CN104773177A - Aided driving method and aided driving device - Google Patents
Aided driving method and aided driving device Download PDFInfo
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- CN104773177A CN104773177A CN201410010655.7A CN201410010655A CN104773177A CN 104773177 A CN104773177 A CN 104773177A CN 201410010655 A CN201410010655 A CN 201410010655A CN 104773177 A CN104773177 A CN 104773177A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
- B60W30/095—Predicting travel path or likelihood of collision
- B60W30/0956—Predicting travel path or likelihood of collision the prediction being responsive to traffic or environmental parameters
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
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- G—PHYSICS
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
- B60W2050/143—Alarm means
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2420/00—Indexing codes relating to the type of sensors based on the principle of their operation
- B60W2420/40—Photo, light or radio wave sensitive means, e.g. infrared sensors
- B60W2420/403—Image sensing, e.g. optical camera
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Abstract
The invention discloses an aided driving method and an aided driving device. The aided driving method comprises the following steps of acquiring images of scenes nearby a vehicle; identifying all objects around the vehicle from the images; evaluating the probability that all the objects collide against the vehicle to obtain the collision probability of all the objects; evaluating consequence severities when all the objects collide against the vehicle based on attributes of all the objects to obtain potential collision consequence severities of all the objects; providing aided driving information for a driver or automatically performing driving control based on the collision probability and the potential collision consequence severities of all the objects.
Description
Technical field
The present invention relates generally to auxiliary driving method and the device of vehicle, relates more specifically to the auxiliary driving method based on image procossing and device.
Background technology
Drive ancillary technique and mainly utilize the such as sensor information such as image, radar, the driving assistance information such as prompting, warning are provided for chaufeur, even carry out the technology of automatic Pilot control.
Propose some and drive ancillary technique.
Such as, in the open US7689359B2 of the US granted patent being entitled as " Running Support System for Vehicle ", propose a kind of vehicle assistant drive technology, wherein utilize both radar and image-recognizing method to detect obstacle, and based on testing result, suitable control is carried out to vehicle.
In the open US8412448B2 of the US granted patent being entitled as " Collision avoidance system and method ", propose a kind of vehicle assistant drive technology, wherein set up the 3D modelling of various object, compare the view data of the vehicle front of in-vehicle camera collection and the characteristic data of 3D modelling to detect front object; If during close together with front object, given the alarm to chaufeur, if chaufeur does not react, then self-actuating brake or deceleration.
Summary of the invention
The present invention controls desirable to provide more fully auxiliary driving information or automatic Pilot.
According to an aspect of the present invention, provide a kind of auxiliary driving method for vehicle, can comprise: the image obtaining du vehicule scene; Each object of vehicle periphery is identified from image; Assess the probability of each object and automobile crash, obtain the collision probability of each object; Attribute based on each object assesses severity degree when each object and automobile crash, obtains the potential collision severity degree of each object; Based on collision probability and the potential collision severity degree of each object, provide driving assistance information to chaufeur or automatically carry out Driving control.
According to a further aspect of the invention, provide a kind of auxiliary driving device for vehicle, can comprise: image acquisition component, obtain the image of du vehicule scene; Object identification parts, identify each object of vehicle periphery from image; Bump against probability assessment parts, assess the probability of each object and automobile crash, obtain the collision probability of each object; Bump against consequence evaluation means, the attribute based on each object assesses severity degree when each object and automobile crash, obtains the potential collision severity degree of each object; Drive auxiliary execution unit, based on collision probability and the potential collision severity degree of each object, provide driving assistance information to chaufeur or automatically carry out Driving control.
According to auxiliary driving method and the device of the embodiment of the present invention, the attribute information of object can be obtained by recognition object, assessment object and the collision probability of vehicle and the severity degree of collision, there is provided driving assistance information to chaufeur or automatically carry out Driving control, the safety of driving can be improved, avoid or reduce loss.
Accompanying drawing explanation
Below in conjunction with accompanying drawing in the detailed description of the embodiment of the present invention, these and/or other side of the present invention and advantage will become clearly and be easier to understand, wherein:
Fig. 1 and Fig. 2 shows the illustrative example of complicated traffic environment.
Fig. 3 shows the application scenarios schematic diagram of the embodiment of the present invention.
Fig. 4 shows the configuration block diagram of DAS (Driver Assistant System) 100 according to an embodiment of the invention.
Fig. 5 schematically shows Current vehicle and its surrounding objects (going out with square frame frame) identified and relevant collision probability.
Fig. 6 schematically shows Current vehicle and its surrounding objects (going out with square frame frame) identified and potential collision severity degree.
Fig. 7 shows the configuration block diagram of auxiliary driving device 100 ' according to another embodiment of the present invention.
