhood window ₀

2) from p _iThe point beginning grows linear portion p downwards _ip _I+1, calculate this linear portion slope S _fReach the height h of neighborhood window Nwf this moment _fAnd the thickness c of this moment _f:

If c _f＜c ₀, then forward direction neighborhood window Nwf continues to next abutment points growth;

If c _f>=c ₀, then forward direction neighborhood window Nwf stops growing, and gets into step 3);

3) from p _iPoint begins upwards a bit to grow linear portion p _ip _I-1, calculate this linear portion slope S _rReach the height h of neighborhood window Nwr this moment _rAnd the thickness c of this moment _r:

If c _r＜c ₀, then upwards abutment points growth is continued to neighborhood window Nwr in the back;

If c _r>=c ₀, then the back stops growing to neighborhood window Nwr;

Through the process of above iteration, for concentrated any 1 p of discrete curve point _i, i>=N, the neighborhood window Nwf that grows forward is by discrete point set p _i, p _I+1..., p _I+fForm, its corresponding point coordinate is (x _i, y _i) ..., (x _I+f, y _I+f); The neighborhood window Nwr that grows backward is by discrete point set p _i, p _I-1..., p _I-rForm, its corresponding point coordinate is (x _i, y _i) ..., (x _I-r, y _I-r), for concentrated any 1 p of discrete curve point _i, i>=N is with the curve arc long l of its neighborhood window process of growing forward _fThe curve arc long l of the neighborhood window process of growing backward _rCarry out approximate calculation with the Euclidean distance of corresponding neighborhood window edge point respectively, that is:

$l_f=\sqrt{{(x_{i+f}-x_i)}^2+{(y_{i+f}-y_i)}^2}$

$l_r=\sqrt{{(x_{i-r}-x_i)}^2+{(y_{i-r}-y_i)}^2}$

To pass through the straight line p of neighborhood window edge point _ip _I+f, p _I-rp _iBe approximately the tangent line of the curve of this neighborhood window process, make θ _f=arctan (S _f), θ _r=arctan (S _r), then according to the defined formula of curvature at continuous space, some p _iThe Curvature Estimation value ρ (p at place _i) be:

$\mathrm{\&rho;}\left(p_i\right)=\frac{1}{2}|{\mathrm{\&theta;}}_f-{\mathrm{\&theta;}}_r|(\frac{1}{l_f}+\frac{1}{l_r})$

3. a kind of automatic alarm method of hitting before the bend vehicle of preventing as claimed in claim 1 is characterized in that: said risk status is estimated module and is adopted risk status to estimate function, and its expression formula is following:

$R=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{P}_i\&times;G_i$

When calculating the risk status discreet value of hitting before vehicle takes place when negotiation of bends, to the hazard event that hits before causing vehicle to take place adopt chaufeur bend following distance, distance and collision avoidance time three parameters are described when following car, the n value is 3.

4. a kind of automatic alarm method of hitting before the bend vehicle of preventing as claimed in claim 2 is characterized in that: said risk status is estimated module and is adopted risk status to estimate function, and its expression formula is following:

$R=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{P}_i\&times;G_i$

When calculating the risk status discreet value of hitting before vehicle takes place when negotiation of bends, to the hazard event that hits before causing vehicle to take place adopt chaufeur bend following distance, distance and collision avoidance time three parameters are described when following car, the n value is 3.

5. like claim 1 or 2 or 3 or 4 described a kind of automatic alarm methods of hitting before the bend vehicle of preventing, it is characterized in that: the risk status in the said step 8) is estimated module and is being adopted risk status to estimate function, and its expression formula is following:

$R=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{P}_i\&times;G_i$

When calculating the risk status discreet value of hitting before vehicle takes place when negotiation of bends, utilize the weighted value G of adaptive weighted blending algorithm to the hazard event that causes hitting before the vehicle generation _iCarrying out optimum distributes in real time.

6. like claim 1 or 2 or 3 or 4 described a kind of automatic alarm methods of hitting before the bend vehicle of preventing; It is characterized in that: the alarm module in the said step 8) hits the risk status estimation results before utilizing the bend vehicle; And consider time of driver's reaction the safe clearance that should reserve, confirm to hit alarm threshold value before the bend vehicle.

7. a kind of automatic alarm method of hitting before the bend vehicle of preventing as claimed in claim 5; It is characterized in that: the alarm module in the said step 8) hits the risk status estimation results before utilizing the bend vehicle; And consider time of driver's reaction the safe clearance that should reserve, confirm to hit alarm threshold value before the bend vehicle.

8. like claim 1 or 2 or 3 or 4 or 7 described a kind of automatic alarm methods of hitting before the bend vehicle of preventing, it is characterized in that: hit alarm threshold value before the said bend vehicle and be divided into two grades: elementary alarm threshold value and senior alarm threshold value.

9. a kind of automatic alarm method of hitting before the bend vehicle of preventing as claimed in claim 5 is characterized in that: hit alarm threshold value before the said bend vehicle and be divided into two grades: elementary alarm threshold value and senior alarm threshold value.

10. a kind of automatic alarm method of hitting before the bend vehicle of preventing as claimed in claim 6 is characterized in that: hit alarm threshold value before the said bend vehicle and be divided into two grades: elementary alarm threshold value and senior alarm threshold value.

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