CN102745194A - Self-adaption alarming method for preventing tailgating with front car on expressway - Google Patents

Abstract

The invention discloses a self-adaption alarming method for preventing tailgating with a front car on an expressway. The method comprises the following steps of: adapting to driver characteristic and driving environment based on a safety time logic algorithm; and performing self-adaption adjustment on a safety time threshold value, namely an alarming threshold to effectively avoid false-alarm. According to the algorithm, an alarm inhibition strategy is set by determining the intension of a driver to reduce false-alarm rate according to the selection of an initial mode and vehicle driving relevant information acquired by a vehicle-mounted sensor so as to calculate collision time (TTC) of the own car and the front car; the current alarm safety threshold value (TW) is calculated in real time according to the driver characteristic and the current driving environment; and finally, whether the potential danger of tailgating with the front car exists is determined according to the calculated collision time (TTC) of the own car and the front car and the alarm safety threshold value (TW); and an alarm instruction is sent when the potential danger exists. By utilizing the method, the potential tailgating danger can be accurately found out in real time according to the driving age of the driver, the driving environment, the vehicle brake type and the like so as to alarm the driver; and therefore, the occurrence of the tailgating accidents of the own car and the front car on the expressway can be effectively prevented.

Description

The adaptive alarm method of the anti-front truck that knocks into the back of a kind of highway automobile

Technical field

The present invention relates to the go research of adaptive alarm method of the anti-front truck that knocks into the back of a kind of highway automobile; Relate in particular to the go foundation of rear-end impact Early-warning Model of a kind of highway automobile that can react chaufeur and running environment characteristic based on the safety time logical algorithm; Its purpose is in vehicle traveling process, can find timely that potential rear-end impact is dangerous, and give chaufeur with warning; Thereby avoid belonging to the safety traffic field from the car front truck that knocks into the back.

Background technology

The statistical information of traffic accident shows that the rear-end impact accident of vehicle on expressway is topmost accident form.The research of Daimler-benz (Daimler-Benz) company shows, if chaufeur can take place to obtain early warning in preceding 0.5 second in collision, just can avoid 50% collision case; If before 1 second, obtained early warning, then can avoid 90% collision case.Therefore, develop the preventing car from close trailing collision warning device, before rear-end impact takes place, give the chaufeur caution; Remind chaufeur to take braking or measure such as change, prevent from the car front truck that knocks into the back, for improving ride safety of automobile; Reduce the generation of rear-end collision, have great importance.Alarm method and the model of setting up the rear-end impact of science then are core and the gordian technique places that the rear-end impact warning device is studied.Have only and set up comparatively accurate, reasonable and comprehensive math modeling in theory, warning device could conform to the practical operation situation of vehicle, just has practicality.

At present, preventing collision alarm method both domestic and external and model mainly comprise two types of safety distance logical algorithm and safety time logical algorithms.The safety distance logical algorithm be meant through calculate running car under precondition, vehicle is avoided vehicle that rear-end impact need keep and the minor increment between obstacle, and compares with the safety distance threshold value, confirms safe condition.The safety time logical algorithm is to compare through the collision time of calculating two workshops and safety time threshold value, confirms safe condition.More for the safety distance Study of model both at home and abroad, also formed fixedly spacing model, fixedly multi-form models such as headway model and kinematics model.But in two important parameter relative distance that influences rear-end impact and relative velocity, what the safety distance model was paid close attention to is this parameter of relative distance, and has ignored this key parameter of relative velocity, thereby has influenced the accuracy of Early-warning Model to a great extent.Simultaneously, the safety distance model also exists very big deficiency on for the individual comformability of chaufeur.

Based on such consideration, the rear-end impact alarm method that is based on the safety time logical algorithm that the present invention adopts.But existing safety time logical algorithm alarm method and model exist bigger problem and shortage.At first; The safety time logical algorithm is to compare with definite precarious position through the collision time of calculating two workshops and safety time threshold value, and in the existing at home and abroad research, no matter is to take what warning; Choose several safety time threshold values; It is constant that its safety time threshold value all adopts empirical value, can't embody the individual difference of different driving environments and different chaufeur, flase alarm under some driving cycles that need not to report to the police; And under other driving cycles that need report to the police, do not report to the police, cause the alert rate of false alarm rate and mistake higher.Secondly; Existing safety time logical algorithm can't be discovered the driving intention of chaufeur, even at chaufeur collision risk is discovered to some extent, and has taked under the situation of behaviors such as braking or change; Still report to the police; This has produced influence to the normal running of chaufeur to a certain extent, causes the dislike of chaufeur easily, has played the effect of running counter to desire.Moreover existing safety time logical algorithm is when calculating the collision time of two cars, and the method for calculating of being taked is divided by two car relative velocities with relative distance; In that two car relative velocities are less when trending towards zero, the collision time value that is calculated is bigger, can not produce warning; But this moment is if two car relative spacings are less; Then can give a kind of unsafe sensation of chaufeur, be in comparatively dangerous situation equally, should remind chaufeur to note.At last, present safety time logical algorithm can't guarantee that chaufeur after accepting warning, has sufficient time proper operation vehicle, prevents the generation of rear-end collision.

