Source: https://patents.google.com/patent/CN105197011A/en
Timestamp: 2020-08-15 15:08:56
Document Index: 66495764

Matched Legal Cases: ['art 130', 'art 130', 'art 130', 'art 130', 'art 130', 'art 130', 'art 130', 'art 130', 'art 130', 'art 130', 'art 130', 'art 130', 'art 130', 'art 130', 'art 130', 'art 130', 'art 132', 'art 132', 'art 132', 'art 132', 'art 132', 'art 132', 'art 163', 'art 161', 'art 161', 'art 161', 'art 161', 'art 161', 'art 161', 'art 161', 'art 161', 'art 161', 'art 161', 'art 161', 'art 161', 'art 161', 'art 161', 'art 161', 'art 161', 'art 161', 'art 161', 'art 161', 'art 161', 'art 161', 'art 161', 'art 161', 'art 161', 'art 161', 'art 161', 'art 161', 'art 161', 'art 161', 'art 161', 'art 161', 'art 161', 'art 161', 'art 161', 'art 163', 'art 163', 'art 163', 'art 163', 'art 163', 'art 161', 'art 163', 'art 161', 'art 163', 'art 163', 'art 163', 'art 163', 'art 163', 'art 161', 'art 163', 'art 161', 'art 163', 'art 161', 'art 161', 'art 163', 'art 163', 'art 161', 'art 163', 'art 163', 'art 161', 'art 163', 'art 161', 'art 163', 'art 161', 'art 163', 'art 163', 'art 163', 'art 163', 'art 161', 'art 163', 'art 163', 'art 163', 'art 163', 'art 163', 'art 163', 'art 161', 'art 163', 'art 163', 'art 163', 'art 163', 'art 163', 'art 163', 'art 163', 'art 163', 'art 163']

CN105197011A - System and method for managing dangerous driving index for vehicle - Google Patents
System and method for managing dangerous driving index for vehicle Download PDF
CN105197011A
CN105197011A CN201510321139.0A CN201510321139A CN105197011A CN 105197011 A CN105197011 A CN 105197011A CN 201510321139 A CN201510321139 A CN 201510321139A CN 105197011 A CN105197011 A CN 105197011A
CN201510321139.0A
CN105197011B (en
2014-06-13 Priority to KR10-2014-0072280 priority Critical
2014-06-13 Priority to KR1020140072280A priority patent/KR102051142B1/en
2015-06-12 Application filed by 现代摩比斯株式会社 filed Critical 现代摩比斯株式会社
2015-12-30 Publication of CN105197011A publication Critical patent/CN105197011A/en
2017-11-21 Publication of CN105197011B publication Critical patent/CN105197011B/en
Provided are a system and method for managing dangerous driving index for vehicle. The system includes a driver's vehicle driving trajectory generator configured to generate a driver's vehicle driving trajectory, based on driver's vehicle driving information input from an internal sensor which senses a driving situation of the driver's vehicle, a peripheral vehicle trajectory generator configured to generate a peripheral vehicle trajectory, based on ambient environment information input from an external sensor which senses the driving situation of the driver's vehicle, a trajectory load calculator configured to calculate a trajectory load representing a comparison result which is obtained by comparing a predetermined threshold value with a trajectory distance which is a difference between the peripheral vehicle trajectory and the driver's vehicle driving trajectory, and a dangerous index manager configured to generate a dangerous driving index corresponding to the calculated trajectory load.
Vehicle steering person's hazard index management system and method thereof
The present invention relates to its method of vehicle steering person's hazard index management system, particularly relate to vehicle steering person's hazard index management system of the chaufeur hazard index that a kind of management generates according to the information that vehicle measures.
Vehicle is usually equipped with for the protection of chaufeur or passenger, assists the adaptive cruise control system driven and improve ride comfort.This adaptive cruise control system utilizes camera identification lane mark, and utilizes this recognition result automatically to control Vehicular turn.
Existing document (Korea S's publication number: 10-2012-0022305) about described adaptive cruise control system discloses explores path and the adaptive cruise technology reflecting the driving performance of chaufeur in traveling action.
This existing document (publication number: 10-2012-0022305) utilizes the filming image portion of the image information obtaining vehicle-surroundings road, from the acceptance division of the vehicle receiver routing information of described vehicle-surroundings, stores the memory device of the driving mode in the past of described vehicle and determine the control part of the driving mode of described vehicle according to the driving mode in the past of the image information of described road, the routing information received from described nearby vehicle and described vehicle, and driving pupil characteristic also controls automatic navigation of vehicle.
This existing document (publication number: 10-2012-0022305) for grasp chaufeur driving habit (in the past driving mode) and collect running data in the past, when collecting because of emergency case in this process from running datas in the past different at ordinary times, namely, when according to the mistake collected, running data provides vehicle adaptive cruise in the past, the repulsion sense caused to chaufeur is greater than general adaptive cruise.
Therefore, the vehicle self-adaption cruise system of the correct driving habit of exploitation reflection chaufeur is needed.
Therefore, the object of the present invention is to provide a kind of the vehicle steering person's hazard index management system and the method thereof that reflect the correct driving habit of chaufeur.
For reaching above-mentioned purpose, vehicle steering person's hazard index management system according to one aspect of the invention comprises: this vehicle driving trace generating unit, and it utilizes this vehicle traveling information received from the internal sensor of the travel conditions of this vehicle of sensing to generate this vehicle driving trace; Surrounding vehicles driving trace generating unit, it utilizes the ambient environment information received from the external sensor of the surrounding condition of sensing described vehicle to generate the surrounding vehicles driving trace of described vehicle; Track lifting capacity calculating part, it calculates the track lifting capacity of the comparative result between the distance of the track between driving trace and described surrounding vehicles driving trace and the critical value preset representing described vehicle; And hazard index management department, it generates the chaufeur hazard index corresponding to the described track lifting capacity calculated.
Vehicle steering person's hazard index management process according to another aspect of the invention comprises: utilize this vehicle traveling information received from the internal sensor of the travel conditions of this vehicle of sensing to generate the step of this vehicle driving trace; The ambient environment information received from the external sensor of the surrounding condition of sensing described vehicle is utilized to generate the step of the surrounding vehicles driving trace of described vehicle; The step of the track lifting capacity of the track distance between calculating expression described vehicle driving trace and described surrounding vehicles driving trace and the comparative result between the critical value preset; And generate the step of the chaufeur hazard index corresponding to the described track lifting capacity calculated.
According to the present invention, by accurately grasping the driving habit of chaufeur, to adjust the vehicle lateral control start time point in adaptive cruise, that therefore can eliminate chaufeur turns to sense of insecurity.
Fig. 1 is the block diagram showing vehicle steering person's hazard index management system according to an embodiment of the invention;
Fig. 2 is the block diagram of the concrete formation of ambient environment identification part shown in display Fig. 1;
Fig. 3 is for surrounding vehicles lifting capacity calculating part shown in display Fig. 2 is according to collision required time (TimeToCollision; The schematic diagram of the three kinds of hazardous locations TTC) divided;
Fig. 4 is for display is by the pie graph of the sensor of hazardous location detection surrounding vehicles each shown in Fig. 3;
Fig. 5 is for display is by the schematic diagram of the example of the formation of sensor shown in Fig. 4 detection surrounding vehicles;
Fig. 6 is the diagram of circuit showing vehicle steering person's hazard index management process according to an embodiment of the invention;
Fig. 7 is the block diagram of another embodiment of ambient environment identification part shown in instruction diagram 2;
Fig. 8 is the block diagram of the concrete formation of surveyed area generating unit shown in display Fig. 7;
Fig. 9 is the diagram of circuit of the method showing optimum detection region according to an embodiment of the invention;
Figure 10 is the block diagram of the concrete formation of driver status sensing system shown in display Fig. 1;
Figure 11 is the schematic diagram of the device showing obtaining information according to an embodiment of the invention;
Figure 12 illustrates the schematic diagram confirming the method whether chaufeur closes one's eyes according to an embodiment of the invention;
Figure 13 is the diagram of curves that display hinders the sleepy load travelling chaufeur in load according to an embodiment of the invention;
Figure 14 is the diagram of circuit that display confirms the step whether chaufeur closes one's eyes according to an embodiment of the invention;
Figure 15 be illustrate hinder the chaufeur travelled in load according to an embodiment of the invention watch the schematic diagram of neglecting load attentively;
Figure 16 is the schematic diagram that display exports the example of picture according to an embodiment of the invention;
Figure 17 is the diagram of circuit showing driver status method for sensing according to an embodiment of the invention.
The preferred embodiments of the present invention are described in detail referring to accompanying drawing.First, it should be noted that adding in Reference numeral the inscape of each figure, even if show on different accompanying drawings, still as far as possible identical Reference numeral being added to identical inscape.And if judge when illustrating of the present invention to think and omit relevant detailed description in detail to during the illustrating and may obscure content of the present invention of related known structure or function.Following in addition preferred embodiments of the present invention will be described, but technical scheme of the present invention does not limit or is limited to this, and the technical personnel of affiliated technical area can do various deformation and implement, and this is self-evident.