Fig. 8 shows the schematic diagram of regional assessment result, and the region security wherein indicated by label 1 is the highest, and the region security that label 2 indicates is the poorest, and the region security that label 3 indicates is placed in the middle.
Fig. 9 shows according to an embodiment of the invention for the overview flow chart of the auxiliary driving method 200 of vehicle.
Figure 10 shows the block diagram of the exemplary computer system 600 be suitable for for realizing embodiment of the present invention.
Detailed description of the invention
In order to make those skilled in the art understand the present invention better, below in conjunction with the drawings and specific embodiments, the present invention is described in further detail.
Before introducing the specific embodiment of the invention in detail, generally hold for ease of those skilled in the art and understand the present invention better, first summarized introduction invention thought of the present invention once.The present inventor finds the obstacle in normally inspection vehicle front in prior art, and judges relative distance, and then provides warning and carry out slowing down or braking automatically controlling.Such as, but in actual life, situation is usually much complicated, and, vehicle not only has dead ahead, and also have front, side, and rear area.In case of emergency, not only can be confined to the operation of slowing down or braking, especially when slowing down or brake is still difficult to collision free, the security situation of regional around can be investigated, and provide suitable suggestion, prompting, warning to driver or automatically carry out Driving control: as left-hand rotation or right-hand rotation, changing, even in case of emergency, drive on road serrated edge to avoid accident to occur.In addition, the security situation of regional is relevant with the direct intimate in regional, in the unavoidable situation of collision, bump against pedestrian obviously much serious than the consequence bumping against fence, therefore the object in regional can be detected, and the probability that bumps against of assessment and the severity degree of collision, and then the safety indexes of assessment regional, thus it is stronger and consider more fully driving assistance information and/or automatically carry out Driving control to provide operability.
Contemporary society's traffic environment becomes increasingly complex, and this drives to driver safety and make timely, correct judgement and reaction brings huge challenge when emergency case.Fig. 1 and Fig. 2 shows the illustrative example of complicated traffic environment.
Fig. 3 shows the application scenarios schematic diagram of the embodiment of the present invention.As shown in Figure 3, at the front erection plan of vehicle as capturing means, such as binocular solid camera, it continuously gathers disparity map and/or the gray-scale map of frontal scene.Vehicle is configured with drive assist system, such as be integrated in vehicle control system, drive assist system receives the image that pick up camera is caught, through image procossing and analysis, provide auxiliary driving information, and such as with voice or video mode, so auxiliary driving information is conveyed to chaufeur.Or drive assist system can provide Driving control instruction, control the associate components of vehicle, such as brake system, heading control loop, airbag ejecting mechanism etc.It should be noted that, drive assist system is except the image that the image capture parts receiving such as binocular camera are caught, the running data of vehicle interior and vehicle interior and outside various sensor information and out of Memory can also be received, such as, moving velocity, acceleration/accel, oil mass that vehicle is current, and ambient weather situation, condition of road surface etc. information, and comprehensive analysis vehicle interior and extraneous information provide the auxiliary suggestion of driving, prompting, warn, or directly automatically carry out Driving control.
In figure 3, camera and drive assist system are depicted as part separately, but this is only example, optionally both can be integrated, and is arranged in a shell.
Fig. 4 shows the configuration block diagram of DAS (Driver Assistant System) 100 according to an embodiment of the invention.
As shown in Figure 4, drive assist system 100 can comprise image acquisition component 110, object identification parts 120, bumps against probability assessment parts 130, bump against consequence evaluation means 140.
Image acquisition component 110 can obtain the image of du vehicule scene, and image can comprise gray level image and/or anaglyph.Image acquisition component 110 had both obtained gray level image and had also obtained anaglyph in one example.The range information between object and vehicle can be obtained by acquisition anaglyph.Such as, image acquisition component 110 can with the wired or wireless connections of monocular camera, binocular camera, many orders camera etc., with receive from its transmit image.
The method of any existing acquisition disparity map all may be used for the present invention.Such as, when detect to as if road on object as vehicles or pedestrians, can pass through vehicle-mounted binocular camera, many orders camera, stereoscopic camera shooting gray-scale map, and calculate correspondence disparity map.Particularly, such as, left image and right image can be clapped to obtain by vehicle-mounted binocular camera, wherein obtain disparity map based on left image and right image calculation, in addition alternatively when gray-scale map may be used, can using left image (or right image) as corresponding gray-scale map.It should be noted that, the acquisition of disparity map can be directly realize at camera internal, and also can realize in camera outside, these implementations are not construed as limiting the invention.
In addition, in this manual, using binocular camera as the example of stereo camera, but also can apply the stereo camera of other types, such as, use and initiatively launch the stereo camera that infrared light carrys out Aided Generation steric information.Such pick up camera example, has the Kinect of Microsoft, also has the type based on infrared light flight time (Time of Flight, TOF) technology, or the type of structure based light (structured light) etc.