Summary of the invention

To the problems referred to above and deficiency; The adaptive alarm method that the purpose of this invention is to provide the anti-front truck that knocks into the back of a kind of highway automobile based on the safety time logical algorithm; Monitor this car with the relative motion state of front truck in the track, effectively prevention is from the car front truck that knocks into the back.It is individual with different chaufeurs that this algorithm can adapt to different running environments, real-time adjustment safety time threshold value, and combine the chaufeur driving intention to formulate warning shielding strategy, effectively reduce false alarm rate and mistake police rate.When two workshop relative velocities are zero, keep certain vehiclar gap to reduce the sense of insecurity of chaufeur, guarantee that simultaneously chaufeur after accepting to report to the police, has the generation of time proper operation vehicle to prevent to knock into the back of abundance.

For realizing above-mentioned purpose, the present invention takes following technical scheme, and the adaptive alarm method of the anti-front truck that knocks into the back of a kind of highway automobile is characterized in that, may further comprise the steps:

1) selects current vehicle type of retardation, road surface types and chaufeur driving age

Before the driving; Select current vehicle type of retardation, road surface types and chaufeur driving age, wherein, the car brakeing type comprises vapour-pressure type and fluid pressure type; Road surface types comprises dry pavement, moist road surface, snowy road surface and ice-patch surface; The chaufeur driving age is the actual driving age of chaufeur, and option was divided into less than 1 year, 1～3 year and greater than 3 years three types;

2) gather front truck with respect to from the relative distance of car, relative velocity and from speed, accelerator pedal signal, steering indicating light signal, brake pedal signal and the current visbility signal of car

Utilize millimeter wave radar sensor or vision sensor gather front truck with from car relative distance and relative velocity; Utilize from the vehicle speed sensor acquisition from vehicle speed information; Utilize Das Gaspedal force gauge and brake pedal force gauge to obtain Das Gaspedal and brake pedal signal respectively, and obtain the information whether steering indicating light is opened in real time through automobile CAN-bus; Utilize visibility sensor to obtain current visibility information;

3), set shielding and report to the police tactful according to the information of being gathered in the step 2

According to gathered in the step 2 from the speed V of car with respect to front truck _r, if unit: m/s is V _r≤0, then collisionless is dangerous, and shielding is reported to the police, otherwise, according to the brake pedal of being gathered in the step 2, Das Gaspedal and steering indicating light signal, judge the chaufeur driving intention, following warning shielding strategy is set: chaufeur is stepped on glancing impact, the shielding of reporting to the police; During chaufeur pine tar door, the shielding of reporting to the police; When chaufeur is opened steering indicating light, the shielding of reporting to the police;

4) according to the information of being gathered in step 2 and the step 1, when from car with respect to preceding vehicle speed V _r, calculate the collision time TTC and the safe threshold T of reporting to the police at＞0 o'clock from car and front truck _W

The calculating of said collision time TTC from car and front truck is following:

$\mathrm{TTC}=\frac{L}{V_r}$

In the formula, L be front truck with respect to distance from car, unit: m, V _rFor from the speed of car with respect to front truck, unit: m/s, both obtain by step 2;

The safe threshold T of said warning _WCalculating following:

T _W=T ₁+T ₂+T ₃+T ₄

In the formula, T ₁Be time of driver's reaction, promptly accept early warning to its time of taking brake measure to experience, unit: s from chaufeur;

T ₂Be the brake coordination time, promptly take brake measure to produce the time that braking force experienced, unit: s to brake system from chaufeur;

T ₃Be the deceleration/decel build up time, promptly braking deceleration from 0 rise to maximum braking deceleration time, unit: s;

T ₄For continuing braking time, arrive Ben Che and the static relatively required time of front truck, unit: s when promptly braking deceleration reaches maxim;

Described time of driver's reaction T ₁The span statistical value be 0.3～1s, it calculates as follows:

Confirm driver reaction time through setting up a time of driver's reaction identification BP Artificial Neural Network model; Artificial neural net (ANN) comprises input layer, hidden layer and output layer; Wherein input layer receives and calculates the needed input vector of time of driver's reaction; This input vector is 4 dimensional vectors, and each dimension was respectively from the car speed of a motor vehicle, visbility, road surface types and chaufeur driving age;

Hidden layer comprises 6 neuron nodes, and each neuron node can calculate according to the input vector of input layer, and the output valve of calculating is calculated the input value of time of driver's reaction as output layer; The function that neuron node calculates according to input vector is tansig function: tansig (x)=1/ (1+e ^-x), concrete, the expression formula of this tansig function as shown in the formula:

$m^j=\tan \mathrm{sig}[\underset{i=1}{\overset{4}{\mathrm{\Σ}}}(w^i\×p^i)+b^j]$

In the following formula, i=1,2 ... 4, be input vector dimension, p ⁱBe i input vector; J=1,2 ... 6, be the number of hidden layer neuron, m ^jBe hidden layer j neuronic output valve, w ⁱBe corresponding p in the hidden layer ⁱThe network weight coefficient, b ^jBe j threshold value coefficient of hidden layer;