Fig. 1 is the block diagram showing vehicle steering person's hazard index management system according to an embodiment of the invention.
With reference to Fig. 1, vehicle steering person's hazard index management system 100 is not the driving habit of the analysis of the driving mode in the past chaufeur by learning in advance according to an embodiment of the invention, but according to the information accurate analysis driving habit by various sensor Real-time Obtaining.
Chaufeur hazard index management system 100 utilizes the information by various sensor Real-time Obtaining according to an embodiment of the invention, therefore analyzing the existing driving habit analysis method of driving habit compared to the driving mode in the past by learning in advance, more adequately can analyze the driving habit of chaufeur.
Vehicle steering person's hazard index management system 100 comprises internal sensor 110, external sensor 120, ambient environment identification part 130, travel conditions sensing interface 140, driver status sensing system 160 and efferent 150.
Internal sensor 110 obtains this vehicle traveling information by the travel conditions sensing this vehicle.This vehicle traveling information comprises speed information 11-1, rate of yaw (yawrate) information 11-3, deflection angle information 11-5, acceleration information 11-7 and wheel speed information 11-9.
For obtaining these these vehicle traveling informations, internal sensor 110 comprises the car speed sensor 110-1 obtaining speed information 11-1, the yaw rate sensor 110-3 obtaining rate of yaw information 11-3, obtains the steering angle sensor 110-5 of deflection angle information 11-5, obtains the acceleration pick-up (Gsensor) of acceleration information 11-7 and obtain the wheel speed sensors 110-9 of wheel speed information 11-9.
External sensor 120 obtains ambient environment information by the surrounding condition of this vehicle of sensing.Ambient environment information comprises front/rear side radar information 12-1, front/rear side's image information 12-3, side ultrasound information 12-5, panoramic view monitoring (AroundViewMonitoring; AVM) image information 12-7 and navigation information 12-9.
For obtaining these ambient environment information, external sensor 120 can comprise the front/rear Fanglei obtaining front/rear side radar information 12-1 and reach 120-1, obtains front/rear side's camera 120-3 of front/rear side's image information 12-3, obtains the side super sonic 120-5 of side ultrasound information 12-5, obtain the AVM camera 120-7 of AVM image information 12-7 and obtain navigating instrument (NAV) 120-9 of navigation information 12-9.
Ambient environment identification part 130 utilizes this vehicle traveling information received from described internal sensor 110 to calculate track lifting capacity with the ambient environment information received from described external sensor 120, and according to management of calculated results chaufeur hazard index.Referring to Fig. 2, this is described in further details.
Travel conditions sensing interface 140 is connected to efferent 150, and the chaufeur hazard index that the driver status information provided by driver status sensing system 160 by described efferent 150 and described ambient environment identification part 130 are provided is supplied to chaufeur.
Efferent 150 exports with vision or auditory information form the described chaufeur hazard index being reflected in described driver status information and is supplied to chaufeur.For this reason, efferent 150 comprises loud speaker 150-1, image and sound guidance system (AudioVideoNavigation; AVN) 150-3 and head-up display (HUD) 150-5.
Further, for adjusting the crosswise joint start time point of vehicle, described efferent 150 can also comprise Engine Control System 150-7, braking control system 150-9 and steering control system 150-11.
Driver status sensing system 160 senses the driving conditions such as sleepy driving.Driver status sensing system is described in detail with reference to Figure 10 to Figure 17.
Fig. 2 is the block diagram of the concrete formation of ambient environment identification part shown in display Fig. 1.
With reference to Fig. 2, ambient environment identification part 130 is described above, this vehicle traveling information received from described internal sensor 110 is utilized to calculate track lifting capacity with the ambient environment information received from described external sensor 120, and according to the track lifting capacity calculating chaufeur hazard index calculated (or around hazard index).
For managing chaufeur hazard index more exactly, ambient environment identification part 130 also utilizes surrounding vehicles lifting capacity and road load gauge to calculate chaufeur hazard index except track lifting capacity.
Ambient environment identification part 130 comprises this vehicle driving trace generating unit 130-1, surrounding vehicles driving trace generating unit 130-3, track lifting capacity calculating part 130-5, surrounding vehicles lifting capacity calculating part 130-7, road load amount calculating part 130-9 and chaufeur hazard index management department 130-11.
The speed information that this vehicle driving trace generating unit 130-1 utilizes internal sensor 110 to provide, deflection angle information, acceleration and deceleration information and this vehicle driving trace of rate of yaw acquisition of information 13-1.
Surrounding vehicles driving trace generating unit 130-3 utilizes the ambient environment acquisition of information surrounding vehicles driving trace 13-3 comprising front/rear side radar information 12-1 that external sensor 120 provides, image information 12-3,12-7 and side ultrasound information 12-5.
When utilizing described front/rear side radar information 12-1 to differentiate object, accuracy is low, but can obtain range information (longitudinal direction) accurately.In contrast to this, described image information 12-3 and 12-7 of acquisition is simple eye image, and therefore the accuracy of range information (longitudinal direction) is low, but can differentiate object, acquisition horizontal information accurately exactly.
Reach 120-1 by front/rear Fanglei in target vehicle model and obtain longitudinal range information, obtain transverse distance information by front/rear side's camera 120-3, AVM camera 120-7 and side super sonic 120-5.
Following mathematical expression 1 is that surrounding vehicles driving trace generating unit 130-3 is for predicting the target vehicle pattern of surrounding vehicles driving trace.
A = 1 &Delta;t 0 0 0 1 0 0 0 0 1 &Delta;t 0 0 0 1 , H = 1 0 0 0 0 0 1 0
x = x v x y v y
Wherein, x, Vx, y, Vy are the state variables of target vehicle, and x, y are the positions of target vehicle, measured obtain by image camera.Vx, Vy are the speed of target vehicle.A is auto model, and H is measured value model, and state variable represents distance, speed, the distance in y-axis direction, the speed in x-axis direction.System noise and measured value noise are white Gauss noises.
Track lifting capacity calculating part 130-5 calculates track lifting capacity W trj.Track lifting capacity W trjbe the comparative result between track distance value and the critical value preset, wherein said track distance value is the difference of described surrounding vehicles driving trace 13-3 and described vehicle driving trace 13-1.
Predict that the situation of collision risk that has that this vehicle driving trace 13-1 and surrounding vehicles driving trace 13-3 draws is the situation needing high driver to note.This condition values is changed into track lifting capacity W by track lifting capacity calculating part 130-5 trj.
Track lifting capacity W trjcalculate by following mathematical expression 2.
W Trj(i)＝|T Trj(i)-D Trj(i)ifW Trj(i)＞Threshold，0
W Trj(i)＜Threshold，1
Wherein, D trjthis vehicle driving trace, T trjit is surrounding vehicles driving trace.I (1,2 ... n) be the surrounding vehicles detected.
According to above-mentioned mathematical expression 1, track lifting capacity W trjcomparison detection to the track of surrounding vehicles and the track distance that obtains of the track of this vehicle be less than critical value (Threshold) time be set as 1, be set as 0 when being greater than critical value.
Surrounding vehicles lifting capacity calculating part 130-7 according to ambient environment information analysis front/rear/the surrounding vehicles quantity of side, analyze described surrounding vehicles with or without change lane, according to this Analysis result calculation surrounding vehicles lifting capacity Ws.The quantity of described surrounding vehicles (can be multiple) and the trail change of described surrounding vehicles (can be multiple) are the lifting capacity that chaufeur should be noted that.
For calculating surrounding vehicles lifting capacity, first according to collision required time (TimeToCollision; TTC) calculate three hazardous locations (region 1., region 2. and region 3.).Three hazardous locations as shown in Figure 3.Wherein, TTC is when the close velocity (closingspeed) of subject vehicle is constant, the time needed for subject vehicle collision target vehicle.Described TTC calculates by speed information 11-1 and deflection angle information 11-5.
Such as shown in Figures 4 and 5, distance between the surrounding vehicles (20 in Fig. 5,30,40,50) that can detect by the search coverage 43,47 of the past/back radars 120-1 and the search coverage 41 of front/rear side's camera 120-3, the search coverage 45 of side super sonic 120-5 and the vehicle detected by TTC value calculates the time divided by the relative velocity of the vehicle detected by the TTC value about the surrounding vehicles detected (20 in Fig. 5,30,40,50), sets hazardous location (region 1., 2. and 3.) with this.
After setting three hazardous locations (region is 1., 2. and 3.), surrounding vehicles lifting capacity calculating part 130-7 analyze from each regionally detecting to vehicle quantity and with or without change lane, according to this Analysis result calculation surrounding vehicles lifting capacity Ws.
1. the number of vehicles detected from region is more, and corresponding vehicle change lane is more, and surrounding vehicles lifting capacity Ws is higher.On the contrary, when detect around almost there is no vehicle or detect be arranged in region 3. or detect region 3. track of vehicle change little time, surrounding vehicles lifting capacity Ws declines.
Surrounding vehicles lifting capacity Ws can represent by following mathematical expression 3.