It should be noted that, the position that on vehicle, binocular camera is installed and number can be arranged as required.Such as, binocular camera can be equipped on vehicle front, to take vehicle front scene.But, additionally camera can also be settled, to take rear view of vehicle scene at vehicle rear.Alternatively, camera can also be settled, to take left and right vehicle wheel both sides scene in vehicle left side or right side.In another example, wide-angle and/or image mosaic function can be incorporated in the camera, to make the angle of a phase function shooting wider, even can realize 360 degree of full-shape shoot functions.
In addition, range information between camera and shot object can be obtained by disparity map, but also can obtain range information between object and vehicle, such as radar ranging, infrared distance measurement means etc. by other distance measuring method.
Object identification parts 120 are configured to each object identifying vehicle periphery from image.
Any technology carrying out recognition object based on image procossing all can be applied to the present invention, such as, may relate to rim detection, feature extraction, the technology such as template matches, or also can utilize the hidden Markov mode identification technology etc. based on probability.The technology utilizing the 3D modelling of object to carry out object in recognition image in aforementioned US8412448B2 also can be applied to the present invention.For main points of the present invention of avoiding confusion, object recognition technique is not too much described here.
It should be noted that, object recognition technique here can utilize the historical information of previous frame.This can relate to object tracking technology, such as, can adopt the tracking technique such as Kalman filtering and particle filter.
After recognizing object, the attribute of each object can be identified based on the status information of recognition result, historical information, priori and Current vehicle (as speed, acceleration/accel etc.).
Such as, the attribute of object can comprise the type of object, as pedestrian or vehicle or traffic sign; Geometry and physical features, as the distance, speed, material etc. of article size, distance Current vehicle.
The identification of thingness can based on existing image procossing, pattern-recognition and computer vision methods.Such as can utilize HoG(Histogram of oriented gradient, histograms of oriented gradients) feature judges that in gray-scale map an object is pedestrian.
After the Attribute Recognition of all objects is complete, all information can be stored, and upgrade historical information.
Bump against probability assessment parts 130 and be configured to the probability assessing each object and automobile crash, obtain the collision probability of each object.
Bump against probability and represent that the possibility bumped against occurs for object and Current vehicle.In one example, the value bumping against probability is set up between zero and one.
In one example, the probability of object and automobile crash can be determined based on the Distance geometry relative velocity between object and vehicle.Such as, as previously mentioned, based on disparity map, the distance of object and the Current vehicle detected in present frame can be obtained.Object can be utilized in nearly a few frame to obtain the relative velocity between object and vehicle relative to the distance change of Current vehicle and temporal information.
In one example, formula 1 can be utilized to calculate the collision probability of a kth object and Current vehicle.
PC
k=P(Disk
k,Speed
k) (1)
Wherein Dist
kthe relative distance of a kth object and Current vehicle, Speed
kbe the relative velocity of a kth object and Current vehicle, and meet following relation between the two: work as Dist
kspeed during reduction
kfor just, working as Dist
kspeed during increase
kbe negative.
About the function P () in formula (1), relative to Dist
k, it is monotone decreasing function, and namely between object and Current vehicle, relative distance is larger, and the probability of collision is lower, and relative to Speed
k, it is monotone increasing function, and namely between object and Current vehicle, relative velocity is larger, and the probability of collision is larger.
In one example, function P () can adopt the form of formula 2.
It should be noted that, formula (2) is only example, as long as meet value between zero and one, relative to variables D ist
kmonotone decreasing, and relative to Speed
kmonotone increasing, function P () can adopt other functional form as required.
Fig. 5 schematically shows Current vehicle and its surrounding objects (going out with square frame frame) identified and relevant collision probability, wherein label 1 indicates Current vehicle, the probability P C that the object of label 2 instruction and Current vehicle bump against is higher, the probability P C that object and Current vehicle that label 4 indicates bump against is lower, and the probability P C that the object of label 3 instruction and Current vehicle bump against is placed in the middle.
It should be noted that, in the computation process bumping against probability, it is also conceivable to other factors, such as road conditions (smooth or jolt), weather conditions (whether sleety weather, visbility, haze etc.), it is also conceivable to some personal information of chaufeur in addition, the such as driving age, continuous driving time, even can also monitor that individual facial expression of driver etc. is to judge driver's whether fatigue driving.The collision probability of object and Current vehicle comprehensively can be determined based on various factors.
Bump against consequence evaluation means 140, the attribute based on each object assesses severity degree when each object and automobile crash, obtains the potential collision severity degree of each object.
In one example, as previously mentioned, the attribute of object can comprise the type of object, material, physical dimension, object relative at least one in the speed of vehicle.