Output layer comprises an output layer neuron node, and this neuron node calculates time of driver's reaction according to the hidden layer result calculated, and output layer neuron node function is purelin function: purelin (x)=kx; Concrete, the function of output layer neuron node calculating time of driver's reaction is formula as follows:

$m=\mathrm{purelin}[\underset{j=1}{\overset{6}{\mathrm{\Σ}}}(w^j\×m^j)+b]$

In the formula, m ^jBe hidden layer j neuronic output valve, w ^jBe corresponding m in the output layer ^jThe network weight coefficient,

B is the threshold value coefficient of output layer, and m is the neuronic output valve of output layer, i.e. output vector;

Matlab function newff, formula have as follows been adopted in the foundation of whole BP artificial neural net (ANN):

net＝newff(min?max(p),[6,1],{′tansig′,′purlin′},′trainlm′)

In the following formula, net is a neural network, and p is an input vector, and minmax is that matlab obtains vectorial extremal function, and trainlm is a learning algorithm;

Input vector p according to above-mentioned artificial neural net (ANN) gathers, and comprises measuring difference from the car speed of a motor vehicle, different visbilitys, different road surface types, different chaufeur driving ages and time of driver's reaction; Utilize above-mentioned collection value off-line training time of driver's reaction identification BP artificial neural net (ANN), obtain the network weight coefficient w of hidden layer ⁱWith threshold value coefficient b ^j, and the network weight coefficient w of output layer ^jWith threshold value coefficient b;

The input vector of time of driver's reaction identification BP artificial neural net (ANN) defines as follows: from car speed of a motor vehicle input value is actual measured value; The visbility input value also is an actual measured value; Road surface types is divided into dry pavement, moist road surface, snowy road surface and ice-patch surface; The dry pavement input value is 1, and moist road surface input value is 2, and the snowy road surface input value is 3, and the ice-patch surface input value is 4; The chaufeur driving age was divided into less than 1 year, 1～3 year and greater than 3 years; Less than 1 year input value was that 1,1～3 years input values are 2, was 3 greater than 3 years input values;

The output vector of BP artificial neural network is defined as follows: output vector value scope is 0～3, represents the statistical value 0.3～1.0s of time of driver's reaction, and 0 represents driver's real reaction time short; Get 0.3s, 1 represents time of driver's reaction shorter, gets 0.5s; On behalf of the driver, 2 reflect that reality is longer; Get 0.8s, 3 represent time of driver's reaction long, get 1s;

The main purpose of BP artificial neural net (ANN) training is to obtain network weight coefficient w ⁱ, w ^jWith threshold value coefficient b ^j, b, make output vector satisfy certain precision; The training of BP artificial neural net (ANN) directly adopts matlab function train to carry out off-line training, formula as follows:

net＝train(net,p,t)

In the following formula: net is the BP artificial neural net (ANN), and p is the training sample input vector, and t is the training sample output vector;

Network weight coefficient w with above-mentioned training acquisition ⁱ, w ^jWith threshold value coefficient b ^j, in the b substitution neural network, according to the reality input from the car speed of a motor vehicle, visbility, road surface types and chaufeur chaufeur driving age, calculate output vector;

According to the output vector that calculates, and, judge time of driver's reaction according to defined output vector.BP neural network 1 output vector judges then that near 0 time of driver's reaction is 0.3s; Output vector judges then that near 1 time of driver's reaction is 0.5s; Output vector judges then that near 2 time of driver's reaction is 0.8s; Output vector judges then that near 3 time of driver's reaction is 1s;

Described brake coordination time T ₂, according to related data, getting empirical value is 0.2s;

Described deceleration/decel build up time T ₃According to the selected type of retardation of chaufeur in the step 1, hydraulic brake system and Pneumatic braking system are got 0.2s, 0.7s respectively;

Described lasting braking time T ₄Calculating following:

$T_4=\frac{V_r}{\mathrm{\μ}\×g}$

In the formula, V _rFor from the speed of car with respect to front truck, unit: m/s is by obtaining in the step 2; G is an acceleration due to gravity, and its value is 9.8m/s ²μ is a coefficient of road adhesion, confirms according to road surface types in the step 1: the adhesion value of dry pavement gets 0.61, and moist ground-surface adhesion value gets 0.46, and the adhesion value of snowy road surface gets 0.20, and the adhesion value of ice-patch surface gets 0.07;

5) according to the collision time TTC and the safe threshold T of reporting to the police that are calculated from car and front truck _W, differentiate whether there is potential rear-end impact danger, thereby need determining whether reports to the police

Described differentiation is tactful as follows, as TTC＞T _WDuring+τ, differentiate and be safety, do not send alarm command; As TTC≤T _WDuring+τ, differentiate and be danger, send alarm command and remind chaufeur; Type of alarm can be taked different type of alarms such as voice prompt alarm, does not limit at this;

Wherein, TTC is the collision time from car and front truck, unit: s, T _WBe warning safety door limit value unit: s, τ is the time of giving warning in advance [s]; The described time τ that gives warning in advance calculates as follows:

τ＝d/V

In the formula, d is safe stopping distance [m], and getting empirical value is 3m; V is this vehicle speed [m/s], by obtaining in the step 2.