W S = &alpha; &Sigma; i = 1 n S i + &beta; &Sigma; i = 1 n L i
Wherein, the position (region 1., 2. and 3.) of α, β to be weighting factor (weightingFactor), S be vehicle detected, L represents that the surrounding vehicles detected is with or without change lane.Wherein, detecting L when surrounding vehicles changes track is 1, is 0 without L when changing.I (1<i<n, wherein n is natural number) is the vehicle detected.
Road load amount calculating part 130-9 utilizes Road form, pavement state and volume of traffic state computation road load amount in described ambient environment information.
Curved road is compared to linear road, and cross way, compared to Ordinary road, more needs chaufeur to note during upcoming traffic situation difference, therefore needs to calculate road load amount to this.
The pavement state information got containing navigation information 12-9 and the past/rear camera 120-3 of road status information that road load amount provides by navigating instrument 120-9 calculates.Road load amount calculates by following mathematical expression 4.
W R＝α×A+β×B+γ×C
Wherein, A is the value representing road condition.Such as, A has larger value when the curvature value of road ahead is larger, has larger value when running into signal lamp change, pedestrian, speed limit, children protection area.B is pavement state value, and represent paved road, non-paving road, C represents the volume of traffic of road ahead, has larger value when volume of traffic is large.Whole to A, B, C specification can be changed into 0 ~ 5 scope.
The lifting capacity W that chaufeur hazard index management department 130-11 calculated integrated each stage trj, the chaufeur hazard index (around hazard index) that obtains of Ws, WR manages.
Chaufeur hazard index management department 130-11 is added described track lifting capacity W trj, described surrounding vehicles lifting capacity Ws and described road load amount WR, calculate described chaufeur hazard index according to addition result.
When the chaufeur hazard index calculated is greater than the critical value preset, the described chaufeur hazard index calculated is sent to vehicle control system 150-7,150-9,150-11 by travel conditions sensing interface 140 by chaufeur hazard index management department 130-11.
Vehicle control system 150-7,150-9,150-11 hold up action, braking maneuver and handling maneuver receiving the chaufeur hazard index time control making calculated being greater than the critical value preset, restriction vehicle driving function.
Chaufeur hazard index can represent by following mathematical expression 5.
Chaufeur hazard index=W trj+ W s+ W r
Fig. 6 is the diagram of circuit showing vehicle steering person's hazard index management process according to an embodiment of the invention.
With reference to Fig. 6, first in step S610, generate this vehicle driving trace, this vehicle driving trace by generating in step S612 predicts the position of this vehicle.Described vehicle driving trace can generate according to speed information, deflection angle information, acceleration and deceleration information and rate of yaw information.
Afterwards, in step S614, generate surrounding vehicles driving trace, in step S616, predict the position of surrounding vehicles.Described surrounding vehicles driving trace can be generated according to the fore-and-aft distance information obtained by radar and the transverse distance information obtained by camera and ultrasonic generator.Wherein, fore-and-aft distance information is with this vehicle for benchmark is to the fore-and-aft distance information of surrounding vehicles, and transverse distance information is for benchmark is to the transverse distance information of surrounding vehicles with this vehicle.
Afterwards, in step S618, track lifting capacity is calculated.Track lifting capacity can calculate according to this vehicle driving trace and surrounding vehicles driving trace.Such as, the driving trace of the surrounding vehicles that comparison detection arrives and this vehicle driving trace, when track distance is between less than critical value, result is 1, and when being greater than critical value, result is 0.
Afterwards, in step S620, surrounding vehicles lifting capacity is calculated.Surrounding vehicles lifting capacity be the multiple hazardous locations divided centered by this vehicle according to TTC number of vehicles with calculate with or without change lane.Radar, camera and super sonic identification surrounding vehicles can be passed through, calculate the time with the relative distance of the vehicle detected according to TTC value divided by relative velocity, obtain multiple hazardous location with this.
Afterwards, in step S622, road load amount is calculated.Road load amount calculates by navigation information, pavement state information, traffic information etc.
Afterwards, in step S624, chaufeur hazard index is calculated.Chaufeur hazard index is by obtaining the summation of track lifting capacity, surrounding vehicles lifting capacity and road load amount.
Afterwards, in step S626, compare the chaufeur hazard index and critical value that calculate, when chaufeur hazard index is more than critical value in step S628 according to the size of this critical value by the stage to chaufeur alarm driving dangerousness situation.Wherein, in step S626, when driving dangerousness index, lower than re-executing step S610 to S624 during critical value, this connects process.
In step S630, provided the chaufeur hazard index representing driving dangerousness situation to chaufeur by the loud speaker in vehicle, HUD and AVN etc.
Fig. 7 is the block diagram of another embodiment of ambient environment identification part shown in instruction diagram 2.
With reference to Fig. 7, ambient environment identification part 130 comprises the zoning (hereinafter referred to as surveyed area) for inspection optimization and in the surveyed area optimized, identifies the surveyed area generating unit 132 of ambient environment according to another embodiment of the present invention.Other formations except surveyed area generating unit 132 are all identical with the formation in the identification part of ambient environment shown in Fig. 2 130, therefore replace illustrating all the other formations with reference to Fig. 2 to the explanation that ambient environment identification part 130 is carried out.
Surveyed area generating unit 132 is optimized the surveyed area containing target object, makes it possible to accurately identify practical obstacle thing from the target object of vehicle-surroundings.
Fig. 8 is the block diagram of the concrete formation of surveyed area generating unit shown in display Fig. 7.
As shown in Figure 8, the surveyed area generating unit 132 in optimum detection region comprises distance sensor 132-1, target tracking portion 132-3, track (track) management department 132-5 and storage part 132-9 according to an embodiment of the invention.
The position of distance sensor 132-1 senses vehicle periphery object output detection signal.Now, distance sensor 132-1 can be at least one in laser radar sensor, radar sensor and camera sensing device.
Target tracking portion 132-3 is according to detection signal cognitive disorders thing and generation comprises the track of the position deduction value corresponding with the described obstacle recognized and error covariance with trace location.
For overcoming the error of sensor, the position of sensing movement object, usually adopt Kalman filtering.
The process of Kalman filtering to be a kind of repetition according to the measured value of the presumed value of the object space of a upper time and object space the calculate presumed value of object space, the error occurred during to offset and to measure object space, the accurately technology of presumption object space.At this, the presumed value of the object space of a supreme time is first utilized to calculate the presumed value only using the current time of the measured value of a supreme time.
The measured value correction of the covariance of current time and the object space of current time only using the measured value of a supreme time to calculate is utilized only to use the presumed value of the current time of the measured value of a supreme time, to calculate the presumed value of the object space of current time afterwards.
Target tracking portion 132-3 sets the quantity of described track and corresponds to obstacle initial position and the surveyed area of each track according to the object space that detection signal represents.Now, the initial position of obstacle and surveyed area calculate by the Kalman filtering shown in following mathematical expression 6.
x ^ ( k | k - 1 ) = F ( k - 1 ) x ^ ( k - 1 | k - 1 )
z ^ ( k | k - 1 ) = H ( k ) x ^ ( k | k - 1 )
Now, represent the presumed value by object state value during the time k that estimates to the information of time k-1, represent the presumed value by object state value during the time k-1 that estimates to the information of time k-1. represent the presumed value of object space during the time k by estimating to the information of time k-1.
At this, each track can comprise the Kalman filtering (kalmanfilter) of following the trail of the certain objects that distance sensor 132-1 senses.That is, each track includes the covariance of the presumed value of the position of the obstacle of tracking and the error by measured value correction position, and the presumed value that each time calculates, measured value and covariance can be used as historic records and be stored into storage part 132-9.
The formation of the Kalman filtering in track represents by following mathematical expression 7.
x(k)＝F(k-1)x(k-1)+v(k-1)
z(k)＝H(k)x(k)+w(k)
P ( k | k ) = &Sigma; i &beta; ( k , i ) [ P ( k | k , i ) + ( x ^ ( k | k , i ) - x ^ ( k | k ) ) ( x ^ ( k | k , i ) - x ^ ( k | k ) ) T ]
P(k|k-1)＝F(k-1)P(k-1|k1)F(k-1) T+Q(k-1)
The state value of object when x (k) represents time k in above-mentioned mathematical expression 7, F (k-1) is the state conversion model of the change represented when changing from time k-1 to time k, the position of object when z (k) represents time k, H (k) represents the observing and nursing changed to object space in object state, process noise when v (k-1) is time k-1, measures noise when w (k) is time k.Further, the error covariance of Kalman filtering when P (k|k) is the time k according to calculating to the information of time k, the error covariance of Kalman filtering when P (k|k-1) is the time k calculated according to the information to time k-1.Q (k-1) estimates covariance when representing time k-1.
Whether target tracking portion 132-3 is contained in the surveyed area corresponding to track according to the object space that the covariance of the measured value of object space and the error of predictor and error judges represented by detection signal.