In one example, the severity degree of object and automobile crash can be determined based on the type of object, material and the speed relative to vehicle.
The type of object can comprise pedestrian, vehicle, traffic sign etc.Traffic sign such as has guardrail, forthright direction board etc.Loss when Current vehicle bumps against from dissimilar object is different, and such as generally speaking, the severity degree that Current vehicle and pedestrian collision cause is more than large with the loss of other automobile crash.In one example, the type of object is quantified as the numerical indication Type that instruction causes severity degree with regard to type
k, its value between 0 to 1, can represent the order of severity of latent consequences.Such as, if recognize the type of object for pedestrian, Type is set
kbe 1, be namely set to the highest by the potential collision severity degree with regard to type, vehicle is 0.3, and traffic sign is 0.1.
The material of object can as the index of object crashworthiness degree.In one example, the material of object can be obtained indirectly by its type, such as, when recognizing the type of an object, also just determines its material.Such as, the material of pedestrian is flesh and blood, and the injury caused Current vehicle when this causes bumping against with Current vehicle is very little, and the injury caused pedestrian itself is very large; The material of vehicle is iron and steel in general; Traffic sign may be stone matter, also may be wooden etc.In one example, similar to the type of object, the material of object is quantified as numerical indication Material
k, value 0 to 1.The Material of such as flesh and blood
kbe 1, the Material of ferrous materials
kbe 0.4, the Material of stone matter
kbe 0.2.
Relative velocity can be the factor that loss is bumped against in another impact, usually can cause larger injury because bump against at a high speed.Described before its calculating, namely can be calculated by relative distance and temporal information.
In one example, and the severity degree CC that bumps against of kth object
kformula (3) can be utilized to calculate.
CC
K=C(Type
k,Material
k,Speed
k) (3)
For aforesaid object type Type
k, material Material
kwith relative velocity Speed
kany one, function C () should be monotone increasing function.
In one example, function C () can adopt the form of formula (4).
C(Type
k,Material
k,Speed
k)=Type
k*Material
k*Speed
k(4)
It should be noted that, formula (4) is only example, as long as meet relative to variable Type
k, Material
kand Speed
kmonotone increasing, function C () can adopt other functional form as required.
Fig. 6 schematically shows Current vehicle and its surrounding objects (going out with square frame frame) identified and potential collision severity degree, wherein label 1 indicates Current vehicle, the severity degree CC that the object of label 2 instruction and Current vehicle bump against is higher, the severity degree CC that object and Current vehicle that label 4 indicates bump against is lower, and the severity degree CC that the object of label 3 instruction and Current vehicle bump against is then placed in the middle.
Get back to Fig. 4, drive auxiliary execution unit 150 and be configured to collision probability based on each object and potential collision severity degree, provide driving assistance information to chaufeur or automatically carry out Driving control.
In one example, driving assistance information can be voice messaging, such as " there is pedestrian right front; probably knock, and consequence is serious, please dodges left ", or read-out can be shown in driver dead ahead, provide word or the image instruction of operation to driver.In another example, if determine the collision probability of object and vehicle for high, and to bump against severity degree be height, and the situation is critical, then now directly such as can brake process (brake) or to the automatic control departed from dangerous article opposite sense.
According to the auxiliary driving device 100 of the embodiment of the present invention, the attribute information of object can be obtained by recognition object, and then the collision probability of assessment object and vehicle, and the severity degree bumped against, there is provided driving assistance information to chaufeur or automatically carry out Driving control, improve the safety of driving, avoid or reduce loss.
Fig. 7 shows the configuration block diagram of auxiliary driving device 100 ' according to another embodiment of the present invention.Auxiliary driving device shown in Fig. 7 100 ' and the difference of the auxiliary driving device 100 shown in Fig. 4 have been Region dividing parts 160 and region security evaluation means 170 many.Emphasis is described these different parts below, and omit the description of other function with the similar parts of configuration.
The surrounding of Current vehicle ground is divided into the multiple regions do not overlapped each other by Region dividing parts 160, and determines the object of existence in regional.
About region partitioning method, in one example, region can be divided into vehicle running region and non-vehicle running region.Non-vehicle running region is footway, curb stone area, road central authorities flower bed region etc. such as.About vehicle running region, can divide according to track further again, such as, be divided into track, Current vehicle place, left-lane, right lane etc.But, may not be and divide according to track, but the area that any vehicle can drive to can as region.
In another example, the area of vehicle periphery region according to vehicle wheeled can be divided as unit area size.
In another example, the identification of binding object can carry out the division in region, such as, the area that the enough vehicles between object travel is divided into an independent region, and using the area at object place as a region.
It should be noted that, the region of division not only comprises the region of vehicle front, can also comprise the region at vehicle side front, side, proceeds posterolateral and rear.