The present invention is owing to adopt above technical scheme, and it has the following advantages:

(1) the present invention adopts the preventing collision algorithm based on the safety time logic; Take into full account two key factors that influence rear-end impact: relative distance and relative velocity; Can be in real time Ben Che and front truck relative motion state be calculated and judge precarious position; Thereby when having the potential front truck that knocks into the back dangerous, generate alarm command, actv. has prevented from the car front truck that knocks into the back.

(2) the present invention adopts the BP neural network method, confirms driver reaction time, adapts to different chaufeurs and different driving cycles, and it is long or too short effectively to have avoided the reaction time to obtain, and has avoided the alert and false-alarm of mistake.

(3) the present invention is according to chaufeur driving age and concrete running environment, car brakeing type etc.; Real-time computationally secure time gate limit value; Realized adaptations individual to different chaufeurs and different running environments, solved orthodox method and adopted the not enough problem of constant threshold comformability.

(4) the present invention sets the shielding strategy of reporting to the police according to the driving intention of judging chaufeur, judge that chaufeur has been discovered danger and the operating mode of taking measures under, shielding is reported to the police, actv. has been avoided false-alarm.

(5) the less dangerous operating mode of two car relative distances under the less situation of relative velocity has been avoided in the setting of safe stopping distance, actv..Simultaneously, the setting of the time that gives warning in advance also guarantees chaufeur after accepting early warning, and sufficient time proper operation vehicle is arranged.

Description of drawings

Fig. 1 is the workflow diagram of alarm method method of the present invention.

Fig. 2 is the structured flowchart of alarm method of the present invention.

Fig. 3 is time of driver's reaction identification BP artificial neural network structure.

The specific embodiment

Highway rear-end collision prevention front truck early warning and alarming method is the core technology of highway rear-end collision prevention warning device; Have only and set up comparatively accurate, reasonable and comprehensive alarm method and math modeling in theory, the anti-warning device that knocks into the back could conform to the practical operation situation of vehicle, just has practicality; Could really play and prevent from the car front truck that knocks into the back; Improve ride safety of automobile, reduce traffic accident, improve the effect of safety traffic.

Below in conjunction with embodiment, the present invention is carried out detailed description.

As shown in Figure 1, the present invention includes following steps:

1) chaufeur is manually selected current vehicle type of retardation, road surface types and chaufeur driving age.

As shown in Figure 2, preset originate mode module 21 was manually selected current vehicle type of retardation, road surface types and chaufeur driving age by chaufeur before driving.The car brakeing type comprises vapour-pressure type and fluid pressure type; Road surface types comprises dry pavement, moist road surface, snowy road surface and ice-patch surface, is differentiated by chaufeur, and the chaufeur driving age is the actual driving age of chaufeur; Option was divided into less than 1 year, 1～3 year and greater than 3 years three types; All beginning to drive the corresponding pattern of manually selecting before by chaufeur.And selected pattern information is sent to early warning discrimination module 23 shown in Figure 2.

2) gather front truck with respect to from the relative distance of car, relative velocity and from speed, accelerator pedal signal, steering indicating light signal, brake pedal signal and the visibility information of car.

As shown in Figure 2, on-vehicle information acquisition module 22, utilize millimeter wave radar sensor or vision sensor gather front truck with from car relative distance and relative velocity, do not limit at this.Radar sensor can adopt Delphi (delphi) the ESR type millimeter wave radar that company produced; Vision sensor can adopt dimension to look (Microvision) VS078FC type digital camera that company produced; Utilize from the vehicle speed sensor acquisition from vehicle speed information, can adopt JK08/NS43NCT1 photoelectric vehicle speed sensors.Utilize Das Gaspedal force gauge and brake pedal force gauge to obtain Das Gaspedal and brake pedal signal respectively, and obtain the information whether steering indicating light is opened through the CAN Bus Real Time.Visibility information can be obtained by FYG11 type visibility sensor.The on-vehicle information of being gathered is sent to early warning discrimination module 23 shown in Figure 2.

3), set shielding and report to the police tactful according to the information of being gathered in the step 2

In early warning discrimination module 23 shown in Figure 2, according to gathered in the step 2 from the speed V of car with respect to front truck _r[m/s] is if V _r≤0, then collisionless is dangerous, and shielding is reported to the police, if V _r＞0, then begin to start alarming logic (step 4,5).

Simultaneously, according to the brake pedal of being gathered in the step 2, Das Gaspedal and steering indicating light signal, judge the chaufeur driving intention; Following warning shielding strategy is set: chaufeur is stepped on glancing impact; The reaction chaufeur is driven energy and is concentrated, and is just taking brake measure to avoid knocking into the back, the shielding of reporting to the police; During chaufeur pine tar door, the reaction chaufeur is driven energy and is concentrated, and is just taking the deceleration measure to avoid knocking into the back, the shielding of reporting to the police; When chaufeur was opened steering indicating light, the reaction chaufeur was driven energy and is concentrated, and is just carrying out conscious changing, the shielding of reporting to the police.