Now, target tracking portion 132-3, according to the scope of the state value setting surveyed area of Kalman filtering in current track, utilizes the measured value in surveyed area to upgrade the state value of Kalman filtering.First target tracking portion 132-3 calculates error according to the measured value of object space and the predictor of object space, then according to covariance and the observing and nursing calculation error covariance of estimation error in Kalman filtering, whether surveyed area is entered according to error and error covariance judgment object.
At this, surveyed area is set as taking error covariance as the region representing the value of below particular probability value during discrete gaussian probability distributes, and this probable value is called threshold probability (gateprobability).Therefore by calculating error covariance and setting threshold probability value to calculate surveyed area, error covariance and threshold probability value is optimized in time by Kalman filtering, therefore, it is possible to optimum detection region in time.
The condition that the calculation method of error and error covariance and object comprise within a detection region can represent by following mathematical expression 8.
v ( k , i ) = z ( k , i ) - z ^ ( k | k - 1 )
S(k)＝H（k)P(k|k-1)H(k) T+R(k)
v(k,i) TS(k) -1v(k,i)＜r
In above-mentioned mathematical expression 8, the error of object i when v (k, i) represents time k, z (k, i) represents the measured value of the position of object i.And, P (k|k-1) represents the covariance of the estimation error of Kalman filtering, H (k) is the calculation matrix representing the observing and nursing that the position from the state of object to object is changed, R (k) is the covariance of mensuration noise when representing time k, and S (k) represents that described P (k|k-1) is multiplied by described H (k) and the error covariance be added with described R (k).R represents the scope of surveyed area.
Track management department 132-5 upgrades the position deduction value in track according to the position deduction value corresponded in the track of object in surveyed area and detection signal.
Now for upgrading described position deduction value, track management department 132-5 calculates kalman gain (kalmangain) according to the covariance of estimation error and error covariance, and calculates the position deduction value using the information to current time to estimate according to the position deduction value utilizing kalman gain, object position estimation value and the information of a supreme time to estimate.The available following mathematical expression 9 of renewal of position deduction value represents.
K(k)＝P(k|k-1)H TS(k) -1
x ^ ( k | k , i ) = x ^ ( k | k - 1 ) i = 0 x ^ ( k | k - 1 ) + K ( k ) v ( k , i ) i > 0
x ^ ( k | k ) = &Sigma; i &beta; ( k , i ) x ^ ( k | k , i )
In above-mentioned mathematical expression 9, K (k) represents kalman gain.As mentioned above, track management department 132-5 constantly updates position deduction value, therefore, it is possible to more adequately try to achieve position deduction value according to measured value.
When distance between the object space presumed value of track management department 132-5 in the first track and the object space presumed value in the second track is less than a reference value preset, according to historic records initialization first track and the second track that are stored in storage part 132-9.
Storage part 132-9 storage track upgrades historic records.Now, the historic records being stored in storage part can comprise the position deduction value of each time of the Kalman filtering in track and the covariance of measured value and estimation error.
When as above upgrading position presumed value, sometimes the object that two tracks represent may collide, when the position deduction value of the object that track represents is brought down below a reference value prestored, track management department 132-5 can be judged as that the object that two tracks represent can collide, and carries out initialization according to the data in the historic records of two tracks that will collide to track.
Further, when all objects position deduction value of track management department 132-5 in track is not all contained in the surveyed area corresponding to track, according to be stored in storage part track historic records initialization described in track.That is, the object of trajectory track all move on to outside surveyed area or be judged as noise or mistake and track is followed the trail of object disappear time, mean trajectory track object failure, therefore track management department 132-5 carries out initialization to track, makes to follow the trail of new object.
As mentioned above, when track utilizes Kalman filtering to follow the trail of the obstacle of movement, when the trajectory track of tracking objects is failed or two tracks are impinging one another, initialization track, makes to follow the trail of new object, therefore, it is possible to improve the object identification performance of periphery state detection system.
As mentioned above, target tracking portion 132-3 and track management department 132-5 follow the trail of obstacle and generate and data transmission in the track that upgrades to vehicle control section 132-7, vehicle control section 132-7 controls vehicle with this and makes vehicle avoiding obstacles or give the alarm to chaufeur.
Fig. 9 is the diagram of circuit of the method showing optimum detection region according to an embodiment of the invention.
With reference to Fig. 9, first in step S910, target tracking portion 132-3 is according to distance sensor 132-1 sensing object position and the detection signal of corresponding output generates containing corresponding to the position deduction value of the obstacle recognized and the track of error covariance.
Now described above, distance sensor can be at least one in laser radar sensor or radar sensor.
Further, the track that target tracking portion 132-3 generates comprises the Kalman filtering containing position deduction value and error covariance.Now, the explanation about the formation of Kalman filtering in track is identical with the content illustrated in conjunction with above-mentioned mathematical expression 1 and mathematical expression 2.
Afterwards in step S920, target tracking portion 132-3 calculates surveyed area according to track, and described surveyed area is detection of obstacles scope.
Now, the object space that can represent according to described detection signal, is set to initial value by the size of surveyed area.Further, surveyed area can be set to and take error covariance as the region representing the value of below threshold probability value in the distribution of discrete gaussian probability.
Afterwards in step S930, the object space that target tracking portion 132-3 selects detection signal to represent from detection signal is contained in effective detection signal of described surveyed area.
As mentioned above, the measured value of the object space that the periphery detection system that detection signal comprises vehicle is followed the trail of, target tracking portion 132-3 selects the effective measured value being contained in surveyed area from these measured values, and this value is for upgrading Kalman filtering and tracking objects.
Now, with the covariance of the error of predictor and error, target tracking portion 132-3 judges whether the position of the object that detection signal represents is contained in the surveyed area corresponding with track according to the measured value of object space.
Target tracking portion 132-3 arranges the scope of surveyed area according to the state value of Kalman filtering in current track, utilizes the measured value in surveyed area to upgrade the state value of Kalman filtering.Wherein, first target tracking portion 132-3 calculates error according to the measured value of object space and the predictor of object space, calculate error covariance according to the covariance of the estimation error in Kalman filtering and observation model, whether enter surveyed area according to error and error covariance judgment object.The calculation method of error and error covariance and object are contained in the condition of surveyed area with shown in above-mentioned mathematical expression 3.Kalman filtering continues to optimize error covariance and threshold probability value, therefore, it is possible to continue to optimize surveyed area.
Afterwards in step S940, when the distance in object space presumed value in the first track and the second track between object space presumed value is less than a reference value preset, track management department 132-5 is according to historic records initialization first track of the track be stored in storage part 132-9 and the second track.
The historic records be stored in storage part 132-9 can comprise the position deduction value of each time of the Kalman filtering in track and the covariance of measured value and estimation error.
Afterwards in step S950, track management department 132-5 upgrades the position deduction value in the track of the described detection signal being contained in and choosing and the object corresponding to the object space comprised in described surveyed area.
At this, for upgrading described position deduction value, track management department 132-5 calculates kalman gain according to the covariance of estimation error and error covariance, and calculates the position deduction value using the information to current time to estimate according to the position deduction value utilizing kalman gain, object position estimation value and the information of a supreme time to estimate.The renewal of position deduction value is as shown in above-mentioned mathematical expression 9.
Afterwards, when all objects position deduction value of track management department 132-5 in track is not all contained in the surveyed area corresponding to track, in step S960, basis is stored in the historic records initialization track of the track of storage part and terminates process.
That is, the object of trajectory track all move on to outside surveyed area or be judged as noise or mistake and track is followed the trail of object disappear time, mean trajectory track object failure, therefore track management department 132-5 carries out initialization to track, makes to follow the trail of new object.
By the data transmission in the track of as above method generation and renewal to vehicle control section 132-7, vehicle control section 132-7 controls vehicle with this and makes vehicle avoiding obstacles or give the alarm to chaufeur.
As mentioned above, according to car brakeing equal controller and the method thereof of the present embodiment, the periphery state detection system of vehicle is that sensing obstacle dynamically updates the effective gate limit value representing the region-of-interest followed the trail of, accurately to follow the trail of the obstacle of vehicle-surroundings, laser radar or radar sensor is only needed to get final product accurate trace location, the distance of distance obstacle can be extended, prevent Peril Incident.
Figure 10 is the block diagram of driver status sensing system shown in display Fig. 1.
With reference to Figure 10, driver status sensing system 160 comprises acquisition unit 161, control part 163 and efferent 165.
Acquisition unit 161 is for the not attention state information of the mobility operation information and chaufeur that obtain vehicle.At this, acquisition unit 161 comprises mobility operation detecting part 161A and not attention state detecting part 161B, can respectively by mobility operation detecting part 161A and not attention state detecting part 161B obtain the not attention state information of mobility operation information and chaufeur.
Wherein, mobility operation detecting part 161A is used for the operation of the requisite control unit in senses vehicle traveling aspect, can be the electronic control unit (ElectronicControlUnit of vehicle; ECU) other modules or in electronic control unit.
Mobility operation detecting part 161A can comprise acceleration pedal operation detecting part d 1, brake pedal operation detecting part d 2, multi-functional operation detecting part d 3, steering wheel operation detecting part d 4etc. multiple operation detecting part.