About the determination of the object existed in regional, can determine based on the location of the bounds in region and object.As previously mentioned, vehicle is provided with binocular camera or other distance measuring equipment (DME), the three-dimensional world coordinate of object can be obtained, thus based on the coordinate range in region and the position coordinate of object, can determine whether object is positioned at region.
Region security evaluation means 170 is configured to collision probability based on the object existed in regional and potential collision severity degree, the safety indexes of assessment regional.
In one example, region security evaluation means 170, based on the collision probability of object and Current vehicle and potential collision severity degree, calculates the potentially danger index DC of object relative to Current vehicle
k.
Such as, the potentially danger index DC of a kth object for Current vehicle can be calculated according to formula (5)
k.
DC
K=PC
K*CC
K(5)
Then region security evaluation means 170 can assess a region Region according to formula 6
msafety indexes Safe (Region
m),
That is, by region Region
minterior all objects i is relative to the potentially danger DC of Current vehicle
isummation carry out the safety indexes of assessment area.Safety indexes is higher, shows that safety is better.
It should be noted that, formula 5 and formula 6 are only example, can select other formula form as required.
Fig. 8 shows the schematic diagram of regional assessment result, and the region security wherein indicated by label 1 is the highest, and the region security that label 2 indicates is the poorest, and the region security that label 3 indicates is placed in the middle.
Get back to Fig. 7, driving auxiliary execution unit 150 based on the safety indexes of regional, can provide driving assistance information to chaufeur or automatically carries out Driving control.Such as, in one example, can indicate please to the area format that this safety indexes is higher to chaufeur with graphicform.In another example, chaufeur " shown in right front, region is comparatively safe, please to the right traveling ahead " can be pointed out with speech form.
Utilize the auxiliary driving device according to the embodiment of the present invention, the assessment of probability and potential collision severity degree can be bumped against based on object identification and Region dividing, object and Current vehicle, and the safety of assessment area, thus provide chaufeur more comprehensively driving assistance information.
Fig. 9 shows according to an embodiment of the invention for the overview flow chart of the auxiliary driving method 200 of vehicle.Each step of auxiliary driving method 200 in Fig. 9 can be realized by each corresponding component such as shown in Fig. 4.Therefore about the realization of each step with reference to the description that shown in composition graphs 4, all parts carries out above, only can be briefly described below here.
As shown in Figure 9, in step S210, obtain the image of du vehicule scene.
In step S220, from image, identify each object of vehicle periphery.
In step S230, assess the probability of each object and automobile crash, obtain the collision probability of each object.
In step S240, the attribute based on each object assesses severity degree when each object and automobile crash, obtains the potential collision severity degree of each object.
In step s 250, based on collision probability and the potential collision severity degree of each object, provide driving assistance information to chaufeur or automatically carry out Driving control.
In one example, the attribute of object comprises the type of object, material, physical dimension, object relative at least one in the speed of vehicle.
In one example, when identifying that the type determining object is behaved, the potential collision severity degree of object is set to the highest.
In one example, based on collision probability and the potential collision severity degree of each object, driving assistance information is provided to chaufeur or automatically carries out Driving control and comprise: the surrounding of Current vehicle ground is divided into the multiple regions do not overlapped each other; Determine the object existed in regional; Based on collision probability and the potential collision severity degree of the object existed in regional, the safety indexes of assessment regional; And based on the safety indexes of regional, determine driving assistance information.
In one example, the plurality of region can comprise non-vehicle running region.
In one example, from image, identify that each object of vehicle periphery can comprise: Region Segmentation is carried out to image; Based on gradient orientation histogram and the comparison of the gradient orientation histogram template of the predetermined object prestored of regional, judge which kind of object this region comprises.
In one example, assess the probability of each object and automobile crash, the collision probability obtaining each object can comprise: the probability determining object and automobile crash based on the Distance geometry relative velocity between each object and vehicle.
The present invention can also by a kind of for computing system implement.Figure 10 shows the block diagram of the exemplary computer system 600 be suitable for for realizing embodiment of the present invention.As shown in Figure 10, computing system 600 can comprise: CPU(central processing unit) 601, RAM(random access memory) 602, ROM(read-only memory (ROM)) 603, system bus 604, hard disk controller 605, keyboard controller 606, serial interface controller 607, parallel interface controller 608, display control switch 609, hard disk 610, keyboard 611, serial peripheral equipment 612, concurrent peripheral equipment 613 and telltale 614.In such devices, what be coupled with system bus 604 has CPU601, RAM602, ROM603, hard disk controller 605, keyboard controller 606, serial interface controller 607, parallel interface controller 608 and display control switch 609.Hard disk 610 is coupled with hard disk controller 605, keyboard 611 is coupled with keyboard controller 606, serial peripheral equipment 612 is coupled with serial interface controller 607, and concurrent peripheral equipment 613 is coupled with parallel interface controller 648, and telltale 614 is coupled with display control switch 609.Should be appreciated that the structured flowchart described in Figure 20 is only used to the object of example, instead of limitation of the scope of the invention.In some cases, can increase or reduce some equipment as the case may be.