4) according to the information of being gathered in step 2 and the step 1, when from car with respect to preceding vehicle speed V _r, calculate the collision time TTC and the safe threshold T of reporting to the police at＞0 o'clock from car and front truck _W

The calculating of said collision time TTC from car and front truck is suc as formula (1):

$\mathrm{TTC}=\frac{L}{V_r}---\left(1\right)$

In the formula (1), L is that front truck is with respect to the distance [m] from car, V _rFor from the speed [m/s] of car with respect to front truck.

Both obtain by step 2.

The safe threshold T of said warning _WCalculating suc as formula (2):

T _W=T ₁+T ₂+T ₃+T ₄ （2）

In the formula (2), T ₁Be time of driver's reaction, promptly accept early warning to time [s] that chaufeur takes brake measure to experience from chaufeur.

T ₂Be the brake coordination time, promptly take brake measure to produce the time [s] that braking force experienced to brake system from chaufeur.T ₃Be the deceleration/decel build up time, promptly braking deceleration from 0 rise to maximum braking deceleration time [s].T ₄For continuing braking time, arrive Ben Che and the static relatively required time of front truck [s] when promptly braking deceleration reaches maxim.

1. described time of driver's reaction T ₁Calculating following:

The skill level of time of driver's reaction and chaufeur, degree of fatigue etc. are closely bound up; And receive the influence of factors such as the speed of a motor vehicle, weather, visbility; Therefore be a uncertain value, a large amount of experimental datas show that the statistical value of the action in response time of chaufeur is 0.3～1.0s.According to this reaction time range, the present invention confirms driver reaction time through setting up a time of driver's reaction identification BP artificial neural net (ANN) 1.

Consider that the factor that influences time of driver's reaction is from the car speed of a motor vehicle, visbility, road surface types and chaufeur proficiency; And the chaufeur proficiency is the factor of a bad tolerance; It is generally acknowledged; Long more chaufeur of driving age often has better proficiency and reaction time faster, and therefore, the present invention chooses from the car speed of a motor vehicle, visbility, road surface types and chaufeur driving age and differentiates time of driver's reaction.

As shown in Figure 3, artificial neural net (ANN) 1 comprises input layer 11, hidden layer 12 and output layer 13.

Wherein input layer 11 receives and calculates the needed input vector of time of driver's reaction, and this input vector is 4 dimensional vectors, and each dimension is respectively from car speed of a motor vehicle U1, visbility U2, road surface types U3 and chaufeur driving age U4.

Hidden layer 12 comprises 6 neuron nodes 121, and this neuron node 121 can calculate according to the input vector of input layer 11, and the output valve of calculating is calculated the input value of time of driver's reaction as output layer 13; The function that neuron node 121 calculates according to input vector is the tansig function:

Tansig (x)=1/ (1+e ^-x), concrete, the expression formula of this tansig function is seen formula (3)

$m^j=\tan \mathrm{sig}[\underset{i=1}{\overset{4}{\mathrm{\Σ}}}(w^i\×p^i)+b^j]---\left(3\right)$

In the formula (3), i=1,2 ... 4, be input vector dimension, p ⁱBe i input vector; J=1,2 ... 6, be the number of hidden layer neuron 121, m ^jBe the output valve of hidden layer 12 a j neuron 121, w ⁱBe corresponding p in the hidden layer 12 ⁱThe network weight coefficient, b ^jBe hidden layer 12 a j threshold value coefficient.

Output layer 13 comprises an output layer neuron node 131, and this neuron node 131 calculates time of driver's reaction according to hidden layer 12 result calculated, and output layer neuron node 131 functions are purelin function: purelin (x)=kx.Concrete, the function that output layer neuron node 131 calculates time of driver's reaction is seen formula (4)

$m=\mathrm{purelin}[\underset{j=1}{\overset{6}{\mathrm{\Σ}}}(w^j\×m^j)+b]---\left(4\right)$

In the formula (4), m ^jBe the output valve of hidden layer 12 a j neuron 121, w ^jBe corresponding m in the output layer 13 ^jThe network weight coefficient, b is the threshold value coefficient of output layer 13, m is the output valve of output layer neuron 131, i.e. output vector.

Matlab function newff has been adopted in the foundation of whole BP artificial neural net (ANN) 1, sees formula (5)

net＝newff(min?max(p),[6,1],{′tansig′,′purlin′},′trainlm′) （5）

In the formula (5), net is a neural network, and p is an input vector, and min max is that matlab obtains vectorial extremal function, and trainlm is a learning algorithm.

Input vector p according to above-mentioned artificial neural net (ANN) 1 measures, and comprises measuring difference from the car speed of a motor vehicle, different visbilitys, different road surface types, different chaufeur driving ages and time of driver's reaction.