As shown in (a) in Figure 11, operation needed for multiple operation detecting part senses vehicle in mobility operation detecting part 161A travel, specifically senses at least one mobility operation in acceleration pedal (ACCpad) operation, brake pedal (Breakpad) operation, multifunction switch (Multi-functionS/W) operation and bearing circle (Steeringwheel) operation.
Further, mobility operation detecting part 161A can obtain the pedal of clutch operation or this mobility operation information of transmission operating that also comprise manual gear vehicle.Now, mobility operation detecting part 161A can confirm the speed of vehicle, obtains mobility operation information when car speed is more than predetermined speed (such as, 10km/h).
Such as, mobility operation detecting part 161A lasts the work times n of schedule time sensing by the acceleration pedal of driver's operation in units of certain hour a.Such as, mobility operation detecting part 161A last in units of 50ms 200ms continue confirm acceleration pedal whether work (ON).
Equally, mobility operation detecting part 161A can sense brake pedal and pedal of clutch (is respectively n by the work times operated in each certain hour b, n c).
Again such as, mobility operation detecting part 161A lasts the work times n of schedule time sensing by each multifunction switch of driver's operation m.Wherein, multifunction switch can be the switch of the car light such as wiper, turning indicator for starting vehicle.Such as, mobility operation detecting part 161A lasts the work times that 200ms counts multifunction switch.
Again such as, mobility operation detecting part 161A lasts the cireular frequency n of schedule time sensing by the wheel flutter of driver's operation θ.At this, mobility operation detecting part 161A can not measure cireular frequency separately, but in units of certain hour, last the angle variable quantity that the schedule time measures wheel flutter, calculates cireular frequency with this.Such as, mobility operation detecting part 161A can last the mode of angle variable quantity that 200ms measures wheel flutter and calculates cireular frequency in units of 50ms.
Attention state detecting part 161B is not used for the operation of control unit and the action of chaufeur of additional work when senses vehicle travels, and can comprise multiple detecting parts such as sound signal input sensing portion T1, Air Conditioner Singnal input sensing portion T2 and navigation signal input sensing portion T3.
Further, for sensing the action (VisualFactor, VerbalFactor) of chaufeur, attention state detecting part 161B can also not comprise sleepy assessment of indices portion P, direction of visual lines determination part E and voice detecting part V.Multiple detecting part in above-mentioned not attention state detecting part 161B and determination part as shown in Figure 2, can be arranged on the desired location of vehicle, utilize at least one the acquisition of information not attention state information in peripheral equipment operation, chaufeur image of face, chaufeur voice.
Such as, not attention state detecting part 161B can in senses vehicle driving process chaufeur to the number of operations n of peripheral equipment t.Wherein, peripheral equipment is vehicle audio-visual navigating instrument (Audio, Video, Navigation; AVN) though, that Vehicular air-conditioning apparatus etc. is not that vehicle travels is essential but can by being operating as the control unit for vehicle that chaufeur provide convenient or regulate vehicle environment.Such as, attention state detecting part 161B can at vehicle at a predetermined velocity (not such as, during speeds 10km/h), lasting the schedule time (such as, 200ms) senses the input number of times of chaufeur to the operating switch of peripheral equipment and obtains not attention state information.
Again such as, not attention state detecting part 161B by being arranged on the voice of the microphone senses chaufeur in desired location in vehicle.Such as, not attention state detecting part 161B can confirm in the given time from microphones to the impulse magnitude (voice size) of speech data and voice time of origin.Preferably, attention state detecting part does not utilize the pulse critical value be prestored in memory device to sense the schedule time (such as, the time of the speech data of the impulse magnitude with more than pulse critical value is received, as not attention state information 200ms).
Again such as, attention state detecting part 161B does not receive the image of face of chaufeur by the camera be arranged on vehicle desired location, to obtain the eye closing information of chaufeur and to watch the information of neglecting attentively.Now, camera can comprise the near infrared ray LED (NearInfraredLED) in Figure 11 shown in (b), with guarantee in the daytime with recording image at night.
Preferably, attention state detecting part 161B can not extract separately the eye areas image of chaufeur from the image of face of chaufeur.Now, eye areas image is extracted by carrying out image processing to the image of face of chaufeur.That is, attention state detecting part 161B can not utilize the eye areas image capturing chaufeur the image of face of chaufeur and described image of face obtained from camera to close one's eyes and watch attentively and the not attention state information such as to neglect.
Attention state detecting part 161B can not utilize the eye closing information of the eye areas image capturing chaufeur extracted from the image of face of chaufeur.
With reference to Figure 12, attention state detecting part 161B is not from eye areas image sensing eyelid region, confirms as chaufeur close one's eyes when the angle ∠ A of eyelid and ∠ B sum are predetermined critical below angle ∠ C (the ∠ A+ ∠ B≤∠ C) that preset.
Attention state detecting part 161B does not confirm the angle of eyelid in units of certain hour, senses chaufeur eye closing number of times with this, calculates the sleepy time (sleepy index) of chaufeur according to the number of times of closing one's eyes in predetermined amount of time and certain hour value.Such as, attention state detecting part 161B was not divided into each interval by one second is 250ms, and measures (counting) and to close one's eyes in one second number of times, and the chaufeur closed-eye time obtained when count value is 3 (sleepy time) is 750ms.
Further, not attention state detecting part 161B can utilize the eye areas image capturing chaufeur in the image of face of chaufeur and described image of face watch the information of neglecting attentively.
With reference to Figure 15, watching the scope of neglecting attentively is not for foundation with the angle of bearing circle, but the angle changed from vehicle central with the angle of wheel and wheel (Figure 15 the Θ A of (a) or Θ B) is for foundation, during by confirming that vehicle travels, whether the field range (direction of visual lines) of chaufeur obtains at uncharge visual range η and watches the information of neglecting attentively.After can calculating face's angle (α) of (b) (in the Figure 15) according to the image of face of the chaufeur obtained from camera, from the position (direction of pupil) of eye areas eikonometry pupil (β) of (C) (the Figure 15) to confirm field range.
Specifically, when steering wheel angle be within predetermined angular and the gear of change-speed box be D shelves or N shelves, attention state detecting part 161B can not enter in the sight line of chaufeur and travels zero load visual range (a), (b) and be judged as watching attentively when reaching more than the schedule time neglecting and obtaining according to this time and watch the information of neglecting attentively.Such as, when car speed be more than 10Km/h, Transmission gear be D or N, steering wheel angle within ± 15 °, attention state detecting part 161B can not enter in the sight line of chaufeur and travels zero load visual range (a), (b) be judged as watching attentively vehicle front when reaching more than 1.5 seconds neglecting.
Control part 163, for controlling all working of driver status sensing system 160, can be electronic control unit.Such as, control part 163 can be called traveling lifting capacity calculating unit (DrivingWorkloadComputeUnit; DWCUnit).
Specifically, control part 163 utilize from acquisition unit 161 obtain mobility operation information and attention state information calculate mobility operation lifting capacity and hinder travel lifting capacity.
Further, travels lifting capacity and judge whether chaufeur is in safe driving state by comparing mobility operation lifting capacity with hindering, in judged result for control efferent 165 when being not safe driving state makes output warning information.
First, control part 163 utilize from acquisition unit 161 obtain mobility operation information and attention state information calculate mobility operation lifting capacity and hinder travel lifting capacity.
Such as, control part 163 utilizes the mobility operation information obtained from the mobility operation detecting part 161A of acquisition unit 161 to calculate mobility operation lifting capacity W d.
Specifically, control part 163 utilizes the mobility operation information obtained from mobility operation detecting part 161A and the weighted value calculating mobility operation lifting capacity corresponding to projects mobility operation information read from memory device.
Wherein, weighted value can be in advance through kinds of experiments, extracts and the value preset by the projects travelled in operation information.Weighted value also can be that operating personnel is in advance by the value that project sets arbitrarily.In addition, memory device, for storing data, can be non-volatility memorizer.
Preferably, control part 163 can utilize the project in mobility operation information and calculate the lifting capacity of projects corresponding to the weighted value of this project.Such as, from acquisition unit 161 get comprise acceleration pedal operation, brake pedal operation, multifunction switch operation and the mobility operation information of steering wheel operation time, control part 163 can read weighted value D about acceleration pedal operation from memory device a, about the weighted value D of brake pedal operation b, about multifunction switch operation weighted value D mand about the weighted value θ of steering wheel operation.
If from acquisition unit 161 obtain in units of 50ms every object time and number information reach 200ms time, the mobility operation lifting capacity W lasting 200ms that control part 163 calculates dcan as shown in mathematical expression 10.
W d＝D A×n A×50ms+D B×n B×50ms+D C×n C×50ms+D M×n M+θ×n θ×50ms
According to circumstances can add and subtract the projects in above-mentioned mobility operation information.When according to vehicle class, when mobility operation information comprises pedal of clutch operation and transmission operating, control part 163 can also recycle and calculate mobility operation lifting capacity about the weighted value of pedal of clutch operation and the weighted value of transmission operating.