Person of ordinary skill in the field knows, the present invention can be implemented as system, device, method or computer program.Therefore, the present invention can be implemented as following form, that is: can be completely hardware, also can be software (comprising firmware, resident software, microcode etc.) completely, can also be the form that hardware and software combines, be commonly referred to as " circuit ", " module ", " device " or " system " herein.In addition, in certain embodiments, the present invention can also be embodied as the form of the computer program in one or more computer-readable medium, comprises computer-readable program code in this computer-readable medium.
The combination in any of one or more computer-readable medium can be adopted.Computer-readable medium can be computer-readable signal media or computer-readable recording medium.Computer-readable recording medium can be such as but be not limited to the system of electricity, magnetic, optical, electrical magnetic, infrared ray or quartz conductor, device or device, or combination above arbitrarily.The example more specifically (non exhaustive list) of computer-readable recording medium comprises: the combination with the electrical connection of one or more wire, portable computer diskette, hard disk, random-access memory (ram), read-only memory (ROM) (ROM), erasable type Programmable Read Only Memory (EPROM or flash memory), optical fiber, Portable, compact disk read-only memory (ROM) (CD-ROM), light storage device, magnetic memory device or above-mentioned any appropriate.In this document, computer-readable recording medium can be any comprising or stored program tangible medium, and this program can be used by instruction execution system, device or device or be combined with it.
The data-signal that computer-readable signal media can comprise in a base band or propagate as a carrier wave part, wherein carries computer-readable program code.The data-signal of this propagation can adopt various ways, includes but not limited to the combination of electromagnetic signal, optical signal or above-mentioned any appropriate.Computer-readable signal media can also be any computer-readable medium beyond computer-readable recording medium, and this computer-readable medium can send, propagates or transmit the program for being used by instruction execution system, device or device or be combined with it.
The program code that computer-readable medium comprises with any suitable medium transmission, can include but not limited to wireless, electric wire, optical cable, RF etc., or the combination of above-mentioned any appropriate.
The computer program code operated for performing the present invention can be write with one or more programming languages or its combination, described programming language comprises object oriented program language-such as Java, Smalltalk, C++, also comprises conventional process type programming language-such as " C " language or similar programming language.Program code can fully perform on the user computer, partly perform on the user computer, as one, independently software package performs, partly part performs on the remote computer or performs on remote computer or server completely on the user computer.In the situation relating to remote computer, remote computer can by the network of any kind-comprise local area network (LAN) or wide area network (WAN)-be connected to subscriber computer, or, outer computer (such as utilizing ISP to pass through Internet connection) can be connected to.
The present invention is described above with reference to the diagram of circuit of the method for the embodiment of the present invention, device (system) and computer program and/or block diagram.Should be appreciated that the combination of each square frame in each square frame of diagram of circuit and/or block diagram and diagram of circuit and/or block diagram, can be realized by computer program instructions.These computer program instructions can be supplied to the treater of general computer, single-purpose computer or other programmable data processing equipment, thus produce a kind of machine, these computer program instructions are performed by computing machine or other programmable data processing equipment, create the device of the function/operation specified in the square frame in realization flow figure and/or block diagram.
Also can these computer program instructions be stored in the computer-readable medium that computing machine or other programmable data processing equipment can be made to work in a specific way, like this, the instruction be stored in computer-readable medium just produces the manufacture of the command device of the function/operation specified in a square frame comprising in realization flow figure and/or block diagram.
Also can computer program instructions be loaded on computing machine, other programmable data processing equipment or miscellaneous equipment, make to perform sequence of operations step on computing machine, other programmable data processing equipment or miscellaneous equipment, to produce computer implemented process, thus make the instruction performed on computing machine or other programmable device can provide the process of the function/operation specified in the square frame in realization flow figure and/or block diagram.
Diagram of circuit in accompanying drawing and block diagram show system according to multiple embodiment of the present invention, the architectural framework in the cards of method and computer program product, function and operation.In this, each square frame in diagram of circuit or block diagram can represent a part for module, program segment or a code, and a part for described module, program segment or code comprises one or more executable instruction for realizing the logic function specified.Also it should be noted that at some as in the realization of replacing, the function marked in square frame also can be different from occurring in sequence of marking in accompanying drawing.Such as, in fact two continuous print square frames can perform substantially concurrently, and they also can perform by contrary order sometimes, and this determines according to involved function.Also it should be noted that, the combination of the square frame in each square frame in block diagram and/or diagram of circuit and block diagram and/or diagram of circuit, can realize by the special hardware based system of the function put rules into practice or operation, or can realize with the combination of specialized hardware and computer instruction.