Wherein, Road surface types and chaufeur driving age are through chaufeur manually input before driving; Utilize JK08/NS43NCT1 photoelectric vehicle speed sensors to measure from the car speed of a motor vehicle; Visbility utilizes FYG11 type visibility sensor to obtain, and time of driver's reaction is measured through counting machine 74LS190, also can obtain through expert's questionnaire.

Utilize above-mentioned observed reading off-line training time of driver's reaction identification BP artificial neural net (ANN) 1, obtain the network weight coefficient w of hidden layer 12 ⁱWith threshold value coefficient b ^j, and the network weight coefficient w of output layer 13 ^jWith threshold value coefficient b.

The input vector of time of driver's reaction identification BP artificial neural net (ANN) 1 defines as follows: from car speed of a motor vehicle input value is actual measured value; The visbility input value also is an actual measured value; Road surface types is divided into dry pavement, moist road surface, snowy road surface and ice-patch surface.The dry pavement input value is 1, and moist road surface input value is 2, and the snowy road surface input value is 3, and the ice-patch surface input value is 4; The chaufeur driving age was divided into less than 1 year, 1～3 year and greater than 3 years.Less than 1 year input value was that 1,1～3 years input values are 2,2 to be 3 greater than 3 years input values.

The output vector of BP artificial neural net (ANN) 1 defines as follows: output vector value scope is 0～3, represents the statistical value 0.3～1.0s of time of driver's reaction, and 0 represents the chaufeur real reaction time short; Get 0.3s, 1 represents time of driver's reaction shorter, gets 0.5s; 2 represent chaufeur reflection reality longer; Get 0.8s, 3 represent time of driver's reaction long, get 1s.

The main purpose of BP artificial neural net (ANN) 1 training is to obtain network weight coefficient w ⁱ, w ^jWith threshold value coefficient b ^j, b, make output vector satisfy certain precision.The training of BP artificial neural net (ANN) directly adopts matlab function tranin to carry out off-line training, sees formula (6)

net＝train(net,p,t) （6）

In the formula (6): net is a BP artificial neural net (ANN) 1, and p is the training sample input vector, and t is the training sample output vector.

Network weight coefficient w with above-mentioned training acquisition ⁱ, w ^jWith threshold value coefficient b ^j, in the b substitution neural network, according to the reality input from the car speed of a motor vehicle, visbility, road surface types and chaufeur chaufeur driving age, and convolution (3) and (4), calculating output vector.

According to the output vector that calculates, and, judge time of driver's reaction according to defined output vector.BP neural network 1 output vector judges then that near 0 time of driver's reaction is 0.3s; Output vector judges then that near 1 time of driver's reaction is 0.5s; Output vector judges then that near 2 time of driver's reaction is 0.8s; Output vector judges then that near 3 time of driver's reaction is 1s.

2. described brake coordination time T ₂, according to data, getting statistical value is 0.2s.

3. described deceleration/decel build up time T ₃Depend on type of retardation, hydraulic brake system is approximately 0.15～0.3s, and the pneumatic brake system is approximately 0.3～0.9s.Among the present invention, according to the selected type of retardation of chaufeur in the step 1, hydraulic brake system and Pneumatic braking system be value 0.2s, 0.7s respectively.

4. described lasting braking time T ₄Calculating following:

$T_4=\frac{V_r}{\mathrm{\μ}\×g}---\left(7\right)$

In the formula (7), V _rFor from the speed [m/s] of car, by obtaining in the step 2 with respect to front truck.G is an acceleration due to gravity, and its value is 9.8m/s ²μ is a coefficient of road adhesion, confirms according to road surface types in the step 1: the adhesion value of dry pavement gets 0.61, and moist ground-surface adhesion value gets 0.46, and the adhesion value of snowy road surface gets 0.20, and the adhesion value of ice-patch surface gets 0.07.In fact, the value that μ * g calculated is vehicle maximum braking deceleration [m/s under the current coefficient of road adhesion ²].

The T that draws according to aforementioned calculation ₁, T ₃And T ₄, substitution T again ₂The empirical value 0.2s that is got utilizes formula (2) to calculate the safe threshold T of reporting to the police _W

5) according to the collision time TTC and the safe threshold T of reporting to the police that are calculated from car and front truck _W, differentiate whether there is potential rear-end impact danger, thereby need determining whether reports to the police.

Above-mentioned each motion feature parameter is analyzed, can be drawn as drawing a conclusion: when this vehicle speed during smaller or equal to preceding vehicle speed, i.e. V _r≤0 o'clock, also can not bump even do not implement braking, so this algorithm is only worked as V _r＞0 starts warning; If this vehicle speed is greater than preceding vehicle speed, i.e. V _r＞0, as long as reserve sufficient chaufeur braking time, promptly at TTC>=T _WIn time, brake, and then can not bump after the braking.

It is thus clear that the limiting conditions of implementing braking are TTC=T _WGoing with car after braking this moment is the state that a kind of car is close to front truck, produces the mental burden of having a lingering fear in order not make chaufeur, should keep necessary spacing d between two cars of braking back, i.e. safe stopping distance d [m].Be embodied on the time quantum, promptly advance time τ carries out early warning.