Again such as, control part 163 utilizes the not attention state information obtained from the not attention state detecting part 161B of acquisition unit 161 to calculate and hinders traveling lifting capacity W i.Specifically, control part 163 can calculate the lifting capacity of the projects in the not attention state information that never attention state detecting part 161B obtains, and the lifting capacity of projects that obtains of addition calculation can obtain hindering and travel lifting capacity.
Control part 163 can utilize the peripheral equipment number of operations obtained from the not attention state detecting part 161B of acquisition unit 161 to calculate peripheral equipment operational load amount T (n).
Such as, control part 161 can also recycle the weighted value about peripheral equipment operation prestored in memory and calculate peripheral equipment operational load amount.When lasting 200ms acquisition chaufeur from acquisition unit 161 and inputting number of times to the operation of peripheral equipment, control part 163 can calculate peripheral equipment operational load amount T (n) according to peripheral equipment operation input number of times and weighted value.
Further, control part 163 can utilize the speech data of the chaufeur obtained from the not attention state detecting part 161B of acquisition unit 161 to calculate voice load amount V (n).Specifically, control part 163 can calculate voice load amount according to the pulse critical value of the speech data stored in memory.Such as, control part 163 can last the schedule time (such as according to the not attention state detecting part 161B from acquisition unit 161, the speech data of chaufeur 200ms) got calculates the time receiving the speech data of more than pulse critical value, calculates voice load amount V (t) with this.
Further, control part 163 can utilize the eye closing information the not attention state information obtained from the not attention state detecting part 161B of acquisition unit 161 and watch the information of neglecting attentively and calculate sleepy lifting capacity P (t) and travel and watch attentively and neglect lifting capacity E (t).
With reference to the diagram of curves shown in Figure 13, highly stable when chaufeur is in waking state, curvilinear motion amplitude is little, and when being in doze state, the change of catacleisis degree (PERCLOS) value is very large, and numerical value is overall higher than waking state.Wherein, the available following mathematical expression 11 of PERCLOS value represents.
Diagram of curves according to Figure 13, control part 163 is when confirming PERCLOS value and being more than predetermined percentage (30%), namely confirm that the schedule time (such as by not attention state detecting part 161B, 250 seconds) in chaufeur closed-eye time reach certain hour (such as, about 75 seconds) time, control part 163 makes efferent 165 export vehicle alert sound by control.Sleepy driving is factor the most dangerous during vehicle travels, and therefore above-mentioned purpose is, confirming that chaufeur closed-eye time is judged as when reaching more than the schedule time that chaufeur is in sleepy driving condition, to export alarm tone immediately and give the alarm to chaufeur.
Now, control part 163 can confirm the eye closing action of chaufeur according to the diagram of circuit shown in Figure 14.
Specifically, in step S1410, control part 163 judges whether car speed V is more than predetermined speed (such as, V >=10Km/h).When judged result is more than predetermined speed, control part 163 can last predetermined minute N by not attention state detecting part 161B and confirm each certain hour x (such as, represent with 250ms or 0.25s) angle of interior eyelid, to confirm eye closing number of times y, calculate the closed-eye time (sleepy time) of chaufeur.
In step S1420, control part 163 judges whether the angle of eyelid is ∠ A+ ∠ B≤∠ C.When the angle of eyelid is ∠ A+ ∠ B≤∠ C, be judged as in step S1430 close one's eyes and to eye closing counting how many times (y++).
Control part 163 counts the number of times y (x+=0.25 closed one's eyes in each schedule time x, y++), number of times of closing one's eyes in one second in step S1440 reaches three (x=1, y=3) time, to conversion factor P value counting (P++) in step S1450, each increase by a second (N++) also repeats above process.
If conversion factor P reaches more than 100 in the minute of 250 seconds in step S1460, then in step S1470, control part 163 makes efferent 165 give the alarm (WARNING) event by control.Now, conversion factor P value is changed to 99 by control part 163, and after making minute N deduct 1 second, (P=99, N-=1) repeats above process, calculates the closed-eye time (sleepy time) of chaufeur.Further, control part 163 PERCLOS value in the time of can confirming a reservation reaches the situation of more than 30% and calculates corresponding lifting capacity P (t).
Further, the information of neglecting that what control part 163 utilized never attention state detecting part 161B to obtain watch attentively calculates to watch attentively neglects lifting capacity E (t).
As shown in (a) in Figure 15, when field range departs from traveling zero load visual range η, the factor values of load factor V can change with disengaging scope.
The scope (a') departed from, (b') can preset, and the factor values that can read the load factor of the field range corresponding to current driver's from memory device by scope calculates to watch attentively neglects lifting capacity E (t).
Control part 163 can be neglected lifting capacity E (t) according to peripheral equipment operational load amount T (n) calculated, voice load amount V (t), sleepy lifting capacity P (t) and watching attentively and be calculated to hinder and travel lifting capacity W i.
In addition according to state and the situation of vehicle, utilize in the lifting capacity of not attention state acquisition of information the lifting capacity that may have and calculate and hinder and do not consider when travelling lifting capacity.
Control part 163 is by comparing mobility operation lifting capacity W dlifting capacity W is travelled with obstruction ijudge whether chaufeur is in safe driving state.
Specifically, control part 163 is at traveling operational load amount W ddeduct to hinder and travel lifting capacity W ithe enough and to spare lifting capacity W that the value obtained is presetting ltime following, be judged as that the state of chaufeur is not safe driving state.Wherein, enough and to spare lifting capacity W lbeing by testing by subject's condition, extracting from traveling lifting capacity, driving information (variable gear state, vehicle acceleration, turn to and carry out) and prestoring value in memory, (W can be changed with driving conditions d∝ W l).That is, the condition that must meet mathematical expression 12 could judge that the driving condition of chaufeur is safe driving state.
&Sigma; t = t 1 t = t 2 { W d ( t , n ) - ( v ( t ) + T ( n ) + E ( t ) + P ( t ) ) } &GreaterEqual; W l W d = d 1 t 1 + d 2 t 2 + d 3 t 3 + d 4 t 4
Wherein, t is the time, and n is number of times.
Efferent 165, for exporting picture and alarm tone, comprises liquid crystal indicator (LCD) and loud speaker.Preferably, as shown in (b) in Figure 16, efferent 165 can export picture and alarm tone by instrument carrier panel.Efferent 165 also can export picture by Audio presentation device.Further, the safe driving condition adjudgement result of control part 163 and this end value as (a) in Figure 16, can be presented on picture with column diagram form by efferent 165.At this, column diagram can show with multiple color.
Such as, control part 163 can by controlling to make efferent 165 show the current load amount information travelling the difference of lifting capacity as mobility operation lifting capacity and obstruction in column diagram mode on picture.
Specifically, can display control unit 163 calculates in units of the percentum of column diagram mobility operation lifting capacity W dlifting capacity W is travelled with obstruction idifference divided by enough and to spare lifting capacity W lthe end value C obtained.
Suppose in advance the moment that vehicle starts to be difficult to travel to be set to 75%, now control part 163 can make efferent 165 show with red column diagram when end value C is more than 75% by control, controlling efferent 165 makes column diagram glimmer, and vehicle traveling is dangerous to allow chaufeur recognize.
Further, control part 163 can make efferent 165 export alarm tone by control in this case, gives the alarm to chaufeur.
Can show with green column diagram when end value C is less than 75%, inform that chaufeur is current in a safe condition.
Further, control part 163 can at traveling operational load amount W dlifting capacity W is travelled with obstruction idifference divided by enough and to spare lifting capacity W lwhen the end value C obtained is more than predeterminated level (such as, 85%), hard closing (OFF) does not affect the power supply of the AVN of vehicle driving safety.
Further, the volume of the alarm tone that control part 163 can make efferent 165 export increase to end value C square, travel dangerous to the powerful alarm of chaufeur.
As mentioned above, the present invention hinders traveling obstruction by comparing the traveling lifting capacity that occurs in vehicle travel process and peripheral equipment and driver drowsy etc. travels the safe driving state that lifting capacity confirms chaufeur, give the alarm to chaufeur when hindering the lifting capacity height of vehicular drive, and according to circumstances force stop vehicle to travel non-essential device (such as, peripheral equipment) work, travel lifting capacity to reduce the obstruction hindering vehicle to travel, guarantee that chaufeur can safe driving.
First, in step S1710, driver status sensing system 160 obtains the not attention state information of mobility operation information and chaufeur.
Specifically, driver status sensing system 160 senses vehicle travels the mobility operation of necessary traveling control unit to obtain mobility operation information.
Such as, driver status sensing system 160 obtains mobility operation information by least one mobility operation in sensing acceleration pedal (ACCpad) operation, brake pedal (Breakpad) operation, multifunction switch (Multi-functionS/W) operation and bearing circle (Steeringwheel) operation.Now, driver status sensing system 160 can confirm the speed of vehicle, obtains mobility operation information when car speed is more than predetermined speed (such as, 10km/h).
Such as, driver status sensing system 160 lasts the work times n of schedule time sensing by the acceleration pedal of driver's operation in units of certain hour a.Equally, driver status sensing system 160 can sense and (is respectively n by the brake pedal that operates and the work times of pedal of clutch in each certain hour b, n c).