Be described above various embodiments of the present invention, above-mentioned explanation is exemplary, and non-exclusive, and be also not limited to disclosed each embodiment.When not departing from the scope and spirit of illustrated each embodiment, many modifications and changes are all apparent for those skilled in the art.The selection of term used herein, is intended to explain best the principle of each embodiment, practical application or the improvement to the technology in market, or makes other those of ordinary skill of the art can understand each embodiment disclosed herein.
Claims (10)
1., for an auxiliary driving method for vehicle, comprising:
Obtain the image of du vehicule scene;
Each object of vehicle periphery is identified from image;
Assess the probability of each object and automobile crash, obtain the collision probability of each object;
Attribute based on each object assesses severity degree when each object and automobile crash, obtains the potential collision severity degree of each object; And
Based on collision probability and the potential collision severity degree of each object, provide driving assistance information to chaufeur or automatically carry out Driving control.
2. auxiliary driving method according to claim 1, the attribute of described object comprises the type of object, material, physical dimension, object relative at least one in the speed of vehicle.
3. auxiliary driving method according to claim 2, when identifying that the type determining object is behaved, is set to the highest by the potential collision severity degree of object.
4. auxiliary driving method according to claim 1, the described collision probability based on each object and potential collision severity degree, provide driving assistance information to chaufeur or automatically carry out Driving control and comprise:
The surrounding of Current vehicle ground is divided into the multiple regions do not overlapped each other;
Determine the object existed in regional;
Based on collision probability and the potential collision severity degree of the object existed in regional, the safety indexes of assessment regional; And
Based on the safety indexes of regional, determine driving assistance information.
5. auxiliary driving method according to claim 4, described multiple region comprises non-vehicle running region.
6. auxiliary driving method according to claim 1, describedly identifies that from image each object of vehicle periphery comprises:
Region Segmentation is carried out to image;
Based on gradient orientation histogram and the comparison of the gradient orientation histogram template of the predetermined object prestored of regional, judge which kind of object this region comprises.
7. auxiliary driving method according to claim 1, the probability of each object of described assessment and automobile crash, the collision probability obtaining each object comprises:
The probability of object and automobile crash is determined based on the Distance geometry relative velocity between each object and vehicle.
8., for an auxiliary driving device for vehicle, comprising:
Image acquisition component, obtains the image of du vehicule scene;
Object identification parts, identify each object of vehicle periphery from image;
Bump against probability assessment parts, assess the probability of each object and automobile crash, obtain the collision probability of each object;
Bump against consequence evaluation means, the attribute based on each object assesses severity degree when each object and automobile crash, obtains the potential collision severity degree of each object;
Drive auxiliary execution unit, based on collision probability and the potential collision severity degree of each object, provide driving assistance information to chaufeur or automatically carry out Driving control.
9. auxiliary driving device according to claim 8, the attribute of described object comprises the type of object, material, physical dimension, object relative at least one in the speed of vehicle.
10. auxiliary driving device according to claim 8, when identifying that the type determining object is behaved, the potential collision severity degree of object is set to the highest by described collision consequence evaluation means.
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| CN201410010655.7A CN104773177A (en) | 2014-01-09 | 2014-01-09 | Aided driving method and aided driving device |
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| CN201410010655.7A CN104773177A (en) | 2014-01-09 | 2014-01-09 | Aided driving method and aided driving device |
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Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106240458A (en) * | 2016-07-22 | 2016-12-21 | 浙江零跑科技有限公司 | A kind of vehicular frontal impact method for early warning based on vehicle-mounted binocular camera |