The computing formula of time τ of giving warning in advance is:

τ＝d/V （8）

In the formula (8), d is safe stopping distance [m], and getting empirical value is 3m.V is this vehicle speed [m/s], by obtaining in the step 2.

The setting of τ can guarantee chaufeur after accepting early warning, and sufficient time proper operation vehicle is arranged.At this moment, the limiting conditions of enforcement braking are TTC=T _W+ τ.

Therefore, described differentiation is tactful as follows, as TTC＞T _WDuring+τ, differentiate and be safety, do not send alarm command; As TTC≤T _WDuring+τ, differentiate and be danger, send alarm command and remind chaufeur.

In the present embodiment, type of alarm can be taked different type of alarms such as voice prompt alarm, does not limit at this.

Claims (1)

1. the adaptive alarm method of the anti-front truck that knocks into the back of highway automobile is characterized in that, may further comprise the steps:

1) selects current vehicle type of retardation, road surface types and chaufeur driving age

Before the driving; Select current vehicle type of retardation, road surface types and chaufeur driving age, wherein, the car brakeing type comprises vapour-pressure type and fluid pressure type; Road surface types comprises dry pavement, moist road surface, snowy road surface and ice-patch surface; The chaufeur driving age is the actual driving age of chaufeur, and option was divided into less than 1 year, 1～3 year and greater than 3 years three types;

2) gather front truck with respect to from the relative distance of car, relative velocity and from speed, accelerator pedal signal, steering indicating light signal, brake pedal signal and the current visbility signal of car

Utilize millimeter wave radar sensor or vision sensor gather front truck with from car relative distance and relative velocity; Utilize from the vehicle speed sensor acquisition from vehicle speed information; Utilize Das Gaspedal force gauge and brake pedal force gauge to obtain Das Gaspedal and brake pedal signal respectively, and obtain the information whether steering indicating light is opened in real time through automobile CAN-bus; Utilize visibility sensor to obtain current visibility information;

3), set shielding and report to the police tactful according to the information of being gathered in the step 2

According to gathered in the step 2 from the speed V of car with respect to front truck _r, if unit: m/s is V _r≤0, then collisionless is dangerous, and shielding is reported to the police, otherwise, according to the brake pedal of being gathered in the step 2, Das Gaspedal and steering indicating light signal, judge the chaufeur driving intention, following warning shielding strategy is set: chaufeur is stepped on glancing impact, the shielding of reporting to the police; During chaufeur pine tar door, the shielding of reporting to the police; When chaufeur is opened steering indicating light, the shielding of reporting to the police;

4) according to the information of being gathered in step 2 and the step 1, when from car with respect to preceding vehicle speed V _r, calculate the collision time TTC and the safe threshold T of reporting to the police at＞0 o'clock from car and front truck _W

The calculating of said collision time TTC from car and front truck is following:

$\mathrm{TTC}=\frac{L}{V_r}$

In the formula, L be front truck with respect to distance from car, unit: m, V _rFor from the speed of car with respect to front truck,

Unit: m/s, both obtain by step 2;

The safe threshold T of said warning _WCalculating following:

T _W=T ₁+T ₂+T ₃+T ₄

In the formula, T ₁Be time of driver's reaction, promptly accept early warning to its time of taking brake measure to experience, unit: s from chaufeur;

T ₂Be the brake coordination time, promptly take brake measure to produce the time that braking force experienced, unit: s to brake system from chaufeur;

T ₃Be the deceleration/decel build up time, promptly braking deceleration from 0 rise to maximum braking deceleration time, unit: s;

T ₄For continuing braking time, arrive Ben Che and the static relatively required time of front truck, unit: s when promptly braking deceleration reaches maxim;

Described time of driver's reaction T ₁The span statistical value be 0.3～1s, it calculates as follows:

Confirm driver reaction time through setting up a time of driver's reaction identification BP Artificial Neural Network model; Artificial neural net (ANN) comprises input layer, hidden layer and output layer; Wherein input layer receives and calculates the needed input vector of time of driver's reaction; This input vector is 4 dimensional vectors, and each dimension was respectively from the car speed of a motor vehicle, visbility, road surface types and chaufeur driving age;

Hidden layer comprises 6 neuron nodes, and each neuron node can calculate according to the input vector of input layer, and the output valve of calculating is calculated the input value of time of driver's reaction as output layer; The function that neuron node calculates according to input vector is tansig function: tansig (x)=1/ (1+e ^-x), concrete, the expression formula of this tansig function as shown in the formula:

$m^j=\tan \mathrm{sig}[\underset{i=1}{\overset{4}{\mathrm{\Σ}}}(w^i\×p^i)+b^j]$

In the following formula, i=1,2 ... 4, be input vector dimension, p ⁱBe i input vector; J=1,2 ... 6, be the number of hidden layer neuron, m ^jBe hidden layer j neuronic output valve, w ⁱBe corresponding p in the hidden layer ⁱThe network weight coefficient, b ^jBe j threshold value coefficient of hidden layer;