Again such as, driver status sensing system 160 lasts the work times n of schedule time sensing by each multifunction switch of driver's operation m.Wherein, multifunction switch can be the switch of the car light such as wiper, turning indicator for starting vehicle.
Again such as, driver status sensing system 160 lasts the cireular frequency n of schedule time sensing by the wheel flutter of driver's operation θ.At this, driver status sensing system 160 can not measure cireular frequency separately, but in units of certain hour, last the angle variable quantity that the schedule time measures wheel flutter, calculates cireular frequency with this.
Further, driver status sensing system 160 obtains not attention state information by the peripheral equipment of chaufeur selectivity control, the voice of chaufeur and facial information in vehicle travel process.
Such as, driver status sensing system 160 can in senses vehicle driving process chaufeur to the number of operations n of peripheral equipment t.Wherein, peripheral equipment is vehicle audio-visual navigating instrument (Audio, Video, Navigation; AVN) though, that Vehicular air-conditioning apparatus etc. is not that vehicle travels is essential but can by being operating as the control unit for vehicle that chaufeur provide convenient or regulate vehicle environment.
Again such as, driver status sensing system 160 is by being arranged on the voice of the microphone senses chaufeur in desired location in vehicle.Such as, driver status sensing system 160 can confirm from microphones to the impulse magnitude (voice size) reaching the speech data of the schedule time and voice time of origin.
Again such as, driver status sensing system 160 receives the image of face of chaufeur, to obtain the eye closing information of chaufeur and to watch the information of neglecting attentively by the camera be arranged on vehicle desired location.Now, camera can comprise the near infrared ray LED (NearInfraredLED) in Figure 11 shown in (b), with guarantee in the daytime with recording image at night.
Preferably, driver status sensing system 160 can extract separately the eye areas image of chaufeur from the image of face of chaufeur.Now, eye areas image is extracted by carrying out image processing to the image of face of chaufeur.That is, driver status sensing system 160 can utilize the image of face of chaufeur that obtains from camera and eye areas image capturing chaufeur to close one's eyes and watch attentively and the not attention state information such as neglect.
Driver status sensing system 160 can utilize the eye closing information of the eye areas image capturing chaufeur extracted from the image of face of chaufeur.With reference to Figure 12, driver status sensing system 160, from eye areas image sensing eyelid region, is confirmed as chaufeur when the angle ∠ A of eyelid and ∠ B sum are predetermined critical below angle ∠ C (the ∠ A+ ∠ B≤∠ C) that preset and is closed one's eyes.
Driver status sensing system 160 confirms the angle of eyelid in units of certain hour, senses chaufeur eye closing number of times with this, calculates the eye closing information (sleepy time) of chaufeur according to the number of times of closing one's eyes in predetermined amount of time and certain hour value.
Further, what driver status sensing system 160 can utilize the image of face of chaufeur and eye areas image capturing chaufeur watches the information of neglecting attentively.
With reference to Figure 15, watching the scope of neglecting attentively is not for foundation with the angle of bearing circle, but the angle changed from vehicle central with the angle of wheel and wheel (Figure 15 the Θ A of (a) or Θ B) is for foundation, during by confirming that vehicle travels, whether the field range of chaufeur obtains at uncharge visual range η and watches the information of neglecting attentively.At this, after can calculating face's angle (α) of (b) (in the Figure 15) according to the image of face of the chaufeur obtained from camera, from the position (direction of pupil) of eye areas eikonometry pupil (β) of (c) (the Figure 15) to confirm field range.
Specifically, when steering wheel angle be within predetermined angular and the gear of change-speed box be D shelves or N shelves, driver status sensing system 160 can not enter in the sight line of chaufeur and travels zero load visual range (a), (b) and be judged as watching attentively when reaching more than the schedule time neglecting and obtaining according to this time and watch the information of neglecting attentively.
In step S1720, driver status sensing system 160 utilize step S1710 obtain mobility operation information and attention state information calculate mobility operation lifting capacity and hinder travel lifting capacity.
Such as, driver status sensing system 160 utilizes mobility operation information to calculate mobility operation lifting capacity W d.Specifically, driver status sensing system 160 utilizes mobility operation information and calculates mobility operation lifting capacity from the weighted value corresponding to projects mobility operation information of memory fetch.
Wherein, weighted value can be in advance through kinds of experiments, extracts and the value preset by the projects travelled in operation information.Weighted value also can be that operating personnel is in advance by the value that project sets arbitrarily.
Preferably, driver status sensing system 160 can utilize the project in mobility operation information and calculate the lifting capacity of projects corresponding to the weighted value of this project.Such as, driver status sensing system 160 get comprise acceleration pedal operation, brake pedal operation, multifunction switch operation and the mobility operation information of steering wheel operation time, driver status sensing system 160 can extract the weighted value D about acceleration pedal operation prestored a, about the weighted value D of brake pedal operation b, about multifunction switch operation weighted value D mand about the weighted value θ of steering wheel operation.When lasting 200ms and get every object time in mobility operation information and number information in units of 50ms, last the mobility operation lifting capacity W of 200ms dcan as shown in mathematical expression 10.
Again such as, driver status sensing system 160 utilizes not attention state information to calculate and hinders traveling lifting capacity W i.Specifically, driver status sensing system 160 can calculate the lifting capacity of the projects in not attention state information, the lifting capacity of projects that obtains of addition calculation can obtain hindering and travel lifting capacity.
Driver status sensing system 160 can utilize peripheral equipment number of operations to calculate peripheral equipment operational load amount T (n).Such as, driver status sensing system 160 can recycle the weighted value about peripheral equipment operation prestored and calculate peripheral equipment operational load amount.When last the schedule time get chaufeur to the operation of peripheral equipment input number of times time, driver status sensing system 160 can calculate peripheral equipment operational load amount T (n) according to peripheral equipment operation input number of times and weighted value.
Further, driver status sensing system 160 can utilize the speech data of chaufeur to calculate voice load amount V (n).Specifically, driver status sensing system 160 can calculate voice load amount according to the pulse critical value of the speech data prestored.Such as, driver status sensing system 160 can calculate according to the speech data lasting the chaufeur that the schedule time gets the time receiving the speech data of more than pulse critical value, calculates voice load amount V (t) with this.
Further, driver status sensing system 160 can utilize the eye closing information in not attention state information and watch the information of neglecting attentively and calculate sleepy lifting capacity P (t) and travel to watch attentively and neglect lifting capacity E (t).
Now, driver status sensing system 160 can confirm the eye closing action of chaufeur according to the diagram of circuit shown in Figure 14, it can last schedule time N under car speed V is the condition of more than predetermined speed in units of certain hour x, eye closing number of times y is confirmed, to calculate chaufeur closed-eye time (sleepy time) by eyelid angle.Further, the value that situation counting P PERCLOS value in the schedule time being reached to more than 30% obtains can be converted into load factor, calculate sleepy lifting capacity P (t) with this.
Further, driver status sensing system 160 utilizes the information of neglecting of watching attentively in not attention state information to calculate to watch attentively and neglect lifting capacity E (t).As shown in (a) in Figure 15, when field range departs from traveling zero load visual range η, the factor values of load factor V can change with disengaging scope.The scope (a') of the disengaging in Figure 15, (b') can preset, and the factor values that can read the load factor of the field range corresponding to current driver's from memory device by scope calculates to watch attentively neglects lifting capacity E (t).
Driver status sensing system 160 can be neglected lifting capacity E (t) according to peripheral equipment operational load amount T (n) calculated, voice load amount V (t), sleepy lifting capacity P (t) and watching attentively and be calculated to hinder and travel lifting capacity W i.
In step S1730, driver status sensing system 160 compares mobility operation lifting capacity W dlifting capacity W is travelled with obstruction idifference and enough and to spare lifting capacity W l.
Specifically, driver status sensing system 160 confirms mobility operation lifting capacity W ddeduct to hinder and travel lifting capacity W iwhether the value obtained is the enough and to spare lifting capacity W preset lbelow.Wherein, enough and to spare lifting capacity W lbeing by testing by subject's condition, extracting from traveling lifting capacity, driving information (variable gear state, vehicle acceleration, turn to and advance) and prestoring value in memory, (W can be changed with driving conditions d∝ W l).Now, driver status sensing system 160 can use mobility operation lifting capacity W dlifting capacity W is travelled with obstruction idifference divided by enough and to spare lifting capacity W lobtain end value C.
Suppose in advance the moment that vehicle starts to be difficult to travel to be set to critical percentage, the comparison result value C of step S1730 is more than critical percentage, then in step S1740, driver status sensing system 160 gives the alarm to chaufeur.
Suppose in advance the moment that vehicle starts to be difficult to travel to be set to 75%, the comparison result value C of step S1730 is more than 75%, and now driver status sensing system 160 gives the alarm to chaufeur.Such as, driver status sensing system 160 can show current load amount with red column diagram and column diagram be glimmered on picture, and vehicle traveling is dangerous to allow chaufeur recognize.Further, driver status sensing system 160 can be given the alarm to chaufeur by alarm tone in this case.