| CN106394553A (en) * | 2015-08-03 | 2017-02-15 | Lg电子株式会社 | Driver assistance apparatus and control method for the same |
| EP3208165A1 (en) * | 2016-01-21 | 2017-08-23 | Volvo Car Corporation | Vehicle safety assist system |
| CN107128303A (en) * | 2017-06-05 | 2017-09-05 | 北京汽车集团有限公司 | Vehicle collision avoidance method, device, storage medium, equipment, system and vehicle |
| CN109117709A (en) * | 2017-06-23 | 2019-01-01 | 优步技术公司 | Collision avoidance system for automatic driving vehicle |
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| CN110435672A (en) * | 2019-08-11 | 2019-11-12 | 西藏宁算科技集团有限公司 | One kind is based on scene anticipation safe driving householder method and its system |
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| CN111319628A (en) * | 2018-12-13 | 2020-06-23 | 通用汽车环球科技运作有限责任公司 | Method and system for evaluating false threat detection |
| CN112249029A (en) * | 2020-10-30 | 2021-01-22 | 高新兴科技集团股份有限公司 | AR-based method and system for assisting vehicle to adjust posture in short distance |
| CN113821022A (en) * | 2020-06-18 | 2021-12-21 | 百度（美国）有限责任公司 | Speed planning for buffer based on relative speed |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7689359B2 (en) * | 2004-01-28 | 2010-03-30 | Toyota Jidosha Kabushiki Kaisha | Running support system for vehicle |
| CN102145688A (en) * | 2010-02-08 | 2011-08-10 | 鸿富锦精密工业（深圳）有限公司 | Vehicle anti-collision monitoring system and method |
| TW201217200A (en) * | 2010-10-20 | 2012-05-01 | yan-hong Jiang | capable of controlling throttle, braking or steering according to the re-constructed environment information so as to improve driving safety |
| CN103158657A (en) * | 2011-12-15 | 2013-06-19 | 福特全球技术公司 | Estimating real-time crash parameters of a vehicle |
-
2014
- 2014-01-09 CN CN201410010655.7A patent/CN104773177A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7689359B2 (en) * | 2004-01-28 | 2010-03-30 | Toyota Jidosha Kabushiki Kaisha | Running support system for vehicle |
| CN102145688A (en) * | 2010-02-08 | 2011-08-10 | 鸿富锦精密工业（深圳）有限公司 | Vehicle anti-collision monitoring system and method |
| TW201217200A (en) * | 2010-10-20 | 2012-05-01 | yan-hong Jiang | capable of controlling throttle, braking or steering according to the re-constructed environment information so as to improve driving safety |
| CN103158657A (en) * | 2011-12-15 | 2013-06-19 | 福特全球技术公司 | Estimating real-time crash parameters of a vehicle |
Cited By (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106394553B (en) * | 2015-08-03 | 2019-04-16 | Lg电子株式会社 | Driver assistance and its control method |
| CN106394553A (en) * | 2015-08-03 | 2017-02-15 | Lg电子株式会社 | Driver assistance apparatus and control method for the same |
| EP3208165A1 (en) * | 2016-01-21 | 2017-08-23 | Volvo Car Corporation | Vehicle safety assist system |
| US10053067B2 (en) | 2016-01-21 | 2018-08-21 | Volvo Car Corporation | Vehicle safety assist system |
| CN106240458B (en) * | 2016-07-22 | 2017-12-12 | 浙江零跑科技有限公司 | A kind of vehicular frontal impact method for early warning based on vehicle-mounted binocular camera |
| CN106240458A (en) * | 2016-07-22 | 2016-12-21 | 浙江零跑科技有限公司 | A kind of vehicular frontal impact method for early warning based on vehicle-mounted binocular camera |
| CN107128303A (en) * | 2017-06-05 | 2017-09-05 | 北京汽车集团有限公司 | Vehicle collision avoidance method, device, storage medium, equipment, system and vehicle |
| CN109117709B (en) * | 2017-06-23 | 2020-09-01 | Uatc有限责任公司 | Collision avoidance system for autonomous vehicles |
| US11402848B2 (en) | 2017-06-23 | 2022-08-02 | Uatc, Llc | Collision-avoidance system for autonomous-capable vehicles |
| US10649464B2 (en) | 2017-06-23 | 2020-05-12 | Uatc Llc | Collision-avoidance system for autonomous-capable vehicles |
| CN109117709A (en) * | 2017-06-23 | 2019-01-01 | 优步技术公司 | Collision avoidance system for automatic driving vehicle |
| WO2020042859A1 (en) * | 2018-08-29 | 2020-03-05 | 上海商汤智能科技有限公司 | Smart driving control method and apparatus, vehicle, electronic device, and storage medium |
| CN109308467A (en) * | 2018-09-18 | 2019-02-05 | 陕西科技大学 | Traffic accident prior-warning device and method for early warning based on machine learning |
| CN109955817B (en) * | 2018-10-29 | 2020-11-17 | 苏州斯莱斯食品有限公司 | Multi-sensor safety automobile |
| CN109955817A (en) * | 2018-10-29 | 2019-07-02 | 永康市臣贸工贸有限公司 | Multisensor secure type automobile |
| CN111319628A (en) * | 2018-12-13 | 2020-06-23 | 通用汽车环球科技运作有限责任公司 | Method and system for evaluating false threat detection |
| CN110435672A (en) * | 2019-08-11 | 2019-11-12 | 西藏宁算科技集团有限公司 | One kind is based on scene anticipation safe driving householder method and its system |
| CN110349425A (en) * | 2019-08-13 | 2019-10-18 | 浙江吉利汽车研究院有限公司 | A kind of important goal generation method for bus or train route collaboration automated driving system |
| CN113821022A (en) * | 2020-06-18 | 2021-12-21 | 百度（美国）有限责任公司 | Speed planning for buffer based on relative speed |
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