Output layer comprises an output layer neuron node, and this neuron node calculates time of driver's reaction according to the hidden layer result calculated, and output layer neuron node function is purelin function: purelin (x)=kx; Concrete, the function of output layer neuron node calculating time of driver's reaction is formula as follows:

$m=\mathrm{purelin}[\underset{j=1}{\overset{6}{\mathrm{\Σ}}}(w^j\×m^j)+b]$

In the formula, m ^jBe hidden layer j neuronic output valve, w ^jBe corresponding m in the output layer ^jThe network weight coefficient, b is the threshold value coefficient of output layer, m is the neuronic output valve of output layer, i.e. output vector;

Matlab function newff, formula have as follows been adopted in the foundation of whole BP artificial neural net (ANN):

net＝newff(min?max(p),[6,1],{′tansig′,′purlin′},′trainlm′)

In the following formula, net is a neural network, and p is an input vector, and minmax is that matlab obtains vectorial extremal function, and trainlm is a learning algorithm;

Input vector p according to above-mentioned artificial neural net (ANN) gathers, and comprises measuring difference from the car speed of a motor vehicle, different visbilitys, different road surface types, different chaufeur driving ages and time of driver's reaction; Utilize above-mentioned collection value off-line training time of driver's reaction identification BP artificial neural net (ANN), obtain the network weight coefficient w of hidden layer ⁱWith threshold value coefficient b ^j, and the network weight coefficient w of output layer ^jWith threshold value coefficient b;

The input vector of time of driver's reaction identification BP artificial neural net (ANN) defines as follows: from car speed of a motor vehicle input value is actual measured value; The visbility input value also is an actual measured value; Road surface types is divided into dry pavement, moist road surface, snowy road surface and ice-patch surface; The dry pavement input value is 1, and moist road surface input value is 2, and the snowy road surface input value is 3, and the ice-patch surface input value is 4; The chaufeur driving age was divided into less than 1 year, 1～3 year and greater than 3 years; Less than 1 year input value was that 1,1～3 years input values are 2, was 3 greater than 3 years input values;

The output vector of BP artificial neural network is defined as follows: output vector value scope is 0～3, represents the statistical value 0.3～1.0s of time of driver's reaction, and 0 represents driver's real reaction time short; Get 0.3s, 1 represents time of driver's reaction shorter, gets 0.5s; On behalf of the driver, 2 reflect that reality is longer; Get 0.8s, 3 represent time of driver's reaction long, get 1s;

The main purpose of BP artificial neural net (ANN) training is to obtain network weight coefficient w ⁱ, w ^jWith threshold value coefficient b ^j, b, make output vector satisfy certain precision; The training of BP artificial neural net (ANN) directly adopts matlab function train to carry out off-line training, formula as follows:

net＝train(net,p,t)

In the following formula: net is the BP artificial neural net (ANN), and p is the training sample input vector, and t is the training sample output vector;

Network weight coefficient w with above-mentioned training acquisition ⁱ, w ^jWith threshold value coefficient b ^j, in the b substitution neural network, according to the reality input from the car speed of a motor vehicle, visbility, road surface types and chaufeur chaufeur driving age, calculate output vector;

According to the output vector that calculates, and, judge time of driver's reaction according to defined output vector.BP neural network 1 output vector judges then that near 0 time of driver's reaction is 0.3s; Output vector judges then that near 1 time of driver's reaction is 0.5s; Output vector judges then that near 2 time of driver's reaction is 0.8s; Output vector judges then that near 3 time of driver's reaction is 1s;

Described brake coordination time T ₂, according to related data, getting empirical value is 0.2s;

Described deceleration/decel build up time T ₃According to the selected type of retardation of chaufeur in the step 1, hydraulic brake system and Pneumatic braking system are got 0.2s, 0.7s respectively;

Described lasting braking time T ₄Calculating following:

$T_4=\frac{V_r}{\mathrm{\μ}\×g}$

In the formula, V _rFor from the speed of car with respect to front truck, unit: m/s is by obtaining in the step 2; G is an acceleration due to gravity, and its value is 9.8m/s ²μ is a coefficient of road adhesion, confirms according to road surface types in the step 1: the adhesion value of dry pavement gets 0.61, and moist ground-surface adhesion value gets 0.46, and the adhesion value of snowy road surface gets 0.20, and the adhesion value of ice-patch surface gets 0.07;

5) according to the collision time TTC and the safe threshold T of reporting to the police that are calculated from car and front truck _W, differentiate whether there is potential rear-end impact danger, thereby need determining whether reports to the police

Described differentiation is tactful as follows, as TTC＞T _WDuring+τ, differentiate and be safety, do not send alarm command; As TTC≤T _WDuring+τ, differentiate and be danger, send alarm command and remind chaufeur; Type of alarm can be taked different type of alarms such as voice prompt alarm, does not limit at this;

Wherein, TTC is the collision time from car and front truck, unit: s, T _WBe warning safety door limit value unit: s, τ is the time of giving warning in advance [s]; The described time τ that gives warning in advance calculates as follows:

τ＝d/V

In the formula, d is safe stopping distance [m], and getting empirical value is 3m; V is this vehicle speed [m/s], by obtaining in the step 2.

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