When the comparison result value C of step S1730 is less than 75%, in step S1750, driver status sensing system 160 can show current load amount, to inform that chaufeur is current in a safe condition with green column diagram.
As mentioned above, the present invention, when multiple driver vehicle that driving habit is different, can solve the problem that cannot identify driver habit well occurred owing to carrying out not considering driving condition when learning and learn according to the different driving models of each chaufeur.
Further, when the present invention can solve backlight and access tunnel, the unidentified problem to lane mark during crosswise joint, expands Lateral Controller usable range.
Further, the present invention also goes for the integrated vehicle controller of Future Development.
The invention is not restricted to formation and the method for embodiment described above, all or part of optionally combination of each embodiment realizes various deformation.
1. vehicle steering person's hazard index management system, is characterized in that, comprising:
This vehicle driving trace generating unit, it utilizes this vehicle traveling information received from the internal sensor of the travel conditions of this vehicle of sensing to generate this vehicle driving trace;
Surrounding vehicles driving trace generating unit, it utilizes the ambient environment information received from the external sensor of the surrounding condition of sensing described vehicle to generate surrounding vehicles driving trace;
Track lifting capacity calculating part, it calculates the track lifting capacity of the comparative result represented between track distance and the critical value preset, and described track distance is the difference of described surrounding vehicles driving trace and described vehicle driving trace; And
Hazard index management department, it generates the chaufeur hazard index corresponding to the described track lifting capacity calculated.
2. vehicle steering person's hazard index management system according to claim 1, is characterized in that, also comprise:
Surrounding vehicles lifting capacity calculating part, it calculates surrounding vehicles lifting capacity according to the quantity of the surrounding vehicles in described ambient environment information and described surrounding vehicles with or without change lane.
3. vehicle steering person's hazard index management system according to claim 2, is characterized in that:
Described surrounding vehicles lifting capacity calculating part is according to collision required time, centered by described vehicle, divide multiple hazardous location, and calculate surrounding vehicles lifting capacity according to the quantity of surrounding vehicles described in each hazardous location divided and described surrounding vehicles with or without change lane.
4. vehicle steering person's hazard index management system according to claim 2, is characterized in that:
Described hazard index management department generates the described chaufeur hazard index corresponding to described track lifting capacity and described surrounding vehicles lifting capacity sum.
5. vehicle steering person's hazard index management system according to claim 1, is characterized in that, also comprise:
Road load amount calculating part, it is according to the Road form in described ambient environment information, pavement state and volume of traffic state computation road load amount.
6. vehicle steering person's hazard index management system according to claim 5, is characterized in that:
Described hazard index management department generates the described chaufeur hazard index corresponding to described track lifting capacity and described road load amount sum.
7. vehicle steering person's hazard index management system according to claim 1, is characterized in that:
Surrounding vehicles lifting capacity calculating part, its according in described ambient environment information front/rear/quantity of side vehicle with calculate surrounding vehicles lifting capacity with or without change lane; And
Road load amount calculating part, it is according to the Road form in described ambient environment information, pavement state and volume of traffic state computation road load amount,
Wherein, described hazard index management department generates the described chaufeur hazard index corresponding to described track lifting capacity, described surrounding vehicles lifting capacity and described road load amount sum.
8. vehicle steering person's hazard index management process, is characterized in that:
This vehicle traveling information received from the internal sensor of the travel conditions of this vehicle of sensing is utilized to generate the step of this vehicle driving trace;
The ambient environment information received from the external sensor of the surrounding condition of sensing described vehicle is utilized to generate the step of the surrounding vehicles driving trace of described vehicle;
Calculate the step of the track lifting capacity of the comparative result represented between track distance and the critical value preset, wherein said track distance is the difference of described surrounding vehicles driving trace and described vehicle driving trace; And
Generate the step of the chaufeur hazard index corresponding to the described track lifting capacity calculated.
9. vehicle steering person's hazard index management process according to claim 8, is characterized in that, also comprise:
The step of surrounding vehicles lifting capacity is calculated with or without change lane according to the quantity of the surrounding vehicles in described ambient environment information and described surrounding vehicles.
10. vehicle steering person's hazard index management process according to claim 9, is characterized in that, the described step calculating surrounding vehicles lifting capacity comprises:
According to collision required time, centered by described vehicle, divide the step of multiple hazardous location; And
The step of surrounding vehicles lifting capacity is calculated with or without change lane according to the quantity of surrounding vehicles described in each hazardous location divided and described surrounding vehicles.
11. vehicle steering person's hazard index management processs according to claim 8, is characterized in that, also comprise:
According to the step of the Road form in described ambient environment information, pavement state and volume of traffic state computation road load amount.
12. vehicle steering person's hazard index management processs according to claim 11, is characterized in that:
The described step generating chaufeur hazard index specifically, generates the step of the described chaufeur hazard index corresponding to described track lifting capacity and described road load amount sum.
CN201510321139.0A 2014-06-13 2015-06-12 Vehicle steering person's hazard index management system and its method CN105197011B (en)
KR10-2014-0072280 2014-06-13
KR1020140072280A KR102051142B1 (en) 2014-06-13 2014-06-13 System for managing dangerous driving index for vehicle and method therof
CN105197011A true CN105197011A (en) 2015-12-30
CN105197011B CN105197011B (en) 2017-11-21
ID=54707030
CN201510321139.0A CN105197011B (en) 2014-06-13 2015-06-12 Vehicle steering person's hazard index management system and its method
US (1) US9623874B2 (en)
KR (1) KR102051142B1 (en)
CN (1) CN105197011B (en)
DE (1) DE102015210630A1 (en)
KR20180010666A (en) 2016-07-22 2018-01-31 엘지전자 주식회사 Electronic device and method for controlling the same
KR20180058405A (en) 2016-11-24 2018-06-01 현대자동차주식회사 Vehicle and method for controlling thereof
US10446031B2 (en) * 2017-03-14 2019-10-15 Hyundai Mobis Co., Ltd. Apparatus and method of safety support for vehicle
US10392012B2 (en) * 2017-04-24 2019-08-27 Adam Benjamin Tannenbaum System and method of use for vehicular driving safety
KR20190051601A (en) * 2017-11-07 2019-05-15 현대자동차주식회사 Apparatus and method for associating sensor data of vehicle
US20190384292A1 (en) * 2018-06-15 2019-12-19 Allstate Insurance Company Processing System For Evaluating Autonomous Vehicle Control Systems Through Continuous Learning
CN102449672A (en) * 2009-06-02 2012-05-09 丰田自动车株式会社 Vehicular peripheral surveillance device
CN103069466A (en) * 2010-06-18 2013-04-24 本田技研工业株式会社 System for inferring driver's lane change intention
CN103250196A (en) * 2010-11-30 2013-08-14 丰田自动车株式会社 Target status determination device for movable object, and program
JP2715528B2 (en) * 1989-03-17 1998-02-18 トヨタ自動車株式会社 Vehicle safety control device
KR20050121571A (en) 2004-06-22 2005-12-27 현대모비스 주식회사 Global positioning system for enforcing road condition driver's recognition
JP2006024106A (en) * 2004-07-09 2006-01-26 Honda Motor Co Ltd Contact avoidance controller of vehicle
DE102004038734A1 (en) * 2004-08-10 2006-02-23 Robert Bosch Gmbh Method and device for triggering emergency braking
JP4924207B2 (en) * 2007-05-29 2012-04-25 トヨタ自動車株式会社 Own vehicle risk acquisition device
KR101340779B1 (en) 2010-03-17 2013-12-11 주식회사 만도 Method for setting basis following position and lane keeping control system
JP4979840B2 (en) * 2010-07-27 2012-07-18 パナソニック株式会社 Moving body detection apparatus and moving body detection method
KR20120022305A (en) 2010-09-02 2012-03-12 주식회사 만도 Autonomous vehicle
KR101617349B1 (en) 2012-08-28 2016-05-03 (주)나노포인트 Diagnostic system and method for the analysis of driving behavior
KR20140072280A (en) 2012-11-29 2014-06-13 남서울대학교 산학협력단 Apparatus and method of detecting/transmitting emergency situation on personal digital terminal
EP2826687B1 (en) * 2013-07-16 2019-03-06 Honda Research Institute Europe GmbH Technique for lane assignment in a vehicle
CN105313898B (en) * 2014-07-23 2018-03-20 现代摩比斯株式会社 Driver status induction installation and its method
2014-06-13 KR KR1020140072280A patent/KR102051142B1/en active IP Right Grant
2015-06-10 US US14/735,528 patent/US9623874B2/en active Active
2015-06-10 DE DE102015210630.6A patent/DE102015210630A1/en active Pending
2015-06-12 CN CN201510321139.0A patent/CN105197011B/en active IP Right Grant
KR102051142B1 (en) 2019-12-02
DE102015210630A1 (en) 2015-12-17
US20150360697A1 (en) 2015-12-17
CN105197011B (en) 2017-11-21
KR20150143139A (en) 2015-12-23
US9623874B2 (en) 2017-04-18